Development of data-driven clinical pathways: the big data clinical evidence-based pathways

Objectives This study developed clinical evidence-based pathways (CEBPWs) to standardise treatment protocols, align diagnosis-reimbursement criteria, detect upcoding and enable early overtreatment warnings. Methods The CEBPWs were developed based on hospitalised patient-level data from 1 January 2022 to 31 June 2024 in 166 public hospitals in 16 administrative districts of Shanghai. It includes a total of 5 319 336 cases, involving 3 688 108 groups of ‘diagnosis+therapy’. 2.61 billion records of hospitalisation charges and 876.45 million records of outpatient charges were collected. GROWTH algorithm was used to find the combination of frequently charged items for examination, treatment, drugs and devices in ‘diagnosis+therapy’ group. Results CEBPWs comprise five key elements: objective evidence identification, accurate classification, value weighting, frequency weighting and temporal sequencing of evidence. We applied CEBPWs to 224 diseases, detecting issues including upcoding, overtreatment and fragmented care episodes to enhance healthcare quality. CEBPWs achieve 100% coverage in diagnostics, therapy and consumables, with 81.81% drug coverage. The pilot evaluation showed that there were violations in 433 cases, with a frequency deviation of 8.64% and cost deviation of 10.82%, with 8.95% for diagnosis, 9.44% for therapy, 14.81% for drugs and 8.98% for consumables. Discussion We were developed CEBPWs, breaking the limitations of the clinical pathways is that the experience of clinical experts rather than objective criterion based on the characteristics of big data and lack of diagnostic and therapy standards integrated with payment standards. Conclusion The results indicate that CEBPW is critical tool for hospital management and regulation, address many drawbacks of clinical pathways.