Risk Adjustment of ICD-10-CM Coded Potential Inpatient Complications Using Administrative Data
Bibliographic Data
| ID | 9099072 |
|---|---|
| Authors | Michael Korvink (0000-0002-4802-4293, ITS Data Science, Premier, Inc., corresponding author), Laura H Gunn (0000-0003-3962-4526, University of North Carolina at Charlotte), German Molina (0000-0003-4693-6907, Statistical Solutions, Bayesian Solutions LLC), Tracy Hayes (0000-0002-3850-9967, Carolinas College of Health Sciences, Atrium Health, Charlotte NC), Esther Selves (ITS Data Science, Premier, Inc.), Esther J Selves (Premier Research Group, corresponding author), Michael Duan (ITS Data Science, Premier, Inc., corresponding author), John Martin (0000-0002-3209-7340, ITS Data Science, Premier, Inc., corresponding author) |
| Year | 2023 |
| Volume | 61 |
| Issue | 8 |
| Pages | 514-520 |
| Publication date | 2023-08-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0000000000001865 |
| PMID | 37219083 |
| OpenAlex | W4377564793 |
| Language | EN |
| References cited | 17 |
OBJECTIVE: To risk-adjust the Potential Inpatient Complication (PIC) measure set and propose a method to identify large deviations between observed and expected PIC counts. DATA SOURCES: Acute inpatient stays from the Premier Healthcare Database from January 1, 2019 to December 31, 2021. STUDY DESIGN: In 2014, the PIC list was developed to identify a broader set of potential complications that can occur as a result of care decisions. Risk adjustment for 111 PIC measures is performed across 3 age-based strata. Using patient-level risk factors and PIC occurrences, PIC-specific probabilities of occurrence are estimated through multivariate logistic regression models. Poisson Binomial cumulative mass function estimates identify deviations between observed and expected PIC counts across levels of patient-visit aggregation. Area under the curve (AUC) estimates are used to demonstrate PIC predictive performance in an 80:20 derivation-validation split framework. DATA COLLECTION/EXTRACTION METHODS: We used N=3,363,149 administrative hospitalizations between 2019 and 2021 from the Premier Healthcare Database. PRINCIPAL FINDINGS: PIC-specific model predictive performance was strong across PICs and age strata. Average area under the curve estimates across PICs were 0.95 (95% CI: 0.93-0.96), 0.91 (95% CI: 0.90-0.93), and 0.90 (95% CI: 0.89-0.91) for the neonate and infant, pediatric, and adult strata, respectively. CONCLUSIONS: The proposed method provides a consistent quality metric that adjusts for the population's case mix. Age-specific risk stratification further addresses currently ignored heterogeneity in PIC prevalence across age groups. Finally, the proposed aggregation method identifies large PIC-specific deviations between observed and expected counts, flagging areas with a potential need for quality improvements
Confidence interval · Environmental health · Health care · Logistic regression · Metric (unit) · Multivariate statistics · Negative binomial distribution · Operations management · Poisson distribution · Poisson regression · Population · Statistics · Emergency Medicine · Healthcare cost, quality, practices · Healthcare Policy and Management · Mathematics · Medical Coding and Health Information · Medicine
| Citation velocity | historical |
|---|---|
| Highly cited | No |