Four essays on the impact of data analytics on hospital operations

Smith-Nino, Isabel C (2018) Four essays on the impact of data analytics on hospital operations. Doctoral thesis, University of London: London Business School.


Healthcare is a complex industry; costs are rising while poor quality persists. There is a growing consensus that current systems are not able to cope with these mounting pressures. One reason cited for this, is the lack of knowledge on how clinical processes should be organised to achieve efficient, timely and cost- effective care. The goal of this thesis is to generate practical managerial insights into four specific scenarios within a hospital environment. Our work is set within a large London hospital where we use data sets from the emergency department (ED) and the acute services. The thesis is divided into four related, but organisationally distinct, chapters. The first paper examines staffing mix decisions in the ED. We compare the performance of senior and junior doctors by using a natural experiment where junior doctors went on strike. Our results indicate that senior doctors do not differ in admission decisions but they process patients more quickly through the ED. The second paper studies bed capacity decisions within the ED and acute services. We present a simulation model where the arrival and length of stay (LoS) distributions are time dependent. Applied to our partner hospital, we find that the current bed establishment is insufficient and make specific recommendations on how it should be amended. For the third paper, we investigate the effect of a mental health comorbidity on LoS. Overall, we find no evidence that mental health impacts LoS, except for patients admitted under the categories of "symptoms and signs", and "multiple injuries", for which LoS is higher. The fourth paper studies the implications of a national waiting time target imposed on UK EDs. This target stipulates that 95% of patients should be seen and treated within 4 hours of arriving to the ED. Prior investigations have not investigated the impact of this target on the quality of admission and discharge decisions. Our results indicate an increase in discharges related to the 4-hour target. However, these discharges are no more likely to return within 7 days and be subsequently re-admitted. Our work provides a more nuanced understanding of several relevant healthcare topics, with the goal of obtaining useful managerial insights that will expand the current scope of knowledge.

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Item Type: Thesis (Doctoral)
Subject Areas: Management Science and Operations
Date Deposited: 10 Feb 2022 10:12
Date of first compliant deposit: 10 Feb 2022
Subjects: Hospital management
Capacity management
Operational research
Last Modified: 15 Feb 2022 05:13

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