A retrospective study on the effectiveness of Artificial Intelligence-based Clinical Decision Support System (AI-CDSS) to improve the incidence of hospital-related venous thromboembolism (VTE).

Abstract

BackgroundArtificial intelligence technology is widely used in the medical industry. Our retrospective study evaluated the effectiveness of an AI-CDSS in improving the incidence of hospital-related VTE and the impact of anticoagulant drug use.MethodsThis study collected relevant data on adult patients over 18 years of age who are not discharged 24 hours, from January to July 2019 and from January to July 2020, the VTE high-risk department of Ruijin Hospital. Before and after using AI-CDSS, the incidence of hospital-related VTE and using anticoagulants were analyzed.ResultsBetween January to July 2019 and January to July 2020, 3,565 and 4,423 adult patients over 18 years old were hospitalized in our hospital and were designed as a control group and intervention group, respectively (7,988 in total). Both groups had similar baseline characteristics. There were 4,716 (59.03%) male patients, the mean age was 60.43±13.09 years, and the mean stay was 7.56±7.76 days. More than half of the patients (4,605, 57.58%) came from the respiratory. VTE events during hospitalization occurred in 41 patients; overall, 5.13/1,000 (41 episodes in 7,988 patients). Compared with the control group, before implementing AI-CDSS, the rate of VTE during hospitalization was reduced from 5.89/1,000 (21 episodes in 3,565 patients) to 4.75/1,000 patients (20 episodes in 4,423 patients) (relative reduction of 19.35%) in the intervention group. The use rate of anticoagulant drugs was increased from 19.97% (712/3,565) in the control group to 22.88% (1,012/4,423) in intervention group [P<0.01, odds ratio (OR): 1.19, 95 percent confidence interval (95% CI) (1.07–1.32)], (relative 14.57% increase). Poisson’s regression results showed that department, age ≥75 years [OR: 3.09, 95% Cl (1.45–6.33)], duration of hospitalization [OR: 1.04, 95% CI (1.03–1.05)], heart failure [OR: 5.13, 95% CI (1.74–13.54)] and renal failure [OR: 3.60, 95% CI (0.90–11.34)] were high-risk factors for VTE events.ConclusionsImplementing AI-CDSS can help clinicians identify hospitalized patients at increased VTE risk, take effective preventive measures, and improve clinicians’ compliance with the American College of Chest Physicians (ACCP) guidelines.