Using Length of Stay to Control for Unobserved Heterogeneity When Estimating Treatment Effect on Hospital Costs with Observational Data: Issues of Reliability, Robustness, and Usefulness

Acute kidney injury associated with increased costs in the neonatal intensive care unit: analysis of Pediatric Health Information System database

OBJECTIVE

Compare neonatal intensive care unit hospitalization costs between neonates with and without AKI; identify predictors of AKI-associated costs. We hypothesized neonates with AKI would amass more costs than those without AKI.

STUDY DESIGN

Retrospective, multicenter cohort study of surviving neonates cared for 2015-2021 in Pediatric Health Information System database. The primary outcome was estimated hospitalization costs.

RESULTS

Data from 304,725 neonates were evaluated, 8774 (3%) with AKI and 295,951 (97%) without AKI. Neonates with AKI had $58,807 greater adjusted costs than those without AKI. AKI-associated costs were most strongly driven by Feudtner Pediatric Complex Chronic Conditions Classifications (cardiovascaular, congenital/genetic, gastrointestinal, medical technology) and gestational age. Adjusted costs decreased with increasing gestational age, regardless of AKI status.

CONCLUSIONS

AKI is independently associated with increased hospital costs. Knowledge of these drivers can help in identifying high-value practices for cost mitigation strategies.

Modeling heterogeneity of Sudanese hospital stay in neonatal and maternal unit: non-parametric random effect models with Gamma distribution

OBJECTIVE

Studies looking into patient and institutional variables linked to extended hospital stays have arisen as a result of the increased focus on severe maternal morbidity and mortality. Understanding the length of hospitalization of patients after delivery is important to gain insights into when hospitals will reach capacity and to predict corresponding staffing or equipment requirements. In Sudan, the distribution of the length of stay during delivery hospitalizations is heavily skewed, with the average length of stay of 2 to 3 days. This study aimed to investigate the use of non-parametric random effect model with Gamma distributed response for analyzing skewed hospital length of stay data in Sudan in neonatal and maternal unit.

METHODS

We applied Gamma regression models with unknown random effects, estimated using the non-parametric maximum likelihood (NPML) technique [5]. The NPML reduces the heterogeneity in the distribution of the response and produce a robust estimation since it does not require any assumptions on the distribution. The same applies to the log-Gamma link that does not require any transformation for the data distribution and it can handle the outliers in the data points. In this study, the models are fitted with and without covariates and compared using AIC and BIC values.

RESULTS

The findings imply that in the context of health care database investigations, Gamma regression models with non-parametric random effect consistently reduce heterogeneity and improve model accuracy. The generalized linear model with covariates and random effect (k = 4) had the best fit, indicating that Sudanese hospital length of stay data could be classified into four groups with varying average stays influenced by maternal, neonatal, and obstetrics data.

CONCLUSION

Identifying factors contributing to longer stays allows hospitals to implement strategies for improvement. Non-parametric random effect model with Gamma distributed response effectively accounts for unobserved heterogeneity and individual-level variability, leading to more accurate inferences and improved patient care. Including random effects can significantly affect variable significance in statistical models, emphasizing the need to consider unobserved heterogeneity when analyzing data containing potential individual-level variability. The findings emphasise the importance of making robust methodological choices in healthcare research in order to inform accurate policy decisions.

Advance Identification of Patients With Chronic Conditions and Acute Respiratory Failure at Greatest Risk for High-Intensity, Costly Care

CONTEXT

Patients with underlying chronic illness requiring mechanical ventilation for acute respiratory failure are at risk for poor outcomes and high costs.

OBJECTIVES

Identify characteristics at time of intensive care unit (ICU) admission that identify patients at highest risk for high-intensity, costly care.

METHODS

Retrospective cohort study using electronic health and financial records (2011-2017) for patients requiring ≥48 hours of mechanical ventilation with ≥1 underlying chronic condition at an academic healthcare system. Main outcome was total cost of index hospitalization. Exposures of interest included number and type of chronic conditions. We used finite mixture models to identify the highest-cost group.

RESULTS

4,892 patients met study criteria. Median cost for index hospitalization was $135,238 (range, $9,748 -$3,176,065). Finite mixture modelling identified three classes with mean costs of $89,980, $150,603, and $277,712. Patients more likely to be in the high-cost class were: 1) < 72 years old (OR: 2.03; 95% CI:1.63, 2.52); 2) with dementia (OR: 1.55; 95% CI:1.17, 2.06) or chronic renal failure (OR: 1.27; 95% CI:1.08, 1.48); 3) weight loss ≥ 5% in year prior to hospital admission (OR: 1.25; 95% CI:1.05, 1.48); and 4) hospitalized during prior year (OR: 1.92; 95% CI:1.58, 2.35).

CONCLUSION

Among patients with underlying chronic illness and acute respiratory failure, we identified characteristics associated with the highest costs of care. Identifying these patients may be of interest to healthcare systems and hospitals and serve as one indication to invest resources in palliative and supportive care programs that ensure this care is consistent with patients' goals.

Using predicted length of stay to define treatment and model costs in hospitalized adults with serious illness: an evaluation of palliative care

BACKGROUND

Economic research on hospital palliative care faces major challenges. Observational studies using routine data encounter difficulties because treatment timing is not under investigator control and unobserved patient complexity is endemic. An individual's predicted LOS at admission offers potential advantages in this context.

METHODS

We conducted a retrospective cohort study on adults admitted to a large cancer center in the United States between 2009 and 2015. We defined a derivation sample to estimate predicted LOS using baseline factors (N = 16,425) and an analytic sample for our primary analyses (N = 2674) based on diagnosis of a terminal illness and high risk of hospital mortality. We modelled our treatment variable according to the timing of first palliative care interaction as a function of predicted LOS, and we employed predicted LOS as an additional covariate in regression as a proxy for complexity alongside diagnosis and comorbidity index. We evaluated models based on predictive accuracy in and out of sample, on Akaike and Bayesian Information Criteria, and precision of treatment effect estimate.

RESULTS

Our approach using an additional covariate yielded major improvement in model accuracy: R² increased from 0.14 to 0.23, and model performance also improved on predictive accuracy and information criteria. Treatment effect estimates and conclusions were unaffected. Our approach with respect to treatment variable yielded no substantial improvements in model performance, but post hoc analyses show an association between treatment effect estimate and estimated LOS at baseline.

CONCLUSION

Allocation of scarce palliative care capacity and value-based reimbursement models should take into consideration when and for whom the intervention has the largest impact on treatment choices. An individual's predicted LOS at baseline is useful in this context for accurately predicting costs, and potentially has further benefits in modelling treatment effects.

Prognosticating Outcomes and Nudging Decisions with Electronic Records in the Intensive Care Unit Trial Protocol

Expert recommendations to discuss prognosis and offer palliative options for critically ill patients at high risk of death are variably heeded by intensive care unit (ICU) clinicians. How to best promote such communication to avoid potentially unwanted aggressive care is unknown. The PONDER-ICU (Prognosticating Outcomes and Nudging Decisions with Electronic Records in the ICU) study is a 33-month pragmatic, stepped-wedge cluster randomized trial testing the effectiveness of two electronic health record (EHR) interventions designed to increase ICU clinicians' engagement of critically ill patients at high risk of death and their caregivers in discussions about all treatment options, including care focused on comfort. We hypothesize that the quality of care and patient-centered outcomes can be improved by requiring ICU clinicians to document a functional prognostic estimate (intervention A) and/or to provide justification if they have not offered patients the option of comfort-focused care (intervention B). The trial enrolls all adult patients admitted to 17 ICUs in 10 hospitals in North Carolina with a preexisting life-limiting illness and acute respiratory failure requiring continuous mechanical ventilation for at least 48 hours. Eligibility is determined using a validated algorithm in the EHR. The sequence in which hospitals transition from usual care (control), to intervention A or B and then to combined interventions A + B, is randomly assigned. The primary outcome is hospital length of stay. Secondary outcomes include other clinical outcomes, palliative care process measures, and nurse-assessed quality of dying and death.Clinical trial registered with clinicaltrials.gov (NCT03139838).

Factors Associated with the Costs of Palliative Care: A Retrospective Cost Analysis at a University Cancer Hospital in Brazil

Background: There have been few studies evaluating the costs of palliative care (PC) in low- and middle-income countries (LMICs), especially for patients with cancer. Objectives: The objective of this study was to identify the sociodemographic and clinical variables that could explain the cost per day of PC for cancer in Brazil. Methods: This was a retrospective cost analysis of PC at a quaternary cancer center in São Paulo, Brazil, between January 2010 and December 2013. Factors influencing the cost per day were assessed with generalized linear models and generalized linear-mixed models in which the random effect was the site of the cancer. Results: The study included 2985 patients. The mean total cost per patient was $12,335 (standard deviation [SD] = 14,602; 95% confidence interval [CI] = 11,803 to 12,851). The mean cost per day per patient was $325.50 (SD = 246.30, 95% CI = 316.60 to 334.30). There were statistically significant differences among cancer sites in terms of the mean cost per day. Multivariate analysis revealed that the drivers of cost per day were Karnofsky performance status, the number of hospital admissions, referral to PC, and place of death. Place of death had the greatest impact on the cost per day; death in a hospital and in hospice care increased the mean cost per day by $1.56 and $1.83, respectively. Conclusion: To allocate resources effectively, PC centers in LMICs should emphasize early enrollment of patients at PC outpatient clinics, to avoid hospital readmission, as well as advance planning of the place of death.

Impact of palliative care consult service in inpatient hospital setting: a systematic literature review

OBJECTIVES

Despite a number of studies on effectiveness of palliative care, there is a lack of complex updated review of the impact of in-hospital palliative care consult service. The objective is to update information on the impact of palliative care consult service in inpatient hospital setting.

METHODS

This study was a systematic literature review, following the standard protocols (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Joanna Briggs Institute tools) to ensure the transparent and robust review procedure. The effect of palliative care consult service was classified as being associated with improvement, no difference, deterioration or mixed results in specific outcomes. PubMed, Scopus, Academic Search Ultimate and SocINDEX were systematically searched up to February 2020. Studies were included if they focused on the impact of palliative care consult service caring for adult palliative care patients and their families in inpatient hospital setting.

RESULTS

After removing duplicates, 959 citations were screened of which 49 full-text articles were retained. A total of 28 different outcome variables were extracted. 18 of them showed positive effects within patient, family, staff and healthcare system domains. No difference was observed in patient survival and depression. Inconclusive results represented patient social support and staff satisfaction with care.

CONCLUSIONS

Palliative care consult service has a number of positive effects for patients, families, staff and healthcare system. More research is needed on factors such as patient spiritual well-being, social support, performance, family understanding of patient diagnosis or staff stress.

Economic Analysis of Hospital Palliative Care: Investigating Heterogeneity by Noncancer Diagnoses

Background. Single-disease-focused treatment and hospital-centric care are poorly suited to meet complex needs in an era of multimorbidity. Understanding variation in palliative care’s association with treatment choices is essential to optimizing interdisciplinary decision making in care of complex patients. Aim. To estimate the association between palliative care and hospital costs by primary diagnosis and multimorbidity for adults with one of six life-limiting conditions: heart failure, chronic obstructive pulmonary disease (COPD), liver failure, kidney failure, neurodegenerative conditions including dementia, and HIV/AIDS. Methods. Data from four studies (2002–2015) were pooled to provide an analytic dataset of 73,304 participants with mean costs $10,483, of whom 5,348 (7%) received palliative care. We estimated average effect of palliative care on direct hospital costs among the treated, using propensity scores to control for observed confounding. Results. Palliative care was associated with a statistically significant reduction in total direct costs for heart failure (estimated treatment effect: −$2666; 95% confidence interval [CI]: −$3440 to −$1892), neurodegenerative conditions (−$3523; −$4394 to −$2651), COPD (−$1613; −$2217 to −$1009), kidney failure (−$3589; −$5132 to −$2045), and liver failure (−$7574; −$9232 to −$5916). The association for liver failure patients was statistically significantly larger than for any other disease group. Cost-saving associations were also statistically larger for patients with multimorbidity than single disease for two of the six groups: neurodegenerative and liver failure. Conclusions. Heterogeneity in treatment effect estimates was observable in assessing association between palliative care and hospital costs for adults with serious life-limiting illnesses other than cancer. The results illustrate the importance of careful definition of palliative care populations in research and practice, and raise further questions about the role of interdisciplinary decision making in treatment of complex medical illness.

Improving palliative and end-of-life care with machine learning and routine data: a rapid review

Introduction: Improving end-of-life (EOL) care is a priority worldwide as this population experiences poor outcomes and accounts disproportionately for costs. In clinical practice, physician judgement is the core method of identifying EOL care needs but has important limitations. Machine learning (ML) is a subset of artificial intelligence advancing capacity to identify patterns and make predictions using large datasets.  ML approaches have the potential to improve clinical decision-making and policy design, but there has been no systematic assembly of current evidence. Methods: We conducted a rapid review, searching systematically seven databases from inception to December 31st, 2018: EMBASE, MEDLINE, Cochrane Library, PsycINFO, WOS, SCOPUS and ECONLIT.  We included peer-reviewed studies that used ML approaches on routine data to improve palliative and EOL care for adults.  Our specified outcomes were survival, quality of life (QoL), place of death, costs, and receipt of high-intensity treatment near end of life.  We did not search grey literature and excluded material that was not a peer-reviewed article. Results: The database search identified 426 citations. We discarded 162 duplicates and screened 264 unique title/abstracts, of which 22 were forwarded for full text review.  Three papers were included, 18 papers were excluded and one full text was sought but unobtainable.  One paper predicted six-month mortality, one paper predicted 12-month mortality and one paper cross-referenced predicted 12-month mortality with healthcare spending.  ML-informed models outperformed logistic regression in predicting mortality but poor prognosis is a weak driver of costs.  Models using only routine administrative data had limited benefit from ML methods. Conclusion: While ML can in principle help to identify those at risk of adverse outcomes and inappropriate treatment near EOL, applications to policy and practice are formative.  Future research must not only expand scope to other outcomes and longer timeframes, but also engage with individual preferences and ethical challenges.

Improving palliative and end-of-life care with machine learning and routine data: a rapid review

Introduction: Improving end-of-life (EOL) care is a priority worldwide as this population experiences poor outcomes and accounts disproportionately for costs. In clinical practice, physician judgement is the core method of identifying EOL care needs but has important limitations. Machine learning (ML) is a subset of artificial intelligence advancing capacity to identify patterns and make predictions using large datasets.  ML approaches have the potential to improve clinical decision-making and policy design, but there has been no systematic assembly of current evidence. Methods: We conducted a rapid review, searching systematically seven databases from inception to December 31st, 2018: EMBASE, MEDLINE, Cochrane Library, PsycINFO, WOS, SCOPUS and ECONLIT.  We included peer-reviewed studies that used ML approaches on routine data to improve palliative and EOL care for adults.  Our specified outcomes were survival, quality of life (QoL), place of death, costs, and receipt of high-intensity treatment near end of life.  We did not search grey literature and excluded material that was not a peer-reviewed article. Results: The database search identified 426 citations. We discarded 162 duplicates and screened 264 unique title/abstracts, of which 22 were forwarded for full text review.  Three papers were included, 18 papers were excluded and one full text was sought but unobtainable.  One paper predicted six-month mortality, one paper predicted 12-month mortality and one paper cross-referenced predicted 12-month mortality with healthcare spending.  ML-informed models outperformed logistic regression in predicting mortality but poor prognosis is a weak driver of costs.  Models using only routine administrative data had limited benefit from ML methods. Conclusion: While ML can in principle help to identify those at risk of adverse outcomes and inappropriate treatment near EOL, applications to policy and practice are formative.  Future research must not only expand scope to other outcomes and longer timeframes, but also engage with individual preferences and ethical challenges.

A quasi-experimental evaluation of an intervention to increase palliative medicine referral in the emergency department

Objective

To evaluate a new intervention intended to increase referral rates from the emergency department (ED) to the palliative medicine service (PMS) in acute hospitals.

Methods

We conducted a quasi-experimental evaluation in an urban teaching hospital in Dublin, Ireland. Data were collected over two eight-week periods in November/December 2013 and May/June 2015, with the PALliative Medicine in the Emergency Department (PAL.M.ED.™) intervention implemented in the intervening period. All adults who were admitted to the hospital via the ED during the two time periods and who received a palliative care consultation during their hospital stay were included in the study. Our primary analysis evaluated the impact of PAL.M.ED.™ on PMS referral in the ED. Our secondary analysis evaluated the impact of PMS referral in the ED on length of stay (LOS) and utilization, compared to PMS referral later in the admission. We controlled for observed confounding between groups using propensity scores.

Results

PAL.M.ED.™ was associated with an increase in PMS referral in the ED ( p < 0.005; odds ratio: 10.5 (95%CI: 3.8 to 28.7)). PMS referral in the ED was associated with shorter hospital LOS ( p < 0.005; −10.9 days (95%CI: −17.7 to −4.1)).

Conclusions

Low PMS referral rates in the ED, and the poor outcomes for patients and hospitals that arise from admissions of those with serious illness, may be mitigated by a proactive intervention to identify appropriate patients at admission.

Evaluating Hospital Readmissions for Persons With Serious and Complex Illness: A Competing Risks Approach

Hospital readmission rate is a ubiquitous measure of efficiency and quality. Individuals with life-limiting illnesses account heavily for admissions but evaluation is complicated by high-mortality rates. We report a retrospective cohort study examining the association between palliative care (PC) and readmissions while controlling for postdischarge mortality with a competing risks approach. Eligible participants were adult inpatients admitted to an academic, safety-net medical center (2009-2015) with at least one diagnosis of cancer, heart failure, chronic obstructive pulmonary disease, liver failure, kidney failure, AIDS/HIV, and selected neurodegenerative conditions. PC was associated with reduced 30-, 60-, and 90-day readmissions (subhazard ratios = 0.57, 0.53, and 0.52, respectively [all p < .001]). Hospital PC is associated with a reduction in readmissions, and this is not explained by higher mortality among PC patients. Performance measures only counting those alive at a given end point may underestimate systematically the effects of treatments with a high-mortality rate.

Variations in hospital inpatient palliative care service use: a retrospective cohort study

OBJECTIVE

Use of palliative care in hospitals for people at end of life varies. We examined rate and time of in-hospital palliative care use and associated interhospital variations.

METHODS

We used admissions from all hospitals in New South Wales, Australia, within a 12-month period, for a cohort of adults who died in 73 public acute care hospitals between July 2010 and June 2014. Receiving palliative care and its timing were based on recorded use.

RESULTS

Among 90 696 adults who died, 27% received palliative care, and the care was initiated 7.6 days (mean; SD: 3.3 days) before death. Over the 5-year period, the palliative care rate rose by 58%, varying extent across chronic conditions. The duration of palliative care before death declined by 7%. Patient (demographics, morbidities and service use) and hospital factors (size, location and availability of palliative care unit) explained half of the interhospital variation in outcomes: adjusted IQR in rate and duration of palliative care among hospitals were 23%-39% and 5.2-8.7 days, respectively. Hospitals with higher rates often initiated palliative care earlier (correlation: 0.39; p<0.01).

CONCLUSION

Despite an increase over time in the palliative care rate, its initiation was late and of brief duration. Palliative care use was associated with patient and hospital characteristics; however, half of the between hospital variation remained unexplained. The observed suboptimal practices and variability indicate the need for expanded and standardised use of palliative care supported by assessment tools, service enhancement and protocols.

Economics of Palliative Care for Hospitalized Adults With Serious Illness: A Meta-analysis

IMPORTANCE

Economics of care for adults with serious illness is a policy priority worldwide. Palliative care may lower costs for hospitalized adults, but the evidence has important limitations.

OBJECTIVE

To estimate the association of palliative care consultation (PCC) with direct hospital costs for adults with serious illness.

DATA SOURCES

Systematic searches of the Embase, PsycINFO, CENTRAL, PubMed, CINAHL, and EconLit databases were performed for English-language journal articles using keywords in the domains of palliative care (eg, palliative, terminal) and economics (eg, cost, utilization), with limiters for hospital and consultation. For Embase, PsycINFO, and CENTRAL, we searched without a time limitation. For PubMed, CINAHL, and EconLit, we searched for articles published after August 1, 2013. Data analysis was performed from April 8, 2017, to September 16, 2017.

STUDY SELECTION

Economic evaluations of interdisciplinary PCC for hospitalized adults with at least 1 of 7 illnesses (cancer; heart, liver, or kidney failure; chronic obstructive pulmonary disease; AIDS/HIV; or selected neurodegenerative conditions) in the hospital inpatient setting vs usual care only, controlling for a minimum list of confounders.

DATA EXTRACTION AND SYNTHESIS

Eight eligible studies were identified, all cohort studies, of which 6 provided sufficient information for inclusion. The study estimated the association of PCC within 3 days of admission with direct hospital costs for each sample and for subsamples defined by primary diagnoses and number of comorbidities at admission, controlling for confounding with an instrumental variable when available and otherwise propensity score weighting. Treatment effect estimates were pooled in the meta-analysis.

MAIN OUTCOMES AND MEASURES

Total direct hospital costs.

RESULTS

This study included 6 samples with a total 133 118 patients (range, 1020-82 273), of whom 93.2% were discharged alive (range, 89.0%-98.4%), 40.8% had a primary diagnosis of cancer (range, 15.7%-100.0%), and 3.6% received a PCC (range, 2.2%-22.3%). Mean Elixhauser index scores ranged from 2.2 to 3.5 among the studies. When patients were pooled irrespective of diagnosis, there was a statistically significant reduction in costs (-$3237; 95% CI, -$3581 to -$2893; P < .001). In the stratified analyses, there was a reduction in costs for the cancer (-$4251; 95% CI, -$4664 to -$3837; P < .001) and noncancer (-$2105; 95% CI, -$2698 to -$1511; P < .001) subsamples. The reduction in cost was greater in those with 4 or more comorbidities than for those with 2 or fewer.

CONCLUSIONS AND RELEVANCE

The estimated association of early hospital PCC with hospital costs may vary according to baseline clinical factors. Estimates may be larger for primary diagnosis of cancer and more comorbidities compared with primary diagnosis of noncancer and fewer comorbidities. Increasing palliative care capacity to meet national guidelines may reduce costs for hospitalized adults with serious and complex illnesses.

A Palliative Radiation Oncology Consult Service Reduces Total Costs During Hospitalization

CONTEXT

Palliative radiation therapy (PRT) is a highly effective treatment in alleviating symptoms from bone metastases; however, currently used standard fractionation schedules can lead to costly care, especially when patients are treated in an inpatient setting. The Palliative Radiation Oncology Consult (PROC) service was developed in 2013 to improve appropriateness, timeliness, and care value from PRT.

OBJECTIVES

Our primary objective was to compare total costs among two cohorts of inpatients with bone metastases treated with PRT before, or after, PROC establishment. Secondarily, we evaluated drivers of cost savings including hospital length of stay, utilization of specialty-care palliative services, and PRT schedules.

METHODS

Patients were included in our observational cohort study if they received PRT for bone metastases at a single tertiary care hospital from 2010 to 2016. We compared total costs and length of stay using propensity score-adjusted analyses. Palliative care utilization and PRT schedules were compared by χ² and Mann-Whitney U tests.

RESULTS

We identified 181 inpatients, 76 treated before and 105 treated after PROC. Median total hospitalization cost was $76,792 (range $6380-$346,296) for patients treated before PROC and $50,582 (range $7585-$620,943) for patients treated after PROC. This amounted to an average savings of $20,719 in total hospitalization costs (95% CI [$3687, $37,750]). In addition, PROC was associated with shorter PRT schedules, increased palliative care utilization, and an 8.5 days reduction in hospital stay (95% CI [3.2,14]).

CONCLUSION

The PROC service, a radiation oncology model integrating palliative care practice, was associated with cost-savings, shorter treatment courses and hospitalizations, and increased palliative care.

Does Modality Matter? Palliative Care Unit Associated With More Cost-Avoidance Than Consultations

CONTEXT

Inpatient palliative care (PC) is associated with reduced costs, but the optimal model for providing inpatient PC is unknown.

OBJECTIVES

To estimate the effect of palliative care consultations (PCCs) and care in a palliative care unit (PCU) on cost of care, in comparison with usual care (UC) only and in comparison with each other.

METHODS

Retrospective cohort study, using multinomial propensity scoring to control for observed confounding between treatment groups. Participants were adults admitted as inpatients between 2009 and 2015, with at least one of seven life-limiting conditions who died within a year of admission (N = 6761).

RESULTS

PC within 10 days of admission is estimated to reduce costs compared with UC in the case of both PCU (-$6333; 95% CI: -7871 to -4795; P < 0.001) and PCC (-$3559; 95% CI: -5732 to -1387; P < 0.001). PCU is estimated to reduce costs compared with PCC (-$2774; 95% CI: -5107 to -441; P = 0.02) and length of stay compared with UC (-1.5 days; -2.2 to -0.9; P < 0.001). The comparatively larger effect of PCU over PCC is not observable when the treatment groups are restricted to those who received PC early in their admission (within six days).

CONCLUSION

Both PCU and PCC are associated with lower hospital costs than UC. PCU is associated with a greater cost-avoidance effect than PCC, except where both interventions are provided early in the hospitalization. Both timely provision of PC for appropriate patients and creation of more PCUs may decrease hospital costs.

Rationale and Design of the Randomized Evaluation of Default Access to Palliative Services (REDAPS) Trial

The substantial nationwide investment in inpatient palliative care services stems from their great promise to improve patient-centered outcomes and reduce costs. However, robust experimental evidence of these benefits is lacking. The Randomized Evaluation of Default Access to Palliative Services (REDAPS) study is a pragmatic, stepped-wedge, cluster randomized trial designed to test the efficacy and costs of specialized palliative care consultative services for hospitalized patients with advanced chronic obstructive pulmonary disease, dementia, or end-stage renal disease, as well as the overall effectiveness of ordering such services by default. Additional aims are to identify the types of services that are most beneficial and the types of patients most likely to benefit, including comparisons between ward and intensive care unit patients. We hypothesize that patient-centered outcomes can be improved without increasing costs by simply changing the default option for palliative care consultation from opt-in to opt-out for patients with life-limiting illnesses. Patients aged 65 years or older are enrolled at 11 hospitals using an integrated electronic health record. As a pragmatic trial designed to enroll between 12,000 and 15,000 patients, eligibility is determined using a validated, electronic health record-based algorithm, and all outcomes are captured via the electronic health record and billing systems data. The time at which each hospital transitions from control, opt-in palliative care consultation to intervention, opt-out consultation is randomly assigned. The primary outcome is a composite measure of in-hospital mortality and length of stay. Secondary outcomes include palliative care process measures and clinical and economic outcomes. Clinical trial registered with www.clinicaltrials.gov (NCT02505035).

Evaluating the Economic Impact of Palliative and End-of-Life Care Interventions on Intensive Care Unit Utilization and Costs from the Hospital and Healthcare System Perspective

Purpose of report: Understanding the impact of palliative care interventions on intensive care unit (ICU) costs and utilization is critical for demonstrating the value of palliative care. Performing these economic assessments, however, can be challenging. The purpose of this special report is to highlight and discuss important considerations when assessing ICU utilization and costs from the hospital perspective, with the goal of providing recommendations on methods to consider for future analyses.

FINDINGS

ICU length of stay (LOS) and associated costs of care are common and important outcome measures, but must be analyzed properly to yield valid conclusions. There is significant variation in costs by day of stay in the ICU with only modest differences between an ICU day at the end of a stay and the first day on the acute care floor; this variation must be appropriately accounted for analytically. Furthermore, reporting direct variable costs, in addition to total ICU costs, is needed to understand short-term and long-term impact of a reduction in LOS. Importantly, incentives for the hospital to realize savings vary depending on reimbursement policies.

SUMMARY

ICU utilization and costs are common outcomes in studies evaluating palliative care interventions. Accurate estimation and interpretation are key to understanding the economic implications of palliative care interventions.

Prospective Cohort Study of Hospitalized Adults With Advanced Cancer: Associations Between Complications, Comorbidity, and Utilization

BACKGROUND

Inpatient hospital stays account for more than a third of direct medical cancer care costs. Evidence on factors driving these costs can inform planning of services, as well as consideration of equity in access.

OBJECTIVE

To measure the association between hospital costs, and demographic, clinical, and system factors, for a cohort of adults with advanced cancer.

DESIGN

Prospective multisite cohort study.

SETTING

Four medical and cancer centers.

PATIENTS

Adults with advanced cancer admitted to a participating hospital between 2007 and 2011, excluding those with dementia. Final analytic sample included 1020 patients.

METHODS

With receipt of palliative care controlled for, the associations between hospital cost and patient factors were estimated. Factors covered the domains of demographics (age, sex, race), socioeconomics and systems (education, insurance, living will, proxy), clinical care (diagnoses, complications deemed to pose a threat to life or bodily functions, comorbidities, symptom burden, activities of daily living), and prior healthcare utilization (home help, analgesic prescribing).

OUTCOME MEASURE

Direct hospital costs.

RESULTS

A major (markedly abnormal) complication (+$8267; P < 0.01), a minor but not a major complication (+$5289; P < 0.01), and number of comorbidities (+$852; P < 0.01) were associated with higher cost, and admitting diagnosis of electrolyte disorders (-$4759; P = 0.01) and increased age (-$53; P = 0.03) were associated with lower cost.

CONCLUSIONS

Complications and comorbidity burden drive inhospital utilization for adults with advanced cancer. There is little evidence of sociodemographic associations and no apparent impact of advance directives. Attempts to control growth of hospital cancer costs require consideration of how the most resource-intensive patients are identified promptly and prioritized for cost-effective care. Journal of Hospital Medicine 2017;12:407-413.

Cost analysis of a prospective multi-site cohort study of palliative care consultation teams for adults with advanced cancer: Where do cost-savings come from?

BACKGROUND

Studies report cost-savings from hospital-based palliative care consultation teams compared to usual care only, but drivers of observed differences are unclear.

AIM

To analyse cost-differences associated with palliative care consultation teams using two research questions: (Q1) What is the association between early palliative care consultation team intervention, and intensity of services and length of stay, compared to usual care only? (Q2) What is the association between early palliative care consultation team intervention and day-to-day hospital costs, compared to a later intervention?

DESIGN

Prospective multi-site cohort study (2007-2011). Patients who received a consultation were placed in the intervention group, those who did not in the comparison group. Intervention group was stratified by timing, and groups were matched using propensity scores.

SETTING/PARTICIPANTS

Adults admitted to three US hospitals with advanced cancer. Principle analytic sample contains 863 patients ( n_UC = 637; n_{PC EARLY} = 177; n_{PC LATE} = 49) discharged alive.

RESULTS

Cost-savings from early palliative care accrue due to both reduced length of stay and reduced intensity of treatment, with an estimated 63% of savings associated with shorter length of stay. A reduction in day-to-day costs is observable in the days immediately following initial consult but does not persist indefinitely. A comparison of early and late palliative care consultation team cost-effects shows negligible difference once the intervention is administered.

CONCLUSION

Reduced length of stay is the biggest driver of cost-saving from early consultation for patients with advanced cancer. Patient- and family-centred discussions on goals of care and transition planning initiated by palliative care consultation teams may be at least as important in driving cost-savings as the reduction of unnecessary tests and pharmaceuticals identified by previous studies.

Analyzing the Impact of Palliative Care Interventions on Cost of Hospitalization: Practical Guidance for Choice of Dependent Variable

CONTEXT

Multiple cost analyses of hospital-based palliative care have been published in recent years, but there are important differences between studies in their choice of dependent variable, complicating interpretation of results.

OBJECTIVES

The purpose of this article was to compare three different established approaches to estimating treatment effect on hospital costs, to highlight that different approaches yield different results, and to provide some practical guidelines for investigators performing hospital cost analysis in future.

METHODS

A simple example is developed using simulated cost data for four hospitalized patients, one of whom receives usual care only and three of whom receive different interventions. The impacts of the interventions are calculated and compared for three different dependent variables: cost of hospitalization, mean daily costs, and "before-and-after" costs.

RESULTS

Both the magnitude of an intervention's cost-saving effect and the relative impact of different interventions vary according to which dependent variable is used. Cost of hospitalization provides the most useful results of the three options for evaluating an intervention's impact on resource use. Alternative approaches visible in the literature can be misleading with respect to cost effects. Where the intervention is first administered to different patients at different points in a hospital admission, incorporating intervention timing is essential to maximize accuracy of cost-effect estimates.

CONCLUSION

Investigators evaluating the impact of palliative care programs on hospital costs ought to use cost of hospitalization as the dependent variable in primary analysis unless the research question specifically justifies an alternative approach. Mean daily costs and "before-and-after" costs should be used only to address relevant research questions, and results must be interpreted carefully. Analyses should also incorporate timing of the intervention where appropriate.