- Title
- Assessing healthcare providers' performance with and without risk adjustment
- Creator
- Morales-Silva, Daniel
- Date
- 2018
- Type
- Text; Thesis; PhD
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/166698
- Identifier
- vital:13479
- Identifier
- https://library.federation.edu.au/record=b2754521
- Abstract
- This study focuses on how healthcare data can be used to draw comparisons between healthcare providers (surgeons or hospitals). Depending on the type of access to datasets, these comparisons can be done with or without risk adjustment. For us, risk adjustment refers to the use of patient-level information to explain variation in healthcare spending, resource utilisation and health outcomes. For unadjusted comparisons, we highlight the diagnostic potential that radar plots offer for reporting on outcome indicators. These outcome indicators were obtained from hospital admissions of patients undergoing certain surgical procedures. We address two drawbacks of radar plots: presence of missing information and order of indicators. By introducing a consolidated view at provider level, we define an uncomplicated ranking of providers which can be used to identify potential low and high performers. For risk adjusted comparisons, we introduce a novel and robust methodology that enables comparisons of healthcare providers across multiple hierarchies, namely, surgeons, teams, departments and hospitals, using a consistent approach. Our methodology puts the patient at the centre of the analysis, and thus, can be used for personalised predictions (e.g. expected length of stay, costs and probability of being transferred to intensive care unit). Our findings suggest that the observed variation in selected outcome indicators, such as length of stay and charges of healthcare providers, cannot be explained by patient characteristics alone. Importantly, we have also observed that the perceived performance, on selected outcome indicators, of providers can change substantially following risk adjustment. Healthcare is unique in that clinical expertise is essential in guiding decision making and in informing all statistical models that seek to describe patient outcomes. For future iterations of our models, we will seek greater clinical input.; Doctor of Philosophy
- Publisher
- Federation University Australia
- Rights
- Copyright Daniel Morales-Silva
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Healthcare data; Healthcare providers; Risk adjustment; Performance; Outcome indicators
- Full Text
- Thesis Supervisor
- Pineda-Villavicencio, Guillermo
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