Orthopedic and ophthalmology surgical service projection modelling in Manitoba: Research approach for a data linkage study - Katz, Alan; Owczar, Hannah; Taylor, Carole; Bowes, John-Micheal; Soodeen, Ruth-Ann (International Journal of Population Data Science, 2023)
Background The healthcare system in Manitoba, Canada has faced long wait times for many surgical procedures and investigations, including orthopedic and ophthalmology surgeries. Wait times for surgical procedures is considered a significant barrier to accessing healthcare in Canada and can have negative health outcomes for patients. We developed models to forecast anticipated surgical procedure demands up to 2027. This paper explores the opportunities and challenges of using administrative data to describe forecasts of surgical service delivery. Methods This study used whole population linked administrative health data to predict future orthopedic and ophthalmology surgical procedure demands up to 2027. Procedure codes (CCI) from hospital discharge abstracts and medical claims data were used in the modelling. A Seasonal Autoregressive Integrated Moving Average model provided the best fit to the data from April 1, 2004 to March 31, 2020. Results Initial analyses of only hospital-based procedures excluded a significant portion of provider workload, namely those services provided in clinics. We identified 500,732 orthopedic procedures completed between April 1, 2004 and March 31, 2020 (349,171 procedures identified from hospital discharge abstracts and 151,561 procedures from medical claims). Procedure volumes for these services are expected to rise 17.7% from 2020 (36,542) to 2027 (43,011), including the forecasted 43.9% increase in clinic-based procedures. Of the 660,127 ophthalmology procedures completed between April 1, 2004 and March 31, 2020, 230,717 procedures were identified from hospital discharge abstracts and 429,410 from medical claims. Models forecasted a 27.7% increase from 2020 (69,598) to 2027 (88,893) with most procedures being performed in clinics. Conclusion Researchers should consider including multiple datasets to add information that may have been missing from the presumed data source in their research approach. Confirming the completeness of the data is critical in modelling accurate predictions. Forecast modelling techniques have evolved but still require validation.