Improving the delivery of palliative care through predictive modeling and healthcare informatics. (21st February 2021)

• Record Type: Journal Article
• Authors: Murphree, Dennis H; Wilson, Patrick M; Asai, Shusaku W; Quest, Daniel J; Lin, Yaxiong; Mukherjee, Piyush; Chhugani, Nirmal; Strand, Jacob J; Demuth, Gabriel; Mead, David; Wright, Brian; Harrison, Andrew; Soleimani, Jalal; Herasevich, Vitaly; Pickering, Brian W; Storlie, Curtis B
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 6(2021)
• Page Start: 1065
• Page End: 1073
• Publication Date: 2021-02-21
• Subjects: palliative care -- machine learning -- decision support systems -- clinical -- precision medicine; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa211
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. Materials and Methods: Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient's corresponding care team. Results: Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an "in-production" AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes. Conclusions: A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.