Medical professional liability in gastroenterology: definitions, trends, risk factors, provider behaviors, and implications.

Introduction: Medical professional liability (MPL) is a notable concern for many clinicians, especially in procedure-intensive specialties such as gastroenterology (GI). Comprehensive understanding of the basis for MPL claims can improve gastroenterologists' practice, lower MPL risk, and improve the overall patient care experience. This is particularly relevant in the setting of the increasing average compensation per paid GI-related MPL claim, and evolving healthcare delivery patterns and regulations.Areas Covered: MPL claims are generally grounded in the concept of negligence, a broad term that may apply to situations involving medical errors, ameliorable adverse events, inadequate informed consent and/or refusal, and numerous others. Though often not directly discussed in GI training or thereafter, there are various mechanisms and behaviors that can alter (decrease or increase) MPL risk. Additional dimensions of MPL include telemedicine, social media, and vicarious liability. We discuss these topics as well as takeaways to mitigate risk, thus reducing unnecessary clinician anxiety, promoting professional development, and optimizing healthcare outcomes.Expert Opinion: MPL risk is modifiable. Strong provider-patient relationships, through effective communication, patient reassurance, and enhanced informed consent, decrease risk, as does thorough documentation. Conversely, provider 'defensive' mechanisms intended to decrease MPL risk, including assurance and avoidance behaviors, may paradoxically increase it.

Machine learning models compared to existing criteria for noninvasive prediction of endoscopic retrograde cholangiopancreatography-confirmed choledocholithiasis.

BACKGROUND AND AIMS: Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics. We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography (ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models (MLMs). METHODS: Clinical data of consecutive patients undergoing first-ever ERCP for suspected choledocholithiasis from 2015-2019 were abstracted from a prospectively-maintained database. Multiple logistic regression was used to identify predictors of ERCP-confirmed choledocholithiasis. MLMs were then trained to predict ERCP-confirmed choledocholithiasis using pre-ERCP ultrasound (US) imaging only and separately using all available noninvasive imaging (US/CT/magnetic resonance cholangiopancreatography). The diagnostic performance of American Society for Gastrointestinal Endoscopy (ASGE) "high-likelihood" criteria was compared to MLMs. RESULTS: We identified 270 patients (mean age 46 years, 62.2% female, 73.7% Hispanic/Latino, 59% with noninvasive imaging positive for choledocholithiasis) with native papilla who underwent ERCP for suspected choledocholithiasis, of whom 230 (85.2%) were found to have ERCP-confirmed choledocholithiasis. Logistic regression identified choledocholithiasis on noninvasive imaging (odds ratio (OR) = 3.045, P = 0.004) and common bile duct (CBD) diameter on noninvasive imaging (OR=1.157, P = 0.011) as predictors of ERCP-confirmed choledocholithiasis. Among the various MLMs trained, the random forest-based MLM performed best; sensitivity was 61.4% and 77.3% and specificity was 100% and 75.0%, using US-only and using all available imaging, respectively. ASGE high-likelihood criteria demonstrated sensitivity of 90.9% and specificity of 25.0%; using cut-points achieving this specificity, MLMs achieved sensitivity up to 97.7%. CONCLUSIONS: MLMs using age, sex, race, presence of diabetes, fever, body mass index (BMI), total bilirubin, maximum CBD diameter, and choledocholithiasis on pre-ERCP noninvasive imaging predict ERCP-confirmed choledocholithiasis with good sensitivity and specificity and outperform the ASGE criteria for patients with suspected choledocholithiasis.