Published on in Vol 4, No 4 (2016): Oct-Dec

Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning

Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning

Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning

Journals

  1. Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. New England Journal of Medicine 2019;380(14):1347 View
  2. Paige N, Apaydin E, Goldhaber-Fiebert J, Mak S, Miake-Lye I, Begashaw M, Severin J, Shekelle P. What Is the Optimal Primary Care Panel Size?. Annals of Internal Medicine 2020;172(3):195 View
  3. Huang H, Shang X, Zhao H, Wu N, Li W, Xu Y, Zhou Y, Fu L. Discovering Medication Patterns for High-Complexity Drug-Using Diseases Through Electronic Medical Records. IEEE Access 2019;7:125280 View
  4. Kamnetz S, Trowbridge E, Lochner J, Koslov S, Pandhi N. A Simple Framework for Weighting Panels Across Primary Care Disciplines: Findings From a Large US Multidisciplinary Group Practice. Quality Management in Health Care 2018;27(4):185 View
  5. Berzin T, Parasa S, Wallace M, Gross S, Repici A, Sharma P. Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force. Gastrointestinal Endoscopy 2020;92(4):951 View
  6. Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database 2020;2020 View
  7. Adler-Milstein J, Zhao W, Willard-Grace R, Knox M, Grumbach K. Electronic health records and burnout: Time spent on the electronic health record after hours and message volume associated with exhaustion but not with cynicism among primary care clinicians. Journal of the American Medical Informatics Association 2020;27(4):531 View
  8. Lin S, Mahoney M, Sinsky C. Ten Ways Artificial Intelligence Will Transform Primary Care. Journal of General Internal Medicine 2019;34(8):1626 View
  9. Wajnberg A, Fishman M, Hernandez C, Kweon S, Coyle A. Empanelment in a Resident Teaching Practice: A Cornerstone to Improving Resident Outpatient Education and Patient Care. Journal of Graduate Medical Education 2019;11(2):202 View
  10. Ganguli I, Sheridan B, Gray J, Chernew M, Rosenthal M, Neprash H. Physician Work Hours and the Gender Pay Gap — Evidence from Primary Care. New England Journal of Medicine 2020;383(14):1349 View
  11. Ganguli I, Gordon W, Lupo C, Sands-Lincoln M, George J, Jackson G, Rhee K, Bates D. Machine Learning and the Pursuit of High-Value Health Care. NEJM Catalyst 2020;1(6) View
  12. Bonfim D, Mafra A, da Costa Palacio D, Rewa T. Assessment of staffing needs for registered nurses and licensed practical nurses at primary care units in Brazil using Workload Indicators of Staffing Need (WISN) method. Human Resources for Health 2022;19(S1) View
  13. Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Medical Oncology 2022;39(8) View
  14. Ganguli I, Mulligan K, Phillips R, Basu S. How the Gender Wage Gap for Primary Care Physicians Differs by Compensation Approach. Annals of Internal Medicine 2022;175(8):1135 View
  15. Mayo-Smith M, Robbins R, Murray M, Weber R, Bagley P, Vitale E, Paige N. Analysis of Variation in Organizational Definitions of Primary Care Panels. JAMA Network Open 2022;5(4):e227497 View
  16. Ganguli I, Rivara F, Inouye S. Gender Differences in Electronic Health Record Work—Amplifying the Gender Pay and Time Gap in Medicine. JAMA Network Open 2022;5(3):e223940 View
  17. Chen H. Managing a multi-panel clinic with heterogeneous patients. Health Care Management Science 2023;26(4):673 View
  18. Ranjbari D, Abbasgholizadeh Rahimi S. Implications of conscious AI in primary healthcare. Family Medicine and Community Health 2024;12(Suppl 1):e002625 View

Books/Policy Documents

  1. . AI Doctor. View
  2. Wojda T, Hoffman C, Kindler K, Desai A, Visweswaran S. Artificial Intelligence in Medicine and Surgery - An Exploration of Current Trends, Potential Opportunities, and Evolving Threats - Volume 2. View