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. 2021 May;259(5):1289-1296.
doi: 10.1007/s00417-020-05040-9. Epub 2021 Jan 2.

Big data simulations for capacity improvement in a general ophthalmology clinic

Christoph Kern  1 , André König  2 , Dun Jack Fu  3 , Benedikt Schworm  2 , Armin Wolf  4 , Siegfried Priglinger  2 , Karsten U Kortuem  2
Affiliations

Big data simulations for capacity improvement in a general ophthalmology clinic

Christoph Kern et al. Graefes Arch Clin Exp Ophthalmol. 2021 May.

Abstract

Purpose: Long total waiting times (TWT) experienced by patients during a clinic visit have a significant adverse effect on patient's satisfaction. Our aim was to use big data simulations of a patient scheduling calendar and its effect on TWT in a general ophthalmology clinic. Based on the simulation, we implemented changes to the calendar and verified their effect on TWT in clinical practice.

Design and methods: For this retrospective simulation study, we generated a discrete event simulation (DES) model based on clinical timepoints of 4.401 visits to our clinic. All data points were exported from our clinical warehouse for further processing. If not available from the electronic health record, manual time measurements of the process were used. Various patient scheduling models were simulated and evaluated based on their reduction of TWT. The most promising model was implemented into clinical practice in 2017.

Results: During validation of our simulation model, we achieved a high agreement of mean TWT between the real data (229 ± 100 min) and the corresponding simulated data (225 ± 112 min). This indicates a high quality of the simulation model. Following the simulations, a patient scheduling calendar was introduced, which, compared with the old calendar, provided block intervals and extended time windows for patients. The simulated TWT of this model was 153 min. After implementation in clinical practice, TWT per patient in our general ophthalmology clinic has been reduced from 229 ± 100 to 183 ± 89 min.

Conclusion: By implementing a big data simulation model, we have achieved a cost-neutral reduction of the mean TWT by 21%. Big data simulation enables users to evaluate variations to an existing system before implementation into clinical practice. Various models for improving patient flow or reducing capacity loads can be evaluated cost-effectively.

Keywords: Big data; Clinic efficiency; Discrete event simulation; Ophthalmology; Waiting time optimisation.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Patient flow diagram. This represents the pathway including waiting times of scheduled general ophthalmology patients from check-in, through examination (± OCT scan) to consultant presentation before leaving the clinic
Fig. 2
Fig. 2
Histogram analysis of TWT before and after implementation of model 2. Intervals on x-axis are set to 20 min
See this image and copyright information in PMC

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