Machine learning can guide suitability of consultation and patient referral through telemedicine for hepatobiliary diseases

Abstract

Background and aim: Telemedicine is an evolving tool to provide health-care services. We evaluated the suitability of telemedicine to deliver effective consultation for hepatobiliary disorders.

Methods: In this prospective study spanning over a year, we interviewed hepatologists delivering the teleconsultations through a pre-validated questionnaire. A consult was deemed suitable based on the physician's judgment in the absence of unplanned hospitalization. We evaluated factors determining the suitability through inferential statistics and machine learning models, namely, extreme gradient boosting (XGB) and decision tree (DT).

Results: Of 1118 consultations, 917 (82.0%) were deemed suitable. On univariable analysis, patients with skilled occupation, higher education, out-of-pocket expenses, and diseases such as chronic hepatitis B, C, and non-alcoholic fatty liver disease (NAFLD) without cirrhosis were associated with suitability (P < 0.05). Patients with cirrhosis (compensated or decompensated), acute-on-chronic liver failure (ACLF), and biliary obstruction were likely unsuitable (P < 0.05). XGB and DT models predicted suitability with an area under the receiver operating curve of 0.808 and 0.780, respectively. DT demonstrated that compensated cirrhosis with higher education or skilled occupation with age < 55 years had 78% chance of suitability whereas hepatocellular carcinoma, decompensated cirrhosis, and ACLF patients were unsuitable with a 60-95% probability. In non-cirrhotic liver diseases, hepatitis B, C, and NAFLD were suitable, with a probability of 89.7%. Biliary obstruction and previous failure of teleconsultation were unsuitable, with a probability of 70%. Non-cirrhotic portal fibrosis, dyspepsia, and dysphagia not requiring intervention were suitable (probability: 88%).

Conclusion: A simple decision tree can guide the referral of unsuitable and the management of suitable patients with hepatobiliary diseases through telemedicine.

Keywords: Artificial intelligence; Cirrhosis; Hepatology; Machine learning; Telemedicine.