Intelligent EHRs: Predicting Procedure Codes From Diagnosis Codes

TL;DR

This paper introduces a deep learning approach to automatically predict procedure codes from diagnosis codes in electronic health records, streamlining the medical billing process.

Contribution

It presents a novel multi-label classification model using distributed representations to improve coding accuracy from diagnosis data.

Findings

Achieved 90% recall at top 3 predictions.

Outperformed existing rule-based and probabilistic methods.

Trained on 2.3 million claims.

Abstract

In order to submit a claim to insurance companies, a doctor needs to code a patient encounter with both the diagnosis (ICDs) and procedures performed (CPTs) in an Electronic Health Record (EHR). Identifying and applying relevant procedures code is a cumbersome and time-consuming task as a doctor has to choose from around 13,000 procedure codes with no predefined one-to-one mapping. In this paper, we propose a state-of-the-art deep learning method for automatic and intelligent coding of procedures (CPTs) from the diagnosis codes (ICDs) entered by the doctor. Precisely, we cast the learning problem as a multi-label classification problem and use distributed representation to learn the input mapping of high-dimensional sparse ICDs codes. Our final model trained on 2.3 million claims is able to outperform existing rule-based probabilistic and association-rule mining based methods and has a recall of 90@3.