Literature DB >> 27637821

Patterns of care for clinically distinct segments of high cost Medicare beneficiaries.

Jeffrey D Clough1, Gerald F Riley2, Melissa Cohen2, Sheila M Hanley2, Darshak Sanghavi2, Darren A DeWalt2, Rahul Rajkumar2, Patrick H Conway2.   

Abstract

BACKGROUND: Efforts to improve the efficiency of care for the Medicare population commonly target high cost beneficiaries. We describe and evaluate a novel management approach, population segmentation, for identifying and managing high cost beneficiaries.
METHODS: A retrospective cross-sectional analysis of 6,919,439 Medicare fee-for-service beneficiaries in 2012. We defined and characterized eight distinct clinical population segments, and assessed heterogeneity in managing practitioners.
RESULTS: The eight segments comprised 9.8% of the population and 47.6% of annual Medicare payments. The eight segments included 61% and 69% of the population in the top decile and top 5% of annual Medicare payments. The positive-predictive values within each segment for meeting thresholds of Medicare payments ranged from 72% to 100%, 30% to 83%, and 14% to 56% for the upper quartile, upper decile, and upper 5% of Medicare payments respectively. Sensitivity and positive-predictive values were substantially improved over predictive algorithms based on historical utilization patterns and comorbidities. The mean [95% confidence interval] number of unique practitioners and practices delivering E&M services ranged from 1.82 [1.79-1.84] to 6.94 [6.91-6.98] and 1.48 [1.46-1.50] to 4.98 [4.95-5.00] respectively. The percentage of cognitive services delivered by primary care practitioners ranged from 23.8% to 67.9% across segments, with significant variability among specialty types.
CONCLUSIONS: Most high cost Medicare beneficiaries can be identified based on a single clinical reason and are managed by different practitioners. IMPLICATIONS: Population segmentation holds potential to improve efficiency in the Medicare population by identifying opportunities to improve care for specific populations and managing clinicians, and forecasting and evaluating the impact of specific interventions.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Accountable care; Payment reform; Population health; Population segmentation; Risk-stratification

Mesh:

Year:  2015        PMID: 27637821     DOI: 10.1016/j.hjdsi.2015.09.005

Source DB:  PubMed          Journal:  Healthc (Amst)        ISSN: 2213-0764


  8 in total

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

Authors:  Brian W Powers; Jiali Yan; Jingsan Zhu; Kristin A Linn; Sachin H Jain; Jennifer L Kowalski; Amol S Navathe
Journal:  J Gen Intern Med       Date:  2018-12-03       Impact factor: 5.128

2.  Persistence of High-Need Status Over Time Among Fee-for-Service Medicare Beneficiaries.

Authors:  Tamra Keeney; Nina R Joyce; David J Meyers; Vincent Mor; Emmanuelle Belanger
Journal:  Med Care Res Rev       Date:  2020-01-23       Impact factor: 3.929

3.  Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

Authors:  Jiali Yan; Kristin A Linn; Brian W Powers; Jingsan Zhu; Sachin H Jain; Jennifer L Kowalski; Amol S Navathe
Journal:  J Gen Intern Med       Date:  2018-12-12       Impact factor: 5.128

4.  Population segments as a tool for health care performance reporting: an exploratory study in the Canadian province of British Columbia.

Authors:  Julia M Langton; Sabrina T Wong; Fred Burge; Alexandra Choi; Niloufar Ghaseminejad-Tafreshi; Sharon Johnston; Alan Katz; Ruth Lavergne; Dawn Mooney; Sandra Peterson; Kimberlyn McGrail
Journal:  BMC Fam Pract       Date:  2020-05-31       Impact factor: 2.497

5.  A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data.

Authors:  Ravi B Parikh; Kristin A Linn; Jiali Yan; Matthew L Maciejewski; Ann-Marie Rosland; Kevin G Volpp; Peter W Groeneveld; Amol S Navathe
Journal:  PLoS One       Date:  2021-02-19       Impact factor: 3.240

6.  No Association Between Pharmacogenomics Variants and Hospital and Emergency Department Utilization: A Mayo Clinic Biobank Retrospective Study.

Authors:  Paul Y Takahashi; Euijung Ryu; Suzette J Bielinski; Matthew Hathcock; Gregory D Jenkins; James R Cerhan; Janet E Olson
Journal:  Pharmgenomics Pers Med       Date:  2021-02-11

7.  Estimating Population Benefits of Prevention Approaches Using a Risk Tool: High Resource Users in Ontario, Canada.

Authors:  Meghan O'Neill; Kathy Kornas; Walter P Wodchis; Laura C Rosella
Journal:  Healthc Policy       Date:  2021-02

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

Authors:  Anna C Davis; Ernest Shen; Nirav R Shah; Beth A Glenn; Ninez Ponce; Donatello Telesca; Michael K Gould; Jack Needleman
Journal:  J Gen Intern Med       Date:  2018-09-04       Impact factor: 6.473

  8 in total

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