Towards More Nuanced Patient Management: Decomposing Readmission Risk with Survival Models

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Abstract

Unplanned hospital readmissions are costly and associated with poorer patient outcomes. Overall readmission rates have also come to be used as performance metrics in reimbursement in healthcare policy, further motivating hospitals to identify and manage high-risk patients. Many models predicting readmission risk have been developed to facilitate the equitable measurement of readmission rates and to support hospital decision-makers in prioritising patients for interventions. However, these models consider the overall risk of readmission and are often restricted to a single time point. This work aims to develop the use of survival models to better support hospital decision-makers in managing readmission risk. First, semi-parametric statistical and nonparametric machine learning models are applied to adult patients admitted via the emergency department at Gold Coast University Hospital (n = 46,659) and Robina Hospital (n = 23,976) in Queensland, Australia. Overall model performance is assessed based on discrimination and calibration, as measured by time-dependent concordance and D-calibration. Second, a framework based on iterative hypothesis development and model fitting is proposed for decomposing readmission risk into persistent, patient-specific baselines and transient, care-related components using a sum of exponential hazards structure. Third, criteria for patient prioritisation based on the duration and magnitude of care-related risk components are developed. The extensibility of the framework and subsequent prioritisation criteria are considered for alternative populations, such as outpatient admissions and specific diagnosis groups, and different modelling techniques. Time-to-event models have rarely been applied for readmission modelling but can provide a rich description of the evolution of readmission risk post-discharge and support more nuanced patient management decisions than simple classification models.
Original languageEnglish
Pages157-158
Number of pages2
Publication statusPublished - 6 Sept 2023
EventThe Operational Research Society's Annual Conference - University of Bath, Bath, United Kingdom
Duration: 12 Sept 202314 Sept 2023
https://www.theorsociety.com/events/previous-annual-conferences/or65/ (Link to conference website)

Conference

ConferenceThe Operational Research Society's Annual Conference
Abbreviated titleOR65
Country/TerritoryUnited Kingdom
CityBath
Period12/09/2314/09/23
OtherThe theme for the OR65 annual conference will be recognising and celebrating 75 years since the beginning of the OR Society. Building on previous years’ conference themes - of demonstrating the impact of OR on decision-making and improving operations across all areas of society and industry – OR65 will ensure to showcase how OR can be used to create a better world. Our 2023 event will provide a platform to both speakers and delegates alike who will challenge and stimulate the community. By sharing their work challenges and successes, they will help the OR community of attendees to stay up-to-date with the latest OR developments and innovations.
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