Segmentation of High-Cost Adults in an Integrated Healthcare System Based on Empirical Clustering of Acute and Chronic Conditions

Davis, Al; Shen, Ernest; Shah, Nirav R.; Glenn, Beth A.; Ponce, Ninez A.; Telesca, Donatello; Gould, Michael K.; Needleman, Jack

doi:10.1007/s11606-018-4626-0

Cited by 28 publications

(37 citation statements)

References 24 publications

(32 reference statements)

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“…An analytical approach similar to audience segmentation in marketing can be applied to identify patterns of sample characteristics that describe baseline variation among participants [16]. In healthcare, segmentation analyses have been used to identify patient subgroups to better understand observed heterogeneity in clinical outcomes and care utilization [17][18][19]. Among several methods for subgroup identification, latent class analysis (LCA) provides a rigorous yet intuitive statistical approach that can accommodate a large number of variables and test competing models of segmentation.…”

Section: Introductionmentioning

confidence: 99%

Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial

et al. 2020

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Participants often vary in their response to behavioral interventions, but methods to identify groups of participants that are more likely to respond are lacking. In this secondary analysis of a randomized clinical trial, we used baseline characteristics to group participants into distinct behavioral phenotypes and evaluated differential responses to a physical activity intervention. Latent class analysis was used to segment participants based on baseline participant data including demographics, validated measures of psychosocial variables, and physical activity behavior. The trial included 602 adults from 40 U.S. states with body mass index �25 who were randomized to control or one of three gamification interventions (supportive, collaborative, or competitive) to increase physical activity. Daily step counts were monitored using a wearable device for a 24-week intervention with 12 weeks of follow-up. The model segmented participants into three classes named for key defining traits: Class 1, extroverted and motivated; Class 2, less active and less social; Class 3, less motivated and at-risk. Adjusted regression models were used to test for differences in intervention response relative to control within each behavioral phenotype. In Class 1, only participants in the competitive arm increased their mean daily steps during the intervention (adjusted difference, 945; 95% CI, 352-1537; P = .002), but it was not sustained during follow-up. In Class 2, participants in all three gamification arms significantly increased their mean daily steps compared to control during the intervention (supportive arm adjusted difference 1172; 95% CI, 363-1980; P = .005; collaborative arm adjusted difference 1119; 95% CI, 319-1919; P = .006; competitive arm adjusted difference 1179; 95% CI, 400-1957; P = .003) and all three had sustained impact during follow-up. In Class 3, none of the interventions had a significant effect on physical activity. Three behavioral phenotypes were identified, each

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Section: Introductionmentioning

confidence: 99%

Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial

et al. 2020

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“…Scholars have identified subpopulations among high‐risk populations including high‐cost and high‐risk cohorts using data from a variety of sources including managed care plans, administrative claims, and health systems . Prior work into HN subpopulations classified subgroups using clinical conditions, risk scores, hospital procedures, and acute utilization.…”

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confidence: 99%

“…7,8 Scholars have identified subpopulations among highrisk populations including high-cost 9-11 and high-risk 12 cohorts using data from a variety of sources including managed care plans, 10 administrative claims, 11 and health systems. 9,12 Prior work into HN subpopulations classified subgroups using clinical conditions, risk scores, hospital procedures, and acute utilization. Although valuable, past work is limited by a lack of focus on post-acute care that accounts for approximately 73% of the regional variation in Medicare spending.…”

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confidence: 99%

High‐Need Phenotypes in Medicare Beneficiaries: Drivers of Variation in Utilization and Outcomes

Keeney

Bélanger

Jones

et al. 2019

J American Geriatrics Society

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OBJECTIVES High‐need (HN) Medicare beneficiaries heavily use healthcare services at a high cost. This population is heterogeneous, composed of individuals with varying degrees of medical complexity and healthcare needs. To improve healthcare delivery and decrease costs, it is critical to identify the subpopulations present within this population. We aimed to (1) identify distinct clinical phenotypes present within HN Medicare beneficiaries, and (2) examine differences in outcomes between phenotypes. DESIGN Latent class analysis was used to identify phenotypes within a sample of HN fee‐for‐service (FFS) Medicare beneficiaries aged 65 years and older using Medicare claims and post‐acute assessment data. SETTING Not applicable. PARTICIPANTS Two cross‐sectional cohorts were used to identify phenotypes. Cohorts included FFS Medicare beneficiaries aged 65 and older who survived through 2014 (n = 415 659) and 2015 (n = 416 643). MEASUREMENTS The following variables were used to identify phenotypes: acute and post‐acute care use, functional dependency in one or more activities of daily living, presence of six or more chronic conditions, and complex chronic conditions. Mortality, hospitalizations, healthcare expenditures, and days in the community were compared between phenotypes. RESULTS Five phenotypes were identified: (1) comorbid ischemic heart disease with hospitalization and skilled nursing facility use (22% of the HN sample), (2) comorbid ischemic heart disease with home care use (23%), (3) home care use (12%), (4) high comorbidity with hospitalization (32%), and (5) Alzheimer's disease/related dementias with functional dependency and nursing home use (11%). Mortality was highest in phenotypes 1 and 2; hospitalizations and expenditures were highest in phenotypes 1, 3, and 4. CONCLUSIONS Our findings represent a first step toward classifying the heterogeneity among HN Medicare beneficiaries. Further work is needed to identify modifiable utilization patterns between phenotypes to improve the value of healthcare provided to these subpopulations. J Am Geriatr Soc 68:70–77, 2019

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“… 36 , 37 , 38 , 39 Results from diagnosis code analyses are useful for identifying which conditions are enriched or cooccur in high-cost conditions (eg, end-stage renal disease, diabetes with multiple comorbidities, and acute on chronic illness) but are less helpful for designing care around patient-centered phenotypes (eg, frail elderly adults, chronic pain management). 40 In addition, we linked our analytic results to clinical interpretation. Studies that apply automated approaches may return clusters of limited clinical interpretability (eg, creating groups distinguished by 1-year outcome risks that lack unifying clinical themes that could inform care redesign).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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Segmentation of High-Cost Adults in an Integrated Healthcare System Based on Empirical Clustering of Acute and Chronic Conditions

Cited by 28 publications

References 24 publications

Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial

Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial

High‐Need Phenotypes in Medicare Beneficiaries: Drivers of Variation in Utilization and Outcomes

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

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