A clinical coding recommender system

Clinical coding of hospital admissions can erroneously omit diagnosis and procedure codes. A consequence of these omissions is that the condition and treatment of the patient are not fully captured by the entered codes, which can then also impact hospital revenue. One way to prevent these errors is through a real-time recommender system which suggests the addition of codes at the point of coding when it appears they have been omitted. Association analysis uncovers patterns between codes, forming a basis for coding recommendations. Combining association analysis with manual expert validation produces more useful recommendations (we refer to this as the expert validated list), but is labour-intensive. In this study, we propose an approach using Bayesian Networks to determine the conditional relationships between codes. Performance is evaluated using a testing strategy which simulates errors through the random removal of codes from episodes of patient care and counts how many of the removed codes are recommended to coders by each recommender. Performance is also based on how many recommended codes were not removed (superfluous recommendations) which we seek to minimise. We develop a recommender system which generates 96% of the number of correct recommendations produced by the expert validated list, while having 68% fewer superfluous recommendations. Our proposed methodology provides a high performance recommender while reducing dependence on labour-intensive effort by clinical coding experts.