Risk Adjustment Models

Risk Adjustment Models is a statistical framework used in healthcare analytics that belongs to the class of predictive modeling techniques, distinguished by its specific purpose of adjusting for differences in patient health status and demographic characteristics to enable fair comparisons of healthcare costs, utilization, or outcomes across providers or plans.

385

Publications

26.5K

Citations

1.5K

Authors

601

Institutions

Publications per year

2017–2026

96

Authors

1.5K

  1. TG
    Thomas G. McGuire

    Harvard University, United States

    10 publications · 389 citations

    10

    h-index

  2. AK
    Amy K. Rosen

    Boston University, United States

    8 publications · 394 citations

    9

    h-index

  3. HC
    Hsien‐Yen Chang

    Johns Hopkins University, United States

    7 publications · 269 citations

    7

    h-index

  4. DR
    Dan R. Berlowitz

    Boston University, United States

    6 publications · 213 citations

    7

    h-index

  5. GC
    Gregory C. Pope

    RTI International, United States

    7 publications · 557 citations

    7

    h-index

1–5 of 1.5K

Venues

  1. Medical Care

    35 publications · 2.7K citations

    28

    h-index

  2. PubMed

    Repository · 28 publications · 1.5K citations

    20

    h-index

  3. BMC Health Services Research

    12 publications · 556 citations

    11

    h-index

  4. Health Services Research

    HSR · 11 publications · 686 citations

    11

    h-index

  5. Clinical Orthopaedics and Related Research

    6 publications · 746 citations

    6

    h-index

1–5