JMIR Publications

ABSTRACT

Background:

Under the New Diagnosis-Related Group payment system (New DRG) in Korea, the Health Insurance Review and Assessment Service (HIRA) applies disease-specific diagnosis-coding approval criteria that differ from the coding guidelines of the Korean Classification of Diseases (KCD), frequently leading to claim denials for conditions such as acute kidney injury, pleural effusion, and malnutrition. Repeated denials also induce conservative down-coding among coders. Because denial notifications are issued 9 to 18 months after discharge, reactive, denial-triggered quality management is insufficient.

Objective:

We implemented a metadata-driven rule repository that operationalizes HIRA's review criteria as data, coupled with a daily-refreshed screening data mart (six clinical data domains, 14 data sources) and a robotic process automation (RPA) notification pipeline, to enable proactive, pre-claim coding-quality management.

Methods:

This Implementation Report describes the design, deployment, and 12-month operational experience of the system at an 820-bed New DRG pilot hospital. HIRA's criteria were externalized into a four-layer rule repository—criteria definitions, clinical concepts, disease-specific attributes, and decision rules. A nightly extract-transform-load pipeline populates a patient-by-attribute screening data mart within the clinical data warehouse; each morning, Python-based RPA compiles and e-mails review-candidate lists to coders. Descriptive statistics summarized coverage and detection outcomes; precision (equivalent to positive predictive value) was calculated with Wilson score 95% confidence intervals.

Results:

As of the end of the evaluation period, the rule repository comprised 11 review criteria, 271 clinical concepts, 77 attributes, and 96 decision rules. Over the 12-month evaluation period (July 2025-June 2026), the system evaluated 17,138 diagnosis- or episode-level targets across 15,388 distinct episodes—67.0% of the hospital's 22,970 New DRG episodes—and flagged 576 review candidates (3.4%), of which 50 (8.7%, 95% CI 6.6%-11.3%) were confirmed by coders as actual coding errors. Precision differed significantly across the three monitored categories (χ²=39.91, P<.001): present-on-admission (POA) coding items showed the highest precision (20/82, 24.4%), followed by the New DRG cost-outlier category (17/134, 12.7%) and frequently denied diagnoses (13/360, 3.6%). The system was designed, built, and operated entirely by existing departmental staff using SQL and Python, without dedicated IT development support, and has continued in daily, weekday operation since launch.

Conclusions:

This metadata-driven screening architecture enabled a transition from retrospective, sample-based monitoring to real-time, comprehensive surveillance, absorbing review-criteria changes through rule-repository data updates rather than program modification. By giving coders timely, data-driven evidence before claim submission, the system offers a replicable, low-resource model for proactive coding-quality management at other New DRG hospitals.