JMIR Publications

ABSTRACT

Background:

As more humanitarian relief organizations are beginning to use electronic medical records in their operations, data from clinical encounters can be leveraged for public health planning. Currently, medical data from humanitarian medical workers are infrequently available in a format that can be analyzed, interpreted, and used for public health.

Objective:

To develop and test a methodology by which diagnosis and procedure codes can be derived from free-text medical encounters by medical relief practitioners for the purposes of data analysis.

Methods:

We conducted a cross-sectional study of clinical encounters from humanitarian clinics for displaced persons in Mexico between August 3, 2021, and December 5, 2022. We developed and tested a method by which free-text encounters were reviewed by medical billing coders and assigned codes from the International Classification of Diseases 10th Revision (ICD-10) and Current Procedural Terminology (CPT). Each encounter was independently reviewed in duplicate and assigned ICD-10 and CPT codes in a blinded manner. Encounters with discordant codes were reviewed and arbitrated by a more experienced medical coder. We compared the ICD codes for concordance across single-diagnosis and multi-diagnosis encounters. We also analyzed the concordance across patient characteristics, such as age, sex, and country of origin.

Results:

We analyzed 8,546 encounters representing 472 unique diagnosis code categories. There were 5,053 (59.13%) encounters where both coders assigned one diagnosis code, 1,570 (18.37%) encounters where both coders assigned multiple diagnosis codes, and 1,923 (22.50%) encounters with a mixed number of codes assigned. Of the 5,053 encounters with a single code, only 644 (12.74%) had a unique diagnosis assigned by the arbitrator which was not assigned by either of the initial two coders. Of the 1,570 encounters with multiple diagnosis codes, only 54 (3.44%) had unique diagnosis codes assigned by the arbitrator which were not initially assigned by either coder. The frequency of complete concordance across diagnosis codes was similar across sex categories and ranged from 29.7–42.2% across age and countries of origin.

Conclusions:

Free-text electronic medical records from humanitarian relief clinics can be used to develop a database of diagnosis and procedure codes. The method developed in this study, which utilized multiple independent reviews of clinical encounters, appears to reliably assign diagnosis codes across a diverse patient population.