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.

Multiple chronic conditions and associated health care expenses in US adults with cancer: a 2010-2015 Medical Expenditure Panel Survey study.

BACKGROUND: Cancer increases the risk of developing one or more chronic conditions, yet little research describes the associations between health care costs, utilization patterns, and chronic conditions in adults with cancer. The objective of this study was to examine the treated prevalence of chronic conditions and the association between chronic conditions and health care expenses in US adults with cancer. METHODS: This retrospective observational study used US Medical Expenditure Panel Survey (MEPS) Household Component (2010-2015) data sampling adults diagnosed with cancer and one or more of 18 select chronic conditions. The measures used were treated prevalence of chronic conditions, and total and chronic condition-specific health expenses (per-person, per-year). Generalized linear models assessed chronic condition-specific expenses in adults with cancer vs. without cancer and the association of chronic conditions on total health expenses in adults with cancer, respectively, by controlling for demographic and health characteristics. Accounting for the complex survey design in MEPS, all data analyses and statistical procedures applied longitudinal weights for national estimates. RESULTS: Among 3657 eligible adults with cancer, 83.9% (n = 3040; representing 16 million US individuals per-year) had at least one chronic condition, and 29.7% reported four or more conditions. Among those with cancer, hypertension (59.7%), hyperlipidemia (53.6%), arthritis (25.6%), diabetes (22.2%), and coronary artery disease (18.2%) were the five most prevalent chronic conditions. Chronic conditions accounted for 30% of total health expenses. Total health expenses were $6388 higher for those with chronic conditions vs. those without (p < 0.001). Health expenses associated with chronic conditions increased by 34% in adults with cancer vs. those without cancer after adjustment. CONCLUSIONS: In US adults with cancer, the treated prevalence of common chronic conditions was high and health expenses associated with chronic conditions were higher than those without cancer. A holistic treatment plan is needed to improve cost outcomes.

Evaluation of the intensive outpatient clinic: study protocol for a prospective study of high-cost, high-need patients in the University of Utah Health system.

INTRODUCTION: The University of Utah (UofU) Health intensive outpatient clinic (IOC) is a primary care clinic for medically complex (high-cost, high-need) patients with Medicaid. The clinic consists of a multidisciplinary care team aimed at providing coordinated, comprehensive and patient-centred care. The protocol outlines the quantitative design of an evaluation study to determine the IOC's effects on reducing healthcare utilisation and costs, as well as improving patient-reported health outcomes and quality of care. METHODS AND ANALYSIS: High-risk patients, with high utilisation and multiple chronic illnesses, were identified in the Medicaid ACO population managed by the UofU Health plans for IOC eligibility. A prospective, case-control study design is being used to match 100 IOC patients to 200 control patients (receiving usual care within the UofU) based on demographics, health utilisation and medical complexity for evaluating the primary outcome of change in healthcare utilisation and costs. For the secondary outcomes of patient health and care quality, a prepost design will be used to examine within-person change across the 18 months of follow-up (ie, before and after IOC intervention). Logistic regression and hierarchical, longitudinal growth modelling are the two primary modelling approaches. ETHICS AND DISSEMINATION: This work has received ethics approval by the UofU Institutional Review Board. Results from the evaluation of primary and secondary outcomes will be disseminated in scientific research journals and presented at national conferences.

Systematic review of high-cost patients' characteristics and healthcare utilisation.

OBJECTIVES: To investigate the characteristics and healthcare utilisation of high-cost patients and to compare high-cost patients across payers and countries. DESIGN: Systematic review. DATA SOURCES: PubMed and Embase databases were searched until 30 October 2017. ELIGIBILITY CRITERIA AND OUTCOMES: Our final search was built on three themes: 'high-cost', 'patients', and 'cost' and 'cost analysis'. We included articles that reported characteristics and utilisation of the top-X% (eg, top-5% and top-10%) patients of costs of a given population. Analyses were limited to studies that covered a broad range of services, across the continuum of care. Andersen's behavioural model was used to categorise characteristics and determinants into predisposing, enabling and need characteristics. RESULTS: The studies pointed to a high prevalence of multiple (chronic) conditions to explain high-cost patients' utilisation. Besides, we found a high prevalence of mental illness across all studies and a prevalence higher than 30% in US Medicaid and total population studies. Furthermore, we found that high costs were associated with increasing age but that still more than halve of high-cost patients were younger than 65 years. High costs were associated with higher incomes in the USA but with lower incomes elsewhere. Preventable spending was estimated at maximally 10% of spending. The top-10%, top-5% and top-1% high-cost patients accounted for respectively 68%, 55% and 24% of costs within a given year. Spending persistency varied between 24% and 48%. Finally, we found that no more than 30% of high-cost patients are in their last year of life. CONCLUSIONS: High-cost patients make up the sickest and most complex populations, and their high utilisation is primarily explained by high levels of chronic and mental illness. High-cost patients are diverse populations and vary across payer types and countries. Tailored interventions are needed to meet the needs of high-cost patients and to avoid waste of scarce resources.

Provider Experiences with Chronic Care Management (CCM) Services and Fees: A Qualitative Research Study.

BACKGROUND: Support for ongoing care management and coordination between office visits for patients with multiple chronic conditions has been inadequate. In January 2015, Medicare introduced the Chronic Care Management (CCM) payment policy, which reimburses providers for CCM activities for Medicare beneficiaries occurring outside of office visits. OBJECTIVE: To explore the experiences, facilitators, and challenges of practices providing CCM services, and their implications going forward. DESIGN: Semi-structured telephone interviews from January to April 2016 with 71 respondents. PARTICIPANTS: Sixty billing and non-billing providers and practice staff knowledgeable about their practices' CCM services, and 11 professional society representatives. KEY RESULTS: Practice respondents noted that most patients expressed positive views of CCM services. Practice respondents also perceived several patient benefits, including improved adherence to treatment, access to care team members, satisfaction, care continuity, and care coordination. Facilitators of CCM provision included having an in-practice care manager, patient-centered medical home recognition, experience developing care plans, patient trust in their provider, and supplemental insurance to cover CCM copayments. Most billing practices reported few problems obtaining patients' consent for CCM, though providers felt that CMS could better facilitate consent by marketing CCM's goals to beneficiaries. Barriers reported by professional society representatives and by billing and non-billing providers included inadequacy of CCM payments to cover upfront investments for staffing, workflow modification, and time needed to manage complex patients. Other barriers included inadequate infrastructure for health information exchange with other providers and limited electronic health record capabilities for documenting and updating care plans. Practices owned by hospital systems and large medical groups faced greater bureaucracy in implementing CCM than did smaller, independent practices. CONCLUSIONS: Improving providers' experiences with and uptake of CCM will require addressing several challenges, including the upfront investment for CCM set-up and the time required to provide CCM to more complex patients.

Incorporating Budget Impact Analysis in the Implementation of Complex Interventions: A Case of an Integrated Intervention for Multimorbid Patients within the CareWell Study.

OBJECTIVES: To develop a framework for the management of complex health care interventions within the Deming continuous improvement cycle and to test the framework in the case of an integrated intervention for multimorbid patients in the Basque Country within the CareWell project. METHODS: Statistical analysis alone, although necessary, may not always represent the practical significance of the intervention. Thus, to ascertain the true economic impact of the intervention, the statistical results can be integrated into the budget impact analysis. The intervention of the case study consisted of a comprehensive approach that integrated new provider roles and new technological infrastructure for multimorbid patients, with the aim of reducing patient decompensations by 10% over 5 years. The study period was 2012 to 2020. RESULTS: Given the aging of the general population, the conventional scenario predicts an increase of 21% in the health care budget for care of multimorbid patients during the study period. With a successful intervention, this figure should drop to 18%. The statistical analysis, however, showed no significant differences in costs either in primary care or in hospital care between 2012 and 2014. The real costs in 2014 were by far closer to those in the conventional scenario than to the reductions expected in the objective scenario. The present implementation should be reappraised, because the present expenditure did not move closer to the objective budget. CONCLUSIONS: This work demonstrates the capacity of budget impact analysis to enhance the implementation of complex interventions. Its integration in the context of the continuous improvement cycle is transferable to other contexts in which implementation depth and time are important.