What Can We Expect Following Anterior THA On A Regular OR Table? A Validation Study of an Artificial Intelligence Algorithm to Monitor Adverse Events in a High-Volume, Non-Academic Setting

Abstract Introduction Quality monitoring is increasingly important to support and assure sustainability of the Orthopaedic practice. Many surgeons in a non-academic setting lack the resources to accurately monitor quality of care. Widespread use of electronic medical records (EMR) provides easier access to medical information and facilitates its analysis. However, manual review of EMRs is inefficient and costly. Artificial Intelligence (AI) software has allowed for the development of automated search algorithms for extracting relevant complications from EMRs. We hypothesized that an AI supported algorithm for complication data extraction would have an accuracy level equal to or higher than a human reviewer to monitor the quality of care following Total Hip Arthroplasty (THA) in a high-volume, non-academic setting. Methods 532 Consecutive patients underwent 613 THA between January 1st and December 31st, 2017. Patients were prospectively followed pre-op, 6 weeks, 3 months and 1 year post-operatively. They were seen by the surgeon who created clinical notes and reported every adverse event. A random derivation cohort (100 patients, 115 hips) was used to determine accuracy. After generation of a gold standard, the algorithm was compared to manual extraction to validate performance in raw data extraction. The full cohort (532 patients, 613 hips) was used to determine its recall, precision and F-value. Results The algorithm had an accuracy value of 95.0%, compared to 94.5% for manual review (p=0.69). Recall of 96.0% (84.0% - 100%) was achieved with precision of 88.0% (33% - 100%) and F-measure of 0.85 (0.5 – 1) for all adverse events. Recovery of 80.6% of patients was uneventful, with no recorded adverse events. Re-intervention was required in 1.3% of cases and 18.1% had a ‘transient’ event such as low back pain. Conclusion An AI supported search algorithm can analyze and interpret large quantities of EMRs at greater speed but with a performance comparable to or even slightly better than manual review. Using this program, new clinical information surfaced. 18.1% Of patients can be expected to have a ‘transient’ problem following a THA procedure. Summary Sentence The use of an automated, AI supported search algorithm for EMRs provided new and continuous feedback on the quality of care with a performance level comparable to manual data extraction.