Improving Quality of Care for Seriously Ill Patients: Opportunities for Hospitalists

Default Palliative Care Consultation for Seriously Ill Hospitalized Patients: A Pragmatic Cluster Randomized Trial

Importance

Increasing inpatient palliative care delivery is prioritized, but large-scale, experimental evidence of its effectiveness is lacking.

Objective

To determine whether ordering palliative care consultation by default for seriously ill hospitalized patients without requiring greater palliative care staffing increased consultations and improved outcomes.

Design, Setting, and Participants

A pragmatic, stepped-wedge, cluster randomized trial was conducted among patients 65 years or older with advanced chronic obstructive pulmonary disease, dementia, or kidney failure admitted from March 21, 2016, through November 14, 2018, to 11 US hospitals. Outcome data collection ended on January 31, 2019.

Intervention

Ordering palliative care consultation by default for eligible patients, while allowing clinicians to opt-out, was compared with usual care, in which clinicians could choose to order palliative care.

Main Outcomes and Measures

The primary outcome was hospital length of stay, with deaths coded as the longest length of stay, and secondary end points included palliative care consult rate, discharge to hospice, do-not-resuscitate orders, and in-hospital mortality.

Results

Of 34 239 patients enrolled, 24 065 had lengths of stay of at least 72 hours and were included in the primary analytic sample (10 313 in the default order group and 13 752 in the usual care group; 13 338 [55.4%] women; mean age, 77.9 years). A higher percentage of patients in the default order group received palliative care consultation than in the standard care group (43.9% vs 16.6%; adjusted odds ratio [aOR], 5.17 [95% CI, 4.59-5.81]) and received consultation earlier (mean [SD] of 3.4 [2.6] days after admission vs 4.6 [4.8] days; P < .001). Length of stay did not differ between the default order and usual care groups (percent difference in median length of stay, -0.53% [95% CI, -3.51% to 2.53%]). Patients in the default order group had higher rates of do-not-resuscitate orders at discharge (aOR, 1.40 [95% CI, 1.21-1.63]) and discharge to hospice (aOR, 1.30 [95% CI, 1.07-1.57]) than the usual care group, and similar in-hospital mortality (4.7% vs 4.2%; aOR, 0.86 [95% CI, 0.68-1.08]).

Conclusions and Relevance

Default palliative care consult orders did not reduce length of stay for older, hospitalized patients with advanced chronic illnesses, but did improve the rate and timing of consultation and some end-of-life care processes.

Trial Registration

ClinicalTrials.gov Identifier: NCT02505035.

Oncology hospitalist impact on hospice utilization

BACKGROUND

Unplanned hospitalizations among patients with advanced cancer are often sentinel events prompting goals of care discussions and hospice transitions. Late referrals to hospice, especially those at the end of life, are associated with decreased quality of life and higher total health care costs. Inpatient management of patients with solid tumor malignancies is increasingly shifting from oncologists to oncology hospitalists. However, little is known about the impact of oncology hospitalists on the timing of transition to hospice.

OBJECTIVE

To compare hospice discharge rate and time to hospice discharge on an inpatient oncology service led by internal medicine-trained hospitalists and a service led by oncologists.

METHODS

At Smilow Cancer Hospital, internal medicine-trained hospitalists were integrated into one of two inpatient medical oncology services allowing comparison between the new, hospitalist-led service (HS) and the traditional, oncologist-led service (TS). Discharges from July 26, 2021, through January 31, 2022, were identified from the electronic medical record. The odds ratio for discharge disposition by team was calculated by logistic regression using a multinomial distribution. Adjusted length of stay before discharge was assessed using multivariable linear regression.

RESULTS

The HS discharged 47/400 (11.8%) patients to inpatient hospice, whereas the TS service discharged 18/313 (5.8%), yielding an adjusted odds ratio of 1.94 (95% CI, 1.07-3.51; p = .03). Adjusted average length of stay before inpatient hospice disposition was 6.83 days (95% CI, 4.22-11.06) for the HS and 16.29 days (95% CI, 7.73-34.29) for the TS (p = .003).

CONCLUSIONS

Oncology hospitalists improve hospice utilization and time to inpatient hospice referral on an inpatient medical oncology service.

PLAIN LANGUAGE SUMMARY

Patients with advanced cancer are often admitted to the hospital near the end of life. These patients generally have a poor chance of long-term survival and may prefer comfort-focused care with hospice. In this study, oncology hospitalists discharged a higher proportion of patients to inpatient hospice with less time spent in the hospital before discharge.

Does Hospitalist Care Enhance Palliative Care and Reduce Aggressive Treatments for Terminally Ill Patients? A Propensity Score-Matched Study

BACKGROUND

Information on the use of palliative care and aggressive treatments for terminally ill patients who receive care from hospitalists is limited.

METHODS

This three-year, retrospective, case-control study was conducted at an academic medical center in Taiwan. Among 7037 patients who died in the hospital, 41.7% had a primary diagnosis of cancer. A total of 815 deceased patients who received hospitalist care before death were compared with 3260 patients who received non-hospitalist care after matching for age, gender, catastrophic illness, and Charlson comorbidity score. Regression models with generalized estimating equations were performed.

RESULTS

Patients who received hospitalist care before death, compared to those who did not, had a higher probability of palliative care consultation (odds ratio (OR) = 3.41, 95% confidence interval (CI): 2.63-4.41), and a lower probability to undergo invasive mechanical ventilation (OR = 0.13, 95% CI: 0.10-0.17), tracheostomy (OR = 0.14, 95% CI: 0.06-0.31), hemodialysis (OR = 0.70, 95% CI: 0.55-0.89), surgery (OR = 0.25, 95% CI: 0.19-0.31), and intensive care unit admission (OR = 0.11, 95% CI: 0.08-0.14). Hospitalist care was associated with reductions in length of stay (coefficient (B) = -0.54, 95% CI: -0.62--0.46) and daily medical costs.

CONCLUSIONS

Hospitalist care is associated with an improved palliative consultation rate and reduced life-sustaining treatments before death.

A Retrospective Study Analyzing a Palliative Care-Hospital Medicine Collaboration to Improve Quality of Care of Patients With Advanced Illness

BACKGROUND

Palliative care seeks to improve outcomes for patients with advanced illness (AI). Advocacy exists for making it part of mainstream care for hospitalized patients with AI.

AIM

To determine if a partnership between hospital-medicine and specialized palliative care would increase identification of AI patients with palliative care needs requiring palliative consultation. Secondary outcomes: Decreasing 30-day readmission, length of stay (LOS) and pain scores, increasing documentation reflecting goals and advanced care planning.

DESIGN

Retrospective chart review of patients with AI admitted to a hospitalist-resident run unit divided into Care Models, "A" and "B," and analyzed over two ten-month periods, "1" and "2." Triggers for palliative needs were provided for "A." During biweekly rounding, needs were assessed and generalist vs. specialist level palliative care concepts were used for consultation.

SETTING

Quaternary-level teaching center in the New York Metropolitan area.

PATIENTS

3,395 AI patients were analyzed, 1,707 from "1," and 1,688 from "2."

RESULTS

Comparing care models and time frames, palliative care consultation increased in "A" (P-value = .0013, P-value = .0005). When investigating "A" in "1" to "2," CMI was higher. Comparing "B" between "1" and "2," found older age and lower LACE. When adjusting for confounders (LACE and CMI), our models did not show a difference. Data on discharge disposition was significant for subacute rehab but not for mortality. There were no differences between care models and time-periods for secondary outcomes.

CONCLUSION

Our study demonstrated the demand for palliative care services integrated into hospital medicine and highlighted areas of focus for future studies.

The Hospital Value-Based Purchasing Program: Do hospitalists improve health care value

INTRODUCTION

As healthcare organizations examine the associated benefits of employing a larger hospitalist workforce, there is a need to better understand the association with patients' quality, experience, and efficiency. However, there is a lack of information regarding how hospital use of hospitalists over time influences hospital scoring on quality programs, such as the Center for Medicare and Medicaid Services (CMS) Hospital Inpatient Value-Based Purchasing (HVBP) Program. This study examines the association between hospitalist staffing between 2014 and 2019 and HVBP scores.

METHODS

We used a cross-sectional panel study design. Total Performance Score (TPS) and its domains were obtained from CMS from 2014 to 2019 and merged with the American Hospital Association Annual Survey Database. We utilized random-effects multivariable panel regression models and zero-inflated negative binomial regression to examine the association between the hospitalist-staffing ratio and the HVBP Program. All models were adjusted for hospital characteristics.

RESULTS

A total of 2126 hospitals were included in the study. The average ratio of hospitalists per staffed bed was 0.06, with a standard deviation of 0.15. This study suggests that hospitals that employ a higher percentage of hospitalists see improvement in their overall TPS (β = 5.40; p < .001), Patient Experience (β = 2.49; p <.05), and Efficiency (incidence-rate ratio= 1.41; p < .001) domain. However, the Clinical Care domain was no different in organizations employing more hospitalists.

CONCLUSION

There are benefits associated with TPS, Patient Experience, and Efficiency from employing hospitalists. Managers should seek opportunities to leverage hospitalists' expertise in providing care, particularly in improving care processes.

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study

BACKGROUND

Predicting the risk of in-hospital mortality on admission is challenging but essential for risk stratification of patient outcomes and designing an appropriate plan-of-care, especially among transferred patients.

OBJECTIVE

Develop a model that uses administrative and clinical data within 24 h of transfer to predict 30-day in-hospital mortality at an Academic Health Center (AHC).

DESIGN

Retrospective cohort study. We used 30 putative variables in a multiple logistic regression model in the full data set (n = 10,389) to identify 20 candidate variables obtained from the electronic medical record (EMR) within 24 h of admission that were associated with 30-day in-hospital mortality (p < 0.05). These 20 variables were tested using multiple logistic regression and area under the curve (AUC)-receiver operating characteristics (ROC) analysis to identify an optimal risk threshold score in a randomly split derivation sample (n = 5194) which was then examined in the validation sample (n = 5195).

PARTICIPANTS

Ten thousand three hundred eighty-nine patients greater than 18 years transferred to the Indiana University (IU)-Adult Academic Health Center (AHC) between 1/1/2016 and 12/31/2017.

MAIN MEASURES

Sensitivity, specificity, positive predictive value, C-statistic, and risk threshold score of the model.

KEY RESULTS

The final model was strongly discriminative (C-statistic = 0.90) and had a good fit (Hosmer-Lemeshow goodness-of-fit test [X² (8) =6.26, p = 0.62]). The positive predictive value for 30-day in-hospital death was 68%; AUC-ROC was 0.90 (95% confidence interval 0.89-0.92, p < 0.0001). We identified a risk threshold score of -2.19 that had a maximum sensitivity (79.87%) and specificity (85.24%) in the derivation and validation sample (sensitivity: 75.00%, specificity: 85.71%). In the validation sample, 34.40% (354/1029) of the patients above this threshold died compared to only 2.83% (118/4166) deaths below this threshold.

CONCLUSION

This model can use EMR and administrative data within 24 h of transfer to predict the risk of 30-day in-hospital mortality with reasonable accuracy among seriously ill transferred patients.

Palliative Care in Coronavirus Disease 2019 Pandemic: Position Statement of the Indian Association of Palliative Care

The global pandemic involving severe acute respiratory syndrome–coronavirus-2 has brought new challenges to clinical practice and care in the provision of palliative care. This position statement of the Indian Association of Palliative Care (IAPC) represents the collective opinion of the experts chosen by the society and reports on the current situation based on recent scientific evidence. It purports to guide all health-care professionals caring for coronavirus disease 2019 (COVID-19) patients and recommends palliative care principles into government decisions and policies. The statement provides recommendations for palliative care for both adults and children with severe COVID-19 illness, cancer, and chronic end-stage organ impairment in the hospital, hospice, and home setting. Holistic care incorporating physical, psychological, social, and spiritual support for patients and their families together with recommendations on the rational use of personal protective equipment has been discussed in brief. Detailed information can be accessed freely from the website of the IAPC http://www.palliativecare.in/. We hope that this position statement will serve as a guiding light in these uncertain times.

The role of geriatric palliative care in hospitalized older adults

Take-Away Points:1. Geriatric palliative care requires integrating the disciplines of hospital medicine and palliative care in pursuit of delivering comprehensive, whole-person care to aging patients with serious illnesses.2. Older adults have unique palliative care needs compared to the general population, different prevalence and intensity of symptoms, more frequent neuropsychiatric challenges, increased social needs, distinct spiritual, religious, and cultural considerations, and complex medicolegal and ethical issues.3. Hospital-based palliative care interdisciplinary teams can take many forms and provide high-quality, goal-concordant care to older adults and their families.

A Model to Improve Hospital-Based Palliative Care: The Palliative Care Redistribution Integrated System Model (PRISM)

Many hospitalized patients have unmet palliative care needs that are exacerbated by gaps in the palliative care subspecialty workforce. Training frontline physicians, including hospitalists, to provide primary palliative care has been proposed as one solution to this problem. However, improving palliative care access requires more than development of the physician workforce. Systemlevel change and interdisciplinary approaches are also needed. Using task shifting as a guiding principle, we propose a new workforce framework (the Palliative care Redistribution Integrated System Model, or PRISM), which utilizes physician and nonphysician providers and resources to their maximum potential. We highlight the central role of hospitalists in this model and provide examples of innovations in screening, workflow, quality, and benchmarking to enable hospitalists to be purveyors of quality palliative care.