At the onset of the COVID‐19 pandemic, hospitals were in dire need of data‐driven analytics to provide support for critical, expensive, and complex decisions. Yet, the majority of analytics being developed were targeted at state‐ and national‐level policy decisions, with little availability of actionable information to support tactical and operational decision‐making and execution at the hospital level. To fill this gap, we developed a multi‐method framework leveraging a parsimonious design philosophy that allows for rapid deployment of high‐impact predictive and prescriptive analytics in a time‐sensitive, dynamic, data‐limited environment, such as a novel pandemic. The product of this research is a workload prediction and decision support tool to provide mission‐critical, actionable information for individual hospitals. Our framework forecasts time‐varying patient workload and demand for critical resources by integrating disease progression models, tailored to data availability during different stages of the pandemic, with a stochastic network model of patient movements among units within individual hospitals. Both components employ adaptive tuning to account for hospital‐dependent, time‐varying parameters that provide consistently accurate predictions by dynamically learning the impact of latent changes in system dynamics. Our decision support system is designed to be portable and easily implementable across hospital data systems for expeditious expansion and deployment. This work was contextually grounded in close collaboration with IU Health, the largest health system in Indiana, which has 18 hospitals serving over one million residents. Our initial prototype was implemented in April 2020 and has supported managerial decisions, from the operational to the strategic, across multiple functionalities at IU Health.

No abstract

Aims Acute Bowel Obstruction (ABO) accounts for 10% of emergency surgical admissions and when surgery is required mortality can exceed 10%. Early diagnosis is associated with improved patient outcomes and timely acquisition of abdominal CT scans can help prevent delays. The NCEPOD 2020 report on ABO identified ‘delays in imaging’ as a key area for improvement in the care of these patients, with these delays being exacerbated if an abdominal X-ray (AXR) was performed as well as an abdominal CT. This study looks at ways to expedite the diagnosis of patients presenting with ABO.   Methods A retrospective audit of 77 patients admitted from A&E or SAU with ABO from April 2019 to February 2020 was conducted. Imaging modality, time-to-CT scan and time-to-diagnosis was recorded. Results and recommendations were presented locally and an evidence based ABO care pathway was implemented and publicised. 20 patients were audited prospectively, post care pathway implementation.  Results 70.1% of patients from the initial audit received a CT-scan and 42% of these patients received an AXR before their eventual CT-scan. The average wait for a definitive radiological diagnosis was 27.8hr. After implementation of the pathway only 18% of patients audited received both modes of imaging and the average time to diagnosis has been reduced to 10.7hr.  Conclusions Raising awareness of the appropriate and timely use of CT-scans in the diagnosis of ABO has reduced the number of concomitant AXR for these patients, expediting the making of a definitive diagnosis and improving patient outcomes.