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A Toolkit for Delirium Identification and Promoting Partnerships Between Carers and Nurses: A Pilot Pre–Post Feasibility Study

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• Published: 22 April 2024

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• Christina Aggar PhD   ORCID: orcid.org/0000-0002-0137-7796 1 , 6 ,
• Alison Craswell PhD   ORCID: orcid.org/0000-0001-8603-3134 2 ,
• Kasia Bail PhD   ORCID: orcid.org/0000-0002-4797-0042 4 ,
• Roslyn M. Compton PhD   ORCID: orcid.org/0000-0002-2069-2767 3 ,
• Mark Hughes PhD   ORCID: orcid.org/0000-0002-1158-5214 1 ,
• Golam Sorwar PhD   ORCID: orcid.org/0000-0002-5294-1895 5 ,
• James Baker PhD   ORCID: orcid.org/0000-0002-8290-3996 1 ,
• Jennene Greenhill PhD   ORCID: orcid.org/0000-0001-8325-2139 1 ,
• Lucy Shinners PhD   ORCID: orcid.org/0000-0002-7160-5838 1 ,
• Belinda Nichols MCN 1 , 6 ,
• Rachel Langheim MSc 6 ,
• Allison Wallis MRes 6 ,
• Karen Bowen MBA   ORCID: orcid.org/0000-0001-5840-3820 6 &
• Hazel Bridgett DTPH 6  

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Delirium is frightening for people experiencing it and their carers, and it is the most common hospital-acquired complication worldwide. Delirium is associated with higher rates of morbidity, mortality, residential care home admission, dementia, and carer stress and burden, yet strategies to embed the prevention and management of delirium as part of standard hospital care remain challenging. Carers are well placed to recognize subtle changes indicative of delirium, and partner with nurses in the prevention and management of delirium.

To evaluate a Pr evention & E arly Delirium I dentification C arer T oolkit (PREDICT), to support partnerships between carers and nurses to prevent and manage delirium.

A pre–post-test intervention and observation study.

Main Measures

Changes in carer knowledge of delirium; beliefs about their role in partnering with nurses and intended and actual use of PREDICT; carer burden and psychological distress. Secondary measures were rates of delirium.

Participants

Participants were carers of Indigenous patients aged 45 years and older and non-Indigenous patients aged 65 years and older.

Intervention

Nurses implemented PREDICT, with a view to provide carers with information about delirium and strategies to address caregiving stress and burden.

Key Results

Participants included 25 carers (43% response rate) ( n  = 17, 68% female) aged 29–88 ( M  = 65, SD  = 17.7 years). Carer delirium knowledge increased significantly from pre-to-post intervention ( p  =  < .001; CI 2.07–4.73). Carers’ intent and actual use of PREDICT was ( n  = 18, 72%; and n  = 17, 68%). Carer burden and psychological distress did not significantly change. The incidence of delirium in the intervention ward although not significant, decreased, indicating opportunity for scaling up.

The prevention and management of delirium are imperative for safe and quality care for patients, carers, and staff. Further comprehensive and in-depth research is required to better understand underlying mechanisms of change and explore facets of nursing practice influenced by this innovative approach.

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INTRODUCTION

Delirium, the most common hospital-acquired complication worldwide, is characterized by shifting attention, incoherence, disorientation, and impaired cognition. 1  It is a frightening experience for the person affected, and their sudden change in behavior and/or emotions can impact family carers’ burden and psychological distress. 1 , 2 , 3  The global rise in ageing populations is expected to exacerbate the impact of delirium in healthcare settings, leading to increased rates of hospital-acquired complications (e.g., falls), delayed discharge, re-admissions, dementia, residential aged care admissions, death, and greater caregiving responsibilities for families. 4  Therefore, the prevention, identification, and early management of delirium are imperative in the provision of safe, high-quality care for both the patient and their family.

The healthcare team, including nurses, are responsible for the initial and ongoing assessment, management, and safety of patients at risk of delirium across hospital settings; however, prevention strategies and risk screening are not consistently practiced, and understanding of and recognition of delirium is poor. 5 , 6 , 7  Reasons for undiagnosed delirium include language barriers, fluctuation of symptoms during the day, a lack of routine screening and assessment, lack of resources, competing clinical priorities, and organization culture. 5 , 8 , 9 , 10 , 11 , 12 , 13  These are compounded by a lack of knowledge of the patient’s prior day-to-day level of functioning by the healthcare team. 14

Rapid deterioration due to delirium begins with subtle changes that are best recognized by family or close ones (referred to as carers here on). 15  Carers can provide not only a valuable cognitive anchor point but also comforting reassurance, and if supported by clinicians, implement preventative non-pharmacological interventions. 14 , 16 , 17 , 18 , 19 , 20 , 21  Interventions implemented with carers to address delirium have been found to improve nurse and carer delirium knowledge, 20  reduce carer psychological distress, 18 , 22  and length of hospital stay. 18 , 23 , 24  However, innovative interventions to support partnerships with carers in the prevention and management of delirium in the hospitalized older patient are needed. 25 , 26

The primary aim of this study was to evaluate a Pr evention & E arly D elirium I dentification C arer T oolkit (PREDICT) to support partnerships between carers and nurses to prevent and manage delirium. Specifically, the study aimed to evaluate changes in the carer:

•Knowledge of delirium prevention and management

•Beliefs about their role in partnering in delirium prevention and management

•Actual and intended use of PREDICT

•Levels of burden and psychological distress

A secondary aim of this study was to evaluate changes in the incidence of delirium. We hypothesized that the involvement of nurses would improve their understanding of delirium and lead to changes in nursing practice and delirium incidence rates.

METHOD AND MATERIALS

A pre–post-test intervention study was conducted on a medical ward in an Australian regional hospital with data collected during admission (pre-intervention) and 4–6 weeks post-discharge (post-intervention). A further observational study to address the secondary aim examined the incidence of delirium during the intervention period compared to the same period 12 months prior.

The Intervention (PREDICT)

Acknowledging and valuing the insight and lived experience, a model of care utilizing a Pr evention & E arly D elirium I dentification C arer T oolkit (PREDICT) was codesigned and validated by carers whose family members had been hospitalized and for some had experienced delirium, consumers, and healthcare professionals working in the acute care setting. 27  PREDICT, available on a digital platform and accessed via QR code, included short videos and information on delirium preventive strategies, risk factors, and non-pharmacological interventions to reorientate older adults who experience delirium. To enable carers to express and communicate their concerns about the person being cared for, an interactive psychometrically tested delirium screening questionnaire suitable for informal or untrained carers was also included. 14  To support carer well-being and address burden and psychological distress, PREDICT also includes information and links to carer resources such as counselling and social prescribing programs (social service programs that provide activities to improve health and well-being). 27  PREDICT was also made available in hard copy.

Participants were carers of Indigenous patients aged 45 years and older and non-Indigenous patients aged 65 years and older. The lower age range for Indigenous patients was set because people who identify as Indigenous Australians are more likely to develop serious medical conditions earlier in life and have a lower life expectancy than non-Indigenous Australians 28 (Australian Institute of Health and Welfare, 2023).

Eligibility

The carer was eligible to receive PREDICT if visiting at the patient’s bedside daily during hospitalization for ≥ 2 days and could communicate in English or with an interpreter. The carer was not eligible to participate if the patient’s hospital stay was less than 48 h, and the patient was receiving end-of-life care or had a diagnosis of advanced dementia and was unable to communicate or interact.

Prior to the implementation of PREDICT (September 2022 to February 2023), nurses received a delirium education session and orientation to PREDICT, and during the study nurses received ongoing weekly briefings from the lead ward nurse for dementia and delirium. This regular communication was to ensure the nursing staff were equipped to answer questions the carer may have regarding PREDICT, the delirium screening questionnaire, and the study evaluation. Posters promoting PREDICT and the study evaluation were placed in strategic areas around the ward, with contact information for further enquiries.

The admitting nurse offered eligible carers access to PREDICT. Carers were advised that they were not required to participate in the study evaluation (that is, complete the study survey) to receive and engage with PREDICT.

Nursing staff were encouraged to support all carers to use PREDICT daily, including the delirium screening questionnaire. 14  Carers were not offered incentives to participate.

Data Collection

Participating carers were invited to complete an anonymous survey online using Qualtrics, 29  or in a paper-based format, at admission (pre-intervention) and 4–6 weeks post-discharge (post-intervention). Pre- and post-intervention surveys were matched using an anonymous participant-generated code (the last 4 digits of participants’ phone numbers, and first initial of their mother’s name). For carers completing a paper-based survey, a secure box was placed at the nurses’ station for surveys returned at admission and a reply-paid envelope for surveys returned at 4–6 weeks follow-up. The incidence of delirium (using the standard unit of measurement of utilization—cases per 1000 occupied bed days (OBDs)) during the intervention period (T2) was compared to the same period 12 months prior (T1).

The following measures were combined into the online survey as a continuous tool.

Demographics

Carer demographic items included age, gender, whether they identified as Aboriginal or Torres Strait Islander, length of time as a carer, their relationship with the person they cared for, and whether they lived together, as well as the age and gender of the person they cared for.

Caregiver Delirium Knowledge Questionnaire (CDKQ) 14

The CDKQ is a validated measure of carer knowledge of delirium risk factors, symptoms, and appropriate actions with good internal consistency reliability (Cronbach’s α  = 0.76). 22  Nineteen true/false items across three subscales include Risk (10 items, e.g., dehydration), Symptoms (5 items, e.g., increasing confusion over several days), and Actions (4 items, e.g., immediately calling a doctor). Total and subscale scores comprise the sum of correct items where higher scores indicate greater knowledge.

Beliefs About Carers’ Role in Partnering in Delirium Prevention and Management

A single item question was asked, rated “yes” or “no.”

“Do you think that carers should be incorporated into delirium identification and management?”

Carers’ Intended and Actual use of PREDICT, Including the Delirium Screening Questionnaire

Two questions were asked, rated “yes” or “no.”

“Do you intend to use/ Did you use the Delirium Toolkit?” “Do you intend to use/ Did you use the delirium screening questionnaire?”

Caregiver Delirium Burden Scale (DEL-B-C) 30

The DEL-B-C is a validated 16-item measure of the burden experienced by carers; Cronbach’s α  = 0.82. 31  Total scores range from 0 to 40 with higher scores indicating greater burden.

Kessler Psychological Distress Scale (K10) 32

The K10 is a widely used and validated measure of psychological distress; Cronbach’s α  = 0.93. 32 , 33  Total scores range from 10 to 50 with higher scores indicating greater psychological distress.

Incidence of Delirium

Routinely collected hospital data was accessed to determine delirium incidence. Data was calculated using cases per 1000 OBDs which were compared from September 2021 to February 2022 (T1) and during the intervention period September 2022–February 2023 (T2).

Ethical Conduct

Ethical approval was provided by [ removed for blinding ].

Data were entered using Qualtrics survey software, 29  downloaded and cleaned, checked, and analyzed in SPSS 27 34 and OpenEpi 35 analysis software. Summary and descriptive statistics were produced including frequencies, totals, and means of participant demographics and study outcome variables. Significance level was set at alpha ( α ) = 0.05. Normality was established by visual inspection of histograms, skew and kurtosis, and Shapiro–Wilk (as n  < 50) tests of normality. 36  Cohen’s d effect sizes were calculated as estimates of clinical significance where 0.2 indicates a small effect size, 0.5 moderate, and 0.8 large. 37  Normally distributed data were assessed for change from admission to post-discharge using paired t -tests (CDKQ, DEL-B-C, K10). Non-parametric data were assessed for change using related-samples McNemar change tests for dichotomous dependent variables (beliefs about partnering, satisfaction with care). Relationships between demographics and outcome variables (years as a carer versus intended and actual use of PREDICT) were assessed using independent-samples Mann–Whitney U tests. Missing values were handled as follows: frequency data (demographics, beliefs about partnering and use of PREDICT) were unchanged and were reported in raw form; missing CDKQ items were scored as incorrect; missing DEL-B-C items were scored as though carers had not experienced the relevant burden; and no K10 items were missing. Change in delirium incidence was analyzed by calculating an incidence rate ratio (IRR)—that is, comparing incidence at T1 and T2, wherein an IRR of 1 (or 95% CI that includes 1) indicates equal rates of delirium and thus a non-significant change; Z (standard) scores and p values are also presented for IRRs. 38 , 39

PREDICT was provided to a total of 56 carers, of whom 25 carers (43%) provided pre- and post-intervention data. Carers were primarily females ( n  = 17, 68%) with an average age of 65 years ( SD  = 17.7) providing care for their partner ( n  = 15, 60%). The majority of carers ( n  = 17, 68%) lived with the patient prior to admission. A total of seven carers (28%) reported the patient was diagnosed with delirium; see Table  1 for demographics.

Carer Delirium Knowledge

Carer delirium knowledge (CDKQ) 22 increased significantly from admission ( M  = 8.7, SD  = 4.62) to post-discharge ( M  = 12.1, SD  = 5.43). Scores increased by an average of 3.4 ( SE  = 0.65, 95% CI [2.07, 4.73]; paired t (24) = 5.27, p  =  < 0.001, d  = 1.1). This very large effect size (d) indicates a meaningful, clinically significant improvement in delirium knowledge. 37

Beliefs about Partnering in Delirium Prevention and Management

During admission, the majority of carers ( n  = 18, 72%) believed carers should be incorporated into delirium identification and management, which increased to 24 (96%) post-discharge. A related-samples McNemar change test indicated this was a significant increase ( p  < 0.001).

Carers’ Intended and Actual Use of PREDICT

At admission, most carers intended to use PREDICT ( n  = 18, 72%), and at post-discharge nearly all carers with positive intentions reported they had used PREDICT ( n  = 17, 68%). Similarly, at admission, most carers intended to use the delirium screening questionnaire ( n  = 17, 68%) and at post-discharge most reported they had used it ( n  = 15, 60%), reflecting an effective intention-behavior link with minimal gap; 40  see Table  2 .

Intended and actual use of PREDICT was associated with total time as a carer, where participants who had been carers for longer were significantly more likely to report intention to use PREDICT (Mann–Whitney U  = 93.5, p  = 0.003) and the delirium screening questionnaire ( U  = 90.0, p  < 0.001) weekly, and actual weekly use PREDICT ( U  = 69.0, p  = 0.039) and delirium screening questionnaire ( U  = 77.0, p  = 0.011).

Carer Burden and Distress

Carer burden (DEL-B-C) 31 and distress (K10) 32 did not change significantly from admission to post-discharge ( p  > 0.05). K10 scores were consistently high with carers reporting moderate–high levels of psychological distress at both timepoints 41 ; see Table  3 .

The incidence of delirium on the medical ward was 16.6 cases per 1000 OBDs for 2022/2023, compared to 27.1 cases per 1000 OBDs in the 2021/2022 matched period. The incident risk ratio (IRR) for delirium during the period PREDICT was introduced, compared to the same period 12 months prior which was 0.61 [95%CI 0.33, 1.13]. The associated z -value was 1.59 with a p -value of 0.056 approaching significance. Given the impacts of COVID-19 on healthcare utilization, for contextual comparison points data was also pulled for the whole of hospital, the whole of health district, and the state for the same time periods, which can be seen in Table  4 . No other dataset showed any changes in OBD nearing significance, demonstrating promise of association related to the intervention rather than external factors.

There is increasing attention on the importance of the prevention and early management of delirium because of the deleterious effect on older patients’ and carers’ health and well-being. 2 , 3 , 30  This study evaluated the introduction of a model of care utilizing PREDICT, an interactive toolkit designed to support partnerships with carers and nurses in the prevention and management of delirium. The results of this study while only indicative, are promising, highlighting a partnership approach with carers may impact delirium prevention and management.

Several recent systematic reviews and meta-analyses highlight the importance of carer involvement in delirium management 42 and the efficacy of education; 11 , 18  however, many key studies omit the carer perspective. 15  While most carers in this study significantly increased their knowledge of delirium, we were also able to demonstrate that they saw a clear role for their ongoing involvement in preventing and managing delirium, particularly carers who had been caring for a longer time. This is important because it presents opportunities for improved long-term patient outcomes as the carer is likely to continue to monitor delirium risk following discharge. These findings respond directly to the expectations of carer involvement in care decisions and delivery, as demonstrated by delirium guidelines and standards worldwide. 43 , 44 , 45

Despite increases in delirium knowledge and the utilization of acquired learnings, carers’ moderately high levels of psychological distress and burden did not significantly improve, contrasting with other studies. 11 , 22  While this finding could be due to differences in characteristics of sample populations, it is consistent with studies reporting carers were often highly distressed when the person they were caring for experienced delirium or was at risk of delirium. 46 , 47  Perception of burden is multifaceted and changes over time, raising questions as to whether equity measurements, such as social needs and barriers to care, such as transportation, food insecurity, and housing, are more relevant outcome indicators of burden for carers. 48  While the focus on partnering with carers in our study maximizes the opportunity for enhanced communication and collaboration between carers and nurses, further research is required to elicit the impact of psychological distress and burden in the management of delirium. 30 , 49  Where health inequities impact vulnerable groups including LGBTQ + and Indigenous communities, 50  further research is required to enable carers to highlight their well-being and support needs.

Finally, in relation to our secondary aim, our findings indicate the potential of partnering with carers in delirium prevention and the broad promotion of PREDICT for reducing the incidence of delirium. Given change in the incidence of delirium was not seen elsewhere in the hospital, local health district, or state figures, it is reasonable to hypothesize that PREDICT might have had a ripple effect at the ward level and improved nurses’ delirium prevention practice. Combined with the pre–post-intervention results, there appears to be merit in proceeding to a randomized controlled trial to further validate and understand this model of care and PREDICT’s broader impact.

When deploying this toolkit in additional facilities, it would be of benefit to specifically explore changes in nurse delirium knowledge levels and self-rated confidence in detecting delirium. This would enable improved measurement of the program’s impact on nurses’ understanding and competence in managing delirium cases. It would also be of benefit to include qualitative interviews to better understand how consciously or unconsciously the program may have influenced their practice, altered perceptions of patient interactions, and transformed their overall approach to care, providing a deeper understanding of any mechanisms of change. Finally, future studies could examine any changes to the way in which nurses work when acting in the role of partner in care, including if there are any changes in shared vigilance, improved communication with carers, or changes in intervention strategies. Understanding any mechanisms of change would be crucial for refining program design and understanding its impact.

Study Limitations

A limitation of this study lies in the small sample size and its location in a single medical ward in an Australian regional hospital. This study did not calculate average length of stay; however, older persons’ hospital service utilization in Australia is reported to average 7.1 days. 51  A further limitation was that PREDICT was validated with carers in the community 27 but not an inpatient setting. Finally, PREDICT is limited to those patients who have carers visit at the bedside. While carers are not always at the bedside 24/7, the provision of PREDICT to carers upon admission will support any non-face to face communication between healthcare professionals and carers about the cognitive status of the patient. Future rigorous research as to whether partnering with carers in the prevention of delirium using PREDICT can reduce the incidence of delirium will be an important next step.

This study focused on engaging and supporting carers as partners in the prevention and management of delirium. While this study presents encouraging preliminary results, more extensive research is required seeking to better understand underlying mechanisms of change and exploring additional facets of nursing practice influenced by this innovative approach.

Data Availability

The data presented in this study are available on request from the corresponding author (Christina Aggar).

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Acknowledgements

Nursing staff who supported the dissemination of Carer Delirium Toolkit and partnering with carers. Nurse leaders who advocated for the Carer Delirium Toolkit: Brenda Paddon, Princy Albert, and Hannah Graves. Tamsin Thomas and Tina Prassos for supporting the analysis of this study and preparation of the paper for publication and most importantly the study participants for sharing their experiences.

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Aggar, C., Craswell, A., Bail, K. et al. A Toolkit for Delirium Identification and Promoting Partnerships Between Carers and Nurses: A Pilot Pre–Post Feasibility Study. J GEN INTERN MED (2024). https://doi.org/10.1007/s11606-024-08734-6

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Critical Care Nursing Research Paper Topics

Critical care nursing research paper topics are an essential part of the academic journey of nursing students. This abstract aims to provide a brief overview of the comprehensive list of research paper topics related to critical care nursing. It encompasses a wide range of categories, including Acute Respiratory Failure, Sepsis and Septic Shock, Cardiogenic Shock, and many more. Moreover, it highlights the importance of critical care nursing in healthcare and encourages students to explore the diverse range of topics provided. Additionally, it introduces iResearchNet’s writing services, which offers students the opportunity to order a custom critical care nursing research paper on any topic, ensuring success in their academic pursuits.

100 Critical Care Nursing Research Paper Topics

Introductory Paragraph: Critical care nursing is a crucial and intricate part of the healthcare system. This specialized field of nursing caters to patients with life-threatening illnesses and injuries, necessitating a deep understanding and specialized knowledge in various aspects of critical care. The keyphrase ‘critical care nursing research paper topics’ underlines the wide array of subjects that nursing students can delve into, to not only enrich their knowledge but also contribute to this vital field.

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• Pathophysiology of Acute Respiratory Failure
• Non-invasive Ventilation in Acute Respiratory Failure
• Oxygen Therapy in Acute Respiratory Failure
• Risk Factors for Acute Respiratory Failure
• Complications of Acute Respiratory Failure
• Nursing Management of Acute Respiratory Failure
• Pharmacological Management of Acute Respiratory Failure
• Role of Physiotherapy in Acute Respiratory Failure
• Prognosis of Acute Respiratory Failure
• Preventive Measures for Acute Respiratory Failure
• Pathophysiology of Sepsis and Septic Shock
• Early Goal-Directed Therapy in Sepsis
• Biomarkers in Sepsis and Septic Shock
• Nursing Interventions for Sepsis and Septic Shock
• Antibiotic Therapy in Sepsis and Septic Shock
• Vasopressor Therapy in Septic Shock
• Fluid Resuscitation in Sepsis and Septic Shock
• Organ Dysfunction in Sepsis and Septic Shock
• Prognostic Factors in Sepsis and Septic Shock
• Preventing Sepsis and Septic Shock
• Pathophysiology of Cardiogenic Shock
• Management of Cardiogenic Shock
• Intra-aortic Balloon Pump in Cardiogenic Shock
• Vasopressor and Inotropes in Cardiogenic Shock
• Nursing Management of Cardiogenic Shock
• Complications of Cardiogenic Shock
• Mechanical Circulatory Support in Cardiogenic Shock
• Prognosis of Cardiogenic Shock
• Preventing Cardiogenic Shock
• Case Studies on Cardiogenic Shock
• Principles of Hemodynamic Monitoring
• Invasive vs Non-Invasive Hemodynamic Monitoring
• Role of Echocardiography in Hemodynamic Monitoring
• Nursing Considerations in Hemodynamic Monitoring
• Complications of Hemodynamic Monitoring
• Accuracy and Limitations of Hemodynamic Monitoring
• Hemodynamic Monitoring in Specific Conditions (e.g., ARDS, Sepsis)
• Future Trends in Hemodynamic Monitoring
• Clinical Case Studies on Hemodynamic Monitoring
• Best Practices in Hemodynamic Monitoring
• Principles of Mechanical Ventilation
• Modes of Mechanical Ventilation
• Setting and Adjusting Ventilator Parameters
• Nursing Care of Patients on Mechanical Ventilation
• Weaning from Mechanical Ventilation
• Complications of Mechanical Ventilation
• Mechanical Ventilation in Specific Conditions (e.g., COPD, ARDS)
• High-Frequency Oscillatory Ventilation
• Non-Invasive Ventilation
• Ethical Considerations in Mechanical Ventilation
• Assessment of Pain in Critical Care
• Pharmacological Management of Pain
• Non-Pharmacological Management of Pain
• Sedation in Critical Care: Principles and Practice
• Assessment of Sedation Levels
• Complications of Sedation
• Special Considerations in Pain and Sedation Management (e.g., Renal Failure)
• Pain and Sedation in Specific Populations (e.g., Elderly, Pediatric)
• Role of Family in Pain and Sedation Management
• Guidelines and Protocols for Pain and Sedation Management
• Pathophysiology of Delirium
• Assessment Tools for Delirium
• Risk Factors for Delirium
• Preventive Strategies for Delirium
• Pharmacological Management of Delirium
• Non-Pharmacological Management of Delirium
• Impact of Delirium on Patient Outcomes
• Role of Family in Managing Delirium
• Delirium in Specific Populations (e.g., Elderly, Postoperative)
• Future Directions in Delirium Research and Management
• Pathophysiology of Acute Kidney Injury
• Risk Factors for Acute Kidney Injury
• Diagnosis and Staging of Acute Kidney Injury
• Prevention of Acute Kidney Injury
• Management of Acute Kidney Injury
• Role of Renal Replacement Therapy in Acute Kidney Injury
• Complications of Acute Kidney Injury
• Prognosis of Acute Kidney Injury
• Acute Kidney Injury in Specific Conditions (e.g., Sepsis, Surgery)
• Nursing Care of Patients with Acute Kidney Injury
• Nutritional Assessment in Critical Care
• Enteral vs Parenteral Nutrition
• Nutritional Requirements in Specific Conditions (e.g., Sepsis, ARDS)
• Complications of Nutritional Support
• Monitoring and Adjustment of Nutritional Support
• Role of Micronutrients in Critical Care
• Ethical Considerations in Nutritional Support
• Impact of Nutritional Support on Patient Outcomes
• Guidelines and Protocols for Nutritional Support
• Future Directions in Critical Care Nutrition
• Principles of End of Life Care in ICU
• Communication Strategies in End of Life Care
• Ethical Considerations in End of Life Care
• Role of Palliative Care in ICU
• Management of Symptoms at End of Life
• Decision Making in End of Life Care
• Role of Family in End of Life Care
• Cultural Considerations in End of Life Care
• Bereavement Support for Families
• Organ Donation and End of Life Care

In conclusion, the breadth of critical care nursing research paper topics available for exploration is vast and diverse. This list provides a comprehensive overview of potential topics that nursing students can choose from, thereby helping them delve deeply into specific areas of interest. It is crucial for aspiring nurses and researchers to explore these topics in depth, as it will not only enhance their knowledge but also contribute to the advancements in the critical care nursing field.

The Range of Critical Care Nursing Research Paper Topics

Critical care nursing is a specialized area of nursing that is dedicated to meeting the needs of patients and families facing life-threatening illnesses or injuries. This involves not only managing acute and often complex health issues but also addressing the psychosocial and communication challenges that arise in these high-stress environments. Due to the nature of the work, critical care nurses need to be highly skilled in assessment, problem-solving, and decision-making. This article delves into the significance of critical care nursing and the wide range of critical care nursing research paper topics it offers, such as acute respiratory failure, sepsis and septic shock, and mechanical ventilation.

The Significance of Critical Care Nursing

Critical care nursing is pivotal in the healthcare system as it deals with life and death situations. Nurses in this field play a crucial role in the management of critically ill patients. They provide specialized care, monitor the patients’ condition, and use advanced technology and medications to optimize outcomes. These nurses also support the patients’ families, helping them to understand the situation and to cope with the stress and anxiety associated with critical illnesses or injuries.

A critical care nurse’s role includes assessing the patient’s condition, planning and implementing care, and evaluating the patient’s response to the treatment. They collaborate with a multidisciplinary team of healthcare professionals, including physicians, pharmacists, respiratory therapists, and dietitians, to provide comprehensive care to the critically ill. This collaborative approach ensures that all aspects of the patient’s well-being are addressed, from physiological to psychological needs.

One of the most crucial skills of a critical care nurse is the ability to make quick and accurate decisions. The condition of critically ill patients can change rapidly, and timely interventions can be the difference between life and death. Therefore, critical care nurses must have a deep understanding of the pathophysiology of critical illnesses and the pharmacology of medications used in critical care settings.

Additionally, critical care nurses play a significant role in end-of-life care. They provide compassionate care to patients who are at the end of their lives and support their families during this difficult time. This includes managing symptoms, providing emotional support, and facilitating communication between the patient, family, and healthcare team.

Acute Respiratory Failure

Acute respiratory failure is a common condition encountered in critical care settings. It occurs when the lungs cannot adequately exchange oxygen and carbon dioxide, leading to hypoxemia (low levels of oxygen in the blood) or hypercapnia (high levels of carbon dioxide in the blood). Acute respiratory failure can be caused by various factors, including pneumonia, acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD), and neuromuscular disorders.

Management of acute respiratory failure involves addressing the underlying cause, supporting oxygenation and ventilation, and preventing complications. This may include administering supplemental oxygen, medications to treat the underlying cause (e.g., antibiotics for pneumonia), and mechanical ventilation in severe cases. Critical care nurses play a vital role in the management of patients with acute respiratory failure by monitoring their respiratory status, administering medications, managing mechanical ventilation, and providing supportive care.

Sepsis and Septic Shock

Sepsis is a life-threatening response to an infection that can lead to tissue damage, organ failure, and death. Septic shock is a subset of sepsis characterized by profound circulatory, cellular, and metabolic abnormalities, leading to a higher risk of mortality. Early recognition and management of sepsis and septic shock are crucial for improving outcomes.

The management of sepsis and septic shock involves administering antibiotics, fluids, and vasopressors (medications that constrict blood vessels and increase blood pressure) as needed. Other treatments may include mechanical ventilation, renal replacement therapy, and surgical interventions to remove the source of infection. Critical care nurses play a crucial role in the early recognition and management of sepsis and septic shock by monitoring the patient’s vital signs, administering medications and fluids, and providing supportive care.

Mechanical Ventilation

Mechanical ventilation is a life-saving intervention used in critical care settings to support or replace the patient’s spontaneous breathing. It is commonly used in patients with acute respiratory failure, ARDS, and during surgical procedures requiring general anesthesia. Mechanical ventilation can be invasive (via an endotracheal tube or tracheostomy) or non-invasive (via a mask or helmet).

Managing a patient on mechanical ventilation requires specialized knowledge and skills. It involves selecting the appropriate mode of ventilation, setting and adjusting the ventilator parameters, monitoring the patient’s response to ventilation, and preventing and managing complications. Critical care nurses play a pivotal role in the management of patients on mechanical ventilation by monitoring their respiratory status, adjusting ventilator settings as needed, and providing supportive care.

Critical care nursing is a vital and specialized field in healthcare that focuses on managing patients with life-threatening illnesses or injuries. This includes addressing various aspects such as acute respiratory failure, sepsis and septic shock, and mechanical ventilation. Critical care nurses play a pivotal role in assessing, planning, implementing, and evaluating the care of critically ill patients. They collaborate with a multidisciplinary team of healthcare professionals to provide comprehensive and compassionate care. Research in critical care nursing is essential for developing evidence-based practices, improving patient outcomes, and advancing the profession. The range of critical care nursing research paper topics is diverse and offers numerous opportunities for in-depth exploration and contribution to this crucial field.

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Coefficients and 95% CIs (shaded areas) are shown from facility-week regressions for an analysis sample of 2967 facilities experiencing a severe COVID-19 outbreak that started between June 14, 2020, and January 1, 2021. There are 456 029 facility-weeks between January 1, 2017, and March 31, 2021, included. Primary independent variables are event-time indicator variables relative to the outbreak start. All regressions also contain facility and week fixed effects. Dependent variables are facility COVID-19 cases reported in the National Healthcare Safety Network data (A), total hours worked by nursing staff at a given facility in a given week from the Centers for Medicare & Medicaid Services’ Payroll Based Journal data (B), resident census in a given week from the National Healthcare Safety Network data (C), and nursing staff hours per resident (ie, the measure from panel B divided by the measure from panel C) (D).

Coefficients and 95% CIs (shaded areas) are shown from facility-week regressions for an analysis sample of 2967 facilities experiencing a severe COVID-19 outbreak that started between June 14, 2020, and January 1, 2021. There are 456 029 facility-weeks between January 1, 2017, and March 31, 2021, included. Primary independent variables are event-time indicator variables relative to the outbreak start. All regressions also contain facility and week fixed effects. A, Dependent variables represent counts of nursing employees who were absent, departed, or hired in each week, scaled by each facility’s average weekly staffing level across all weeks. B, The dependent variable for the dashed line is the weekly nursing staffing level, the blue area uses current absences, the green area uses cumulative (past 12 weeks) departures, and the orange area uses cumulative (past 12 weeks) new hires, again all scaled by the average weekly staffing level. The right y-axis of each panel scales these effect sizes by the mean weekly staffing level across all facilities that experienced a severe outbreak.

Coefficient estimates and 95% CIs (shaded areas) are shown from facility-week regressions for an analysis sample of 2967 facilities experiencing a severe COVID-19 outbreak that started between June 14, 2020, and January 1, 2021. There are 456 029 facility-weeks between January 1, 2017, and March 31, 2021, that are included. Primary independent variables are event-time indicator variables relative to the outbreak start. All regressions also contain facility and week fixed effects. Dependent variables are weekly hours worked by nursing staff, scaled by each facility’s average across all weeks. The dependent variable for the dashed line is total weekly hours worked. The blue area uses regular-time hours (ie, hours worked by noncontract employees up to 40 hours per week), the orange area uses overtime hours (ie, hours worked by noncontract employees that are more than 40 hours in a week), and the gray area uses all hours worked by contract workers. Together, these areas sum to the dashed line shown. The right y-axis multiplies the effect size by the mean average nursing home weekly hours across all facilities that experienced a large outbreak.

Coefficients and 95% CIs (error bars) are shown from facility-week regressions for an analysis sample of 2967 facilities experiencing a severe COVID-19 outbreak that started between June 14, 2020, and January 1, 2021. There are 456 029 facility-weeks between January 1, 2017, and March 31, 2021, included. Primary independent variables are event-time indicator variables relative to the outbreak start. Each bar represents the sum of these coefficients across the first 16 weeks after an outbreak’s start for a different dependent variable. A, The dependent variables are total staffing counts and hours for each of 3 staff types: registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs). B, Dependent variables are absences, departures, and new hires for each staff type. C, Dependent variables are regular-time hours, overtime hours, and contract hours. All dependent variables are expressed as a percentage of the facility’s average staffing level (or hours worked) for each staff type. All regressions also contain facility and week fixed effects.

Coefficients and 95% CIs (shaded areas) are shown from facility-week regressions for an analysis sample of 2967 facilities experiencing a severe COVID-19 outbreak that started between June 14, 2020, and January 1, 2021. There are 456 029 facility-weeks between January 1, 2017, and March 31, 2021, included. Primary independent variables are event-time indicator variables relative to the outbreak start. All regressions also contain facility and week fixed effects. A, The dependent variable is an indicator for a facility reporting a shortage of nursing staff and/or aides in that week. B, The dependent variables are facility resident COVID-19–related deaths and resident non-COVID-19–related deaths in that week, as reported to the National Healthcare Safety Network.

eAppendix. Additional detail on data and methods

eTable. Summary statistics of severe outbreak sample compared to full sample

eFigure 1. Histogram of total outbreak cases per bed for all outbreaks, and definition of severe outbreak

eFigure 2. New hires during a severe outbreak, contract and non-contract

eFigure 3. Staffing measures during outbreaks, by outbreak decile

eFigure 4. Staffing measures during a severe outbreak, using alternative definition of severe outbreaks (total cases)

eFigure 5. Staffing measures during a severe outbreak, after adding additional controls of county COVID-19 prevalence

eFigure 6. Staffing measures during a severe outbreak, estimated using Callaway Sant’Anna estimators

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Shen K , McGarry BE , Grabowski DC , Gruber J , Gandhi AD. Staffing Patterns in US Nursing Homes During COVID-19 Outbreaks. JAMA Health Forum. 2022;3(7):e222151. doi:10.1001/jamahealthforum.2022.2151

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Staffing Patterns in US Nursing Homes During COVID-19 Outbreaks

• 1 Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
• 2 Division of Geriatrics & Aging, Department of Medicine, University of Rochester Medical Center, Rochester, New York
• 3 Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
• 4 Department of Economics, Massachusetts Institute of Technology, Cambridge
• 5 UCLA Anderson School of Management, University of California, Los Angeles

Question   What is the association between severe COVID-19 outbreaks and US nursing home staffing patterns?

Findings   In this cohort study of 2967 nursing homes in 2020, owing to increased absences and departures, there were statistically significant declines in staffing levels during a severe COVID-19 outbreak that remained statistically significantly reduced 16 weeks after the outbreak’s start. Facilities temporarily increased hiring, contract staff, and overtime to bolster staffing during outbreaks, but these measures did not fully replace lost staff, particularly certified nursing assistants.

Meaning   Considerable staffing challenges suggest a potential need for policy action to ensure adequate staffing levels during nursing home outbreaks to protect resident health.

Importance   Staff absences and departures at nursing homes may put residents at risk and present operational challenges.

Objective   To quantify changes in nursing home facility staffing during and after a severe COVID-19 outbreak.

Design, Setting, and Participants   In this cohort study, daily staffing payroll data were used to construct weekly measures of facility staffing, absences, departures, and use of overtime and contract staff among US nursing homes experiencing a severe COVID-19 outbreak that started between June 14, 2020, and January 1, 2021. Facility outbreaks were identified using COVID-19 case data. An event-study design with facility and week fixed effects was used to investigate the association of severe outbreaks with staffing measures.

Exposures   Weeks since the beginning of a severe COVID-19 outbreak (4 weeks prior to 16 weeks after).

Main Outcomes and Measures   Total weekly staffing hours, staff counts, staff absences, departures, new hires, overtime and contract staff hours measured for all nursing staff and separately by staff type (registered nurses, licensed practical nurses, certified nursing assistants), facility self-reported staff shortages, and resident deaths.

Results   Of the included 2967 nursing homes experiencing severe COVID-19 outbreaks, severe outbreaks were associated with a statistically significant drop in nursing staffing levels owing to elevated absences and departures. Four weeks after an outbreak’s start, around when average new cases peaked, staffing hours were 2.6% (95% CI, 2.1%-3.2%) of the mean below preoutbreak levels, despite facilities taking substantial measures to bolster staffing through increased hiring and the use of contract staff and overtime. Because these measures were mostly temporary, staffing declined further in later weeks; 16 weeks after an outbreak’s start, staffing hours were 5.5% (95% CI, 4.5%-6.5%) of the mean below preoutbreak levels. Staffing declines were greatest among certified nursing assistants, primarily owing to smaller increases in new hires of this staff type compared with licensed practical nurses and registered nurses.

Conclusions and Relevance   In this cohort study of nursing homes experiencing severe COVID-19 outbreaks, facilities experienced considerable staffing challenges during and after outbreaks. These results suggest the need for policy action to ensure facilities’ abilities to maintain adequate staffing levels during and after infectious disease outbreaks.

Nursing homes have been an epicenter of the COVID-19 pandemic, and the tragic consequences for residents—including nearly 170 000 resident deaths as of May 2022 and months of extreme social isolation owing to visitor restrictions and reduced communal activities—have been well documented. 1 - 3 Less, however, is known about the effects of the pandemic on the staff charged with caring for this vulnerable population.

The nursing home industry has long been plagued by concerns about the adequacy and turnover of its staffing, 4 - 7 and anecdotal reports indicate that COVID-19 has placed immense additional strain on the industry’s workforce. 8 Staff have been at substantial risk of contracting COVID-19, in part owing to persistent personal protective equipment shortages and inadequate COVID-19 testing. 9 , 10 There were more than 1 million confirmed COVID-19 cases and more than 2300 deaths among nursing home staff as of early May 2022. 2 The latter implies an annual death rate surpassing those of the most deadly occupations in the US, including commercial fishing and logging. 11 , 12 Reports suggest that awareness of this risk has led some staff to leave their positions. 13 , 14 When staff leave or are even temporarily absent owing to sickness or quarantine, facilities must find ways to stretch existing staff or bring on new workers to provide even basic care to residents. 8

Despite these anecdotal reports, few empirical studies have documented the effect of COVID-19 on nursing home staffing. Existing studies have established a large decline in industry-wide employment, which was 13.2% lower in June 2021 than at the pandemic’s start. 15 However, some researchers have also noted that because occupancy levels have also decreased considerably, staff hours per patient day (a common measure of staffing) have not generally decreased. 16 Nevertheless, many facility managers have reported experiencing staffing shortages in survey data, 10 suggesting that they may not feel that typical hours of care per patient are adequate given the challenges of providing care during the pandemic.

This cohort study uses daily individual-level staffing data and an event-study design to quantify the association between severe COVID-19 outbreaks and changes in nursing home staffing. The main research questions were (1) whether severe outbreaks were associated with declines in staffing and by how much; (2) whether these declines were primarily driven by temporary absences, permanent departures, or a lack of new hiring; (3) how facilities may have used overtime or contract staffing to cope with these declines; and (4) how these effects may have varied across the 3 primary nursing staff types: registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs). To the extent that CNAs are paid lower wages and may perform tasks with the highest exposure risk, outbreaks may particularly affect their likelihood of being absent or leaving their jobs, as well as facilities’ abilities to fill these positions. On the other hand, RN and LPN positions could be more affected because as more highly certified and higher-earning workers, they may be most willing or able to leave their jobs.

The primary data for this study are from the Centers for Medicare & Medicaid Services’ Payroll Based Journal (PBJ) system. Nursing homes are required to submit daily employee-level staffing information based on auditable payroll and contract data to the PBJ, including unique employee identifiers (allowing individual staff members to be tracked over time), staff type, employment arrangement (salary or contract), and hours worked. 6 , 7 , 16 While the data cover a broad range of staff types, we restricted this analysis to nursing staff (RNs, LPNs, and CNAs) because these staff provide the majority of direct care for patients, and facilities may have deliberately reduced or deferred the use of other staff (eg, therapists, administrative staff) during outbreaks. These staff care for both short-stay and long-stay patients at nursing homes. We used data from January 1, 2017, through June 31, 2021, which covered an average of 333 million daily shifts for 3.6 million staff members at 15 518 facilities each year.

We obtained data on weekly resident and staff COVID-19 cases, resident COVID-19–related deaths and total deaths, and self-reported staff shortages reported from the National Healthcare Safety Network (NHSN) COVID-19 Nursing Home Data set. 2 These data have been published weekly since May 24, 2020.

Per Harvard institutional review board policy, institutional review board approval and written informed consent were not required because this study uses publicly available data. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines for cross-sectional studies.

The population of interest was all US nursing homes, and the primary research objective was to quantify the association of severe COVID-19 outbreaks with changes in facility staffing. We defined outbreaks as contiguous weeks in which facilities reported new cases among residents or staff. We considered an outbreak to have ended after a 2-week stretch without new reported cases. We considered outbreaks that started between June 14, 2020 (the earliest possible date given available data), and January 1, 2021 (to focus on outbreaks prevaccination). We sorted these outbreaks by their severity, defined as total cases per bed, and defined the top decile of outbreaks across all nursing homes as severe outbreaks. Focusing on the top 10% of outbreaks ensured that we observed staffing patterns when facilities were most challenged and may have had the greatest difficulty maintaining adequate staffing levels.

The final analysis sample consisted of all facilities experiencing severe outbreaks that were able to be matched to the PBJ data. We kept only the first severe outbreak for each facility because later outbreaks may have been less comparable owing to spillover effects (increased immunity, staff departures) from the earlier severe outbreak (additional details in eAppendix in the Supplement ). The key exposure measure was the weeks since each outbreak’s start.

We constructed the following staffing measures at the facility-week level using the PBJ data.

We counted employees as hired in the first week they worked hours at a facility.

We labeled an employee as absent in any week between their first and last week of employment during which they did not log any hours. We capped the length of an absence at 12 weeks, meaning an employee who returned after not working for 13 or more weeks would be classified as a departure followed by a new hire. This ensures that absences are defined consistently throughout the sample.

We counted employees as departing a facility on the last week they logged hours at that facility before a period of at least 13 weeks without logging any additional hours at the facility.

We defined the staff count as the number of unique nursing staff who logged any hours at a facility in a given week.

We calculated total weekly hours worked by nursing staff for each facility-week. We also separated these hours into regular-time hours (hours below 40 hours each week worked by noncontract employees), overtime hours (hours exceeding 40 hours each week worked by noncontract employees), and contract hours (all hours worked by contract employees).

We defined a facility as reporting a staffing shortage in any week they reported a shortage of nursing staff or aides in the NHSN.

We used measures of resident deaths reported as due to COVID-19 and resident deaths not reported as due to COVID-19 from the NHSN data. Non-COVID-19–related deaths represent an extreme resident health outcome that could have been influenced by staff shortages.

We used an event-study framework to study how staffing patterns changed during and after a severe outbreak. We ran multivariable linear regressions where the unit of analysis was all available facility-weeks for facilities that experienced a severe outbreak. The primary exposure variables were event-time indicators defined as weeks relative to the start of the outbreak. Models also included facility and calendar-time fixed effects. The facility fixed effects ensure that the event-time indicators are identified from within-facility variation in staffing over time. The time fixed effects control for secular time trends affecting all facilities (eg, holidays, news about the pandemic). Thus, the specification is a 2-way fixed-effect event-study regression, where the identifying assumption is of parallel time trends (ie, that in the absence of an outbreak, facilities experiencing severe outbreaks would have had similar staffing patterns as facilities not currently experiencing severe outbreaks). We graphed the coefficient estimates on the event-time indicators between −4 and 16 weeks, along with their 95% CIs. The effects prior to the start of the outbreak serve as a test of the plausibility of the parallel trends assumption. We clustered standard errors by facility.

Using this method, we first explored how cases, total weekly nursing staff hours, resident census, and hours per resident evolved for the analysis sample during a severe outbreak. Next, we decomposed staffing changes into changes in absences, departures, new hires, overtime, and contract staffing, and studied the contribution of each of these components to the overall trend using the same event-study method. Next, we ran separate regressions for RNs, LPNs, and CNAs, and compared the average outbreak effect (the average coefficient across the first 16 weeks of an outbreak) across staff types. Finally, we plotted the event-study coefficients for self-reported staff shortages and resident deaths. Analyses were performed using Stata, version 16.1 (StataCorp).

In sensitivity analyses, we presented estimates for every outbreak decile to verify that effects were concentrated among larger outbreaks. We also redid the analysis defining severe outbreaks in terms of total cases, rather than total cases per bed. While this alternate definition is a more conventional way to define severe outbreaks, it naturally identifies larger facilities, which is why we used the per-bed definition in the main text. We also added controls for county-level case rates to provide reassurance that the observed associations are specific to a facility experiencing an outbreak, rather than a broader community-level outbreak. Finally, because the estimates exploit staggered event timing, we followed the literature and redid the analyses using alternative estimators that are robust to negative weighting. 17

There were 39 390 COVID-19 outbreaks identified in the data with a mean of 29.6 resident and staff cases (0.30 cases per bed). The top decile of these outbreaks was labeled as severe outbreaks, which averaged 135.1 cases, corresponding to 1.5 cases per bed (eFigure 1 in the Supplement ). Matching to the PBJ data and keeping the first severe outbreak for each facility resulted in a final analysis sample of 2967 nursing homes with severe outbreaks (additional details in the eAppendix and eTable in the Supplement ).

Figure 1 A shows that average weekly cases for the severe outbreak sample peaked at 11.5 cases in the third week and dropped to 3.3 cases in the 16th week. Figure 1 B and C show that nursing staff hours and resident census dropped sharply at the start of severe outbreaks and continued to drop after the peak in average new cases. In week 4, hours of nursing staff care were down 65 (95% CI, 54-76) hours per week, and by week 16 they were down 138 (95% CI, 113-163) hours per week, a decline of 6.4% of the mean. Resident censuses also dropped precipitously during severe outbreaks to down 4.9 (95% CI, 4.5-5.3) residents after 4 weeks and 9.8 (95% CI, 8.8-10.9) residents after 16 weeks, or 12.4% of the mean decline. Because the decline in residents was steeper than the decline in staffing hours, Figure 1 D shows that nursing hours per resident (a common measure of staffing) actually increased during severe outbreaks.

Figure 2 A shows that average weekly nursing staff absences, departures, and new hires all rose during the first few weeks of an outbreak and peaked around the same time as average cases. In week 4, an additional 3.2% (95% CI, 2.9%-3.5%) of the facility average staffing level was absent, an additional 1.3% (95% CI, 1.1%-1.5%) departed, and an additional 1.5% (95% CI, 1.1%-1.9%) were newly hired. The right y-axis rescales these estimates into counts for a typical facility with 64 nursing staff members working each week—this is equivalent to 2.0 additional absences, 0.8 departures, and 1.0 new hires. Absences remained elevated above normal levels 16 weeks after an outbreak’s start, while departures and new hires both returned closer to baseline levels. However, because departures consistently outnumbered new hires between weeks 5 and 15, the result was a net loss of employees during this time. Figure 2 B graphs how these changes were associated with the facility’s staff count. While absences reduce the staff count only in the week that they occur, departures and new hires have a cumulative effect over time. Thus, in week 4, absences and cumulative departures each reduced the staff count by roughly the same amount. However, in week 16, the reduced staff count of 5.1% of the mean is largely owing to the cumulative effect of additional departures relative to new hires.

Figure 3 explores 2 other ways facilities can try to cope with staffing losses: contract staffing and overtime. On average, regular-time hours dropped sharply during the first few weeks of an outbreak. Partly offsetting these declines, average overtime and contract hours both rose during the initial weeks of an outbreak. In week 4, though regular-time hours fell by 5.5% (95% CI, 5.0%-6.0%) of the mean, increased overtime and contract hours added an additional 1.3% (95% CI, 1.1%-1.5%) and 1.4% (95% CI, 1.1%-1.7%) of the mean to the staffing level, respectively. The net effect of these changes is that staffing hours were only reduced by 2.6% (95% CI, 2.1%-3.1%) in week 4. Consistent with these patterns, additional hiring was concentrated among contract employees (eFigure 2 in the Supplement ). However, following the average peak weeks, the additional use of overtime and contract hours declined. Overtime returned to near-baseline levels around week 9, and additional contract hours declined to roughly 1% of the staffing level by week 10. Because regular-time hours did not recover, total staffing hours continued to decline past these peak weeks, reaching 5.5% (95% CI, 4.5%-6.5%) of the mean below preoutbreak levels by week 16.

Figure 4 shows these changes by staff type, averaged across the 16 weeks after the outbreak start. The largest declines were observed for CNAs. The CNA staff size was down an average of 4.7% (95% CI, 4.0%-5.4%) of the mean staff count and 5.8% (95% CI, 5.1%-6.5%) of mean hours, while RN and LPN staff declines were considerably smaller. Rather than being because of substantially different rates of absences or departures, the main source of the difference appears to be that facilities made substantially fewer additional CNA new hires compared with RN and LPN hires.

Finally, Figure 5 explores 2 additional measures of facility stress. Figure 5 A shows that outbreaks also coincided with increases in the rate of facility administrators reporting nursing staff shortages, which is 10.1 (95% CI, 8.6-11.6) percentage points higher during the fourth week of the outbreak than in other preoutbreak weeks. Figure 5 B shows that resident deaths also increased during a severe outbreak. While this is primarily owing to increased deaths due to COVID-19, facilities also saw a rise in non-COVID-19–related deaths during an outbreak.

Event study coefficients for the weeks prior to an outbreak are near zero for most dependent variables. This finding supports the assumption of parallel trends that forms the basis of this article’s identification. In addition, the findings of clear shocks starting in the exact week of treatment and peaks roughly coinciding with the peak in cases offer additional validation of the identification strategy.

eFigure 3 in the Supplement shows the main findings for the other deciles of outbreaks. There were similar staffing reductions for the second decile, statistically significant reductions for the third decile, and small to no reductions for other deciles. eFigure 4 in the Supplement shows that the main findings are robust to measuring outbreak size using total cases instead of total cases per bed, suggesting that the results are not sensitive to the method of defining severe outbreaks. eFigure 5 in the Supplement shows that the main results are robust to including a control for county COVID-19 rates, suggesting that the patterns observed are specific to facility outbreaks rather than rising rates in the surrounding community. eFigure 6 in the Supplement shows that the findings are robust to using an alternative estimator from the literature that addresses negative weighting in 2-way fixed-effect event studies.

This study found that severe COVID-19 outbreaks were associated with statistically significantly reduced nursing staffing levels at nursing homes. Staffing reductions were driven by both temporary absences and permanent departures. Facilities were only able to partially offset these losses during outbreaks through increased new hires and the potentially costly use of contract staff and overtime. Because the use of contract hours and overtime declined after the peak outbreak weeks, staffing levels fell further after these weeks and did not recover even 16 weeks after the start of an outbreak, suggesting a lasting effect of severe outbreaks on facility staffing.

Previous work found that per-resident staffing levels were stable or higher than prepandemic levels owing to a declining resident census that outpaced reductions in staffing. 16 We confirmed this to be true even during severe outbreaks. However, we also found that facility managers are much more likely to report staffing shortages during severe outbreaks, suggesting that per-resident staffing may not be a suitable benchmark for understanding staffing capacity during these times. The shifting composition of nursing home residents caused by declines in admissions of short-stay rehabilitative residents, 18 and adoption of intensive infection-control practices, likely drastically altered the nature of resident care in nursing homes, potentially necessitating greater staffing per resident.

We found staffing losses were greatest for CNAs, primarily owing to a disproportionate lack of hiring to fill losses created by absences and departures. Previous work has demonstrated that CNA positions have higher rates of turnover, likely owing to their low wages and limited benefits. 6 Similar factors may impede the filling of available CNA positions during outbreaks and raise concerns about the long-term outlook of this occupation. 19 In contrast, RN staffing appeared to be the most stable during outbreaks, largely owing to substantial increases in new RN hires. This study is unable to distinguish whether this pattern reflects the available supply of RNs or a focused effort to bring in additional RN hours owing to their additional training and ability to implement and oversee infection control practices.

Future work is needed to determine whether these staffing reductions had adverse effects on resident quality of life, morbidity, and mortality, including by putting facilities at risk for additional outbreaks. Although descriptive, the contemporaneous increase in staffing shortages and resident non-COVID-19–related deaths is consistent with worsened resident health owing to inadequate staffing.

This study is limited by the fact that we are unable to observe the reasons for changes in absences, departures, and new hires. For example, we do not know whether staff absences were because of sickness and quarantine, fear of exposure, or other factors. In addition, we do not know if facilities intentionally chose to lower staffing levels owing to the occupancy declines or if facilities were unable to achieve their desired staffing because of turnover and hiring constraints. We also miss early outbreaks that were not captured by the NHSN data. 1

In light of the considerable challenges documented in this cohort study, preparations for future infectious disease outbreaks should include emergency staffing plans for nursing homes to ensure resident safety, such as centralized “strike teams” that can be temporarily deployed. 20 , 21 These teams can be organized at the state or federal level and could provide supplemental staffing to facilities experiencing severe outbreaks. 22 The American Rescue Plan Act of 2021 provided temporary funding for such teams, and anecdotal reports indicate that they were helpful in addressing emergency staff shortages. 23 , 24 Policy makers might also consider broad investment in nursing home workers through better pay and benefits, such as increasing Medicaid reimbursements alongside wage pass-through requirements. 25 Finally, policy makers should question whether traditional staffing measures accurately capture the adequacy of staffing levels during a pandemic or if new measures are needed.

Accepted for Publication: May 25, 2022.

Published: July 22, 2022. doi:10.1001/jamahealthforum.2022.2151

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Shen K et al. JAMA Health Forum .

Corresponding Author: Karen Shen, PhD, Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, 624 N Broadway, Hampton House 454, Baltimore, MD 21205 ( [email protected] ).

Author Contributions: Dr Shen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Shen, Gandhi.

Drafting of the manuscript: Shen, McGarry, Gruber, Gandhi.

Critical revision of the manuscript for important intellectual content: McGarry, Grabowski, Gruber, Gandhi.

Statistical analysis: Shen, McGarry, Gruber, Gandhi.

Administrative, technical, or material support: McGarry.

Supervision: Gruber, Gandhi.

Conflict of Interest Disclosures: Dr Grabowski reported personal fees from Analysis Group, AARP, GRAIL, Health Care Lawyers PLC, the Medicare Payment Advisory Commission, and RTI International outside the submitted work. Dr Gruber reported personal fees from Aetna outside the submitted work. Prof Gandhi reported grants from the National Institute on Aging as a predoctoral fellowship through the National Bureau of Economics Research; the University of California, Los Angeles internal funding sources, including Ziman Center for Real Estate, Fink Center for Finance & Investment, Price Center for Entrepreneurship & Innovation, Morrison Center for Marketing & Data Analytics, and Society of Hellman Fellows; the Washington Center for Equitable Growth; and the National Institute for Healthcare Management outside the submitted work. No other disclosures were reported.

• Open access
• Published: 03 September 2021

A literature-based study of patient-centered care and communication in nurse-patient interactions: barriers, facilitators, and the way forward

• Abukari Kwame 1 &
• Pammla M. Petrucka 2  

BMC Nursing volume  20 , Article number:  158 ( 2021 ) Cite this article

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Providing healthcare services that respect and meet patients’ and caregivers’ needs are essential in promoting positive care outcomes and perceptions of quality of care, thereby fulfilling a significant aspect of patient-centered care requirement. Effective communication between patients and healthcare providers is crucial for the provision of patient care and recovery. Hence, patient-centered communication is fundamental to ensuring optimal health outcomes, reflecting long-held nursing values that care must be individualized and responsive to patient health concerns, beliefs, and contextual variables. Achieving patient-centered care and communication in nurse-patient clinical interactions is complex as there are always institutional, communication, environmental, and personal/behavioural related barriers. To promote patient-centered care, healthcare professionals must identify these barriers and facitators of both patient-centered care and communication, given their interconnections in clinical interactions. A person-centered care and communication continuum (PC4 Model) is thus proposed to orient healthcare professionals to care practices, discourse contexts, and communication contents and forms that can enhance or impede the acheivement of patient-centered care in clinical practice.

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Providing healthcare services that respect and meet patients’ and their caregivers’ needs are essential in promoting positive care outcomes and perceptions of quality of care, thus constituting patient-centered care. Care is “a feeling of concern for, or an interest in, a person or object which necessitates looking after them/it” [ 1 ]. The Institute of Medicine (IOM) noted that to provide patient-centered care means respecting and responding to individual patient’s care needs, preferences, and values in all clinical decisions [ 2 ]. In nursing care, patient-centered care or person-centered care must acknowledge patients’ experiences, stories, and knowledge and provide care that focuses on and respects patients’ values, preferences, and needs by engaging the patient more in the care process [ 3 ]. Healthcare providers and professionals are thus required to fully engage patients and their families in the care process in meaningful ways. The IOM, in its 2003 report on Health Professions Education , recognized the values of patient-centered care and emphasized that providing patient-centered care is the first core competency that health professionals’ education must focus on [ 4 ]. This emphasis underscored the value of delivering healthcare services according to patients’ needs and preferences.

Research has shown that effective communication between patients and healthcare providers is essential for the provision of patient care and recovery [ 5 , 6 , 7 , 8 ]. Madula et al. [ 6 ], in a study on maternal care in Malawi, noted that patients reported being happy when the nurses and midwives communicated well and treated them with warmth, empathy, and respect. However, other patients said poor communication by nurses and midwives, including verbal abuse, disrespect, or denial from asking questions, affected their perceptions of the services offered [ 6 ]. Similarly, Joolaee et al. [ 9 ] explored patients’ experiences of caring relationships in an Iranian hospital where they found that good communication between nurses and patients was regarded as “more significant than physical care” among patients.

According to Boykins [ 10 ], effective communication is a two-way dialogue between patients and care providers. In that dialogue, both parties speak and are listened to without interrupting; they ask questions for clarity, express their opinions, exchange information, and grasp entirely and understand what the others mean. Also, Henly [ 11 ] argued that effective communication is imperative in clinical interactions. He observed that health and illness affect the quality of life, thereby making health communication critical and that the “intimate and sometimes overwhelming nature of health concerns can make communicating with nurses and other healthcare providers very challenging” [ 11 ]. Furthermore, Henly [ 11 ] added that patient-centered communication is fundamental to ensuring optimal health outcomes, reflecting long-held nursing values that care must be individualized and responsive to patient health concerns. Given the prevalence of face-to-face and device-mediated communications and interactions in healthcare settings, we must explore and clarify who, what, where, when, why, and how interactions with individuals, families, and communities are receiving care and health services [ 11 ].

The value of effective communication in nurse-patient clinical interactions cannot be overemphasized, as “research has shown that communication processes are essential to more accurate patient reporting and disclosure” [ 12 ]. Respectful communication between nurses and patients can reduce uncertainty, enhance greater patient engagement in decision making, improve patient adherence to medication and treatment plans, increase social support, safety, and patient satisfaction in care [ 12 , 13 ]. Thus, effective nurse-patient clinical communication is essential to enhancing patient-centered care and positive care outcomes.

Patient-centered communication, also known as person-centered communication or client-centered communication, is defined as a process that invites and encourages patients and their families to actively participate and negotiate in decision-making about their care needs, as cited in [ 7 ]. Patient-centered communication is crucial in promoting patient-centered care and requires that patients and their caregivers engage in the care process. As McLean [ 14 ] observed, patient-centered care can be enhanced through patient-centered communication by valuing patients’ dignity and rights. Through open communication and collaboration, where information and care plans are shared among care providers, patients, and their families, care provision becomes patient-centered [ 14 ].

Given the interconnected nature of patient-centered care and communication, we must identify the barriers and enablers of patient-centered care and communication and proposed efficient ways to enhance that because patient-centered communication is essential in achieving patient-centered care. Our aim in this paper is to identify the barriers and facilitators of patient-centered care and communication and propose and present a patient-centered care and communication continuum (PC4) Model to explain how patient-centered care can be enhanced in nurse-patient clinical interactions. As Grant and Booth argued, critical reviews are often used to present, analyse, and synthesized research evidence from diverse sources, the outcome of which is a hypothesis or a model as an interpretation of existing data to enhance evidence-based practice [ 15 ]. Thus, this critical literature review study explores the questions: what are the barriers and facilitators of patient-centered care and how can patient-centered care be enhanced through effective clinical communication?

An earlier version of this study was submitted as part of author AK’s doctoral comprehensive exams in February 2021. An interdisciplinary doctoral committee recommended many of the included literature and the questions explored in this study based on the current discourse of patient-centered care advocated for in many healthcare facilities and in recognition of the universal healthcare access objective of the health sustainable development goal. Additional searches for literature were conducted between September and November 2020 using keywords such as barriers and facilitators of nurse-patient interaction, patient-centered care, patient-centered communication , and nurse-patient communication . Databases searched included CINAHL, PubMed, Medline, and Google Scholar. Included studies in this critical review were empirical research on nurse-patient interactions in different care settings published in English and open access. All relevant articles were read, and their main findings relevant to our review questions were identified and organized into themes and subthemes discussed in this paper. Other published studies were read, and together with those that addressed the review question, a model was developed regarding how to enhance patient-centered care through effective communication.

Barriers to Patient-Centered Care and Communication

Nurses constitute a significant workforce of care providers whose practices can severely impact care outcomes (both positive and negative). Nurses spend much time with patients and their caregivers. As a result, positive nurse-patient and caregiver relationships are therapeutic and constitute a core component of care [ 9 , 13 ]. In many instances, nurses serve as translators or patients’ advocates, in addition to performing their primary care duties. Although good nurse-patient relationships positively impact nurse-patient communication and interaction, studies have shown that several factors impede these relationships with significant consequences on care outcomes and quality [ 6 , 16 , 17 ]. Thus, these barriers limit nurses’ and other care providers’ efforts to provide healthcare that meets patients’ and caregivers’ needs. We categorize the barriers to patient-centered care and communication into four kinds: institutional and healthcare system-related, communication-related, environment-related, and personal and behaviour-related barriers. Although these barriers are discussed in separate subheadings, they are interlinked in complex ways during clinical practice.

Institutional and Healthcare System Related Barriers

Many barriers to providing patient-centered care and communication during nurse-patient interactions emanate from healthcare institutional practices or the healthcare system itself. Some of these factors are implicated in healthcare policy or through management styles and strategies.

Shortage of nursing staff, high workload, burnout, and limited-time constituted one complex institutional and healthcare system-level barrier to effective care delivery [ 18 , 19 ]. For instance, Loghmani et al. [ 20 ] found that staffing shortages prevented nurses from having adequate time with patients and their caregivers in an Iranian intensive care unit. Limitations in nursing staff, coupled with a high workload, led to fewer interactions between nurses, patients, and caregivers. Similarly, Anoosheh et al. [ 16 ] found that heavy nursing workload was ranked highest as a limiting factor to therapeutic communication in nurse-patient interactions in Iran.

In a study on communication barriers in two hospitals affiliated with Alborz University of Medical Sciences, Norouzinia et al. [ 21 ] found that shortage of nurses, work overload, and insufficient time to interact with patients were significant barriers to effective nurse-patient interactions. Similar factors are identified as barriers to nurse-patient communication and interactions in other studies [ 13 , 16 , 18 ]. For instance, Amoah et al. [ 16 ] reported that nursing staff shortage and high workload were barriers to patient-centered care and therapeutic communication among Ghanaian nurses and patients. Amoah and colleagues reported a patient’s statement that:

[B]ecause there are few nurses at the ward, sometimes you would want a nurse to attend to you, but he or she might be working on another patient, so in such case, the nurse cannot divide him or herself into two to attend to you both [ 16 ].

Nurses and patients and their caregivers have noted that limited time affects nurse-patient interactions, communication, and care quality. Besides, Yoo et al. [ 22 ] reported that limited visiting hours affected communications between caregivers and nurses in a tertiary hospital in Seoul, Korea. Since the caregivers had limited time to spend with patients, they had little knowledge about the intensive care unit and distrusted the nurses.

Although nursing staff shortage is a significant barrier to patient-centered care and communication that healthcare institutions and managers must know, some healthcare scholars have critique nurses’ complaints of time limitation. For instance, McCabe [ 7 ] argued that the quality of nurse-patient interactions is what matters and not the quantity of time spent with patients and their caregivers. McCabe maintained that “spending long periods with patients does not always result in positive nurse-patient relationships” [ 7 ]. He argued that implementing patient-centered care does not require additional time; hence, nurses’ perceptions of being too busy cannot excuse poor therapeutic communication during clinical interactions. Instead, nurses are encouraged to develop self-awareness, self-reflection, and a commitment to ensuring that patients receive the needed care.

Another institution-related barrier to patient-centered care and communication is the healthcare system’s emphasis on task-centered care. Care providers are more focused on completing care procedures than satisfying patients’ and caregivers’ needs and preferences. This barrier to patient-centered care and communication is acknowledged in several studies [ 7 , 14 , 20 , 22 , 23 ]. For example, McLean [ 14 ] studied dementia care in nursing homes in the United States. She found that patient-centered care and communication in one nursing home (Snow I) were severely affected when nurses, physicians, and care managers focused on completing tasks or observing care and institutional routines to the detriment of satisfying patients’ care needs. However, in the other care home (Snow II), patient-centered care was enhanced as nurses, physicians, and the care home managers focused on addressing patients’ needs and values rather than completing care routines and tasks.

Similarly, Yoo and colleagues [ 22 ] observed that nurse-patient communication was affected when the ICU nurses placed urgency on completing tasks linked directly to patients’ health (e.g., stabilizing vital signs) than communicating to addressed patients’ specific needs. This evidence shows that when nurses are more task-focused, patients and caregivers are treated as bodies and objects, on which medical and care practices must be performed to restore health. Research has shown that when nurses focus on task-oriented care, it becomes hard to provide holistic care to patients or teach and communicate with patients even when nurses are less busy [ 20 ].

Nursing managers and their management styles can affect patient-centered care and communication. Studies have revealed that the management styles that nursing managers implement can either facilitate or impede patient-centered care [ 14 , 22 ]. When nurse managers orient their nursing staff towards task-centered care practices, it affects nurse-patient interaction and communication. Moreover, when nurse managers fail to address their staff’s mental health needs and personal challenges, it influences how nurses attend to patients’ care needs. For example, nurses have indicated that nurse-patient communication is affected when nurse managers are unsupportive or unresponsive to their needs [ 20 ].

In a study exploring nursing and midwifery managers’ perspectives on obstacles to compassion giving and therapeutic care across 17 countries, Papadopoulos et al. [ 24 ] discovered that nurses and midwifery managers’ characteristics and experiences could facilitate or impede compassion and therapeutic interactions in nursing care. Negative personal attitudes, including selfishness, arrogance, self-centeredness, rudeness, lack of leadership skills, the desire for power, and feelings of superiority among nurses and midwifery managers, were obstacles to compassion building. The study further showed that managers who emphasize rules, tasks, and results do not prioritize relationship-building and see their staff as workers rather than team members [ 24 ]. Therefore, nurse managers and care administrators must monitor nurse-patient interaction and communication to address nurses’ concerns and support them, especially in resource-constrained and high patient turnover contexts [ 25 , 26 ].

Communication-Related Barriers

Effective communication is essential to providing patient-centered care. Studies have shown that poor communication between care providers and patients and their caregivers affects care outcomes and perceptions of care quality [ 7 , 16 , 27 , 28 ]. A consistent communication-related barrier in nurse-patient interaction is miscommunication, which often leads to misunderstandings between nurses, patients, and their families [ 20 ]. Other communication-related barriers include language differences between patients and healthcare providers [ 6 , 16 , 27 ], poor communication skills, and patients’ inability to communicate due to their health state, especially in ICU, dementia, or end-of-life care contexts [ 13 , 22 ]. For instance, in their maternity care study, Madula et al. [ 6 ] noted that language barriers significantly affected effective communication between nurses/midwives and expectant mothers. A patient in their study indicated that although many nurses were polite and communicated well, some nurses had challenges communicating with patients in the Chitumbuka language, which affected those nurses’ ability to interact effectively with patients [ 6 ].

Furthermore, Norouzinia et al. [ 21 ] asserted that effective communication could not be established when nurses and patients have a language difference. Moreover, the meanings of certain non-verbal communication acts (e.g., head nodding, eye gaze, touch) can invoke different interpretations across different cultures, which could impede the interactions between patients and nurses. Even in healthcare contexts where nurses and patients speak the same language, “differences in vocabulary, rate of speaking, age, background, familiarity with medical technology, education, physical capability, and experience can create a huge cultural and communication chasm” between nurses and patients [ 12 ]. In ICU and other similar care settings, nurses find it difficult to effectively communicate with patients because the mechanical ventilators made it hard for patients to talk [ 22 ].

To overcome the communication-related barriers, healthcare institutions must make it a responsibility to engage translators and interpreters to facilitate nurse-patient interactions where a language barrier exists. Moreover, nurses working in ICU and other similar settings should learn and employ alternative forms of communication to interact with patients.

Environment-Related Barriers

The environment of the care setting can impact nurse-patient communication and the resulting care. Thus, “good health care experiences start with a welcoming environment” [ 29 ]. Mastors believed that even though good medicine and the hands working to provide care and healing to the sick and wounded are essential, we must not “forget the small things: a warm smile, an ice chip, a warm blanket, a cool washcloth. A pillow flipped to the other side and a boost in bed” [ 29 ]. The environment-related barriers are obstacles within the care setting that inhibit nurse-patient interaction and communication and may include a noisy surrounding, unkept wards, and beds, difficulties in locating places, and navigating care services. Noisy surroundings, lack of privacy, improper ventilation, heating, cooling, and lighting in specific healthcare units can affect nurse-patient communication. These can prevent patients from genuinely expressing their healthcare needs to nurses, which can subsequently affect patient disclosure or make nursing diagnoses less accurate [ 13 , 18 , 21 ]. For instance, Amoah et al. [ 16 ] revealed that an unconducive care environment, including noisy surroundings and poor ward conditions, affected patients’ psychological states, impeding nurse-patient relationships and communication. Moreover, when care services are not well-coordinated, new patients and their caregivers find it hard to navigate the care system (e.g., locating offices for medical tests and consultations), which can constrain patient-centered care and communication.

Reducing the environment-related barriers will require making the care setting tidy/clean, less noisy, and coordinating care services in ways that make it easy for patients and caregivers to access. Coordinating and integrating care services, making care services accessible, and promoting physical comfort are crucial in promoting patient-centered care, according to Picker’s Eight Principles of Patient-Centered Care [ 30 ].

Personal and Behaviour Related Barriers

The kind of nurse-patient relationships established between nurses and patients and their caregivers will affect how they communicate. Since nurses and patients may have different demographic characteristics, cultural and linguistic backgrounds, beliefs, and worldviews about health and illnesses, nurses’, patients’, and caregivers’ attitudes can affect nurse-patient communication and care outcomes. For instance, differences in nurses’ and patients’ cultural backgrounds and belief systems have been identified as barriers to therapeutic communication and care [ 12 , 13 , 21 ]. Research shows that patients’ beliefs and cultural backgrounds affected their communication with nurses in Ghana [ 16 ]. These scholars found that some patients refused a blood transfusion, and Muslim patients refused female nurses to attend to them because of their religious beliefs [ 16 ]. Further, when nurses, patients, or their caregivers have misconceptions about one another due to past experiences, dissatisfaction about the care provided, or patients’ relatives and caregivers unduly interfere in the care process, nurse-patient communication and patient-centered care were affected [ 16 , 21 ].

Similarly, nurse-patient communication was affected when patients or caregivers failed to observe nurses’ recommendations or abuse nurses due to misunderstanding [ 20 ], while patients’ bad attitudes or disrespectful behaviours towards nurses can inhibit nurses’ ability to provide person-centered care [ 31 ]. The above-reviewed studies provided evidence on how patients’ and caregivers’ behaviours can affect nurses’ ability to communicate and deliver patient-centered care.

On the other hand, nurses’ behaviours can also profoundly affect communication and care outcomes in the nurse-patient dyad. When nurses disrespect, verbally abuse (e.g., shouting at or scolding), and discriminate against patients based on their social status, it affects nurse-patient communication, care outcomes, and patient disclosure [ 6 , 32 ]. For instance, Al-Kalaldeh et al. [ 18 ] believe that nurse-patient communication is challenged when nurses become reluctant to hear patients’ feelings and expressions of anxiety. When nurses ignore patients’ rights to share ideas and participate in their care planning, such denials may induce stress, discomfort, lack of trust in nurses, thereby leading to less satisfaction of care [ 18 ].

Furthermore, when nurses fail to listen to patients’ and caregivers’ concerns, coerce patients to obey their rules and instructions [ 16 , 17 , 20 ], or fail to provide patients with the needed information, nurse-patient communication and patient-centered care practices suffer. To illustrate, in Ddumba-Nyanzia et al.‘s study on communication between HIV care providers and patients, a patient remarked that: “I realized no matter how much I talked to the counselor, she was not listening. She was only hearing her point of view and nothing else, [and] I was very upset” [ 17 ]. This quote indicates how care provider attitudes can constrain care outcomes. Due to high workload, limited time, poor remunerations, and shortage of personnel, some nurses can develop feelings of despair, emotional detachment, and apathy towards their job, which can lead to low self-esteem or poor self-image, with negative consequences on nurse-patient interactions [ 13 , 18 ].

Given the significance of effective communication on care, overcoming the above personal and behaviour related barriers to patient-centered care and communication is crucial. Nurses, patients, and caregivers need to reflect on the consequences of their behaviours on the care process. Thus, overcoming these barriers begins with embracing the facilitators of patient-centered care and communication, which we turn to in the next section.

Facilitators of patient-centered care and communication

Patient-centered care and communication can be facilitated in several ways, including building solid nurse-patient relationships.

First, an essential facilitator of patient-centered care and communication is overcoming practical communication barriers in the nurse-patient dyad. Given the importance of communication in healthcare delivery, nurses, patients, caregivers, nursing managers, and healthcare administrators need to ensure that effective therapeutic communication is realized in the care process and becomes part of the care itself. Studies have shown that active listening among care providers is essential to addressing many barriers to patient-centered care and communication [ 7 , 13 ]. Although handling medical tasks promptly in the care process is crucial, the power of active listening is critical, meaningful, and therapeutic [ 22 ]. By listening to patients’ concerns, nurses can identify patients’ care needs and preferences and address their fears and frustrations.

Another facilitator of patient-centered care is by understanding patients and their unique needs [ 25 ], showing empathy and attending attitudes [ 7 , 13 ], expressing warmth and respect [ 22 ], and treating patients and caregivers with dignity and compassion as humans. For instance, McCabe [ 7 ] noted that attending, which obligates nurses to demonstrate that they are accessible and ready to listen to patients, is a patient-centered care process; a fundamental requirement for nurses to show genuineness and empathy, despite the high workload. Showing empathy, active listening, respect, and treating patients with dignity are core to nursing and care, and recognized in the Code of Ethics for Nurses [ 33 ], and further emphasized in the ongoing revision of the Code of Ethics for nurses [ 34 ].

Besides, engaging patients and caregivers in the care process through sharing information, inviting their opinion, and collaborating with them constitutes another facilitator of patient-centered care and communication. When patients and caregivers are engaged in the care process, misunderstandings and misconceptions are minimized. When information is shared, patients and caregivers learn more about their health conditions and the care needed. As McLean [ 14 ] argued, ensuring open communication between care providers and patients and their families is essential to enhancing patient-centered care. Conflicts ensue when patients or their families are denied information or involvement in the care process. As a result, the Harvard Medical School [ 30 ] identified patient engagement, information sharing, and nurse-patient collaboration during care as essential patient-centered care principles.

Finally, health policy must be oriented towards healthcare practices and management to facilitate patient-centered care and communication. These policies, at a minimum, can involve changes in management styles within healthcare institutions, where nurse managers and healthcare administrators reflect on nursing and care practices to ensure that the Code of Ethics of Nurses and patients’ rights are fully implemented. Resource constraints, staff shortages, and ethical dilemmas mainly affect care practices and decision-making. Nonetheless, if patients are placed at the center of care and treated with dignity and respect, most of the challenges and barriers of patient-centered care will diminish. Empowering practicing nurses, equipping them with interpersonal communication skills through regular in-service training, supporting them to overcome their emotional challenges, and setting boundaries during nurse-patient interactions will enhance patient-centered care practices.

In line with the above discussion, Camara et al. [ 25 ] identify three core dimensions that nurses, patients, and caregivers must observe to enhance patient-centered care: treating the patient as a person and seeing the care provider as a person and a confidant. Regarding the first dimension, care providers must welcome patients, listen to them, share information with them, seek their consent, and show them respect when providing care. The second dimension requires that the healthcare provider be seen and respected as a person, and negative perceptions about care providers must be demystified. According to Camara et al. [ 25 ], care providers must not overemphasize their identities as experts but rather establish good relationships with patients to understand patients’ personal needs and problems. Lastly, patients and caregivers must regard care providers as confidants who build and maintain patients’ trust and encourage patients’ participation in care conversations. With this dimension, patients and caregivers must know that nurses and other care providers have the patient at heart and work to meet their care needs and recovery process.

Camara et al.‘s [ 25 ] three dimensions are essential and position patients, their caregivers, and nurses as partners who must engage in dialogic communication to promote patient-centered care. As a result, effective communication, education, and increased health literacy among patients and caregivers will be crucial in that direction.

Enhancing Patient-Centered Care and Communication: A Proposed Model

Nursing care practices that promote patient-centered communication will directly enhance patient-centered care, as patients and their caregivers will actively engage in the care process. To enhance patient-centered communication, we propose person-centered care and communication continuum (PC4) as a guiding model to understand patient-centered communication, its pathways, and what communication and care practices healthcare professionals must implement to achieve person-centered care. In this PC4 Model, we emphasize the person instead of the patient because they are a person before becoming a patient. Moreover, the PC4 Model is supposed to apply to all persons associated with patient care; thus, respect for the dignity of their personhood is crucial.

Although much is written about patient-centered communication in the healthcare literature, there is a gap regarding its trajectory and what communication content enhances patient-centered communication. Also, little is known about how different clinical discourse spaces influence communication and its content during nurse-patient clinical interactions. Using evidence from Johnsson et al. [ 3 ], Murira et al. [ 23 ], and Liu et al. [ 35 ], among other studies, we outline the components of the PC4 Model and how different discourse spaces in the clinical setting and the content of communication impact patient-centered care and communication.

The proposed PC4 Model in this paper has three unbounded components based on the purpose of and how communication is performed among care providers, patients, and their caregivers. Figure  1 illustrates the PC4 Model, its features, and trajectory.

A Person-Centered Care and Communication Continuum (PC4 Model)

Task-Centered Communication

At the lowest end of the PC4 Model is task-centered communication. Here, the care provider’s role is to complete medical tasks as fast as possible with little or no communication with the patient and their caregivers. Patients and caregivers are treated as bodies or objects whose disease symptoms need to be studied, identified, recorded, treated, or cured. As Johnsson et al. [ 3 ] observed, communication content at this stage is mainly biomedically oriented, where nurses and other healthcare professionals focus on the precise medical information (e.g., history taking, medical examination, test results, medication, etc.) about the patient. With a task-centered orientation, nurses make journal entries about their patients’ disease state and ensure that treatment plans, diagnostic tests, and medical prescriptions are completed. Communication at this stage is often impersonal or rigid (see [ 23 ] for details). Care providers may address patients and their caregivers by using informal attributes (e.g., bed 12, the woman in the red shirt, card 8, etc.), thereby ignoring patients’ and caregivers’ personal and unique identities. Patients’ and caregivers’ nonverbal communication signs are mostly overlooked.

Motivations for task-centered communication can be attributed to time limitation, high workload, and staff shortage, thereby pushing nurses and other care providers to reach as many patients as possible. Moreover, the healthcare system’s orientation towards and preference for biomedically-focused care seems to favour task-centered communication [ 7 , 14 ].

Depending on the clinical discourse space under which patient-provider interactions occur, different forms of communication are employed. Clinical discourse spaces can be public (e.g., in the ward, patient bedside), private (e.g., consulting rooms, medical test labs, nurse staff station, etc.), or semi-private (e.g., along the corridor) [ 35 ]. In these clinical discourse spaces, nurse-patient communication can be uninformed (patients or caregivers are not informed about patients’ care conditions or why specific data and routines are performed). It can be non-private (others can hear what the nurse and patient are talking about) or authoritative (care providers demonstrate power and control and position themselves as experts) [ 23 ]. Finally, in task-centered communication, healthcare providers often use medical jargon or terminologies [ 3 ] since the goal of communication is not to engage the patient in the process. Usually, patients or their caregivers are not allowed to ask questions, or their questions get ignored or receive superficial, incomprehensible responses.

Process-Centered Communication

Process-centered communication is an intermediate stage on the continuum, which could slip back into the task-centered or leap forward into person-centered communication. Through process-centered communication, care providers make an effort to know patients and their caregivers as they perform care routines. Care providers ask patients or their caregivers questions to understand the care conditions but may not encourage patients or caregivers to express their thoughts about their care needs. Patients and caregivers are recognized as persons with uniques care needs but may not have the agency to influence the care process. Care providers may chit-chat with patients or their caregivers to pass the time as they record patients’ medical records or provide care. Unlike task-centered communication, there is informative and less authoritative communication between nurses and patients and their caregivers. The goal of process-centered communication could be a mixture of instrumental and relational, with less display of power and control by nurses.

Person-Centered Communication

This is the highest point of the PC4 Model, where patient-centered care is actualized. At this stage of the communication continuum, patients and caregivers are treated as unique persons with specific care needs and are seen as collaborators in the care process. As McLean [ 14 ] observed, caregiving becomes a transactional relationship between the care provider and receiver at the person-centered stage of the continuum. The care itself becomes intersubjective, a mutual relational practice, and an ongoing negotiation for care providers and receivers [ 14 ].

The content of communication at this stage of the continuum is both “personal” and “explanatory” [ 3 ]. Nurses and other healthcare providers create meaningful relationships with patients and their caregivers, understand patients’ concerns, needs, and problems, use open-ended questions to encourage patients or caregivers to express their thoughts and feelings about the care situation. Nurses and other healthcare professionals explain care routines, patients’ health conditions, and management plans in lay language to patients and caregivers through person-centered communication. Accomplishing this level includes employing alternative forms of communication to meet the needs of intensive care unit patients, deaf patients, and ventilated and intubated patients. For instance, it has been shown that “deaf people […] frequently do not have access to clear and efficient communication in the healthcare system, which deprives them of critical health information and qualified health care” [ 36 ]. Empathetic communication practices, including active listening, showing genuine interest in patients’ care, and respect and warmth, become a significant part of nursing care [ 3 , 7 , 14 , 22 ].

Different communication strategies are employed based on the care situation and context. Chit-chatting, as a form of personal communication [ 3 ], use of humor as a communication strategy [ 7 , 8 ], and even maintaining silence [ 28 ] are essential in enhancing person-centered care and communication. Both care providers and patients or their caregivers use relationship-building and -protecting humor (see [ 28 ] for details) to address difficult situations in the care process.

Implications of the PC4 Model for Nursing Practice

Given the values of effective communication in nurse-patient interactions and care outcomes, nurses and other healthcare providers must ensure that they develop therapeutic relationships with patients, their families, and caregivers to promote person-centered care and communication. Achieving that begins with knowing and reflecting on the barriers of therapeutic communication and ways to minimize them. The PC4 Model draws nurses and all healthcare providers’ attention to patient-centered care pathways and how effective communication is necessary. Healthcare professionals, including nurses, must be aware of how their communication orientation–––either oriented toward completing tasks, following care processes or toward addressing patients’ and their caregivers’ needs––can impact patient-centered care. Healthcare providers must observe the care context, patients’ unique situations, their non-verbal language and action, and whether they belong to historically marginalized groups or cultures.

Mastors [ 29 ] has offered healthcare providers some guidance to reflect on as they communicate and interact with patients and caregivers. Thus, (a) instead of asking patients, “What’s the matter?“ care providers must consider asking them, “What’s important to you?“ With this question, the patient is given a voice and empowered to contribute to their own care needs. Care providers should (b) check with patients in the waiting room to update patients whose waiting time has been longer than usual, based on the care context. They should also (c) try to remember their conversations with patients to build on them during subsequent interactions. This continuity can be enhanced by nurse managers reexamining how they deploy care providers to patients. The same nurse can be assigned to the same patients for the duration of the patient’s stay to help patients feel valued and visible [ 29 ].

Knowledge of cultural competence, sensitivity, humility, and interpersonal communication skills will help achieve and implement the PC4 Model. As Cuellar [ 37 ] argues, “[h]umility is about understanding and caring for all people [and] being empathetic.“ Cultural competence is a “dynamic process of acquiring the ability to provide effective, safe, and quality care to the patients through considering their different cultural aspects” [ 38 ]. The concept of cultural competence entails “cultural openness, awareness, desire, knowledge and sensitivity” during care [ 39 ]. It demands that care providers respect and tailor care to align with patients’ and caregivers’ values, needs, practices, and expectations, based on care and moral ethics and understanding [ 39 ]. Active listening and showing compassion as therapeutic relationship-building skills are essential, and continuous education and mentorship will be crucial to developing these skills among healthcare providers.

We invite qualitative and quantitative studies, especially on language use and communication strategies, to explore and evaluate the PC4 Model. Providing in-depth and experiential data on ways to increase its effectiveness as a tool to guide healthcare providers is highly desired. More knowledge can support healthcare providers in offering evidence-based patient-centered care in different healthcare settings and units.

Conclusions

Effective communication is an essential factor in nurse-patient interactions and a core component of nursing care. When communication in the nurse-patient dyad is patient-centered, it becomes therapeutic. It allows for trust and mutual respect in the care process, thereby promoting care practices that address patients’ and caregivers’ needs, concerns, and preferences. We have identified the barriers and facilitators of patient-centered care and communication and proposed a person-centered care and communication continuum (PC4 Model) to demonstrate how patient-centered communication intersects with patient-centered care.

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Abbreviations

Intensive Care Unit

Institution of Medicine

Person-Centered Care and Communication Continuum

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Acknowledgments

We express our gratitude to the first author’s doctoral committee members for their valuable comments, suggestions, and critique of an earlier version of this paper. We are also grateful to the anonymous reviewers for the insightful comments and suggestions that have helped us improve the study’s quality.

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Abukari Kwame is a Ph.D. candidate in Interdisciplinary Studies in the College of Graduate and Postdoctoral Studies, University of Saskatchewan, interested in patients' rights in nurse-patient clinical communications and interactions in the hospital setting. He holds two Master of Philosophy degrees in Indigenous Studies and English Linguistics. Abukari's research interests include language use in social interaction, health communication, First/Second language acquisition, African traditional medical knowledge, and Indigenous and qualitative research methodologies.

Pammla M. Petrucka is a professor in Nursing and has international research experience with many of her graduate students from Africa, Asia, and the Caribbean. Pammla has published extensively in the field of nursing. Her research interests are vast, including child and maternal health, Indigenous peoples' health, global health, and vulnerable populations, with extensive experiences in qualitative research and indigenous research methodologies. Pammla is co-editor of the BMC Nursing journal and a reviewer for many other academic journals.

The authors have not received any funding for the conduct, preparation, and publication of this paper.

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Both authors conceived the topic. PMP recommended the literature, AK searched and added more sources, and drafted the paper. PMP reviewed the paper for intellectual content. Both AK and PMP read and approved the final version.

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Kwame, A., Petrucka, P.M. A literature-based study of patient-centered care and communication in nurse-patient interactions: barriers, facilitators, and the way forward. BMC Nurs 20 , 158 (2021). https://doi.org/10.1186/s12912-021-00684-2

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DOI : https://doi.org/10.1186/s12912-021-00684-2

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The Impact of the COVID-19 Pandemic on Nursing Care: A Cross-Sectional Survey-Based Study

Marco clari.

1 Department of Public Health and Pediatrics, University of Torino, 10126 Turin, Italy; [email protected] (M.C.); [email protected] (M.L.); [email protected] (P.D.G.); [email protected] (S.C.); [email protected] (V.D.)

Michela Luciani

Alessio conti, veronica sciannameo.

2 Department of Cardiac, Vascular Sciences and Public Health, University of Padova, 35128 Padua, Italy; [email protected]

Paola Berchialla

3 Department of Clinical and Biological Sciences, University of Torino, 10043 Orbassano, Italy; [email protected]

Paola Di Giulio

Sara campagna, valerio dimonte, associated data.

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reason.

The COVID-19 pandemic has had a severe impact on nursing care. This cross-sectional survey-based study compared aspects of nursing care and nurses’ satisfaction with care provided before and during the first wave of the COVID-19 pandemic. A total of 936 registered nurses (RNs) rated the frequency with which they performed fundamental care, nursing techniques, patient education, symptom management, and nurse–patient relationships before and during the pandemic. A recursive partitioning for ordered multivariate response in a conditional inference framework approach was applied. More frequent fundamental cares were associated with their frequency before the pandemic ( p < 0.001), caring for COVID-19 patients ( p < 0.001), and workplace reassignment ( p = 0.004). Caring for COVID-19 patients ( p < 0.001), workplace reassignment ( p = 0.030), and caring for ≤7.4 COVID-19 patients ( p = 0.014) increased nursing techniques. RNs in high-intensity COVID-19 units ( p = 0.002) who educated patients before the pandemic, stopped this task. RNs caring for COVID-19 patients reported increased symptom management ( p < 0.001), as did RNs caring for more non-COVID-19 patients ( p = 0.037). Less frequent nurse–patient relationships before the pandemic and working in high-intensity COVID-19 units decreased nurse–patient relationships ( p = 0.002). Despite enormous challenges, nurses continued to provide a high level of care. Ensuring the appropriate deployment and education of nurses is crucial to personalize care and to maintain nurses’ satisfaction with the care provided.

1. Introduction

From 31 December 2019, when the World Health Organization’s China Office reported a case of pneumonia of unknown etiology in Wuhan, the Coronavirus Disease 2019 (COVID-19) started to spread across the globe [ 1 ]; on 11 March 2020, COVID-19 was declared a pandemic [ 2 ]. Italy was one of the first countries outside of China to report cases [ 1 ]. As of July 2021, Italy has reported more than 4,200,000 confirmed COVID-19 cases and more than 128,000 COVID-19 deaths [ 3 ]. The COVID-19 pandemic has had a severe impact on healthcare systems around the world, affecting the availability of beds in hospitals and intensive care units [ 4 ]. The second and following waves of the COVID-19 pandemic are still challenging healthcare systems and professionals [ 5 ].

Nurses have been recognized as fundamental actors in public health crises and have played a major role in the COVID-19 pandemic; however, the pandemic has had a severe impact on nursing care. This is due to the challenges associated with the preparedness and response to emergencies shown by several healthcare systems in different care settings [ 6 , 7 ]. In the face of a heavy workload, nurses have had to wear personal protective equipment (PPE) [ 4 , 8 ] and have been confronted with a lack of PPE [ 9 , 10 ], staff [ 10 , 11 ], and other resources [ 9 , 12 ], all of which have led to decreased mental health and well-being [ 13 , 14 ], occupational satisfaction [ 15 ], and high infection rates among nurses [ 16 ]. Nurses also reported being reassigned due to changes in human resource allocations, having to quickly learn new skills and competencies, having to work with newly-graduated nurses, and difficulties in communicating with patients and their families due to PPE and isolation. All of the factors mentioned above could have affected personalized care, an essential aspect of nursing, during the COVID-19 pandemic [ 17 ]. The concept of personalized health care in nursing is influenced by the care environment and the ability of general nursing care to meet a patient’s needs, which were inevitably affected by the emergency, thereby potentially impacting clinical outcomes and satisfaction with care.

However, to the best of our knowledge, no rigorous study exists on how the pandemic has impacted nursing care. In particular, no studies have examined differences in the care provided by nurses who cared for COVID-19 and non-COVID-19 patients, or between those who were reassigned due to the pandemic and those who continued to work in their unit. Moreover, there is still a limited understanding of the factors associated with nurses’ satisfaction with the care provided during the pandemic. This information could help decision makers ensure that nurses in a given unit have the appropriate education, skill mix, and patient-to-nurse ratio, thereby improving clinical practice and care personalization during this pandemic [ 5 ] and future health emergencies. Hence, this study aimed to identify changes in nursing care by comparing aspects of nursing care and satisfaction with care provided before and during the first wave of the COVID-19 pandemic, examining differences between nurses who cared for COVID-19 and non-COVID-19 patients, and between those who were reassigned and those who continued to work in their unit.

2. Materials and Methods

2.1. study design and participants.

This cross-sectional study included registered nurses (RNs) in Italy who delivered nursing care in the 3 months before study enrollment. No restrictions regarding the type of patient nor the setting were applied.

2.2. Procedures

RNs were invited to complete an online questionnaire, which was available between 12 May and 31 July 2020. Invitations were disseminated through ads on social media (Facebook, Twitter, Instagram), informational links on the websites of the Nursing Councils, and through texts and e-mails sent directly to RNs, using contact lists obtained from nursing schools in each of the Italian regions. Every 2 weeks, ads and informational links were reposted, and texts and e-mails were resent to RNs. The response rate (RR) was calculated as the RR2 [ 18 ], i.e., the sum of complete (I) and partial (P) questionnaires divided by the sum of complete, partial, non-questionnaires (NC, defined as respondents who logged on to the questionnaire but did not complete any item), and other (O, defined as respondents who could not fit in any of previous classifications; in this study, this category was not present): RR2 = (I + P)/((I + P) + (NC + O)). For this study, the RR2 was 81.4%.

2.3. Instruments

The online questionnaire was composed of six sections that covered: (I) changes in nursing care due to the COVID-19 pandemic; (II) changes in work organization; (III) ethics choices; (IV) most challenging case; (V) additional education needed to care for COVID-19 patients; (VI) socio-demographic characteristics. In the present analysis, only sections I, II, and VI were considered.

In section I, RNs reported the frequency with which they carried out the following tasks: fundamental care (i.e., personal hygiene, elimination, nutrition, mobility) [ 19 ], nursing techniques (i.e., respiratory support, vascular access, device positioning and management), patient education (i.e., respiratory exercises, medication, education), symptom management (pain, dyspnea, fatigue), and nurse–patient relationships (i.e., personal interactions with patients). These frequencies were reported for two time periods: before the COVID-19 pandemic and during what RNs perceived to be the worst week of the pandemic, using a 5-point Likert-type scale (1 = never; 2 = rarely; 3 = sometimes; 4 = often; 5 = most of the time). For each time period, RNs were also asked to rate their overall satisfaction with the care they provided (1 = very poor; 2 = poor; 3 = fair; 4 = good; 5 = excellent).

In section II, RNs reported the number of patients they cared for before the COVID-19 pandemic, if that number increased, remained stable, or decreased during the pandemic, if they were reassigned to another unit, and, if so, how many times. RNs were also asked if they thought there was enough time to prepare for the pandemic in terms of work organization, education/training, and their personal lives, with responses given on 5-point Likert-type scale (1 = not at all; 2 = not really; 3 = neutral; 4 = somewhat; 5 = very much). Finally, RNs were asked to report the number of patients they personally cared for during their last shift, and those caring for COVID-19 patients were also asked to report the number of patients who required no respiratory support, high-flow oxygen, non-invasive ventilation, and mechanical ventilation. The number of patients was then weighted based on the respiratory support provided, with a higher coefficient for patients with mechanical ventilation and a lower coefficient for those with no support.

2.4. Analysis

Continuous variables were described using medians and interquartile ranges (IQRs), or means and standard deviations (SDs). The Mann–Whitney U test was performed to evaluate differences in quantitative variables, and Chi-square or Fisher’s exact test was used for categorical variables as appropriate.

A recursive partitioning for ordered multivariate response in a conditional inference framework approach was applied. Conditional inference trees were constructed to identify the pattern of work organization, nurse education/training, and personal lives associated to the different levels of fundamental care, nursing techniques, patient education, symptom management, and nurse–patient relationships in post COVID-19.

Independent variables were selected and split through multiplicity-adjusted p -values following the Bonferroni’s criterion. The split of variables determines a set of rules associated to different values of the dependent variable. In more detail, for each variable, the conditional regression tree determines the optimal split and a partitioning is performed, selecting the input variable with the highest multiplicity-adjusted p -value. Then, a binary split is performed on the selected input variable, and this process is recursively performed until a stopping criterion is reached. The stopping criterion was based on significant results, i.e., splitting continues until the minimum of the adjusted p -values is less than a pre-specified level of significance (0.05) or otherwise stops [ 20 ].

In our analysis, a conditional inference tree was constructed for each nursing task as dependent variables (fundamental care, nursing techniques, patient education, symptom management, and nurse–patient relationships). Independent variables included in the trees were: values from before the pandemic for investigated nursing tasks, caring for COVID-19 patients, gender, age, education, geographic area, work experience, working unit, workplace reassignment, preparedness (in terms of work organization, education/training, and personal lives), decrease/increase in number of patients, number of non-COVID-19 patients, a weighted sum of COVID-19 patients with different respiratory support, and type of contract.

We also performed sensitivity analyses using multivariate logistic regression. Likert scores for the investigated nursing tasks before and during the COVID-19 pandemic were dichotomized into never/rarely/sometimes (0) and often/most of the time (1), and odds ratios and 95% confidence intervals were computed. Missing data were deleted listwise. Analyses were performed using R version 3.6.1 [ 21 ].

2.5. Ethics

RNs were informed about the study before accessing the online questionnaire, and consent was obtained before they began the questionnaire. RNs were not compensated for their role in the study, and participation was voluntary. All data were collected anonymously, and respondents could leave the questionnaire at any time. The study was approved by the University of Torino Ethics Committee (Approval no. 279061–01/07/2020) and conducted following the Declaration of Helsinki guidelines.

A total of 936 RNs completed the online questionnaire (68.2% female); the median age in the sample was 39 years (IQR 30–49), and 40.7% of RNs had a bachelor’s degree ( Table 1 ).

Participants’ characteristics.

1 Presence of missing data.

Most RNs worked in Northern Italy during the pandemic (86.5%). The median work experience was 13 years (IQR 5–25). Most RNs worked in a hospital setting (67.8%), 77.1% cared for COVID-19 patients, 28% worked in a dedicated COVID-19 unit, and 35.9% were reassigned following reorganizations to increase beds for COVID-19 patients. Almost half of RNs reported that they cared for fewer patients during the pandemic (45.8%): the median patient-to-nurse ratio among RNs caring for non-COVID-19 patients was 8 (IQR 3–15), compared to 2.4 (IQR 1–5) among those caring for COVID-19 patients. RNs caring for COVID-19 patients were significantly younger and had less work experience. Furthermore, there was a higher number of male RNs among those caring for COVID-19 patients. Lastly, in areas of high COVID-19 prevalence (i.e., Northern Italy and the Lombardy Region) more RNs were reassigned to COVID-19 hospital units. RNs reported there was little time to prepare for the pandemic in terms of work organization (median 2 (rarely), IQR 1–3), education/training (median 2, IQR 1–2), and their personal lives (median 1 (no time), IQR 1–2) ( Table 1 ).

The highest number of reassigned RNs was observed in the Lombardy Region (19.0% versus 10.1%; p = 0.003). Reassigned RNs ( p = 0.042; median 38, IQR 29–48 versus median 40, IQR 30–50 among those not reassigned) felt poorly prepared in terms of work organization ( p = 0.004; median 1, IQR 1–2 versus median 2, IQR 1–3) and their personal lives ( p = 0.009; median 1, IQR 1–2 versus median 1, IQR 1–2) ( Table 2 ).

Differences between nurses who changed their work unit.

1 Presence of missing data; NA Not available.

Reassigned nurses reported caring for a higher number of patients ( p < 0.001), and they comprised a higher number of self-employed (9.2% versus 3.8% among those not reassigned; p = 0.028) and temporary contract workers (public temporary 3.3% versus 2.6%; private temporary 3.9 versus 2.9). RNs were mostly reassigned from medium–low-intensity facilities, and especially from community care to tertiary care hospitals ( p = 0.003); RNs reassigned to COVID-19 units were most often transferred from medium–low-intensity facilities to tertiary care hospitals ( p < 0.001). Conversely, RNs who were not reassigned and did not care for COVID-19 patients were significantly older ( p < 0.001; median 45.5, IQR 36–51 versus median 37.5, IQR 30–48 among reassigned RNs who did care for COVID-19 patients) and had more work experience ( p < 0.001; median 20, IQR 10–30 versus median 11, IQR 4–23) ( Table 2 ).

Nursing Tasks

The frequency of fundamental care tasks before the pandemic was associated with the frequency during the pandemic ( p < 0.001; before: median 3, IQR 2–4; during: median 3, IQR 2–5). In Figure 1 , the conditional inference tree with the frequency of fundamental care tasks as a dependent variable, reported at the bottom of the figure as a boxplot, is represented. At the top of Figure 1 , we can see that the first split of the decision tree was performed based on the frequency of fundamental care registered in the pre-pandemic period (node 1). RNs who frequently performed fundamental care before the pandemic continued this practice ( p < 0.001), as we can see in the rightmost branch of the tree. Furthermore, among those who declared to not usually perform fundamental care before the pandemic, caring for COVID-19 patients ( p < 0.001) or being reassigned ( p = 0.004) significantly increased the frequency of fundamental care during the COVID-19 pandemic, as expressed in the boxplot on the Likert scale, reported in the bottom of the Figure 1 , left part.

The conditional inference tree with the frequency of fundamental care tasks as dependent variable, reported at the bottom of the figure as boxplot.

In Figure 2 , the conditional inference tree about fundamental care is represented. RNs caring for COVID-19 patients who frequently performed nursing techniques before the pandemic continued this practice ( p < 0.001; before: median 4, IQR 3–5; during: median 5, IQR 4–5), as we can see by following the rightmost branch. RNs who cared for COVID-19 patients ( p < 0.001), those who were reassigned ( p = 0.030), and those who assisted ≤7.4 patients ( p = 0.014) significantly increased the frequency of nursing techniques, with a median Likert scale of 5, as shown in the leftmost boxplot ( Figure 2 ).

The conditional inference tree with the frequency of nursing techniques as dependent variable, reported at the bottom of the figure as boxplot.

RNs who performed patient education often before the pandemic and worked in high-intensity COVID-19 units ( p = 0.002) stopped performing this task, as shown in the rightmost branch in Figure 3 , while those in other settings continued their usual practice ( p < 0.001; before: median 3, IQR 2–4; during: median 3, IQR 1–4).

The conditional inference tree with the frequency of patient education as dependent variable, reported at the bottom of the figure as boxplot.

In Figure 4 , the decision tree with symptom management as the outcome variable is reported. The frequency of symptom management during the COVID-19 pandemic was similar to that before the pandemic ( p < 0.001; before: median 4, IQR 3–5; during: median 4, IQR 3–5), but it increased for RNs who cared for COVID-19 patients ( p < 0.001), as shown in the leftmost branch. Caring for a higher number of non-COVID-19 patients (>6) increased the frequency of symptom management ( p = 0.037), as reported in the eighth node, with a median Likert scale of 4.

The conditional inference tree with the frequency of symptom management as dependent variable, reported at the bottom of the figure as boxplot.

Nurse–patient relationships before the pandemic were associated with those during the pandemic ( p < 0.001; before: median 5, IQR 4–5; during: median 4, IQR 2–5), but nurses working in high-intensity COVID-19 units who reported frequent nurse–patient relationships before the pandemic had no chance to relate with patients during the pandemic ( p = 0.002), as shown in the rightmost branch of the conditional decision tree reported in Figure 5 .

The conditional inference tree with the frequency of nurse–patient relationships as dependent variable, reported at the bottom of the figure as boxplot.

RNs’ satisfaction with the care provided remained stable during the pandemic ( p < 0.001; before: median 4, IQR 3–4; during: median 3, IQR 2–4), but younger nurses (aged ≤27 years) tended to judge their care as poor ( p = 0.047), with a median Likert scale of 2 (node 12, Figure 6 ). Similarly, nurses with less work experience (≤13 years) reported a decreased quality of care ( p = 0.032). RNs who reported high preparedness in terms of education/training showed increased care satisfaction ( p = 0.039), with a median Likert scale of 4 (node 10, Figure 6 ).

The conditional inference tree with the RNs’ satisfaction with the care provided as dependent variable, reported at the bottom of the figure as boxplot.

The results of sensitivity analyses, performed through logistic regressions, confirmed those obtained from the decision trees. In the logistic regression models, the variables associated with the care provided during the COVID-19 pandemic were the same as those identified in the conditional inference trees.

4. Discussion

The present study described how nursing care was affected during the first wave of the COVID-19 pandemic and provided indications as to why nurses changed their practices. The first wave of the COVID-19 pandemic hit healthcare systems hard, affecting all aspects of nursing care; more nursing techniques were performed to care for patients, but the nurse–patient relationship suffered. These changes in nursing care led nurses to report lower satisfaction with the care provided.

The impact of the COVID-19 pandemic on nursing care mainly was felt on a clinical level. COVID-19 is a life-threatening illness that requires complex, clinically-focused, and personalized care; many patients were hospitalized for long periods with unpredictable outcomes. The COVID-19 pandemic was influenced by RNs’ previous care habits, indicating the real professional commitment demonstrated by nurses, regardless of the circumstances [ 22 ]. Although the pandemic caused high levels of stress among nurses due to the high workload and uncertainty [ 23 ], nurses endeavored to guarantee the same level of care they provided before the pandemic to each patient. The prioritization of nursing care provided was affected [ 24 ], likely due to the unknown trajectory of the disease, the increase in fundamental care due to high patient dependency, the acquisition of new technical skills, and difficulties in communication and patient education due to infection containment measures.

Nurses who were reassigned to COVID-19 units increased the amount of fundamental care provided. A possible explanation could be that the COVID-19 context often requires many specialized skills, and newly-hired nurses may have preferred to perform more fundamental care while they learned these skills. Conversely, reassigned nurses could have increased the amount of fundamental care simply because COVID-19 patients require more personalized support for their activities of daily living [ 25 ].

The physiological needs of COVID-19 patients also required the performance of more nursing techniques. In fact, nurses who cared for COVID-19 patients performed these techniques far more frequently during than before the pandemic. Nurses had to adapt to the needs of these patients, often learning or refreshing techniques such as non-invasive ventilation support and arterial puncture for blood gas analysis [ 26 ]. Symptom management was also performed more frequently given the higher number of patients as healthcare systems reached capacity [ 27 ].

A biomedical perspective is often predominant in life-threatening situations, but it should be balanced by the value of caring for others and the individualization of care, something on which technical skills should also focus [ 28 ]. This is particularly crucial for COVID-19 patients, who have to face an unknown disease without the support of their loved ones and likely without a close relationship with healthcare providers. Education is the most commonly omitted task when resources are strained [ 29 ], and nurse–patient relationships and patient education in our study were reduced due to infection prevention measures [ 30 ]. Despite these problems, nurses tried to invent new ways to interact with patients, such as drawing on gowns, printing their pictures to show their faces, and writing their names on face shields [ 31 ].

Another relevant finding was the lower satisfaction with care provided among RNs from areas of high COVID-19 prevalence and those who worked in high-intensity units. This may be due to the lack of therapeutic solutions and the patients’ reduced chance of recovery, especially the elderly, making nurses feel less confident that the care they provided was adequate [ 32 ]. Nurses working in primary care were also less satisfied with the care provided, probably due to the exceptional number of patients that were treated at home and in other community settings [ 33 ].

The devastating, rapid impact that the pandemic has had on the Italian healthcare system might explain some of the lack of preparedness reported by our respondents. During the first wave, COVID-19 was an unknown disease, and a trial and error approach was often applied, with frequent changes in therapies, use of ventilation, and supination [ 34 ]; thus, nurses had to adapt their daily work to rapidly changing guidelines and protocols, individualizing the care they provided. Moreover, in Italy, newly-graduated nurses were hired to work in new community care units specifically dedicated to COVID-19 patients, which aimed to identify those who required medical assistance and those who could stay isolated at home with telephone follow-ups [ 35 ]. More experienced community care nurses were also recruited for these units, which led to a decreased availability of nurses for established home care services. These factors could have influenced the preparedness of nurses working in community settings.

The pandemic globally highlighted the nursing shortage and the lack of adequate, coordinated management responses to population health crises [ 36 ]. However, contextual factors must be considered when evaluating care outcomes, which should not be merely interpreted quantitatively. In this regard, the patient-to-nurse ratio should not be the only index collected to evaluate staffing needs but, perhaps, environmental and organizational factors should also be considered [ 37 ]. Healthcare systems should maintain, and be prepared to implement, plans for pandemic events, and hospital managers should draw up specific emergency plans that define the human resources required in case of a long-term pandemic with recurrent waves, based on competencies, skill mix (e.g., of experienced and novice nurses), and job rotations. Nurse staffing should be proportionate to the clinical complexity of patients and to the organizational complexities to individualize care, minimizing the risk of missed care and patient death [ 38 ]. These actions would improve the healthcare system’s response and alleviate the stress imposed by crises, although maintaining the highest standard of nursing care delivered.

The present study has a number of limitations. The majority of the respondents were from the areas most affected by the pandemic in Italy, and most worked in hospital settings. Moreover, our sample could not completely represent the Italian nursing population, which consists of around 367,000 individuals. This may have produced some response bias as nurses from hospital settings may have felt more implicated in the pandemic and, thus, may have been more interested in the survey. Moreover, considering that data collected were self-reported, findings may be affected by the respondents’ emotional or physical condition. High negative and low positive affect have been associated with an emotional autobiographical memory [ 39 ]; in the emotionally-charged pandemic, recall may also have influenced our results. Our findings should be interpreted considering that data were collected during the first wave in Italy, the first European country hit by the pandemic, and the first country that had to reorganize its healthcare system to respond to the emergency. Moreover, the use of a cross-sectional design means that causality cannot be proven. However, the decision tree analysis used, and the sensitivity analyses performed, increased the confidence in the inferences.

5. Conclusions

This study highlighted the impact of the COVID-19 pandemic on nursing care and the differences between nurses who were and were not reassigned. Despite all of the difficulties faced by nurses, they were generally satisfied with the care provided, except for younger nurses and those with limited working experience. Furthermore, in spite of the healthcare reorganization, and the need to educate, prioritize, and individualize their activities to meet the needs of patients with complex clinical conditions, nurses continued to provide a high level of care, individualizing their practices and ensuring the highest quality of care. Nurses who felt more prepared in terms of education and training were more satisfied with the care provided, and an increase in the number of patients decreased the frequency of fundamental care and nurse–patient relationships. Nurses caring for COVID-19 patients performed nursing techniques more often, to the detriment of patient education. Ensuring the appropriate deployment and education of nurses is crucial to personalize care, especially during a pandemic, and to maintain nurses’ satisfaction with the care provided. Policy makers should consider these results to create structured plans to address long-term pandemics and ensure appropriate nurse staffing in hospitals and primary care settings.

Acknowledgments

We would like to thank all of the nurses who completed the survey in such difficult times.

Author Contributions

Conceptualization, M.C., M.L. and A.C.; methodology, M.C., M.L. and A.C.; validation, V.S. and P.B.; formal analysis, V.S. and P.B.; investigation, V.D.; data curation, M.C., M.L. and A.C.; writing—original draft preparation, M.C., M.L., A.C. and V.S.; writing—review and editing, P.B., P.D.G., S.C. and V.D.; supervision, P.B., P.D.G., S.C. and V.D.; project administration, M.C. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the University of Torino (Approval no. 279061–01/07/2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Biden-Harris Administration Partners with States and Releases Data Recommendations to Strengthen the Direct Care Workforce

As part of the Biden-Harris Administration's Care Workers Recognition Month activities and in support of President Biden's Executive Order on Increasing Access to High-Quality Care and Supporting Caregivers , the U.S. Department of Health and Human Services (HHS) announced today that 20 states have been selected to participate in two separate technical assistance programs that together will help participating states better recruit, train, and retain direct care workers, who provide home and community-based services (HCBS) for older adults and people with disabilities. Both programs are offered through the Administration for Community Living's Direct Care Workforce (DCW) Strategies Center. HHS also announced the first members of the advisory committee that will guide the center's work.

In addition, HHS and the U.S. Department of Labor (DOL) released key recommendations for building data infrastructure to support development of policies and initiatives to strengthen the HCBS direct care workforce. As directed by President Biden's care executive order, the recommendations are the culmination of a year-long partnership with DOL that was led by HHS' Office of the Assistant Secretary for Planning and Evaluation.

"Investing in care is an investment in the future of America's families, workforce, and economy," said HHS Secretary Xavier Becerra. "Better data infrastructure will ensure that these investments are made wisely, and our partnerships with states will accelerate the development and sharing of effective strategies for expanding the direct care workforce. These announcements reflect the Biden-Harris Administration's commitment to ensuring older adults and people with disabilities can get the care they need to live and thrive in their communities."

"The Biden-Harris Administration is committed to ensuring that workers across the country—including those who work in the crucial and growing care economy for older adults and people with disabilities—have access to good jobs," said Acting Secretary of Labor Julie Su. "Today, the Department of Labor is proud to release a joint report with the Department of Health and Human Services about gaps in data surrounding the home- and community-based workforce. We are also partnering with HHS to uplift the work of the Direct Care Workforce Strategies Center to improve job quality for this essential workforce. In order to better understand this critical sector and to improve job quality for these workers, we need the best possible data. Care workers show up every day to ensure people have the support they need. We need to show up for them to ensure these are quality jobs with family-sustaining wages, safety standards, and benefits."

"Urgent action is needed to address the shortage of direct care professionals – which is threatening to reverse decades of progress in community living," said Alison Barkoff, who leads the Administration for Community Living (ACL). "ACL's Direct Care Workforce Strategies Center was developed in partnership with DOL and agencies across HHS to catalyze that action and to improve recruitment, retention, and development of this critical workforce. The technical assistance announced today will help 20 states improve collaboration across state agencies, direct care professionals, people receiving services, and other stakeholders and accelerate progress toward this goal."

The DCW Strategies Center received applications from 20 states to participate in one of two programs to support them in addressing the workforce shortage in their state. After a rigorous review and scoring process, including interviews with state teams, the following six states were selected to participate in the first program, which offers intensive technical assistance:

Each will receive up to 250 hours of individualized technical assistance, have a coach, and have access to subject matter experts to support them in addressing their state's unique DCW challenges. Each team includes representatives from the state's Medicaid, aging, disability, and workforce development agencies, in addition to other stakeholders. Synopses of each state's areas of focus and goals for the program can be found here .

The remaining fourteen states will be offered an opportunity to participate in a second program offering learning collaboratives focused on sharing best practices, innovative strategies, and proven models for growing the direct care workforce. In addition, each participating state will receive up to 70 hours of individual technical assistance on a topic or issue important to the state.

To guide the work of the DCW Strategies Center, twenty-six stakeholders, primarily direct care professionals and people who receive HCBS, were selected to serve as members of the center's inaugural advisory council. A list of the members can be found here .

Making informed policy decisions to expand and strengthen the HCBS workforce can be difficult with the data currently available. Information is often unavailable or incomplete, and much of the data that is available cannot be broken out by state, making it difficult to understand the impact of Medicaid and other policies. That's why President Biden's Executive Order also charged HHS and DOL with identifying opportunities for government and non-government entities to build data infrastructure to inform federal and state policies that strengthen the HCBS workforce, which is disproportionately made up of women, persons of color, and immigrants with little or no formal education beyond high school.

A new issue brief released today by HHS and DOL presents recommendations to improve HCBS workforce data, informed by input from HCBS providers, workers, consumers, and researchers. HHS is taking steps to implement these recommendations, both through the recently finalized Ensuring Access to Medicaid Services rule that will enable greater transparency in rates paid to certain types of HCBS workers, and through the DCW Strategies Center's intensive technical assistance to selected states who have identified data collection, tracking and/or analysis as a key need.

"Implementing these recommendations will build the data infrastructure needed to answer key questions about the HCBS workforce and drive data-informed policy decisions to improve the quality of and access to HCBS for the millions of Americans who are receiving or need these services," said Miranda Lynch-Smith, who is performing the delegable duties of the Assistant Secretary for Planning and Evaluation.

The report is posted on the DCW Strategies Center website . It will be joined in the coming months by other resources developed through the HHS-DOL partnership.

On Tuesday, May 21, HHS and DOL, in collaboration with the DCW Strategies Center, will host a webinar to share highlights and recommendations from the issue brief. Advance registration is required.

To learn more, visit the DCW Strategies Center website .

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