Case mix and coding error detection in Western Cape healthcare facilities - Narayan, Saiheal (Department of Statistical Sciences, 2025)
South Africa has a two-tier structure for the delivery of hospital and health care services: the public sector and the private sector. The private sector is known for having better service quality, cost, and data management. The Clinton Health Access Initiative (CHAI) has been supporting the first steps towards Diagnosis Related Group (DRG) to categorise hospitalisation costs in the public health facilities in South Africa. DRG's are widely used in the private sector for active cost management. Additionally, an issue was raised by the on-site audit clinical coding report of the public hospitals managed by the Western Cape Department of Health, which must be addressed. This dissertation applies case mix adjustment for hospitals in the Western Cape based on DRG weights from the private sector. DRG weights represent the average resources required to care for cases in that particular DRG, relative to the average resources used to treat cases in all DRGs. This is then compared to another metric that uses actual length of stay data from the public sector, which will act as a proxy for resource utilisation (Fetter, Shin, Freeman, Averill, and Thompson, 1980). The objective is to find out if case mix will help in identifying hospitals which take on highly resource intensive procedures on average. The potential of using case mix in the public sector will allow for optimized resourcing. The second part looks at generating classification models that will be used to flag diagnosis coding errors by healthcare staff in the Western Cape. Patient-level data was used which includes length of stay, procedures, and cost centre. Models trained to classify diagnosis include neural networks, multinomial logistic regression, random forests, SMOTE (Synthetic Minority Over-sampling Technique), and finally an ensemble of the top 3 models using majority voting. These models are able to handle multiple response categories. The aim of the error detection model will be to increase data quality in the public sector. The results showed that the DRG weights from the private sector might not be appropriate for the public health sector. Next, it was shown that the best predictive model for diagnosis was a random forest with an accuracy of 57% on the unseen test dataset. Lastly, through the explanatory analysis, this dissertation identified both qualitative and quantitative relationships in the data that could open up avenues for more research and development. These results can be used to help stakeholders make informed decisions and improve data quality in the public sector.