Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Abstract

Characterizing acute service demand is critical for neurosurgery and other emergency-dominant specialties in order to dynamically distribute resources and ensure timely access to treatment. This is especially important in the post-Covid 19 pandemic period, when healthcare centers are grappling with a record backlog of pending surgical procedures and rising acute referral numbers. Healthcare dashboards are well-placed to analyze this data, making key information about service and clinical outcomes available to staff in an easy-to-understand format. However, they typically provide insights based on inference rather than prediction, limiting their operational utility. We retrospectively analyzed and prospectively forecasted acute neurosurgical referrals, based on 10,033 referrals made to a large volume tertiary neurosciences center in London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021 through the use of a novel AI-enabled predictive dashboard. As anticipated, weekly referral volumes significantly increased during this period, largely owing to an increase in spinal referrals (p < 0.05). Applying validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point, with Prophet demonstrating the best test and computational performance. Using a mixed-methods approach, we determined that a dashboard approach was usable, feasible, and acceptable among key stakeholders.

Historical and Future Trends in Emergency Pituitary Referrals: A Machine Learning Analysis

“…This level of discretisation was chosen to account for observed short-term volatility and to increase the level of stationarity needed for accurate time-series modelling [11]. The choice of forecasting algorithm was based on evidence of clinical application of forecasting models with referral data using previously described hyperparameter tuning [9]. In brief, this included three algorithms: an automated pipeline which combined Seasonal and Trend decomposition using Loess (STL) with an automated regression integrated moving average (Auto-ARIMA) model, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) network [12,13] and Prophet [14].…”

“…Manually performing such analyses, on the other hand, is time-and resource-intensive. Electronic databases, bespoke programming and machine learning offer an avenue for streamlined, automatic and accurate analysis of patient pathway data and prediction of future trends [9].…”

Forecasting healthcare service volumes with machine learning algorithms

“…Propelled by technologies such as artificial intelligence, big data, and 5G, telemedicine and online medical platforms have burgeoned rapidly. The demand for online medical care has been addressed and has emerged as an important instrument to redress the imbalance of medical resources (Jiang et al, 2022; Pandit et al, 2022). During the outbreak of the COVID‐19 epidemic, the utilization of online medical care expanded swiftly in numerous countries, especially in developed nations.…”

“…online medical care has been addressed and has emerged as an important instrument to redress the imbalance of medical resources (Jiang et al, 2022;Pandit et al, 2022). During the outbreak of the COVID-19 epidemic, the utilization of online medical care expanded swiftly in numerous countries, especially in developed nations.…”