Assessing the quality of shared decision making for elective orthopedic surgery across a large healthcare system: cross-sectional survey study.

Background: Clinical guidelines recommend engaging patients in shared decision making for common orthopedic procedures; however, limited work has assessed what is occurring in practice. This study assessed the quality of shared decision making for elective hip and knee replacement and spine surgery at four network-affiliated hospitals.

Methods: A cross-sectional sample of 875 adult patients undergoing total hip or knee joint replacement (TJR) for osteoarthritis or spine surgery for lumbar herniated disc or lumbar spinal stenosis was selected.

Development of prediction models for clinically meaningful improvement in PROMIS scores after lumbar decompression.

Background: The ability to preoperatively predict which patients will achieve a minimal clinically important difference (MCID) after lumbar spine decompression surgery can help determine the appropriateness and timing of surgery. Patient-Reported Outcome Measurement Information System (PROMIS) scores are an increasingly popular outcome instrument.

Purpose: The purpose of this study was to develop algorithms predictive of achieving MCID after primary lumbar decompression surgery.

Surgeon-level variance in achieving clinical improvement after lumbar decompression: the importance of adequate risk adjustment.

Background Context: Patient-Reported Outcome Measurement Information System (PROMIS) scores are increasingly utilized in clinical care. However, it is unclear if PROMIS can discriminate surgeon performance on an individual level.

Purpose: The purpose of this study was to examine surgeon-level variance in rates of achieving minimal clinically important difference (MCID) after lumbar decompression.

Development of machine learning and natural language processing algorithms for preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery.

Background: Intraoperative vascular injury (VI) may be an unavoidable complication of anterior lumbar spine surgery; however, vascular injury has implications for quality and safety reporting as this intraoperative complication may result in serious bleeding, thrombosis, and postoperative stricture.

Purpose: The purpose of this study was to (1) develop machine learning algorithms for preoperative prediction of VI and (2) develop natural language processing (NLP) algorithms for automated surveillance of intraoperative VI from free-text operative notes.

Patient Sample: Adult patients, 18 years or age or older, undergoing anterior lumbar spine surgery at two academic and three community medical centers were included in this analysis.

Can natural language processing provide accurate, automated reporting of wound infection requiring reoperation after lumbar discectomy?

Background: Surgical site infections are a major driver of morbidity and increased costs in the postoperative period after spine surgery. Current tools for surveillance of these adverse events rely on prospective clinical tracking, manual retrospective chart review, or administrative procedural and diagnosis codes.

Purpose: The purpose of this study was to develop natural language processing (NLP) algorithms for automated reporting of postoperative wound infection requiring reoperation after lumbar discectomy.