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

Cost consequences of unscheduled emergency admissions in cancer patients in the last year of life

OBJECTIVES

Cancer is a leading cause of death. This paper examines the utilisation of unscheduled emergency end-of-life healthcare and estimates expenditure in this domain. We explore care patterns and quantify the likely benefits from service reconfigurations which may influence rates of hospital admission and deaths.

METHODS

Using prevalence-based retrospective data from the Northern Ireland General Registrar's Office linked by cancer diagnosis to Patient Administration episode data for unscheduled emergency care (1st January 2014 to 31st December 2015), we estimate unscheduled-emergency-care costs in the last year of life. We model potential resources released by reductions in length-of-stay for cancer patients. Linear regression examined patient characteristics affecting length of stay.

RESULTS

A total of 3134 cancer patients used 60,746 days of unscheduled emergency care (average 19.5 days). Of these, 48.9% had ≥1 admission during their last 28 days of life. Total estimated cost was £28,684,261, averaging £9200 per person. Lung cancer patients had the highest proportion of admissions (23.2%, mean length of stay = 17.9 days, mean cost=£7224). The highest service use and total cost was in those diagnosed at stage IV (38.4%), who required 22,099 days of care, costing £9,629,014. Palliative care support, identified in 25.5% of patients, contributed £1,322,328. A 3-day reduction in the mean length of stay with a 10% reduction in admissions, could reduce costs by £7.37 million. Regression analyses explained 41% of length-of-stay variability.

CONCLUSIONS

The cost burden from unscheduled care use in the last year of life of cancer patients is significant. Opportunities to prioritise service reconfiguration for high-costing users emphasized lung and colorectal cancers as offering the greatest potential to influence outcomes.

Local Supply Of Postdischarge Care Options Tied To Hospital Readmission Rates

The extent to which patients' risk for readmission after a hospitalization is influenced by local availability of postdischarge care options is not currently known. We used national, hospital-level data to assess whether the supply of postdischarge care options in hospitals' catchment areas was associated with readmission rates for Medicare patients after hospitalizations for acute myocardial infarction, heart failure, or pneumonia. Overall, readmission rates were negatively associated with per capita supply of primary care physicians (-0.16 percentage points per standard deviation) and licensed nursing home beds (-0.09 percentage points per standard deviation). In contrast, readmission rates were positively associated with per capita supply of nurse practitioners (0.09 percentage points per standard deviation). Our results suggest potential modifications to the Hospital Readmissions Reduction Program to account for local health system characteristics when assigning penalties to hospitals.

Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA)

Background: Demographic ageing is a population health success story but poses unprecedented policy challenges in the 21st century. Policymakers must prepare health systems, economies and societies for these challenges. Policy choices can be usefully informed by models that evaluate outcomes and trade-offs in advance under different scenarios. Methods: We developed a dynamic demographic-economic microsimulation model for the population aged 50 and over in Ireland: the Irish Future Older Adults Model (IFOAM).  Our principal dataset was The Irish Longitudinal Study on Ageing (TILDA). We employed first-order Markovian competing risks models to estimate transition probabilities of TILDA participants to different outcomes: diagnosis of serious diseases, functional limitations, risk-modifying behaviours, health care use and mortality. We combined transition probabilities with the characteristics of the stock population to estimate biennial changes in outcome state.   Results: IFOAM projections estimated large annual increases in total deaths, in the number of people living and dying with serious illness and functional impairment, and in demand for hospital care between 2018 and 2040.  The most important driver of these increases is the rising absolute number of older people in Ireland as the population ages. The increasing proportion of older old and oldest old citizens is projected to increase the average prevalence of chronic conditions and functional limitations. We deemed internal validity to be good but lacked external benchmarks for validation and corroboration of most outcomes. Conclusion: We have developed and validated a microsimulation model that projects health and related outcomes among older people in Ireland.  Future research should address identified policy questions. The model enhances the capacity of researchers and policymakers to quantitatively forecast health and economic dynamics among older people in Ireland, to evaluate ex ante policy responses to these dynamics, and to collaborate internationally on global challenges associated with demographic ageing.

Do Mini-Mental State Examination and Montreal Cognitive Assessment predict high-cost health care users? A competing risks analysis in The Irish Longitudinal Study on Ageing

OBJECTIVES

Policymakers want to better identify in advance the 10% of people who account for approximately 75% of health care costs. We evaluated how well Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) predicted high costs in Ireland.

METHODS/DESIGN

We used five waves from The Irish Longitudinal Study on Ageing, a biennial population-representative survey of people aged 50+ (2010-2018). We used competing risks analysis where our outcome of interest was "high costs" (top 10% at any wave) and the competing outcome was dying or loss to follow-up without first having the high-cost outcome. Our binary predictors of interest were a 'low score' (bottom 10% in the sample) in MMSE (≤25 pts) and MoCA (≤19 pts) at baseline, and we calculated sub-hazard ratios after controlling for sociodemographic, clinical and functional factors.

RESULTS

Of 5856 participants, 1427 (24%) had the 'high cost' outcome; 1463 (25%) had a competing outcome; and 2966 (51%) completed eight years of follow-up without either outcome. In multivariable regressions a low MoCA score was associated with high costs (SHR: 1.38 (95% CI: 1.2-1.6) but a low MMSE score was not. Low MoCA score at baseline had a higher true positive rate (40%) than did low MMSE score (35%). The scores had similar association with exit from the study.

CONCLUSIONS

MoCA had superior predictive accuracy for high costs than MMSE but the two scores identify somewhat different types of high-cost user. Combining the approaches may improve efforts to identify in advance high-cost users.

Projecting future health and service use among older people in Ireland: an overview of a dynamic microsimulation model in The Irish Longitudinal Study on Ageing (TILDA)

Background: Demographic ageing is a population health success story but poses unprecedented policy challenges in the 21st century. Policymakers must prepare health systems, economies and societies for these challenges. Policy choices can be usefully informed by models that evaluate outcomes and trade-offs in advance under different scenarios.

Methods: We developed a dynamic demographic-economic microsimulation model for the population aged 50 and over in Ireland: the Irish Future Older Adults Model (IFOAM).  Our principal dataset was The Irish Longitudinal Study on Ageing (TILDA). We employed first-order Markovian competing risks models to estimate transition probabilities of TILDA participants to different outcomes: diagnosis of serious diseases, functional limitations, risk-modifying behaviours, health care use and mortality. We combined transition probabilities with the characteristics of the stock population to estimate biennial changes in outcome state.  

Results: IFOAM projections estimated large annual increases in total deaths, in the number of people living and dying with serious illness and functional impairment, and in demand for hospital care between 2018 and 2040.  The most important driver of these increases is the rising absolute number of older people in Ireland as the population ages. The increasing proportion of older old and oldest old citizens is projected to increase the average prevalence of chronic conditions and functional limitations. We deemed internal validity to be good but lacked external benchmarks for validation and corroboration of most outcomes.

Conclusion: We have developed and validated a microsimulation model that predicts future health and related outcomes among older people in Ireland.  Future research should address identified policy questions. The model enhances the capacity of researchers and policymakers to quantitatively forecast future health and economic dynamics among older people in Ireland, to evaluate ex ante policy responses to these dynamics, and to collaborate internationally on global challenges associated with demographic ageing.

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.

Inpatient Specialty-Level Palliative Care Is Delivered Late in the Course of Hepatocellular Carcinoma and Associated With Lower Hazard of Hospital Readmission

CONTEXT

Little is known about receipt of specialty-level palliative care by people with hepatocellular carcinoma (HCC) or its impact on health care utilization.

OBJECTIVES

Identify patient characteristics associated with receipt of specialty-level palliative care among hospitalized HCC patients and measure association with time to readmission.

METHODS

We used logistic regression to examine relationships between receipt of inpatient palliative care consultation by HCC patients at an academic center (N = 811; 2012-2016) and clinical and demographic covariates at index hospitalization. We used a survival analysis model accounting for competing risk of mortality to compare time to readmission among individuals who did or did not receive palliative care during the admission and performed a sensitivity analysis using kernel weights to account for selection bias.

RESULTS

Overall, 16% received inpatient palliative care consults. Those who received consults had worse laboratory values than those who did not. In a multivariable model, higher Model for End-Stage Liver Disease Sodium, receipt of sorafenib, and higher pain scores were significantly associated with increased odds of palliative care, whereas liver transplantation and admission to a surgical service were associated with lower odds. For time to readmission (2076 hospitalizations for 811 individuals with 175 palliative care visits), the subhazard ratio for readmission for patients who received consults was 0.26 (95% CI = 0.18-0.38) and 0.35 (95% CI = 0.24-0.52) with a kernel-weighted sample.

CONCLUSION

Inpatient palliative care consultation was received by individuals with more advanced disease and associated with lower readmission hazard. These findings support further research and the development of HCC-specific programs that increase access to specialty-level palliative care.

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.