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Ten Ways Artificial Intelligence Will Transform Primary Care

  • Steven Y. LinEmail author
  • Megan R. Mahoney
  • Christine A. Sinsky
Article

Abstract

Artificial intelligence (AI) is poised as a transformational force in healthcare. This paper presents a current environmental scan, through the eyes of primary care physicians, of the top ten ways AI will impact primary care and its key stakeholders. We discuss ten distinct problem spaces and the most promising AI innovations in each, estimating potential market sizes and the Quadruple Aims that are most likely to be affected. Primary care is where the power, opportunity, and future of AI are most likely to be realized in the broadest and most ambitious scale. We propose how these AI-powered innovations must augment, not subvert, the patient–physician relationship for physicians and patients to accept them. AI implemented poorly risks pushing humanity to the margins; done wisely, AI can free up physicians’ cognitive and emotional space for patients, and shift the focus away from transactional tasks to personalized care. The challenge will be for humans to have the wisdom and willingness to discern AI’s optimal role in twenty-first century healthcare, and to determine when it strengthens and when it undermines human healing. Ongoing research will determine the impact of AI technologies in achieving better care, better health, lower costs, and improved well-being of the workforce.

KEY WORDS

artificial intelligence primary care Quadruple Aim patient–physician relationship 

Notes

Acknowledgments

The authors thank Rebecca Rolfe, MS, for her creative and technical support on the manuscript figure.

Compliance with Ethical Standards

Conflict of Interest

SYL is the PI on a research project sponsored by Google to understand how deep learning techniques and automatic speech recognition can facilitate clinical documentation. Google had no role in the preparation of this manuscript and the decision to approve publication of the finished manuscript. SYL has no financial interests to declare.

MRM and SYL collaborated with Babylon Health to write clinical case vignettes to train a triage and diagnosis software. Babylon Health had no role in the preparation of this manuscript and the decision to approve publication of the finished manuscript. Neither have any conflicts of interest to declare.

CAS has no conflicts of interest to declare.

References

  1. 1.
    Mesko B. Artificial Intelligence is the Stethoscope of the 21st Century. The Medical Futurist. https://medicalfuturist.com/ibm-watson-is-the-stethoscope-of-the-21st-century. Updated July 18, 2017. Accessed 7 March 2019.
  2. 2.
    Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med. 2014;12(6):573–576.CrossRefGoogle Scholar
  3. 3.
    Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–29.CrossRefGoogle Scholar
  4. 4.
    Rui P, Okeyode T. National Ambulatory Medical Care Survey: 2015 State and National Summary Tables. National Center for Health Statistics, Centers for Disease Control and Prevention. https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2015_namcs_web_tables.pdf. Accessed 7 March 2019.
  5. 5.
    Jiang HJ, Russo CA, Barrett ML. Nationwide Frequency and Costs of Potentially Preventable Hospitalizations, 2006. HCUP Statistical Brief #72. U.S. Agency for Healthcare Research and Quality, Rockville, MD. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb72.pdf. Updated April 2009. Accessed 7 March 2019.
  6. 6.
    Fingar KR, Barrett ML, Elixhauser A, Stocks C, Steiner CA. Trends in Potentially Preventable Inpatient Hospital Admissions and Emergency Department Visits. HCUP Statistical Brief #195. U.S. Agency for Healthcare Research and Quality, Rockville, MD. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb195-Potentially-Preventable-Hospitalizations.pdf. Updated November 2015. Accessed 7 March 2019.
  7. 7.
    American Hospital Association. Fast Facts on U.S. Hospitals, 2018. https://www.aha.org/statistics/fast-facts-us-hospitals. Updated February 2018. Accessed 7 March 2019.
  8. 8.
    Rajkomar A, Oren E, Chen K, et al. Scalable and accurate deep learning with electronic health records. Nat Digital Med. 2018;1:18.CrossRefGoogle Scholar
  9. 9.
    Microsoft Industry Blogs. Winners of the 2018 Microsoft Health Innovation Awards. https://cloudblogs.microsoft.com/industry-blog/industry/health/winners-of-the-2018-microsoft-health-innovation-awards/. Updated March 7, 2018. Accessed 7 March 2019.
  10. 10.
    BaseHealth. Predictive Health Platform Optimizes Health and Disease Risk Management, Improves Population Health Outcomes. https://www.basehealth.com/news/2018/02/27/predictive-health-platform-optimizes-health-and-disease-risk-management-improves-population-health-outcomes.html. Updated February 27, 2018. Accessed 7 March 2019.
  11. 11.
    Grand View Research. Population Health Management Market Size Worth $88.9 Billion By 2025. https://www.grandviewresearch.com/press-release/global-population-health-management-phm-market. Updated April 2018. Accessed 7 March 2019.
  12. 12.
    Centers for Medicare & Medicaid Services. Value-Based Programs. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/Value-Based-Programs.html. Updated July 25, 2018. Accessed 7 March 2019.
  13. 13.
    Japsen B. IBM Watson, Siemens Partner To Tap Population Health Industry. Forbes. https://www.forbes.com/sites/brucejapsen/2016/10/11/ibm-watson-siemens-partner-to-tap-population-health-industry/#438447e66600. Updated October 11, 2016. Accessed 7 March 2019.
  14. 14.
    Mukherjee S. You Can Now Download an Artificial Intelligence Doctor. Fortune. http://fortune.com/2017/01/10/healthtap-dr-ai-launch/. Updated January 10, 2017. Accessed 7 March 2019.
  15. 15.
    Razzaki S, Baker A, Perov Y, et al. A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis. Babylon Health. https://www.researchgate.net/publication/326056790_A_comparative_study_of_artificial_intelligence_and_human_doctors_for_the_purpose_of_triage_and_diagnosis. Updated June 2018. Accessed 7 March 2019.
  16. 16.
    Landi H. Telehealth Market Estimated to Reach $19.5B by 2025. Healthcare Innovation. https://www.healthcare-informatics.com/news-item/telemedicine/report-telehealth-market-estimated-reach-195b-2025. Updated April 2, 2018. Accessed 7 March 2019.
  17. 17.
    Grand View Research. U.S. Retail Clinics Market Worth $7.3 Billion by 2025. https://www.grandviewresearch.com/press-release/us-retail-clinics-market-analysis. Updated September 2017. Accessed 7 March 2019.
  18. 18.
    Olson P. Rise Of The AI-Doc: Insurer Prudential Taps Babylon Health In $100 Million Software Licensing Deal. Forbes. https://www.forbes.com/sites/parmyolson/2018/08/02/rise-of-the-ai-doc-insurer-prudential-taps-babylon-health-in-100-million-software-licensing-deal/#386d868d628f. Updated August 2, 2018. Accessed 7 March 2019.
  19. 19.
    Smith CD, Balatbat C, Corbridge S, et al. Implementing Optimal Team-Based Care to Reduce Clinician Burnout. NAM Perspectives. Discussion Paper, National Academy of Medicine, Washington, DC. https://nam.edu/implementing-optimal-team-based-care-to-reduce-clinician-burnout/. Updated September 17, 2018. Accessed 7 March 2019.
  20. 20.
    Shanafelt T, Goh J, Sinsky C. The Business Case for Investing in Physician Well-being. JAMA Intern Med. 2017;177(12):1826–1832.CrossRefGoogle Scholar
  21. 21.
    Rajkomar A, Yim JW, Grumbach K, Parekh A. Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning. JMIR Med Inform. 2016;4(4):e29.CrossRefGoogle Scholar
  22. 22.
    Adams A, Shankar M, Tecco H. 50 Things We Now Know About Digital Health Consumers. Rock Health. https://rockhealth.com/reports/digital-health-consumer-adoption-2016/. Updated 2016. Accessed 7 March 2019.
  23. 23.
    Market Research Engine. Wearable Devices Market By Product Analysis, By Application Analysis, and By Regional Analysis – Global Forecast by 2016–2022. https://www.marketresearchengine.com/wearable-devices-market. Updated July 2017. Accessed 7 March 2019.
  24. 24.
    American Diabetes Association. The Cost of Diabetes. http://www.diabetes.org/advocacy/news-events/cost-of-diabetes.html. Updated March 22, 2018. Accessed 7 March 2019.
  25. 25.
    Jahns RG. Research: 6 Success Factors for Digital-Enabled Health Coaching. HIT Consultant. https://hitconsultant.net/2018/02/09/success-factors-for-digital-enabled-health-coaching/. Updated February 9, 2018. Accessed 7 March 2019.
  26. 26.
    Stein N, Brooks K. A Fully Automated Conversational Artificial Intelligence for Weight Loss: Longitudinal Observational Study Among Overweight and Obese Adults. JMIR Diabetes. 2017;2(2):e28.CrossRefGoogle Scholar
  27. 27.
    Lin SY, Shanafelt TD, Asch SM. Reimagining Clinical Documentation With Artificial Intelligence. Mayo Clin Proc. 2018;93(5):563–565.CrossRefGoogle Scholar
  28. 28.
    Chou K, Chiu CC. Understanding Medical Conversations. Google AI Blog. https://ai.googleblog.com/2017/11/understanding-medical-conversations.html. Updated November 21, 2017. Accessed 7 March 2019.
  29. 29.
    McGrane C. Microsoft and UPMC unveil virtual AI assistant that listens in and takes notes on doctor’s visits. GeekWire. https://www.geekwire.com/2018/microsoft-healthcare/. Updated February 28, 2018. Accessed 7 March 2019.
  30. 30.
    Spitzer J. Nuance brings 1st integrated virtual assistant to Epic EHR. Becker’s Hospital Review. https://www.beckershospitalreview.com/ehrs/nuance-brings-1st-integrated-virtual-assistant-to-epic-ehr.html. Updated September 7, 2018. Accessed 7 March 2019.
  31. 31.
    Miliard M. Athenahealth partners with NoteSwift on AI-powered EHR documentation. Healthcare IT News. https://www.healthcareitnews.com/news/athenahealth-partners-noteswift-ai-powered-ehr-documentation. Updated January 31, 2018. Accessed 7 March 2019.
  32. 32.
    Haenssle HA, Fink C, Schneiderbauer R, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29(8):1836–1842.CrossRefGoogle Scholar
  33. 33.
    Liu Y, Kohlberger T, Norouzi M, et al. Artificial Intelligence-Based Breast Cancer Nodal Metastasis Detection [published online ahead of print: October 8, 2018]. Arch Pathol Lab Med. doi: https://doi.org/10.5858/arpa.2018-0147-OA.
  34. 34.
    Mukherjee S. This New AI Can Detect a Deadly Cancer Early With 86% Accuracy. Fortune. http://fortune.com/2017/10/30/ai-early-cancer-detection/. Updated October 30, 2017. Accessed 7 March 2019.
  35. 35.
    Chinese YL, Beats AI. Doctors in Diagnosing Brain Tumors. Popular Mechanics. https://www.popularmechanics.com/technology/robots/a22148464/chinese-ai-diagnosed-brain-tumors-more-accurately-physicians/. Updated July 14, 2018. Accessed 7 March 2019.
  36. 36.
    Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25(1):65–69.CrossRefGoogle Scholar
  37. 37.
    IDx. University of Iowa Health Care First to Adopt IDx-DR in a Diabetes Care Setting. Cision PR Newswire. https://www.prnewswire.com/news-releases/university-of-iowa-health-care-first-to-adopt-idx-dr-in-a-diabetes-care-setting-300672070.html. Updated June 26, 2018. Accessed 7 March 2019.
  38. 38.
    VisualDx. VisualDx to Launch AI-Enabled Smart Symptom Checker. Cision PR Newswire. https://www.prnewswire.com/news-releases/visualdx-to-launch-ai-enabled-smart-symptom-checker-300697148.html. Updated August 15, 2018. Accessed 7 March 2019.
  39. 39.
    Poplin R, Varadarajan AV, Blumer K, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2:158–164.CrossRefGoogle Scholar
  40. 40.
    Shead S. Tencent Aims to Train AI To Spot Parkinson’s In 3 Minutes. Forbes. https://www.forbes.com/sites/samshead/2018/10/08/tencent-aims-to-train-ai-to-spot-parkinsons-in-3-minutes/#52451b5b6f36. Updated October 8, 2018. Accessed 7 March 2019.
  41. 41.
    Sullivan T. Next up for EHRs: Vendors adding artificial intelligence into the workflow. Healthcare IT News. https://www.healthcareitnews.com/news/next-ehrs-vendors-adding-artificial-intelligence-workflow. Updated March 13, 2018. Accessed 7 March 2019.
  42. 42.
    Shortliffe EH, Sepulveda MJ. Clinical Decision Support in the Era of Artificial Intelligence. JAMA. 2018;320(21):2199–2200.CrossRefGoogle Scholar
  43. 43.
    Sullivan T. eClinicalWorks CEO Girish Navani: Our next EHR will be like a Bloomberg Terminal. Healthcare IT News. https://www.healthcareitnews.com/news/eclinicalworks-ceo-girish-navani-our-next-ehr-will-be-bloomberg-terminal. Updated March 7, 2018. Accessed 7 March 2019.
  44. 44.
    Apixio. Apixio Launches HCC Auditor, the First AI-Powered Risk Adjustment Auditing Solution. https://globenewswire.com/news-release/2018/07/25/1541847/0/en/Apixio-Launches-HCC-Auditor-the-First-AI-Powered-Risk-Adjustment-Auditing-Solution.html. Updated July 25, 2018. Accessed 7 March 2019.
  45. 45.
    Sinsky CA, Sinsky TA, Rajcevich E. Putting Pre-Visit Planning Into Practice. Fam Pract Manag. 2015;22(6):30–38.Google Scholar
  46. 46.
    Arndt BG, Beasley JW, Watkinson MD, et al. Tethered to the EHR: Primary Care Physician Workload Assessment Using EHR Event Log Data and Time-Motion Observations. Ann Fam Med. 2017;15(5):419–426.CrossRefGoogle Scholar
  47. 47.
    Sinsky C, Colligan L, Li L, et al. Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Ann Intern Med. 2016;165(11):753–760.CrossRefGoogle Scholar
  48. 48.
    Shanafelt TD, Dyrbye LN, Sinsky C, et al. Relationship Between Clerical Burden and Characteristics of the Electronic Environment With Physician Burnout and Professional Satisfaction. Mayo Clin Proc. 2016;91(7):836–848.CrossRefGoogle Scholar
  49. 49.
    Shanafelt TD, Hasan O, Dyrbye LN. Changes in Burnout and Satisfaction With Work-Life Balance in Physicians and the General US Working Population Between 2011 and 2014. Mayo Clin Proc. 2015;90(12):1600–1613.CrossRefGoogle Scholar
  50. 50.
    Israni ST, Verghese A. Humanizing Artificial Intelligence. JAMA. 2019;321(1):29–30.CrossRefGoogle Scholar
  51. 51.
    Keel S, Lee PY, Scheetz J, et al. Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep. 2018;8(1):4330.CrossRefGoogle Scholar
  52. 52.
    Al-Taee MA, Kapoor R, Garrett C, Choudhary P. Acceptability of Robot Assistant in Management of Type 1 Diabetes in Children. Diabetes Technol Ther. 2016;18(9):551–554.CrossRefGoogle Scholar
  53. 53.
    Rantanen P, Parkkari T, Leikola S, Airaksinen M, Lyles A. An In-home Advanced Robotic System to Manage Elderly Home-care Patients’ Medications: A Pilot Safety and Usability Study. Clin Ther. 2017;39(5):1054–1061.CrossRefGoogle Scholar
  54. 54.
    Sinsky CA, Privitera MR. Creating a “Manageable Cockpit” for Clinicians: A Shared Responsibility. JAMA Intern Med. 2018;178(6):741–742.CrossRefGoogle Scholar
  55. 55.
    Reuben DB, Sinsky CA. From Transactional Tasks to Personalized Care: A New Vision of Physicians’ Roles. Ann Fam Med. 2018;16(2):168–169.CrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2019

Authors and Affiliations

  • Steven Y. Lin
    • 1
    Email author
  • Megan R. Mahoney
    • 1
  • Christine A. Sinsky
    • 2
  1. 1.Division of Primary Care and Population Health, Department of MedicineStanford University School of MedicineStanfordUSA
  2. 2.American Medical AssociationChicagoUSA

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