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Related Experiment Videos

Finding future high-cost cases: comparing prior cost versus diagnosis-based methods.

A S Ash1, Y Zhao, R P Ellis

  • 1Department of General Internal Medicine, Boston University, MA 02118, USA.

Health Services Research
|September 9, 2005
PubMed
Summary
This summary is machine-generated.

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Identifying high-cost patients is crucial for healthcare management. Diagnosis-based risk models, combined with cost data, effectively pinpoint individuals for targeted interventions, improving outcomes and reducing expenses.

Area of Science:

  • Health Services Research
  • Medical Informatics
  • Healthcare Management

Background:

  • Identifying high-cost patient subgroups is essential for effective healthcare resource allocation and intervention.
  • Traditional methods relying solely on past costs may not fully capture future healthcare needs.

Purpose of the Study:

  • To evaluate the efficacy of patient-level cost and diagnosis data in identifying a small, high-cost population subgroup.
  • To determine if these identified subgroups can benefit from medical management interventions to mitigate future costs.

Main Methods:

  • Utilized the MEDSTAT Market Scan Research Database (1997-1998) containing inpatient and ambulatory healthcare encounter records.
  • Employed prior cost and a diagnostic cost group (DCG) risk model to identify the top 0.5% of individuals with high predicted costs for the subsequent year.

Related Experiment Videos

  • Compared the characteristics, costs, and prevalence of diseases targeted for management between the identified top groups and the general population.

    • Main Results:

    • The overlap between prior cost and DCG-identified top groups was only 38%.
    • Both identified groups comprised individuals with high future costs and significant rates of chronic conditions like diabetes, heart failure, lung disease, and depression.
    • The DCG group identified individuals who were slightly more expensive and more likely to have targetable chronic conditions compared to the prior cost group.

    Conclusions:

    • Diagnosis-based risk models demonstrate comparable or superior power to prior cost data in predicting high future healthcare expenditures.
    • Integrating both cost and diagnostic data offers enhanced predictive power and operational utility for identifying patients for targeted medical management.
    • Diagnostic information is key to identifying specific medical problems amenable to management, leading to improved patient outcomes and reduced healthcare costs.