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Automatic coding of reasons for hospital referral from general medicine free-text reports
L Letrilliart1, C Viboud, P Y Boëlle
1INSERM Unit 444, WHO Collaborating Center for Electronic Disease Surveillance, Paris, France.
Insights
A new automatic coding system simplifies medical data entry for physicians, improving efficiency in healthcare. This system accurately codes reasons for hospital referral, aiding epidemiological research.
Area of Science:
- Medical Informatics
- Public Health
- Epidemiology
Background:
- Manual medical coding is a burdensome task for physicians.
- Accurate coding is essential for patient care and health system efficiency.
- Automating coding can alleviate physician workload and improve data quality.
Purpose of the Study:
- To develop a simple, automatic coding system for free-text reasons for hospital referral.
- To assess the performance of this automated system in terms of match and accuracy rates.
- To facilitate epidemiological research through efficient medical data processing.
Main Methods:
- Developed a string-matching algorithm for automated coding.
- Created a look-up table from 2590 manually coded referral reports (International Classification of Primary Care - ICPC).
- Tested the system on 797 new referral reasons.
Main Results:
- Achieved a 77% match rate for automated coding.
- Attained 80% accuracy at the code level and 92% at the chapter level.
- The system is now in routine use by a national epidemiological network.
Conclusions:
- A simple, automated coding system can effectively process free-text medical referral reasons.
- The system demonstrates high accuracy, supporting its utility in epidemiological studies.
- Routine implementation by sentinel physicians highlights its practical value in public health surveillance.
Abstract:
Although the coding of medical data is expected to benefit both patients and the health care system, its implementation as a manual process often represents a poorly attractive workload for the physician. For epidemiological purpose, we developed a simple automatic coding system based on string matching, which was designed to process free-text sentences stating reasons for hospital referral, as collected from general practitioners (GPs). This system relied on a look-up table, built up from 2590 reports giving a single reason for referral, which were coded manually according to the International Classification of Primary Care (ICPC). We tested the system by entering 797 new reasons for referral. The match rate was estimated at 77%, and the accuracy rate, at 80% at code level and 92% at chapter level. This simple system is now routinely used by a national epidemiological network of sentinel physicians.
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