Enhancing risk stratification for use in integrated care: a cluster analysis of high-risk patients in a retrospective cohort study

Predictive modeling for identification of older adults with high utilization of health and social services

AIM

Machine learning techniques have demonstrated success in predictive modeling across various clinical cases. However, few studies have considered predicting the use of multisectoral health and social services among older adults. This research aims to utilize machine learning models to detect high-risk groups of excessive health and social services utilization at early stage, facilitating the implementation of preventive interventions.

METHODS

We used pseudonymized data covering a four-year period and including information on a total of 33,374 senior citizens from Southern Finland. The endpoint was defined based on the occurrence of unplanned healthcare visits and the total number of different services used. Input features included individual's basic demographics, health status and past usage of healthcare resources. Logistic regression and eXtreme Gradient Boosting (XGBoost) methods were used for binary classification, with the dataset split into 70% training and 30% testing sets.

RESULTS

Subgroup-based results mirrored trends observed in the full cohort, with age and certain health issues, e.g. mental health, emerging as positive predictors for high service utilization. Conversely, hospital stay and urban residence were associated with decreased risk. The models achieved a classification performance (AUC) of 0.61 for the full cohort and varying in the range of 0.55-0.62 for the subgroups.

CONCLUSIONS

Predictive models offer potential for predicting future high service utilization in the older adult population. Achieving high classification performance remains challenging due to diverse contributing factors. We anticipate that classification performance could be increased by including features based on additional data categories such as socio-economic data.

Identifying individuals with intellectual disability who access mental health support and are at high risk for adverse clinical outcomes: cohort study

BACKGROUND

People with intellectual disability often experience aggressive challenging behaviour and mental health issues. It can be difficult to identify those who are at higher risk of adverse clinical outcomes when in clinical care.

AIMS

To characterise potential subgroups in adults with intellectual disability referred to mental health services in those presenting with aggressive behaviour or common mental disorders (CMDs).

METHOD

There were 836 adults (≥18 years) with intellectual disability and a record of aggressive challenging behaviour, and 205 patients with intellectual disability and CMDs, who were seen in specialist mental health services over a 5-year period. Cluster analysis was used to define patient characteristics associated with clinical outcome.

RESULTS

Distinct patient groups with differentiated profiles were observed in people with intellectual disability displaying aggressive challenging behaviour, and in those presenting with CMDs. Characteristics of the aggressive behaviour group who experienced adverse outcomes included being <30 years old, being male, more mentions of aggression and agitation in their clinical record, a diagnosis of pervasive developmental disorder and prescription of psychotropic medication. Characteristics of the CMD cluster that experienced adverse clinical outcomes were being older, being a White male, having a mild intellectual disability and physical health concerns.

CONCLUSIONS

People with intellectual disability who experience adverse clinical outcomes can be identified with a cluster analysis approach of common features, but differ by clinical presentation. This could be used not only to stratify this clinically heterogeneous population in terms of response to interventions, but also improve precision in the development of tailored interventions.

A systematic review of risk stratification tools internationally used in primary care settings

BACKGROUND AND AIMS

In our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been developed as an approach to balance quality with increasing costs. This approach identifies sub-populations with comparable health risks, to tailor interventions for those that will benefit the most. Worldwide, the use of routine healthcare data extracted from electronic health registries for risk stratification approaches is increasing. Different risk stratification tools are used on different levels of the healthcare continuum. In this systematic literature review, we aimed to explore which tools are used in primary healthcare settings and assess their performance.

METHODS

We performed a systematic literature review of studies applying risk stratification tools with health outcomes in primary care populations. Studies in Organisation for Economic Co-operation and Development countries published in English-language journals were included. Search engines were utilized with keywords, for example, "primary care," "risk stratification," and "model." Risk stratification tools were compared based on different measures: area under the curve (AUC) and C-statistics for dichotomous outcomes and R 2 for continuous outcomes.

RESULTS

The search provided 4718 articles. Specific election criteria such as primary care populations, generic health utilization outcomes, and routinely collected data sources identified 61 articles, reporting on 31 different models. The three most frequently applied models were the Adjusted Clinical Groups (ACG, n = 23), the Charlson Comorbidity Index (CCI, n = 19), and the Hierarchical Condition Categories (HCC, n = 7). Most AUC and C-statistic values were above 0.7, with ACG showing slightly improved scores compared with the CCI and HCC (typically between 0.6 and 0.7).

CONCLUSION

Based on statistical performance, the validity of the ACG was the highest, followed by the CCI and the HCC. The ACG also appeared to be the most flexible, with the use of different international coding systems and measuring a wider variety of health outcomes.

Demographic and Clinical Correlates of the Cost of Potentially Preventable Hospital Encounters in a Community Health Center Cohort

This study sought to describe the cost of hospital care for ambulatory care-sensitive conditions (ACSCs) and to identify independent predictors of high-cost hospital encounters related to an ACSC among an urban community health center cohort. The authors conducted a retrospective cohort study of individuals engaged in care in a large, multisite community health center in New Haven, Connecticut, with any Medicaid claims between June 1, 2018 and March 31, 2020. Prevention Quality Indicators of the Agency for Healthcare Research and Quality were used to identify ACSCs. The primary outcome was a high-cost episode of care for an ACSC (in the top quartile within a 7-day period). Multivariable logistic regression was used to identify independent predictors of high-cost episodes by ACSCs among sociodemographic and clinical variables as covariates. Among 8019 included individuals, a total of 751 episodes of hospital care involving ACSCs were identified. The median episode cost was $793, with the highest median cost of care related to heart failure ($4992), followed by diabetes ($1162), and chronic obstructive pulmonary disease ($1141). In adjusted analyses, male gender (P < 0.01), increasing age (P = 0.02), and ACSC type (P < 0.01) were associated with higher costs of care; race/ethnicity was not. Community health centers in urban settings seeking to reduce the cost of care of potentially preventable hospitalizations may target disease-/condition-specific groups, particularly individuals of increasing age with congestive heart failure and diabetes mellitus. These findings may inform return-on-investment calculations for care coordination and other enabling services programming.

Elements and Performance Indicators of Integrated Healthcare Programmes on Chronic Diseases in Six Countries in the Asia-Pacific Region: A Scoping Review

Background and Aims:

Globally, hospital-based healthcare models targeting acute care, are not effective in addressing chronic conditions. Integrated care programmes for chronic diseases have been widely developed and implemented in Europe and North America and to a much lesser extent in the Asia-Pacific region to meet such challenges. We completed a scoping review aiming to examine the elements of programmes identified in the literature from select study countries in the Asia-Pacific, and discuss important facilitators and barriers for design and implementation.

Methods:

The study design adopted a scoping review approach. Integrated care programmes in the study countries were searched in electronic databases using a developed search strategy and key words. Elements of care integration, barriers and facilitators were identified and charted following the Chronic Care Model (CCM).

Results:

Overall the study found a total of 87 integrated care programmes for chronic diseases in all countries, with 44 in China, 21 in Singapore, 12 in India, 5 in Vietnam, 4 in the Philippines and 1 in Fiji. Financial incentives were found to play a crucial role in facilitating integrated care and ensuring the sustainability of programmes. In many cases, the performance of programmes was found not to have been adequately assessed.

Conclusion:

Integrated care is important for addressing the challenges surrounding the delivery of long-term care and there is an increasing trend of integrated care programmes for chronic diseases in the Asia-Pacific. Evaluating the performance of integrated care programmes is crucial for developing strategies for implementing future programmes and improving already existing programmes.

A pragmatic methodology for the evaluation of digital care management in the context of multimorbidity

Multimorbidity is a defining challenge for health systems and requires coordination of care delivery and care management. Care management is a clinical service designed to remotely engage patients between visits and after discharge in order to support self-management of chronic and emergent conditions, encourage increased use of scheduled care and address the use of unscheduled care. Care management can be provided using digital technology - digital care management. A robust methodology to assess digital care management, or any traditional or digital primary care intervention aimed at longitudinal management of multimorbidity, does not exist outside of randomized controlled trials (RCTs). RCTs are not always generalizable and are also not feasible for most healthcare organizations. We describe here a novel and pragmatic methodology for the evaluation of digital care management that is generalizable to any longitudinal intervention for multimorbidity irrespective of its mode of delivery. This methodology implements propensity matching with bootstrapping to address some of the major challenges in evaluation including identification of robust outcome measures, selection of an appropriate control population, small sample sizes with class imbalances, and limitations of RCTs. We apply this methodology to the evaluation of digital care management at a U.S. payor and demonstrate a 9% reduction in ER utilization, a 17% reduction in inpatient admissions, and a 29% increase in the utilization of preventive medicine services. From these utilization outcomes, we drive forward an estimated cost saving that is specific to a single payor's payment structure for the study time period of $641 per-member-per-month at 3 months. We compare these results to those derived from existing observational approaches, 1:1 and 1:n propensity matching, and discuss the circumstances in which our methodology has advantages over existing techniques. Whilst our methodology focuses on cost and utilization and is applied in the U.S. context, it is applicable to other outcomes such as Patient Reported Outcome Measures (PROMS) or clinical biometrics and can be used in other health system contexts where the challenge of multimorbidity is prevalent.

Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles

IMPORTANCE

Medically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date.

OBJECTIVE

To define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles.

MAIN OUTCOMES AND MEASURES

Complex patient profiles, 1-year health care utilization, and mortality outcomes by profile.

RESULTS

The analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management.

CONCLUSIONS AND RELEVANCE

The findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.

Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study

OBJECTIVES

To use Population Health Management (PHM) methods to identify and characterise individuals at high-risk of severe COVID-19 for which shielding is required, for the purposes of managing ongoing health needs and mitigating potential shielding-induced harm.

DESIGN

Individuals at 'high risk' of COVID-19 were identified using the published national 'Shielded Patient List' criteria. Individual-level information, including current chronic conditions, historical healthcare utilisation and demographic and socioeconomic status, was used for descriptive analyses of this group using PHM methods. Segmentation used k-prototypes cluster analysis.

SETTING

A major healthcare system in the South West of England, for which linked primary, secondary, community and mental health data are available in a system-wide dataset. The study was performed at a time considered to be relatively early in the COVID-19 pandemic in the UK.

PARTICIPANTS

1 013 940 individuals from 78 contributing general practices.

RESULTS

Compared with the groups considered at 'low' and 'moderate' risk (ie, eligible for the annual influenza vaccination), individuals at high risk were older (median age: 68 years (IQR: 55-77 years), cf 30 years (18-44 years) and 63 years (38-73 years), respectively), with more primary care/community contacts in the previous year (median contacts: 5 (2-10), cf 0 (0-2) and 2 (0-5)) and had a higher burden of comorbidity (median Charlson Score: 4 (3-6), cf 0 (0-0) and 2 (1-4)). Geospatial analyses revealed that 3.3% of rural and semi-rural residents were in the high-risk group compared with 2.91% of urban and inner-city residents (p<0.001). Segmentation uncovered six distinct clusters comprising the high-risk population, with key differentiation based on age and the presence of cancer, respiratory, and mental health conditions.

CONCLUSIONS

PHM methods are useful in characterising the needs of individuals requiring shielding. Segmentation of the high-risk population identified groups with distinct characteristics that may benefit from a more tailored response from health and care providers and policy-makers.

Tailoring integrated care services for high-risk patients with multiple chronic conditions: a risk stratification approach using cluster analysis

Background

The purpose of this study was to produce a risk stratification within a population of high-risk patients with multiple chronic conditions who are currently treated under a case management program and to explore the existence of different risk subgroups. Different care strategies were then suggested for healthcare reform according to the characteristics of each subgroup.

Methods

All high-risk multimorbid patients from a case management program in the Navarra region of Spain were included in the study (n = 885). A 1-year mortality risk score was estimated for each patient by logistic regression. The population was then divided into subgroups according to the patients’ estimated risk scores. We used cluster analysis to produce the stratification with Ward’s linkage hierarchical algorithm. The characteristics of the resulting subgroups were analyzed, and post hoc pairwise tests were performed.

Results

Three distinct risk strata were found, containing 45, 38 and 17% of patients. Age increased from cluster to cluster, and functional status, clinical severity, nursing needs and nutritional values deteriorated. Patients in cluster 1 had lower renal deterioration values, and patients in cluster 3 had higher rates of pressure skin ulcers, higher rates of cerebrovascular disease and dementia, and lower prevalence rates of chronic obstructive pulmonary disease.

Conclusions

This study demonstrates the existence of distinct subgroups within a population of high-risk patients with multiple chronic conditions. Current case management integrated care programs use a uniform treatment strategy for patients who have diverse needs. Alternative treatment strategies should be considered to fit the needs of each patient subgroup.

A 4-Year Investigation of Ambulatory Health Care Expenditure Concentration and High-Cost Patients: An Experience From a Developing Country

The aim of this study is to investigate the concentration of ambulatory health care expenditure in a large Iranian outpatient population. This study used 2013-2016 individual-level claims data of Iranian Health Insurance Organization in East Azerbaijan province. All ambulatory care utilizers were included in the study. We determined characteristics and utilization pattern of high-cost patients as well as their predictors. A total of 1 128 149 patients were included. The top 10% of patients accounted for 62.56% of the total expenditure. This skewed expenditure pattern remained relatively stable over the study period. Female sex, older age, cancer, chronic obstructive pulmonary disease, cardiovascular disease, and diabetes increase the odds of being high cost.

Evaluating case management as a complex intervention: Lessons for the future

The methodological challenges to effectiveness evaluation of complex interventions has been widely discussed. Bottom-up case management for frail older person was implemented in Belgium, and indeed, it was evaluated as a complex intervention. This paper presents the methodological approach we developed to respond to four main methodological challenges regarding the evaluation of case management: (1) the standardization of the interventions, (2) stratification of the frail older population that was used to test various modalities of case management with different risks groups, (3) the building of a control group, and (4) the use of multiple outcomes in evaluating case management. To address these challenges, we developed a mixed-methods approach that (1) used multiple embedded case studies to classify case management types according to their characteristics and implementation conditions; and (2) compared subgroups of beneficiaries with specific needs (defined by Principal Component Analysis prior to cluster analysis) and a control group receiving 'usual care', to evaluate the effectiveness of case management. The beneficiaries' subgroups were matched using propensity scores and compared using generalized pairwise comparison and the hurdle model with the control group. Our results suggest that the impact of case management on patient health and the services used varies according to specific needs and categories of case management. However, these equivocal results question our methodological approach. We suggest to reconsider the evaluation approach by moving away from a viewing case management as an intervention. Rather, it should be considered as a process of interconnected actions taking place within a complex system.

Health Care Professionals' Perspectives on the Secondary Use of Health Records to Improve Quality and Safety of Care in England: Qualitative Study

BACKGROUND

Health care professionals (HCPs) are often patients' first point of contact in what concerns the communication of the purposes, benefits, and risks of sharing electronic health records (EHRs) for nondirect care purposes. Their engagement is fundamental to ensure patients' buy-in and a successful implementation of health care data sharing schemes. However, their views on this subject are seldom evaluated.

OBJECTIVE

This study aimed to explore HCPs' perspectives on the secondary uses of health care data in England. Specifically, we aimed to assess their knowledge on its purposes and the main concerns about data sharing processes.

METHODS

A total of 30 interviews were conducted between March 27, 2017, and April 7, 2017, using a Web-based interview platform and following a topic guide with open-ended questions. The participants represented a variety of geographic locations across England (London, West Midlands, East of England, North East England, and Yorkshire and the Humber), covering both primary and secondary care services. The transcripts were compiled verbatim and systematically reviewed by 2 independent reviewers using the framework analysis method to identify emerging themes.

RESULTS

HCPs were knowledgeable about the possible secondary uses of data and highlighted its importance for patient profiling and tailored care, research, quality assurance, public health, and service delivery planning purposes. Main concerns toward data sharing included data accuracy, patients' willingness to share their records, challenges on obtaining free and informed consent, data security, lack of adequacy or understanding of current policies, and potential patient exposure and exploitation.

CONCLUSIONS

These results suggest a high level of HCPs' understanding about the purposes of data sharing for secondary purposes; however, some concerns still remain. A better understanding of HCPs' knowledge and concerns could inform national communication policies and improve tailoring to maximize efficiency and improve patients' buy-in.

Characterization of high healthcare utilizer groups using administrative data from an electronic medical record database

Background

High utilizers (HUs) are a small group of patients who impose a disproportionately high burden on the healthcare system due to their elevated resource use. Identification of persistent HUs is pertinent as interventions have not been effective due to regression to the mean in majority of patients. This study will use cost and utilization metrics to segment a hospital-based patient population into HU groups.

Methods

The index visit for each adult patient to an Academic Medical Centre in Singapore during 2006 to 2012 was identified. Cost, length of stay (LOS) and number of specialist outpatient clinic (SOC) visits within 1 year following the index visit were extracted and aggregated. Patients were HUs if they exceeded the 90th percentile of any metric, and Non-HU otherwise. Seven different HU groups and a Non-HU group were constructed. The groups were described in terms of cost and utilization patterns, socio-demographic information, multi-morbidity scores and medical history. Logistic regression compared the groups’ persistence as a HU in any group into the subsequent year, adjusting for socio-demographic information and diagnosis history.

Results

A total of 388,162 patients above the age of 21 were included in the study. Cost-LOS-SOC HUs had the highest multi-morbidity and persistence into the second year. Common conditions among Cost-LOS and Cost-LOS-SOC HUs were cardiovascular disease, acute cerebrovascular disease and pneumonia, while most LOS and LOS-SOC HUs were diagnosed with at least one mental health condition. Regression analyses revealed that HUs across all groups were more likely to persist compared to Non-HUs, with stronger relationships seen in groups with high SOC utilization. Similar trends remained after further adjustment.

Conclusion

HUs of healthcare services are a diverse group and can be further segmented into different subgroups based on cost and utilization patterns. Segmentation by these metrics revealed differences in socio-demographic characteristics, disease profile and persistence. Most HUs did not persist in their high utilization, and high SOC users should be prioritized for further longitudinal analyses. Segmentation will enable policy makers to better identify the diverse needs of patients, detect gaps in current care and focus their efforts in delivering care relevant and tailored to each segment.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4239-2) contains supplementary material, which is available to authorized users.

Two morbidity indices developed in a nationwide population permitted performant outcome-specific severity adjustment

OBJECTIVE

The objective of the study was to develop and validate two outcome-specific morbidity indices in a population-based setting: the Mortality-Related Morbidity Index (MRMI) predictive of all-cause mortality and the Expenditure-Related Morbidity Index (ERMI) predictive of health care expenditure.

STUDY DESIGN AND SETTING

A cohort including all beneficiaries of the main French health insurance scheme aged 65 years or older on December 31, 2013 (N = 7,672,111), was randomly split into a development population for index elaboration and a validation population for predictive performance assessment. Age, gender, and selected lists of conditions identified through standard algorithms available in the French health insurance database (SNDS) were used as predictors for 2-year mortality and 2-year health care expenditure in separate models. Overall performance and calibration of the MRMI and ERMI were measured and compared to various versions of the Charlson Comorbidity Index (CCI).

RESULTS

The MRMI included 16 conditions, was more discriminant than the age-adjusted CCI (c-statistic: 0.825 [95% confidence interval: 0.824-0.826] vs. 0.800 [0.799-0.801]), and better calibrated. The ERMI included 19 conditions, explained more variance than the cost-adapted CCI (21.8% vs. 13.0%), and was better calibrated.

CONCLUSION

The proposed MRMI and ERMI indices are performant tools to account for health-state severity according to outcomes of interest.