Diagnostic Items: A New Framework for Disease Surveillance, Prediction, and Risk Adjustment

JAMA Health Forum, 2022

Read the full paper here! You can also access the DXI software (in SAS) at this link: http://tinyurl.com/DXI-Software.

Ellis RP, Hsu HE, Siracuse JJ, et al. Development and Assessment of a New Framework for Disease Surveillance, Prediction, and Risk Adjustment: The Diagnostic Items Classification System. JAMA Health Forum. 2022;3(3):e220276. doi:10.1001/jamahealthforum.2022.0276

Joint work with Randall P. Ellis, Heather E. Hsu, Jeffrey J. Siracuse, Allan J. Walkey, Karen E. Lasser, Brian C. Jacobson, Corinne Andriola, Ying Liu, Chenlu Song, Tzu-Chun Kuo, and Arlene S. Ash.

Key Points


How can diagnostic information in the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) be organized to improve the accuracy and usefulness of predictive models used for plan payment and disease surveillance?


This diagnostic modeling study used insurance claims for 65 901 460 privately insured adults and children in the US from 2016 to 2018 to create new diagnostic items using ICD-10-CM codes that achieved a validated R2 almost 1.5 times that of Affordable Care Act Marketplace risk-adjustment model, with meaningful improvements for other outcomes.


Rich multidimensional diagnostic classification systems can improve predictive models for performance benchmarking and risk adjustment.



Current disease risk-adjustment formulas in the US rely on diagnostic classification frameworks that predate the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM).


To develop an ICD-10-CM–based classification framework for predicting diverse health care payment, quality, and performance outcomes.

Design, Setting, and Participants

Physician teams mapped all ICD-10-CM diagnoses into 3 types of diagnostic items (DXIs): main effect DXIs that specify diseases; modifiers, such as laterality, timing, and acuity; and scaled variables, such as body mass index, gestational age, and birth weight. Every diagnosis was mapped to at least 1 DXI. Stepwise and weighted least-squares estimation predicted cost and utilization outcomes, and their performance was compared with models built on (1) the Agency for Healthcare Research and Quality Clinical Classifications Software Refined (CCSR) categories, and (2) the Health and Human Services Hierarchical Condition Categories (HHS-HCC) used in the Affordable Care Act Marketplace. Each model’s performance was validated using R2, mean absolute error, the Cumming prediction measure, and comparisons of actual to predicted outcomes by spending percentiles and by diagnostic frequency. The IBM MarketScan Commercial Claims and Encounters Database, 2016 to 2018, was used, which included privately insured, full- or partial-year eligible enrollees aged 0 to 64 years in plans with medical, drug, and mental health/substance use coverage.

Main Outcomes and Measures

Fourteen concurrent outcomes were predicted: overall and plan-paid health care spending (top-coded and not top-coded); enrollee out-of-pocket spending; hospital days and admissions; emergency department visits; and spending for 6 types of services. The primary outcome was annual health care spending top-coded at $250 000.


A total of 65 901 460 person-years were split into 90% estimation/10% validation samples (n = 6 604 259). In all, 3223 DXIs were created: 2435 main effects, 772 modifiers, and 16 scaled items. Stepwise regressions predicting annual health care spending (mean [SD], $5821 [$17 653]) selected 76% of the main effect DXIs with no evidence of overfitting. Validated R2 was 0.589 in the DXI model, 0.539 for CCSR, and 0.428 for HHS-HCC. Use of DXIs reduced underpayment for enrollees with rare (1-in-a-million) diagnoses by 83% relative to HHS-HCCs.


In this diagnostic modeling study, the new DXI classification system showed improved predictions over existing diagnostic classification systems for all spending and utilization outcomes considered.

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