Challenges in predicting future high-cost patients for care management interventions
Abstract Background To test the accuracy of a segmentation approach using claims data to predict Medicare beneficiaries most likely to be hospitalized in a subsequent year. Methods This article uses a 100-percent sample of Medicare beneficiaries from 2017 to 2018. This analysis is designed to illust...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Health Services Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12913-023-09957-9 |
| _version_ | 1797577262768324608 |
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| author | Chris Crowley Jennifer Perloff Amy Stuck Robert Mechanic |
| author_facet | Chris Crowley Jennifer Perloff Amy Stuck Robert Mechanic |
| author_sort | Chris Crowley |
| collection | DOAJ |
| description | Abstract Background To test the accuracy of a segmentation approach using claims data to predict Medicare beneficiaries most likely to be hospitalized in a subsequent year. Methods This article uses a 100-percent sample of Medicare beneficiaries from 2017 to 2018. This analysis is designed to illustrate the actuarial limitations of person-centered risk segmentation by looking at the number and rate of hospitalizations for progressively narrower segments of heart failure patients and a national fee-for-service comparison group. Cohorts are defined using 2017 data and then 2018 hospitalization rates are shown graphically. Results As the segments get narrower, the 2018 hospitalization rates increased, but the percentage of total Medicare FFS hospitalizations accounted for went down. In all three segments and the total Medicare FFS population, more than half of all patients did not have a hospitalization in 2018. Conclusions With the difficulty of identifying future high utilizing beneficiaries, health systems should consider the addition of clinician input and ‘light touch’ monitoring activities to improve the prediction of high-need, high-cost cohorts. It may also be beneficial to develop systemic strategies to manage utilization and steer beneficiaries to efficient providers rather than targeting individual patients. |
| first_indexed | 2024-03-10T22:05:43Z |
| format | Article |
| id | doaj.art-be55638242ff4886bb908be56db885c6 |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-03-10T22:05:43Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-be55638242ff4886bb908be56db885c62023-11-19T12:49:12ZengBMCBMC Health Services Research1472-69632023-09-012311710.1186/s12913-023-09957-9Challenges in predicting future high-cost patients for care management interventionsChris Crowley0Jennifer Perloff1Amy Stuck2Robert Mechanic3West Health InstituteInstitute for Accountable Care and Brandeis UniversityWest Health InstituteInstitute for Accountable Care and Brandeis UniversityAbstract Background To test the accuracy of a segmentation approach using claims data to predict Medicare beneficiaries most likely to be hospitalized in a subsequent year. Methods This article uses a 100-percent sample of Medicare beneficiaries from 2017 to 2018. This analysis is designed to illustrate the actuarial limitations of person-centered risk segmentation by looking at the number and rate of hospitalizations for progressively narrower segments of heart failure patients and a national fee-for-service comparison group. Cohorts are defined using 2017 data and then 2018 hospitalization rates are shown graphically. Results As the segments get narrower, the 2018 hospitalization rates increased, but the percentage of total Medicare FFS hospitalizations accounted for went down. In all three segments and the total Medicare FFS population, more than half of all patients did not have a hospitalization in 2018. Conclusions With the difficulty of identifying future high utilizing beneficiaries, health systems should consider the addition of clinician input and ‘light touch’ monitoring activities to improve the prediction of high-need, high-cost cohorts. It may also be beneficial to develop systemic strategies to manage utilization and steer beneficiaries to efficient providers rather than targeting individual patients.https://doi.org/10.1186/s12913-023-09957-9MedicareHigh-cost patientsUtilizationCare managementRisk-stratificationSegmentation |
| spellingShingle | Chris Crowley Jennifer Perloff Amy Stuck Robert Mechanic Challenges in predicting future high-cost patients for care management interventions BMC Health Services Research Medicare High-cost patients Utilization Care management Risk-stratification Segmentation |
| title | Challenges in predicting future high-cost patients for care management interventions |
| title_full | Challenges in predicting future high-cost patients for care management interventions |
| title_fullStr | Challenges in predicting future high-cost patients for care management interventions |
| title_full_unstemmed | Challenges in predicting future high-cost patients for care management interventions |
| title_short | Challenges in predicting future high-cost patients for care management interventions |
| title_sort | challenges in predicting future high cost patients for care management interventions |
| topic | Medicare High-cost patients Utilization Care management Risk-stratification Segmentation |
| url | https://doi.org/10.1186/s12913-023-09957-9 |
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