Observational studyJournal of general internal medicine2018
Segmentation of High-Cost Adults in an Integrated Healthcare System Based on Empirical Clustering of Acute and Chronic Conditions.
Observational study in Journal of general internal medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping Healthcare Needs: A Systematic Review of Population Stratification Tools.Medical sciences (Basel, Switzerland) · 2025Pooled it
- Trial
- Time to care: the lived experience of receiving care from a primary care service for people with frequent hospital admissions.Family medicine and community health · 2026Article
- Characterizing hospitalization trajectories in the high-need, high-cost population using electronic health record data.Health affairs scholar · 2023Article
- Differential Impact of a Plan-Led Standardized Complex Care Management Intervention on Subgroups of High-Cost High-Need Medicaid Patients.Population health management · 2023Article
- Experiences of implementation of the group-based adaptation training intervention for patients with chronic somatic illnesses or disabilities among multi-professional teams in specialized healthcare.Annals of medicine · 2023Article
- Two-year change in latent classes of comorbidity among high-risk Veterans in primary care: a brief report.BMC health services research · 2022Article
- Clinical Outcome and Utilization Profiles Among Latent Groups of High-Risk Patients: Moving from Segmentation Towards Intervention.Journal of general internal medicine · 2022Article
- Identifying patterns of clinical conditions among high-cost older adult health care users using claims data: a latent class approach.International journal for equity in health · 2022Article
- Empiric segmentation of high-risk patients: a structured literature review.The American journal of managed care · 2022Review
- High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program.BMC health services research · 2021Article
- Emerging models of care for individuals with multiple chronic conditions.Health services research · 2021Article
- Outcomes of a randomized quality improvement trial for high-risk Veterans in year two.Health services research · 2021Article
- A systematic review of risk stratification tools internationally used in primary care settings.Health science reports · 2021Review
- Comparative Effectiveness of a Complex Care Program for High-Cost/High-Need Patients: a Retrospective Cohort Study.Journal of general internal medicine · 2021Article
- A sequence analysis of hospitalization patterns and service utilization in patients with major psychiatric disorders in China.BMC psychiatry · 2021Article
- Clustering Complex Chronic Patients: A Cross-Sectional Community Study From the General Practitioner's Perspective.International journal of integrated care · 2021Article
- Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles.JAMA network open · 2020Article
- High-Need Phenotypes in Medicare Beneficiaries: Drivers of Variation in Utilization and Outcomes.Journal of the American Geriatrics Society · 2020Article
- Characterization of high healthcare utilizer groups using administrative data from an electronic medical record database.BMC health services research · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundHigh-cost patients are a frequent focus of improvement projects based on primary care and other settings. Efforts to characterize high-cost, high-need patients are needed to inform care planning, but such efforts often rely on a priori assumptions, masking underlying complexities of a heterogenous population.
objectiveTo define recognizable subgroups of patients among high-cost adults based on clinical conditions, and describe their survival and future spending.
designRetrospective observational cohort study.
participantsWithin a large integrated delivery system with 2.7 million adult members, we selected the top 1% of continuously enrolled adults with respect to total healthcare expenditures during 2010. MAIN MEASURES: We used latent class analysis to identify clusters of alike patients based on 53 hierarchical condition categories. Prognosis as measured by healthcare spending and survival was assessed through 2014 for the resulting classes of patients.
resultsAmong 21,183 high-cost adults, seven clinically distinctive subgroups of patients emerged. Classes included end-stage renal disease (12% of high-cost population), cardiopulmonary conditions (17%), diabetes with multiple comorbidities (8%), acute illness superimposed on chronic conditions (11%), conditions requiring highly specialized care (14%), neurologic and catastrophic conditions (5%), and patients with few comorbidities (the largest class, 33%). Over 4 years of follow-up, 6566 (31%) patients died, and survival in the classes ranged from 43 to 88%. Spending regressed to the mean in all classes except the ESRD and diabetes with multiple comorbidities groups.
conclusionsData-driven characterization of high-cost adults yielded clinically intuitive classes that were associated with survival and reflected markedly different healthcare needs. Relatively few high-cost patients remain persistently high cost over 4 years. Our results suggest that high-cost patients, while not a monolithic group, can be segmented into few subgroups. These subgroups may be the focus of future work to understand appropriateness of care and design interventions accordingly.
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