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Effect of a multidisciplinary ward-based intervention on end-of-life care for general medicine patients

Published online by Cambridge University Press:  19 October 2021

Michelle Soo Rui Ting*
Affiliation:
Division of Advanced Internal Medicine, Department of Medicine, National University Hospital, Singapore, Singapore
Norshima Binte Nashi
Affiliation:
Division of Advanced Internal Medicine, Department of Medicine, National University Hospital, Singapore, Singapore
Kai Ang Lin Elaine
Affiliation:
Division of Oncology Nursing, National Cancer Institute Singapore, National University Hospital, Singapore, Singapore
Benjamin M.Y. Hooi
Affiliation:
Division of Advanced Internal Medicine, Department of Medicine, National University Hospital, Singapore, Singapore
*
Author for correspondence: Michelle Soo Rui Ting, Department of Advanced Internal Medicine, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074. E-mail: michelle_soo@nuhs.edu.sg
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Abstract

Objective

Providing good end-of-life (EOL) care for noncancer patients has been made a national priority in Singapore. A combined medical and nursing ward-based intervention known as the EOL care plan was piloted in a general medicine ward at our institution, aiming to guide key aspects of EOL care. The aim of this study is to assess the EOL care plan's effect on EOL care for general medicine patients.

Method

We conducted a retrospective cohort study on inpatients who died in a general ward under the discipline “General Medicine” from May to October 2019. We collected data around symptom management, rationalization of care and communication with families. The primary analysis compared care received by patients who died in the pilot ward with that of a control group of patients who died in other wards.

Results

In total, 112 records were included in the analysis. Pain assessment was more common in the pilot ward compared with the control group (35.3% vs. 6.3%, p < 0.001), as were anti-psychotic prescriptions for delirium (64.7% vs. 24.4%, p = 0.001). Fewer patients received blood glucose monitoring in the last 48 h of life in the pilot ward (69.5% vs. 35.3%, p = 0.007). There were also less frequent parameters monitoring in the pilot ward (p < 0.004).

Significance of results

The implementation of the EOL care plan was associated with process-level indicators of better EOL care, suggesting that it could have a significant positive impact when implemented on a wider scale.

Type
Original Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Introduction

The provision of high-quality end-of-life (EOL) care for general medicine patients can be challenging due to the unpredictable trajectories of this predominantly noncancer group. Unlike patients with cancer, those with organ failures, frailty, or advanced dementia may have a fluctuating course of deterioration (Murray et al., Reference Murray, Kendall and Boyd2005). Medical health professionals with little or no palliative care experience may not be comfortable managing patients toward the EOL. The failure to recognize a dying patient may lead to a delayed communication of prognosis, which causes distress to patients and their families (Gomes and Higginson, Reference Gomes and Higginson2008; Gibbins et al., Reference Gibbins, McCoubrie and Alexander2009). It may also lead to poor assessment and management of symptoms at the EOL. Deficiencies in teamwork between doctors and nurses and conflicting role definitions have also been shown to affect the holistic delivery of appropriate EOL care (Farber et al., Reference Farber, Egnew and Herman-Bertsch2003; Brueckner et al., Reference Brueckner, Schumacher and Schneider2009). In Singapore, the provision of good palliative care for noncancer patients has been identified as a key priority in the National Strategy for Palliative Care (Di Leo et al., Reference Di Leo, Beccaro and Finelli2011).

Integrated care pathways are a viable option to guide key aspects of patient care at the EOL. Such pathways have been implemented in countries such as Australia, China, and the United Kingdom (Jackson et al., Reference Jackson, Mooney and Campbell2009; Phillips et al., Reference Phillips, Halcomb and Davidson2011), and have been shown to improve the quality of care (Phillips et al., Reference Phillips, Halcomb and Davidson2011). One of the well-known integrated care pathways was the United Kingdom's Liverpool Care Pathway for the Dying Patient, which was developed from a model of care that had been successfully used in hospices. While the Liverpool Care Pathway for the Dying Patient received criticism for being associated with poor care, an independent review published in 2013 also concluded that where implemented properly, patients died a peaceful and dignified death (Neuberger et al., Reference Neuberger, Guthrie and Aaronvitch2013; Finn and Malhotra, Reference Finn and Malhotra2019). There was also evidence that it improved physicians’ confidence with pain management and with discontinuing inappropriate treatment (Di Leo et al., Reference Di Leo, Beccaro and Finelli2011). In Singapore, EOL care pathways have been implemented for hospitalized cancer patients, with promising results on patient care (Neo et al., Reference Neo, Poon and Peh2012). An audit of patients cared for on such a pathway did not find any evidence of compromised care (Koon et al., Reference Koon, Shan and Shivananda2015). However, there has not been any published data on their effectiveness among noncancer patients.

The aim of this study was to assess the effect of a multidisciplinary ward-based intervention on EOL care for general medicine patients. Prior to May 2019, there was no structured approach to the care of general medicine inpatients at the EOL at our institution, which is a 1,239-bedded teaching hospital in Singapore.

In May 2019, a combined medical and nursing ward-based intervention collectively known as the EOL care plan was piloted in a general medical ward. The medical component involved a specialized documentation template for doctors in their daily review of patients who were identified to be dying. This EOL care plan was designed to address gaps in care that were picked up during prior audits, where some of the key findings were poor documentation of symptom assessment, polypharmacy, frequent investigations, and blood glucose monitoring at the EOL. These translated into the “boxes” in the EOL care plan template. This template aimed to focus the medical team's efforts on symptom assessment and management, and regular communication with the family (Supplementary 1). Following the lessons gleaned from the review of the Liverpool Care Pathway for the Dying Patient (Finn and Malhotra, Reference Finn and Malhotra2019), there was a deliberate effort made to (i) clearly define the inclusion criteria, which was a dying patient assessed by the primary team consultant to be at the last hours to short days of life and (ii) keep the template as brief and non-prescriptive as possible, to avoid it becoming a “tick-box exercise” for medical teams.

The pilot “End-of-life Nursing Care Bundle” was created to guide generalist nurses reassess the needs of patients approaching the EOL and facilitate discussion between staff, patients, and families. There are no studies to date to prove the effectiveness of nursing care bundle in improving staffs’ attitudes and competency in providing EOL care. Clark et al. (Reference Clark, Curry and Byfieldt2015) reported that the nursing care bundle will be relevant and potentially impactful in the acute care setting where there were an increased number of patient deaths. In the five key priorities in the last days of life, a person's physical, emotional, cultural, and religious needs were identified to be paramount in creating a personalized care plan (Bussooa and North, Reference Bussooa and North2016). The information provided in the EOL nursing care bundle were contextualized to local practice and in particular, the drug formulary was referenced from the electronic palliative care handbook available to healthcare professionals in Singapore (Koh et al., Reference Koh, Yee and Yang2021). The content was also reviewed by the palliative care consultants in our institution.

Methods

A retrospective medical records review was conducted for general medicine inpatients who died between 01 May 2019 and 31 October 2019. We collected data on patients’ demographics, comorbidities, communication with the patients’ families, and treatment provided during the admission preceding their deaths. The primary analysis compared the treatment received by patients who died in our pilot ward, with that of a control group consisting of general medicine patients who died in other wards. While all nurses in the pilot ward received the nursing educational intervention (hereafter known as the “nursing” EOL care plan), not all patients who died in the pilot ward were started on the specialized documentation template (“medical” EOL care plan) by the primary medical teams. In view of this, a secondary analysis was conducted comparing the medical EOL care plan subgroup with the control group.

Primary outcome measures for the study included are as follows: (i) symptom assessment and documentation; (ii) prescription and administration of symptom-directed medications; (iii) frequency of vitals monitoring at the point of demise; (iv) time interval between last blood tests/basal glucose monitoring and demise; (v) number of medications prescribed, including antibiotic use in the last 48 h of life; and (vi) whether the patient's preferred place of death, if previously mentioned, was honored.

We included all adult patients who died in the general wards within the study period under the discipline “General Medicine.” We excluded patients who were younger than 21 years old, elective admissions, pregnant women and patients who did not die in the hospital (even if they had been started on the EOL care plan). Patients who died in the intensive care unit or our institution's affiliated community hospital were also excluded.

Study subjects were de-identified on the data collection sheet. Statistical analysis was performed using SPSS, with two-sided chi-squared tests used to compare categorical data with a significance value of p < 0.05. We expressed variables as number (%) and median (interquartile range).

This study was approved by our institution's ethics committee Domain Specific Review Board (Reference number: 2019/01072).

Results

A total of 117 patients who died under the discipline “General Medicine” were identified. Five patients who had died in the intensive care unit and community hospital were excluded, leaving 112 patients for analysis (Figure 1). Seventeen patients were identified in the pilot ward, of which 11 had been initiated on the “medical” EOL care plan. Ninety-five patients had died in other general wards.

Fig. 1. Flow diagram for patient selection.

The baseline characteristics of the patients in our cohort are outlined in Table 1. Our patients were mostly elderly, with a median age of 83 years old. The majority (66.1%) were bed or chairbound, and 60.7% were confused or uncommunicative. While 107 (95%) deaths were expected, only 21 (18.8%) had a documented advance care plan. Noncancer conditions accounted for 95.5% of deaths, of which pneumonia was the most frequent cause of death accounting for 68 (60.7%) patients, followed by ischemic heart disease which accounted for 14 (12.5%) deaths.

Table 1. Demographic data

The results of our primary analysis are detailed in Table 2. Compared with the patients who had died in other wards, there were significantly more patients in the pilot ward who had pain assessment documentation (35.3% vs. 6.3%) or prescription of an anti-psychotic for delirium (64.7% vs. 24.4%). Fewer patients in the pilot ward had blood glucose monitoring in the last 48 h of life (35.3% vs. 69.5%). There was also less frequent parameters monitoring in those who had died in the pilot ward with a median of 8-hourly parameters monitoring compared with 4-hourly parameters monitoring in those who died in the other wards. Our secondary analysis comparing the “medical” EOL care plan subgroup with the control group yielded similar findings.

Table 2. Primary analysis

Discussion

In this exploratory study, we found that the implementation of a multidisciplinary ward-based intervention was associated with improved care for general medicine inpatients toward the EOL. There was evidence of better symptom assessment and management, as well as less frequent routine monitoring of parameters and blood glucose in the intervention group. These results were consistent with what has been demonstrated in other settings. A before-after trial of an educational intervention to enable staff to identify and manage actively dying patients found similar results (Bailey et al., Reference Bailey, Burgio and Woodby2005). In addition, an integrated review of 26 studies demonstrated better overall care of dying patients (Phillips et al., Reference Phillips, Halcomb and Davidson2011).

Our study used process-level outcomes to obtain evidence around the quality of EOL care. The first broad area was that of symptom assessment and management, a key priority in the care of dying patients. Our results demonstrated a 29% absolute increase in documented pain assessment, as well as a 40.3% absolute increase in anti-psychotic prescriptions. While the sample size of our exploratory study was insufficient to demonstrate a statistically significant benefit in other categories, our study results suggested a possible increase in documented dyspnea assessment, as well as the prescription and administration of other comfort-directed treatments. As a limitation that there was a lack of patient-level symptom assessment including delirium as a whole, but we measured pre-emptive prescription for common symptoms encountered at the EOL as one of the process-level indicators for good care. One notable finding was that all patients in the pilot ward who were prescribed opioids were also administered opioids, whereas 15.8% of patients in the control group who were prescribed opioids did not have them administered. In practice, physicians often put up “as-required” prescriptions of comfort-directed medications for patients at the EOL. The administration of these medications is, thus, contingent on good symptom assessment by the nurses, a skill which was taught in the pilot ward as part of the EOL nursing care bundle (Supplementary 2). Improving the team-based, interdisciplinary delivery of EOL care was a key aim of the EOL care plan.

The second key area was the rationalization of monitoring and treatment. The EOL care plan aimed to shift the medical team's focus away from the routine monitoring that takes place for hospitalized patients, which do not improve the quality of life for imminently dying patients. Frequent monitoring of vital parameters may contribute to anxiety in patients and caregivers, and blood taking for laboratory investigations or glucose monitoring may cause unnecessary discomfort. Our results revealed a 34.2% absolute decrease in the proportion of patients who had blood glucose monitoring in the last 48 h of life, as well as a significant decrease in the frequency of vital parameters monitoring. There was also a suggestion of decreased blood tests and an increase in documented orders to avoid antibiotic escalation, although the small numbers in this pilot were insufficient to demonstrate statistical significance. Avoiding unnecessary, potentially uncomfortable investigations and monitoring is another important component of inpatient EOL care (Paterson et al., Reference Paterson, Duncan and Conway2009).

The descriptive characteristics of our study cohort also revealed a low uptake of advance care planning conversations. While the majority of our cohort comprised of patients who were bed or chairbound (66.1%) and minimally communicative (60.7%), with most deaths (95.5%) classified as “expected,” only 18.8% had a documented advance care plan. This likely reflects the challenges in prognosticating noncancer patients, which accounted for the majority of our general medicine cohort. A systematic review of 42 studies found that there was a wide disparity between physicians in prognostic accuracy, varying between overestimating by 93 days or underestimating by 86 days (White et al., Reference White, Reid and Harris2016).

To the authors’ knowledge, this is the first study evaluating the effect of a multidisciplinary palliative care intervention in Singapore. The EOL care plan was also unique in its multidisciplinary implementation, which aimed to empower healthcare teams to deliver well-integrated, holistic care to patients at the EOL. The main limitation of our exploratory pilot study was the small sample size, which was insufficient to demonstrate a significant benefit in several of the key outcomes. The small number of patients also did not allow adjustment for potential confounders between groups. We plan to use the results of this study to support the wider implementation of the EOL care plan at our institution, including a larger-scale study that is adequately powered for a more robust evaluation of patient outcomes.

Conclusion

Our study's results show promise that the implementation of a structured ward-based intervention could improve the quality of EOL care. Given the high burden of palliative care needs among noncancer patients near the end of their lives, this could prove an invaluable tool for healthcare providers in this setting. The study team plans to iterate the EOL care plan in regular “Plan-Do-Study-Act” (PDSA) cycles; future research will focus on evaluating its effectiveness when implemented on a larger scale.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S1478951521001723.

Author's contribution

All authors were involved in the preparation of the manuscript.

Funding

This research received no specific grant from any funding agency, commercial, or not-for-profit sectors.

Conflict of interest

None of the authors have any conflict of interest to declare.

References

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Figure 0

Fig. 1. Flow diagram for patient selection.

Figure 1

Table 1. Demographic data

Figure 2

Table 2. Primary analysis

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