Prediction of Surgery Time: Capacity Utilization of Operating Theatre
Translated title
Prediction of Surgery Time: Kapacitetsudnyttelse af Operationsstuer
Author
Olsen, Mikkel Lyngholm
Term
4. term
Publication year
2020
Submitted on
2020-06-03
Pages
108
Abstract
Dette kandidatspeciale fra Operations & Innovation Management ved Aalborg Universitet er udarbejdet i tæt samarbejde med Aalborg Universitetshospital (UH) og adresserer et praktisk og akademisk relevant planlægningsproblem. Afdeling O6 håndterer udelukkende elektive operationer på to operationsstuer, som er åbne mandag til fredag kl. 8.00–15.00. Fem specialer har faste tidsblokke inden for denne åbningstid. I forbindelse med flytningen til New Aalborg University Hospital (NAU) får O6 ikke egne operationsstuer, og stuerne skal fremover deles og bookes på tværs af afdelinger. En foranalyse af historiske data viser, at produktiviteten er faldende. I 2019 blev der i gennemsnit behandlet 6,90 patienter per dag, svarende til 3,45 patienter per stue per dag. Samtidig passer antallet af planlagte patienter og de estimerede varigheder ofte dårligt med åbningstiden, hvilket giver både underbelægning (spildt kapacitet) og overbelægning (overarbejde). I 2019 medførte den ikke udnyttede kapacitet et produktionstab på 6,65 % målt på volumen. Uden bedre præcision vil konsekvenserne forstærkes, når operationsstuerne skal deles på NAU. Specialet undersøger den nuværende planlægningsprocedure og analyserer, om operationstider kan forudsiges mere præcist ved hjælp af mønstergenkendelse og statistisk proceskontrol (SPC). SPC bruges til at skelne mellem tilfældig variation og tilskrivbar variation, så man kan identificere, hvornår afvigelser skyldes særlige årsager. Med udgangspunkt i procedurekoder, kirurger og patientkarakteristika anvendes statistiske algoritmer til at estimere nødvendig operationstid. På baggrund af de mere præcise estimater præsenteres tre løsningsforslag, der illustrerer planlægning med forskellige grader af sikkerhed i forhold til den usikkerhed, man ønsker at dække ved fremtidig patientbooking.
This master’s thesis in Operations & Innovation Management at Aalborg University was conducted in close collaboration with Aalborg University Hospital (UH) and addresses a practically and academically relevant scheduling problem. Department O6 handles only elective surgeries in two operating rooms that are open Monday to Friday, 8:00–15:00. Five specialties have fixed time blocks within these hours. As part of the move to the New Aalborg University Hospital (NAU), O6 will not have its own operating rooms; instead, rooms will be shared and booked across departments. A pre-analysis of historical data shows declining productivity. In 2019, an average of 6.90 patients were treated per day, equivalent to 3.45 patients per room per day. At the same time, the number of scheduled patients and their estimated durations often do not fit the opening hours, causing both underutilization (unused capacity) and overutilization (overtime). In 2019, unused capacity resulted in a productivity loss of 6.65% by volume. Without better precision, these effects will also impact other departments when operating rooms are shared at NAU. The thesis examines the current planning procedure and analyzes whether surgery times can be predicted more accurately using pattern recognition and statistical process control (SPC). SPC is used to distinguish random variation from assignable variation, helping identify when deviations are due to special causes. Based on procedure codes, surgeons, and patient characteristics, statistical algorithms are used to estimate the required operating time. Using these more precise estimates, three solution proposals are presented to illustrate planning with different levels of certainty, depending on how much uncertainty one wishes to cover when scheduling future patients.
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