Subgroups of High-Cost Medicare Advantage Patients: an Observational Study

Powers, Brian; Yan, Jiali; Zhu, Jingsan; Linn, Kristin A.; Jain, Sachin H.; Kowalski, Jennifer; Navathe, Amol S.

doi:10.1007/s11606-018-4759-1

Cited by 32 publications

(51 citation statements)

References 16 publications

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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%

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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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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“…2,5,36,37 Two recently published approaches offer other cluster-based solutions to elucidate subgroups of high-cost patients with some notable successes. 38,39 However, these were not applied to evaluate changes in spending, outcomes over more than 1 year, or to elucidate patients with rising Downloaded From: https://jamanetwork.com/ on 11/01/2020 costs. 38,39 They also focused on Medicare Advantage populations, which can differ from fee-forservice beneficiaries.…”

Section: Discussionmentioning

confidence: 99%

“…38,39 However, these were not applied to evaluate changes in spending, outcomes over more than 1 year, or to elucidate patients with rising Downloaded From: https://jamanetwork.com/ on 11/01/2020 costs. 38,39 They also focused on Medicare Advantage populations, which can differ from fee-forservice beneficiaries. 40,41 Patients may have dynamic patterns of spending over longer periods of time that can be potentially meaningful, with implications on whom to outreach for intervention as well as when and perhaps how to do so.…”

Section: Discussionmentioning

confidence: 99%

Use of Data-Driven Methods to Predict Long-term Patterns of Health Care Spending for Medicare Patients

2020

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IMPORTANCE Current approaches to predicting health care costs generally rely on a single composite value of spending and focus on short time horizons. By contrast, examining patients' spending patterns using dynamic measures applied over longer periods may better identify patients with different spending and help target interventions to those with the greatest need. OBJECTIVE To classify patients by their long-term, dynamic health care spending patterns using a data-driven approach and assess the ability to predict spending patterns, particularly using characteristics that are potentially modifiable through intervention. DESIGN, SETTING, AND PARTICIPANTS This cohort study used a retrospective cohort design from a random nationwide sample of Medicare fee-for-service administrative claims data to identify beneficiaries aged 65 years or older with continuous eligibility from 2011 to 2013. Statistical analysis was performed from August 2018 to December 2019. MAIN OUTCOMES AND MEASURES Group-based trajectory modeling was applied to the claims data to classify the Medicare beneficiaries by their total health care spending patterns over a 2-year period. The ability to predict membership in each trajectory spending group was assessed using generalized boosted regression, a data mining approach to model building and prediction, with splitsample validation. Models were estimated using (1) prior-year predictors and (2) prior-year predictors potentially modifiable through intervention measured in the claims data. These models were evaluated using validated C-statistics. The relative influence of individual predictors in the models was evaluated. RESULTS Among the 329 476 beneficiaries, the mean (SD) age was 76.0 (7.2) years and 190 346 (57.8%) were female. This final 5-group model included a minimal-user group (group 1, 37 572 individuals [11.4%]), a low-cost group (group 2, 48 575 individuals [14.7%]), a rising-cost group (group 3, 24 736 individuals [7.5%]), a moderate-cost group (group 4, 83 338 individuals [25.3%]), and a high-cost group (group 5, 135 255 individuals [41.2%]). Potentially modifiable characteristics strongly predicted these patterns (C-statistics range: 0.68-0.94). For groups with progressively increasing spending in particular, the most influential factors were number of medications (relative influence: 29.2), number of office visits (relative influence: 30.3), and mean medication adherence (relative influence: 33.6). CONCLUSIONS AND RELEVANCE Using a data-driven approach, distinct spending patterns were identified with high accuracy. The potentially modifiable predictors of membership in the rising-cost group represent important levers for early interventions that may prevent later spending increases. This approach could be adapted by organizations to target quality improvement interventions, (continued) Key Points Question What are the long-term spending patterns by Medicare beneficiaries, and do baseline patient factors that are potentially modifiable predict these patterns? Findings In this cohort st...

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“…Taken together, our results suggest that density-based clustering with the OPTICS algorithm performs best for identifying clinically distinct and operationally significant subgroups of high-cost patients in our data, and may be a promising and feasible approach for subgroup analysis in health plan or health system data. An accompanying article provides an indepth description of the clinical composition, utilization patterns, and spending trajectories of the subgroups identified by the OPTICS algorithm 19 .…”

Section: Discussionmentioning

confidence: 99%

“…The study population consisted of patients enrolled in a national Medicare Advantage plan who were in the top decile of total per-patient spending in 2014 (n = 6154). See an accompanying article for a full description of the study population 19 . We extracted demographic and clinical data directly from the health plan's electronic data warehouse (EDW).…”

Section: Study Population and Datamentioning

confidence: 99%

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations

et al. 2018

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BACKGROUND: Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients. OBJECTIVE: To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients. DESIGN: We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics. PARTICIPANTS: Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154). MAIN MEASURES: Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures. KEY RESULTS: Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering. CONCLUSIONS: Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.

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Subgroups of High-Cost Medicare Advantage Patients: an Observational Study

Cited by 32 publications

References 16 publications

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

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

Use of Data-Driven Methods to Predict Long-term Patterns of Health Care Spending for Medicare Patients

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations

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