Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes

Jinghui Liu,Anthony Nguyen,Daniel Capurro,Karin Verspoor

doi:10.1038/s41746-021-00474-9

Abstract

As healthcare providers receive fixed amounts of reimbursement for given services under DRG (Diagnosis-Related Groups) payment, DRG codes are valuable for cost monitoring and resource allocation. However, coding is typically performed retrospectively post-discharge. We seek to predict DRGs and DRG-based case mix index (CMI) at early inpatient admission using routine clinical text to estimate hospital cost in an acute setting. We examined a deep learning-based natural language processing (NLP) model to automatically predict per-episode DRGs and corresponding cost-reflecting weights on two cohorts (paid under Medicare Severity (MS) DRG or All Patient Refined (APR) DRG), without human coding efforts. It achieved macro-averaged area under the receiver operating characteristic curve (AUC) scores of 0·871 (SD 0·011) on MS-DRG and 0·884 (0·003) on APR-DRG in fivefold cross-validation experiments on the first day of ICU admission. When extended to simulated patient populations to estimate average cost-reflecting weights, the model increased its accuracy over time and obtained absolute CMI error of 2·40 (1·07%) and 12·79% (2·31%), respectively on the first day. As the model could adapt to variations in admission time, cohort size, and requires no extra manual coding efforts, it shows potential to help estimating costs for active patients to support better operational decision-making in hospitals.

Highlights

The payment system based on diagnosis-related groups, or DRGs, was designed to manage healthcare costs and maintain sustainable operations for inpatients and it has become a significant component of healthcare payments in many countries to promote risk-sharing between healthcare providers and payers1,2
We developed a deep learning-based natural language processing (NLP) model on ICU patients using MIMICIII11 for early classification of two DRG systems, namely Medicare severity-DRG (MS-DRG) and all patient refined-DRG (APR-DRG), which was subsequently applied to estimate cost for patient populations and assessed its potential to provide cost indicators, such as case mix index (CMI), for hospital administration
MS-DRG test cohort was reduced to 1648 hospital stays and APR-DRG test cohort to 2252

Summary

INTRODUCTION

The payment system based on diagnosis-related groups, or DRGs, was designed to manage healthcare costs and maintain sustainable operations for inpatients and it has become a significant component of healthcare payments in many countries to promote risk-sharing between healthcare providers and payers. Calculating DRGs is a time-consuming process requiring expert efforts to manually identify information from patient records, standardize it to ICD (International Classification of Diseases) format, and obtain DRGs. The rapid spread of electronic health records (EHRs) has created large amounts of patient data and provides an opportunity to estimate DRGs and related costs at early patient admission using machine learning. We developed a deep learning-based natural language processing (NLP) model on ICU patients using MIMICIII11 for early classification of two DRG systems, namely Medicare severity-DRG (MS-DRG) and all patient refined-DRG (APR-DRG), which was subsequently applied to estimate cost for patient populations and assessed its potential to provide cost indicators, such as case mix index (CMI), for hospital administration

RESULTS

DISCUSSION

CODE AVAILABILITY

ETHICAL APPROVAL