Machine Learning Clinical Order Recommendations for Specialty Consultation Care A future vision of clinical decision support must transcend constraints in scalability, maintainability, and adaptability. The shortage of 100,000 physicians by 2030 reflects unmet (and unlimited) demand for the scarcest healthcare resource, clinical expertise. Over 25 million in the US alone have deficient access to medical specialty care, with delays contributing to 20% higher mortality. There is no quality without access. Our goal is to develop a radically different paradigm for outpatient specialty consultations by inductively learning clinical workups embedded in clinical data. We focus on predicting the concrete clinical orders for medications and diagnostic tests that result from specialty consultations. This can power a tier of fully automated guides that will enable clinicians to initiate care that would otherwise await in-person specialty visits, opening access for more patients. The major scientific barriers are advances in data science and decision support methods for collating clinical knowledge, with continuous improvement through clinical experience, crowdsourcing, and machine learning. Our innovative approach is inspired by collaborative filtering algorithms that power ?Customers like you also bought this...? recommender systems with the scalability to answer unlimited queries, maintainability through statistical learning, and adaptability to respond to evolving clinical practices. Our team uniquely combines expertise in clinical medicine, electronic medical records, clinical decision support, statistics and machine learning to enhance medical specialty consultations through aims that seek to: (1) Develop methods to generate clinical decision support by predicting the clinical orders that will result from Endocrinology and Hematology specialty consultations; (2) Evaluate and iteratively design clinical collaborative filtering prototypes based on clinical user input on usability and acceptability; and (3) Determine which consult clinical order patterns are associated with better results through reinforcement learning and causal inference frameworks. Completion of these aims will yield a sustained, powerful impact on clinical information retrieval and knowledge discovery for synthesizing clinical practices from real-world data. By addressing grand challenges in clinical decision support, adoption of these methods will fulfill a vision that empowers clinicians to practice to the top of their license, making healthcare more scalable in reach, responsiveness, and reproducibility

Public Health Relevance

There can be no quality without access, and over 25 million in the US alone have deficient access to the scarcest healthcare resource: Human medical expertise. Building on methods analogous to commercial product recommender systems, the proposed research will automatically learn practice patterns from electronic medical records. Distributing predictable practices of medical specialty consultations can then enable healthcare systems to achieve broader patient access to timely and consistent care.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
High Priority, Short Term Project Award (R56)
Project #
1R56LM013365-01A1
Application #
10265158
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Sim, Hua-Chuan
Project Start
2020-09-25
Project End
2021-08-31
Budget Start
2020-09-25
Budget End
2021-08-31
Support Year
1
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305