Managing High‐Cost Healthcare Users: The International Search for Effective Evidence‐Supported Strategies

Older Adults' Experiences With Participation and eHealth in Care Coordination: Qualitative Interview Study in a Primary Care Setting

BACKGROUND

Owing to the demographic changes in the elderly population worldwide, delivering coordinated care at home to multimorbid older adults is of great importance. Older adults living with multiple chronic conditions need information to manage and coordinate their care. eHealth can be effective for gaining sufficient information, communicating, and self-managing chronic conditions. However, incorporating older adults' health preferences and ensuring active involvement remain challenging. More knowledge is needed to ensure successful participation and eHealth use in care coordination.

OBJECTIVE

This study aimed to explore multimorbid older adults' experiences with participation and eHealth in care coordination with general practitioners (GPs) and district nurses (DNs).

METHODS

The study had a qualitative explorative approach. Data collection included semistructured interviews with 20 older adults with multimorbidity receiving primary care services from their GPs and DNs. The participants were included by their GPs or nurses at a local intermunicipal acute inpatient care unit. The data analysis was guided by systematic text condensation.

RESULTS

We identified 2 categories: (1) older adults in charge of and using eHealth in care coordination, and (2) older adults with a loss of control in care coordination. The first category describes how communication with GPs and DNs can facilitate participation, the importance of managing own medication, and how eHealth can support older adults' information needs. The second category focuses on older adults who depend on guidance and help from their GPs and DNs to manage their health, describing how a lack of capacity and system support to be involved makes these adults lose control of their care coordination.

CONCLUSIONS

Being in charge of care coordination is important for older multimorbid adults. The results show that older adults are willing to use eHealth to be informed and to seek information, which ensures high levels of participation in care coordination. Future research should investigate how older adults can be involved in electronic information sharing with health care providers.

Positive Psychology Interventions in Medical Populations: Critical Issues in Intervention Development, Testing, and Implementation

Positive psychological well-being is prospectively associated with superior health outcomes. Positive psychology interventions have promise as a potentially feasible and effective means of increasing well-being and health in those with medical illness, and several initial studies have shown the potential of such programs in medical populations. At the same time, numerous key issues in the existing positive psychology literature must be addressed to ensure that these interventions are optimally effective. These include (1) assessing the nature and scope of PPWB as part of intervention development and application; (2) identifying and utilizing theoretical models that can clearly outline potential mechanisms by which positive psychology interventions may affect health outcomes; (3) determining consistent, realistic targets for positive psychology interventions; (4) developing consistent approaches to the promotion of positive psychological well-being; (5) emphasizing the inclusion of diverse samples in treatment development and testing; and (6) considering implementation and scalability from the start of intervention development to ensure effective real-world application. Attention to these six domains could greatly facilitate the generation of effective, replicable, and easily adopted positive psychology programs for medical populations with the potential to have an important impact on public health.

The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data

Our aim was to predict future high-cost patients with machine learning using healthcare claims data. We applied a random forest (RF), a gradient boosting machine (GBM), an artificial neural network (ANN) and a logistic regression (LR) to predict high-cost patients in the following year. Therefore, we exploited routinely collected sickness funds claims and cost data of the years 2016, 2017 and 2018. Various specifications of each algorithm were trained and cross-validated on training data (n = 20,984) with claims and cost data from 2016 and outcomes from 2017. The best performing specifications of each algorithm were selected based on validation dataset performance. For performance comparison, selected models were applied to unforeseen data with features of the year 2017 and outcomes of the year 2018 (n = 21,146). The RF was the best performing algorithm measured by the area under the receiver operating curve (AUC) with a value of 0.883 (95% confidence interval (CI): 0.872-0.893) on test data, followed by the GBM (AUC = 0.878; 95% CI: 0.867-0.889). The ANN (AUC = 0.846; 95% CI: 0.834-0.857) and LR (AUC = 0.839; 95% CI: 0.826-0.852) were significantly outperformed by the GBM and the RF. All ML algorithms and the LR performed ´good´ (i.e. 0.9 > AUC ≥ 0.8). We were able to develop machine learning models that predict high-cost patients with 'good' performance facilitating routinely collected sickness fund claims and cost data. We found that tree-based models performed best and outperformed the ANN and LR.

Identifying patterns of clinical conditions among high-cost older adult health care users using claims data: a latent class approach

Objectives

To identify patterns of clinical conditions among high-cost older adults health care users and explore the associations between characteristics of high-cost older adults and patterns of clinical conditions.

Methods

We analyzed data from the Shanghai Basic Social Medical Insurance Database, China. A total of 2927 older adults aged 60 years and over were included as the analysis sample. We used latent class analysis to identify patterns of clinical conditions among high-cost older adults health care users. Multinomial logistic regression models were also used to determine the associations between demographic characteristics, insurance types, and patterns of clinical conditions.

Results

Five clinically distinctive subgroups of high-cost older adults emerged. Classes included “cerebrovascular diseases” (10.6% of high-cost older adults), “malignant tumor” (9.1%), “arthrosis” (8.8%), “ischemic heart disease” (7.4%), and “other sporadic diseases” (64.1%). Age, sex, and type of medical insurance were predictors of high-cost older adult subgroups.

Conclusions

Profiling patterns of clinical conditions among high-cost older adults is potentially useful as a first step to inform the development of tailored management and intervention strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-022-01688-3.

Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review

BACKGROUND

In an effort to improve both quality of care and cost-effectiveness, various care-management programmes have been developed for high-need high-cost (HNHC) patients. Early identification of patients at risk of becoming HNHC (i.e. case finding) is crucial to a programme's success. We aim to systematically identify prediction models predicting future HNHC healthcare use in adults, to describe their predictive performance and to assess their applicability.

METHODS

Ovid MEDLINE® All, EMBASE, CINAHL, Web of Science and Google Scholar were systematically searched from inception through January 31, 2021. Risk of bias and methodological quality assessment was performed through the Prediction model Risk Of Bias Assessment Tool (PROBAST).

RESULTS

Of 5890 studies, 60 studies met inclusion criteria. Within these studies, 313 unique models were presented using a median development cohort size of 20,248 patients (IQR 5601-174,242). Predictors were derived from a combination of data sources, most often claims data (n = 37; 62%) and patient survey data (n = 29; 48%). Most studies (n = 36; 60%) estimated patients' risk to become part of some top percentage of the cost distribution (top-1-20%) within a mean time horizon of 16 months (range 12-60). Five studies (8%) predicted HNHC persistence over multiple years. Model validation was performed in 45 studies (76%). Model performance in terms of both calibration and discrimination was reported in 14 studies (23%). Overall risk of bias was rated as 'high' in 40 studies (67%), mostly due to a 'high' risk of bias in the subdomain 'Analysis' (n = 37; 62%).

DISCUSSION

This is the first systematic review (PROSPERO CRD42020164734) of non-proprietary prognostic models predicting HNHC healthcare use. Meta-analysis was not possible due to heterogeneity. Most identified models estimated a patient's risk to incur high healthcare expenditure during the subsequent year. However, case-finding strategies for HNHC care-management programmes are best informed by a model predicting HNHC persistence. Therefore, future studies should not only focus on validating and extending existing models, but also concentrate on clinical usefulness.

Emergency hospital admissions among older adults living alone in the community

Background

Among older adults, living alone is often associated with higher risk of Emergency Department (ED) admissions. However, older adults living alone are very heterogeneous in terms of health. As more older adults choose to live independently, it remains unclear if the association between living alone and ED admissions is moderated by health status. We studied the association between living alone and ED admission outcomes (number of admissions, inpatient days and inpatient costs) among older adults with and without multimorbidity.

Methods

We used data from 16,785 individuals of the third follow-up of the Singapore Chinese Health Study, a population-based cohort of older Singapore Chinese (mean age: 73(61-96) years). Participants were interviewed face-to-face from 2014 to 2016 for sociodemographic/health factors and followed-up for one year on ED admission outcomes using Singapore Ministry of Health’s Mediclaim Database. We first applied multivariable logistic regression and two-part models to test if living alone is a risk factor for ED admission outcomes. We then ran stratified and joint effect analysis to examine if the associations between living alone and ED admission outcomes were moderated by multimorbidity.

Results

Compared to living with others, living alone was associated with higher odds of ED admission [Odds Ratio (OR) 1.28, 95 % Confidence Interval(CI) 1.08-1.51)], longer inpatient days (+0.61, 95 %CI 0.25-0.97) and higher inpatient costs (+322 USD, 95 %CI 54-591). The interaction effects of living arrangement and multimorbidity on ED admissions and inpatient costs were not statistically different, whereas the interaction between living arrangements and multimorbidity on inpatient days was borderline significant (p-value for interaction=0.050). Compared to those living with others and without multimorbidity, the relative mean increase was 1.13 inpatient days (95 %CI 0.39-1.86) for those living alone without multimorbidity, and 0.73 inpatient days ( 95 %CI 0.29-1.17) for those living alone with multimorbidity.

Conclusions

Older adults living alone were at higher risk of ED admission and higher inpatient costs regardless of multimorbidity, while those living alone without multimorbidity had the longest average inpatient days. To enable aging in place while avoiding ED admissions, interventions could provide instrumental support and regular health monitoring to older adults living alone, regardless of their health status.

Medication use and its impact on high-cost health care users among older adults: protocol for the population-based matched cohort HiCOSTT study

BACKGROUND

Health interventions and policies for high-cost health care users (HCUs) who are older adults need to be informed by a better understanding of their multimorbidity and medication use. This study aims to determine the financial contribution of medications to HCU expenditures and explore whether potentially inappropriate prescribing is associated with incident HCU development.

METHODS

This is a protocol for a retrospective population-based matched cohort analysis of incident older adult HCUs (those with the highest 5% of costs and 66 years of age or older) in Ontario during fiscal year 2013. We will obtain person-level data for the index year and year before HCU status from health administrative databases and match each HCU to 3 non-HCUs based on age, sex and geographic location. Average annual medication costs (per patient) and the ratio of medication to total health care costs (at population level) will be examined over the HCU transition period and compared with non-HCUs. We will explore potential quality improvement areas for prescribing by analyzing chronic conditions and the use of medications with a strong evidence base for either clinical benefit or risk of harms outweighing benefits in older adults with these diagnoses. The relation between these medication classes and incident HCU status will be explored using logistic regression.

INTERPRETATION

Using a matched cohort design and focusing on incident rather than prevalent HCUs, this protocol will explore our hypotheses that medications and the quality of their prescribing may be important triggers of HCU status and facilitate the identification of potential preventive clinical interventions or policies. Dissemination of results will occur via publications in peer-reviewed journals, presentations at conferences and academic settings, and knowledge translation activities with relevant health system and patient stakeholder groups.

STUDY REGISTRATION

Clinicaltrials.gov, no. NCT02815930.

Factors associated with high-cost hospitalizations in elderly ovarian cancer patients

OBJECTIVE

To characterize factors associated with high-cost inpatient admissions for ovarian cancer.

METHODS

Operative hospitalizations for ovarian cancer patients ≥65 years of age were identified using the 2010-2017 National Inpatient Sample. Admissions with high-cost were defined as those incurring ≥90th percentile of hospitalization costs each year, while the remainder were considered low-cost. Multivariable logistic regression models were developed to assess independent predictors of being in the high-cost cohort.

RESULTS

During the study period, an estimated 58,454 patients met inclusion criteria. 5827 patient admissions (9.98%) were classified as high-cost. Median hospitalization cost for this high-cost group was $55,447 (interquartile range (IQR) $46,744-$74,015) compared to $16,464 (IQR $11,845-$23,286, p < 0.001) for the low-cost group. Patients with high-cost admissions were more likely to have received open (adjusted odds ratio (AOR) 2.23, 1.31-3.79) or extended (AOR 5.64, 4.79-6.66) procedures and be admitted non-electively (AOR 3.32, 2.74-4.02). Being in the top income quartile (AOR 1.77, 1.39-2.27) was also associated with high-cost. Age and hospital factors, including bed size and volume of gynecologic oncology surgery, did not affect cost group.

CONCLUSION

High-cost ovarian cancer admissions were three times more expensive than low-cost admissions. Fewer open and extended procedures with subsequently shorter lengths of stay may have contributed to decreasing inpatient costs over the study period. In this cohort of patients largely covered by Medicare, clinical factors outweigh socioeconomic factors as cost drivers. Understanding the relationship of disease-specific and social factors to cost will be important in informing future value-based quality improvement efforts in gynecologic cancer care.

Identifying and understanding determinants of high healthcare costs for breast cancer: a quantile regression machine learning approach

BACKGROUND

To identify and rank the importance of key determinants of high medical expenses among breast cancer patients and to understand the underlying effects of these determinants.

METHODS

The Oncology Care Model (OCM) developed by the Center for Medicare & Medicaid Innovation were used. The OCM data provided to Mount Sinai on 2938 breast-cancer episodes included both baseline periods and three performance periods between Jan 1, 2012 and Jan 1, 2018. We included 11 variables representing information on treatment, demography and socio-economics status, in addition to episode expenditures. OCM data were collected from participating practices and payers. We applied a principled variable selection algorithm using a flexible tree-based machine learning technique, Quantile Regression Forests.

RESULTS

We found that the use of chemotherapy drugs (versus hormonal therapy) and interval of days without chemotherapy predominantly affected medical expenses among high-cost breast cancer patients. The second-tier major determinants were comorbidities and age. Receipt of surgery or radiation, geographically adjusted relative cost and insurance type were also identified as important high-cost drivers. These factors had disproportionally larger effects upon the high-cost patients.

CONCLUSIONS

Data-driven machine learning methods provide insights into the underlying web of factors driving up the costs for breast cancer care management. Results from our study may help inform population health management initiatives and allow policymakers to develop tailored interventions to meet the needs of those high-cost patients and to avoid waste of scarce resource.

Identifying factors associated with high use of acute care in Canada: protocol of a population-based retrospective cohort study

INTRODUCTION

High-cost users (HCUs) account for a small proportion of the population but use a disproportionately large share of healthcare resources. Although HCUs exist in all healthcare types, acute care is the most expensive type of service and the most significant contributor to expenditures among HCUs. This study aims to determine demographic, socioeconomic and clinical factors associated with being HCUs in adult patients (≥18 years) receiving acute care in Canada.

METHODS AND ANALYSIS

This is a population-based analysis using a national linked dataset. Adult patients who had at least one interaction with acute care facilities each year from 2011 to 2014 were captured in the dataset, and those living in institutions or other collective residences were not covered. The primary outcome is HCU of acute care (yes/no), which is defined as whether a patient is within the top 10% of the highest acute care cost users in his/her province. Multilevel logistic regression will be used to identify factors associated with HCU and to examine the provincial variations of these identified risk factors. Sensitivity analyses investigating the influences of different high user definitions and missing data on the study results will also be performed.

ETHICS AND DISSEMINATION

All researchers will follow the codes and rules set by Statistics Canada and the Research Data Centre and give priority to the confidentiality of the data during and after this study. The study findings will be published in peer-review journals and disseminated at academic conferences.

Value-based healthcare delivery through metabolomics-based personalized health platform

Type 2 diabetes is routinely identified in clinical practice by tests that rely on a hyperglycemic index. However, people at risk for developing type 2 diabetes may not present with hyperglycemia. We identified several underlying risks for type 2 diabetes, insulin resistance, and associated co-morbidities, using a liquid chromatography mass spectrometry-based analysis of blood metabolites, in participants with normoglycemia and no clinical symptoms. Personalized lifestyle recommendations, including diet, exercise, and nutritional supplement recommendations, were conveyed to these participants by a web-based platform, and after 100 days of following their recommendations, these participants reported reductions in the health risks associated with type 2 diabetes and associated diseases. Our comprehensive metabolite-based assay can be used for type 2 diabetes risk stratification, and our personalized lifestyle recommendation system could be deployed as a preventative treatment option to improve health outcomes, reduce the incidence of chronic disease, and live healthier lives in an evidence-based way.

Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore

OBJECTIVE

We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. Subsequently we apply machine learning algorithms to predict which HUs will persist as PHUs, to inform future trials testing the effectiveness of interventions in reducing healthcare utilisation in PHUs.

DESIGN AND SETTING

This is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.

PARTICIPANTS

Patients who had at least one inpatient admission to the AMC between 2005 and 2013 were included in this study. HUs incurred Singapore Dollar 8150 or more within a year. PHUs were defined as HUs for three consecutive years, while THUs were HUs for 1 or 2 years. Non-HUs did not incur high healthcare costs at any point during the study period.

OUTCOME MEASURES

PHU status at the end of the third year was the outcome of interest. Socio-demographic profiles, clinical complexity and utilisation metrics of each group were reported. Area under curve (AUC) was used to identify the best model to predict persistence.

RESULTS

PHUs were older and had higher comorbidity and mortality. Over the three observed years, PHUs' expenditure generally increased, while THUs and non-HUs' spending and inpatient utilisation decreased. The predictive model exhibited good performance during both internal (AUC: 83.2%, 95% CI: 82.2% to 84.2%) and external validation (AUC: 79.8%, 95% CI: 78.8% to 80.8%).

CONCLUSIONS

The HU population could be stratified into PHUs and THUs, with distinctly different utilisation trajectories. We developed a model that could predict at the end of 1 year, whether a patient in our population will continue to be a HU in the next 2 years. This knowledge would allow healthcare providers to target PHUs in our health system with interventions in a cost-effective manner.

Discharge Information and Support for Patients Discharged from the Emergency Department: Results from a Randomized Controlled Trial

BACKGROUND

Little research has been done on primary care-based models to improve health care use after an emergency department (ED) visit.

OBJECTIVE

To examine the effectiveness of a primary care-based, nurse telephone support intervention for Veterans treated and released from the ED.

DESIGN

Randomized controlled trial with 1:1 assignment to telephone support intervention or usual care arms (ClinicalTrials.gov: NCT01717976).

SETTING

Department of Veterans Affairs Health Care System (VAHCS) in Durham, NC.

PARTICIPANTS

Five hundred thirteen Veterans who were at high risk for repeat ED visits.

INTERVENTION

The telephone support intervention consisted of two core calls in the week following an ED visit. Call content focused on improving the ED to primary care transition, enhancing chronic disease management, and educating Veterans and family members about VHA and community services.

MAIN MEASURES

The primary outcome was repeat ED use within 30 days.

KEY RESULTS

Observed rates of repeat ED use at 30 days in usual care and intervention groups were 23.1% and 24.9%, respectively (OR = 1.1; 95% CI = 0.7, 1.7; P = 0.6). The intervention group had a higher rate of having at least 1 primary care visit at 30 days (OR = 1.6, 95% CI = 1.1-2.3). At 180 days, the intervention group had a higher rate of usage of a weight management program (OR = 3.5, 95% CI = 1.6-7.5), diabetes/nutrition (OR = 1.8, 95% CI = 1.0-3.0), and home telehealth services (OR = 1.7, 95% CI = 1.0-2.9) compared with usual care.

CONCLUSIONS

A brief primary care-based nurse telephone support program after an ED visit did not reduce repeat ED visits within 30 days, despite intervention participants' increased engagement with primary care and some chronic disease management services.

TRIALS REGISTRATION

ClinicalTrials.gov NCT01717976.

Incremental healthcare utilisation and costs among new senior high-cost users in Ontario, Canada: a retrospective matched cohort study

OBJECTIVES

To describe healthcare use and spending before and on becoming a new (incident) senior high-cost user (HCU) compared with senior non-HCUs; to estimate the incremental costs, overall and by service category, attributable to HCU status; and to quantify its monetary impact on the provincial healthcare budget in Ontario, Canada.

DESIGN

We conducted a retrospective, population-based comparative cohort study using administrative healthcare records. Incremental healthcare utilisation and costs were determined using the method of recycled predictions allowing adjustment for preincident and incident year values, and covariates. Estimated budget impact was computed as the product of the mean annual total incremental cost and the number of senior HCUs.

PARTICIPANTS

Incident senior HCUs were defined as Ontarians aged ≥66 years who were in the top 5% of healthcare cost users during fiscal year 2013 (FY2013) but not during FY2012. The incident HCU cohort was matched with senior non-HCUs in a ratio of 1 HCU:3 non-HCU.

RESULTS

Senior HCUs (n=175 847) reached the annual HCU threshold of CAD$10 192 through different combinations of incurred costs. Although HCUs had higher healthcare utilisation and costs at baseline, HCU status was associated with a substantial spike in both, with prolonged hospitalisations playing a major role. Twelve per cent of HCUs reached the HCU expenditure threshold without hospitalisation. Compared with non-HCUs (n=5 27 541), HCUs incurred an additional CAD$25 527 per patient in total healthcare costs; collectively CAD$4.5 billion or 9% of the 2013 Ontario healthcare budget. Inpatient care had the highest incremental costs: CAD$13 427, 53% of the total incremental spending.

CONCLUSIONS

Costs attributable to incident senior HCU status accounted for almost 1/10 of the provincial healthcare budget. Prolonged hospitalisations made a major contribution to the total incremental costs. A subgroup of patients that became HCU without hospitalisation requires further investigation.

Unplanned index hospital admissions among new older high-cost health care users in Ontario: a population-based matched cohort study

BACKGROUND

Most health care spending is concentrated within a small group of high-cost health care users. To inform health policies, we examined the characteristics of index hospital admissions and their predictors among incident older high-cost users compared to older non-high-cost users in Ontario.

METHODS

Using Ontario administrative data, we identified incident high-cost users aged 66 years or more and matched them 1:3 on age, gender and Local Health Integration Network with non-high-cost users aged 66 years or more. We defined high-cost users as patients within the top 5% most costly high-cost users during fiscal year 2013/14 but not during 2012/13. An index hospital admission, the main outcome, was defined as the first unplanned hospital admission during 2013/14, with no hospital admissions in the preceding 12 months. Descriptively, we analyzed the attributes of index hospital admissions, including costs. We identified predictors of index hospital admissions using stratified logistic regression.

RESULTS

Over half (95 375/175 847 [54.2%]) of all high-cost users had an unplanned index hospital admission, compared to 8838/527 541 (1.7%) of non-high-cost users. High-cost users had a poorer health status, longer acute length of stay (mean 7.5 d v. 2.9 d) and more frequent designation as alternate level of care before discharge (20.8% v. 1.7%) than did non-high-cost users. Ten diagnosis codes accounted for roughly one-third of the index hospital admission costs in both cohorts. Although many predictors were similar between the cohorts, a lower risk of an index hospital admission was associated with residence in long-term care, attachment to a primary care provider and recent consultation by a geriatrician among high-cost users.

INTERPRETATION

The high prevalence of index hospital admissions and the corresponding costs are a distinctive feature of incident older high-cost users. Improved access to specialist outpatient care, home-based social care and long-term care when required are worth further investigation.

Regional variation in healthcare spending and mortality among senior high-cost healthcare users in Ontario, Canada: a retrospective matched cohort study

Background

Senior high cost health care users (HCU) are a priority for many governments. Little research has addressed regional variation of HCU incidence and outcomes, especially among incident HCU. This study describes the regional variation in healthcare costs and mortality across Ontario’s health planning districts [Local Health Integration Networks (LHIN)] among senior incident HCU and non-HCU and explores the relationship between healthcare spending and mortality.

Methods

We conducted a retrospective population-based matched cohort study of incident senior HCU defined as Ontarians aged ≥66 years in the top 5% most costly healthcare users in fiscal year (FY) 2013. We matched HCU to non-HCU (1:3) based on age, sex and LHIN. Primary outcomes were LHIN-based variation in costs (total and 12 cost components) and mortality during FY2013 as measured by variance estimates derived from multi-level models. Outcomes were risk-adjusted for age, sex, ADGs, and low-income status. In a cost-mortality analysis by LHIN, risk-adjusted random effects for total costs and mortality were graphically presented together in a cost-mortality plane to identify low and high performers.

Results

We studied 175,847 incident HCU and 527,541 matched non-HCU. On average, 94 out of 1000 seniors per LHIN were HCU (CV = 4.6%). The mean total costs for HCU in FY2013 were 12 times higher that of non-HCU ($29,779 vs. $2472 respectively), whereas all-cause mortality was 13.6 times greater (103.9 vs. 7.5 per 1000 seniors).

Regional variation in costs and mortality was lower in senior HCU compared with non-HCU. We identified greater variability in accessing the healthcare system, but, once the patient entered the system, variation in costs was low. The traditional drivers of costs and mortality that we adjusted for played little role in driving the observed variation in HCUs’ outcomes. We identified LHINs that had high mortality rates despite elevated healthcare expenditures and those that achieved lower mortality at lower costs. Some LHINs achieved low mortality at excessively high costs.

Conclusions

Risk-adjusted allocation of healthcare resources to seniors in Ontario is overall similar across health districts, more so for HCU than non-HCU. Identified important variation in the cost-mortality relationship across LHINs needs to be further explored.

Electronic supplementary material

The online version of this article (10.1186/s12877-018-0952-7) contains supplementary material, which is available to authorized users.