Literature DB >> 30815133

Longitudinal Clustering of High-cost Patients' Spend Trajectories:Delineating Individual Behaviors from Aggregate Trends.

Andrew M Placona1, Rich King1, Fengjuan Wang1.   

Abstract

For the past decade, there has been a concerted effort to fulfill the promises of the Triple Aim-improving patient experience and quality while lowering costs by focusing on high-cost patients via Care Management. Despite the well-known fact that high-cost patients make up roughly half of all annual medical expenses, little has been studied about high-cost patients' cost trajectories. This paper focuses on the trajectory patterns for high-cost Medicare patients, which provides another dimension to understanding optimal program intervention. We performed a retrospective observational study employing Longitudinal K-Means Clustering. We discovered there are two major categories based on overall utilization: "persistent" and "episodic". Both cohorts churn to some degree in the post high-cost year. These results highlight that high-cost patients churn, and the existence of high-cost patient sub-cohorts warrant further exploration around the patient profile for each cohort. This finding could influence the current dialogue about the understanding and ability to impact high-cost patients through appropriate intervention and, more importantly, at the right time to attenuate the cost and improve quality of care.

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Year:  2018        PMID: 30815133      PMCID: PMC6371335     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  17 in total

1.  Long-term trends in the concentration of Medicare spending.

Authors:  Gerald F Riley
Journal:  Health Aff (Millwood)       Date:  2007 May-Jun       Impact factor: 6.301

2.  K-means clustering versus validation measures: a data-distribution perspective.

Authors:  Hui Xiong; Junjie Wu; Jian Chen
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  2008-12-12

3.  The triple aim: care, health, and cost.

Authors:  Donald M Berwick; Thomas W Nolan; John Whittington
Journal:  Health Aff (Millwood)       Date:  2008 May-Jun       Impact factor: 6.301

4.  A 3-year study of high-cost users of health care.

Authors:  Walter P Wodchis; Peter C Austin; David A Henry
Journal:  CMAJ       Date:  2016-01-11       Impact factor: 8.262

5.  High-Cost Patients: Hot-Spotters Don't Explain the Half of It.

Authors:  Natalie S Lee; Noah Whitman; Nirav Vakharia; Glen B Taksler; Michael B Rothberg
Journal:  J Gen Intern Med       Date:  2016-08-01       Impact factor: 5.128

6.  Focusing on High-Cost Patients - The Key to Addressing High Costs?

Authors:  J Michael McWilliams; Aaron L Schwartz
Journal:  N Engl J Med       Date:  2017-03-02       Impact factor: 91.245

7.  Identification Of Four Unique Spending Patterns Among Older Adults In The Last Year Of Life Challenges Standard Assumptions.

Authors:  Matthew Allen Davis; Brahmajee K Nallamothu; Mousumi Banerjee; Julie P W Bynum
Journal:  Health Aff (Millwood)       Date:  2016-06-15       Impact factor: 6.301

8.  Attributing patients to accountable care organizations: performance year approach aligns stakeholders' interests.

Authors:  Valerie A Lewis; Asha Belle McClurg; Jeremy Smith; Elliott S Fisher; Julie P W Bynum
Journal:  Health Aff (Millwood)       Date:  2013-03       Impact factor: 6.301

9.  Models of Care for High-Need, High-Cost Patients: An Evidence Synthesis.

Authors:  Douglas McCarthy; Jamie Ryan; Sarah Klein
Journal:  Issue Brief (Commonw Fund)       Date:  2015-10

10.  Who Are the High-Cost Users? A Method for Person-Centred Attribution of Health Care Spending.

Authors:  Sara J T Guilcher; Susan E Bronskill; Jun Guan; Walter P Wodchis
Journal:  PLoS One       Date:  2016-03-03       Impact factor: 3.240

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