Article Sidebar
Published:
Feb 20, 2023Article Details
Copyright remains in the Author’s name. The work is licensed under a Creative Commons Attribution - Noncommercial Works License. Authors are required to complete and sign an Author Agreement form that outlines Author and Publisher rights and terms of publication. The Agreement form should be uploaded along with other submissions files and any submission will be considered incomplete without it [forthcoming].
Material submitted for publication in the SAMJ is accepted provided it has not been published or submitted for publication elsewhere. Please inform the editorial team if the main findings of your paper have been presented at a conference and published in abstract form, to avoid copyright infringement. The SAMJ does not hold itself responsible for statements made by the authors.
Previously published images
If an image/figure has been previously published, permission to reproduce or alter it must be obtained by the authors from the original publisher and the figure legend must give full credit to the original source. This credit should be accompanied by a letter indicating that permission to reproduce the image has been granted to the author/s. This letter should be uploaded as a supplementary file during submission.
Main Article Content
A critical analysis of Discovery Health’s claims-based risk adjustment of mortality rates in South African private sector hospitals
Abstract
In 2019, Discovery Health published a risk adjustment model to determine standardised mortality rates across South African private hospital systems, with the aim of contributing towards quality improvement in the private healthcare sector. However, the model suffers from limitations due to its design and its reliance on administrative data. The publication’s aim of facilitating transparency is unfortunately undermined by shortcomings in reporting. When designing a risk prediction model, patient-proximate variables with a sound theoretical or proven association with the outcome of interest should be used. The addition of key condition-specific clinical data points at the time of hospital admission will dramatically improve model performance. Performance could be further improved by using summary risk prediction scores such as the EUROSCORE II for coronary artery bypass graft surgery or the GRACE risk score for acute coronary syndrome. In general, model reporting should conform to published reporting standards, and attempts should be made to test model validity by using sensitivity analyses. In particular, the limitations of machine learning prediction models should be understood, and these models should be appropriately developed, evaluated and reported.
