Browsing by Author "Silal Sheetal"

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    Open Access
    Anomaly detection in laboratory tests subject to gatekeeping in selected health facilities
    (2023) Nantongo, Ssozi Margaret Eva; Er Sebnem; Silal Sheetal
    The cost of healthcare is currently a huge burden to governments and health care organisations across the world. In South Africa, laboratory tests administered by government facilities are delivered by the National Health Laboratory Service (NHLS) regardless of payment, and hence there is a possibility that certain tests are over ordered by doctors at government health facilities. Gatekeeping is a demand management tool utilised by facilities across the world to manage costs of laboratory testing. Electronic gatekeeping addresses inappropriate laboratory test ordering to reduce the over ordering or under ordering of tests. In South Africa, the electronic gatekeeping (eGK) system is a standardised set of rules that was developed by the National Department of Health (NDOH) as well as NHLS pathologists and clinicians from the individual provinces (NHLS, 2017; Pema et al., 2018; Smit et al., 2015). The eGK system restricts test ordering by applying a given set of rules to tests ordered by a medical official for each patient. The protocols followed by the eGK system are defined using criteria such as date or result of previous test and location/ward of patient. This project aims to identify facilities and wards that are incurring high violations of tests subject to eGK rules. Anomaly detection methods are utilised to identify these facilities and wards together with the tests that require intervention to address the high violations. Three methods were utilised for anomaly detection and included K-means clustering, isolation forests and one-class Support Vector Machines (SVM). The recommended wards for intervention were mostly the maternity related wards at major hospitals. Within these wards, there was evidence of ordering tests that violated the eGK rules more than other wards. Other wards with evidence of over-ordering and violation of eGK rules included ARV Clinic ward, cardiac wards, high care units and respiratory ICU wards. The tests that were selected for intervention in these wards included calcium, magnesium, inorganic phosphate, total protein, albumin, bilirubin tests, creactive protein, procalcitonin and rubella PCR. The facilities selected for intervention included major hospitals for example Nelson Mandela Academic Hospital, Port Elizabeth Provincial Hospital, Livingstone Hospital and Dora Nginza Hospital. In addition, district hospitals and specialised TB hospitals were selected amongst those recommended for intervention. The tests selected for intervention in these facilities included calcium, magnesium, inorganic phosphate, total protein, albumin, c-reactive protein, procalcitonin, Hepatitis B DNA and CA 15-3. The results of the analysis were compared with results from published literature, and it was found that some of the tests recommended for intervention were also highlighted by previous researchers, for example c-reactive protein tests. A comparison of the results from the K-means clustering, one-class SVM and isolation forests anomaly detection showed that the same wards, facilities, and tests were recommended for intervention. Therefore, anomaly detection is a suitable method for identification of wards and facilities that are violating test ordering rules more than other facilities.
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    Open Access
    ETD: Case mix and coding error detection in Western Cape healthcare facilities
    (2024) Narayan, Saiheal; Ngwenya, Mzabalazo; Silal Sheetal
    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.