Research ArticleSpecial Communication

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care

Richard A. Young, Carmel M. Martin, Joachim P. Sturmberg, Sally Hall, Andrew Bazemore, Ioannis A. Kakadiaris and Steven Lin
The Journal of the American Board of Family Medicine March 2024, 37 (2) 332-345; DOI: https://doi.org/10.3122/jabfm.2023.230219R1

References

  1. 1.↵
    Scientific American. World Changing Ideas 2015. Scientific American. 2015.
  2. 2.↵
    1. Liaw W,
    2. Kakadiaris IA
    . Primary care artificial intelligence: a branch hiding in plain sight. Ann Fam Med May 2020;18:194–5.
    OpenUrlFREE Full Text
  3. 3.↵
    1. Lin SY,
    2. Mahoney MR,
    3. Sinsky CA
    . Ten ways artificial intelligence will transform primary care. J Gen Intern Med 2019;34:1626–30.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Kueper JK,
    2. Terry A,
    3. Bahniwal R,
    4. et al
    . Connecting artificial intelligence and primary care challenges: findings from a multi stakeholder collaborative consultation. BMJ Health Care Inform 2022;29:e100493
    OpenUrl
  5. 5.↵
    1. Lin S
    . A clinician's guide to artificial intelligence (AI): why and how primary care should lead the health care AI revolution. J Am Board Fam Med 2022;35:175–84.
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    1. McWhinney IR
    . ‘An acquaintance with particulars…’ Fam Med 1989;21:296–8.
    OpenUrlPubMed
  7. 7.↵
    1. Blease C,
    2. Kaptchuk TJ,
    3. Bernstein MH,
    4. Mandl KD,
    5. Halamka JD,
    6. DesRoches CM
    . Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners' views. J Med Internet Res 2019;21:e12802.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Boudette N,
    2. Metz C,
    3. Ewing J
    . Tesla autopilot and other driver-assist systems linked to hundreds of crashes. New York Times. New York Times. June 15, 2022. Available at: https://www.nytimes.com/2022/06/15/business/self-driving-car-nhtsa-crash-data.html.
  9. 9.↵
    1. Milmo D
    . Two US lawyers fined for submitting fake court citations from ChatGPT. The Guardian. June 23, 2023. Accessed Aug 2, 2023. Available at: https://www.theguardian.com/technology/2023/jun/23/two-us-lawyers-fined-submitting-fake-court-citations-chatgpt.
  10. 10.↵
    1. Kung TH,
    2. Cheatham M,
    3. Medenilla A,
    4. et al
    . Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health 2023;2:e0000198.
    OpenUrlCrossRef
  11. 11.↵
    1. van Dis EAM,
    2. Bollen J,
    3. Zuidema W,
    4. van Rooij R,
    5. Bockting CL
    . ChatGPT: five priorities for research. Nature 2023;614:224–6.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Chen L,
    2. Zaharia M,
    3. Zou J
    . How is ChatGPT’s behavior changing over time? 2023. arXiv230709009
  13. 13.↵
    1. Martin CM,
    2. Sturmberg JP
    . General practice–chaos, complexity and innovation. Med J Aust 2005;183:106–9.
    OpenUrlPubMed
  14. 14.↵
    1. Burton C,
    2. Elliott A,
    3. Cochran A,
    4. Love T
    . Do healthcare services behave as complex systems? Analysis of patterns of attendance and implications for service delivery. BMC Med 2018;16:138.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Martin CM
    . Self-rated health: patterns in the journeys of patients with multi-morbidity and frailty. Evaluation Clinical Practice 2014;20:1010–6.
    OpenUrl
  16. 16.↵
    1. Surate Solaligue DE,
    2. Hederman L,
    3. Martin CM
    . What weekday? How acute? An analysis of reported planned and unplanned GP visits by older multi-morbid patients in the Patient Journey Record System database. Evaluation Clinical Practice 2014;20:522–6.
    OpenUrl
  17. 17.↵
    1. Burton C,
    2. Stone T,
    3. Oliver P,
    4. Dickson JM,
    5. Lewis J,
    6. Mason SM
    . Frequent attendance at the emergency department shows typical features of complex systems: analysis of multicentre linked data. Emerg Med J . 2021;39:3–9.;
    OpenUrl
  18. 18.↵
    1. Katerndahl D,
    2. Wood R,
    3. Jaen CR
    . Complexity of ambulatory care across disciplines. Healthcare 2015;3:89–96.
    OpenUrl
  19. 19.↵
    1. Kueper JK,
    2. Terry AL,
    3. Zwarenstein M,
    4. Lizotte DJ
    . Artificial intelligence and primary care research: a scoping review. Ann Fam Med 2020;18:250–8.
    OpenUrlAbstract/FREE Full Text
  20. 20.↵
    1. Schmidt-Erfurth U,
    2. Sadeghipour A,
    3. Gerendas BS,
    4. Waldstein SM,
    5. Bogunovic H
    . Artificial intelligence in retina. Prog Retin Eye Res 2018;67:1–29.
    OpenUrlCrossRefPubMed
  21. 21.↵
    1. Becker AS,
    2. Marcon M,
    3. Ghafoor S,
    4. Wurnig MC,
    5. Frauenfelder T,
    6. Boss A
    . deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest Radiol 2017;52:434–40.
    OpenUrl
  22. 22.↵
    1. Perez MV,
    2. Mahaffey KW,
    3. Hedlin H
    , Apple Heart Study Investigatorset al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med 2019;381:1909–17.
    OpenUrlCrossRefPubMed
  23. 23.↵
    1. Jain A,
    2. Way D,
    3. Gupta V,
    4. et al
    . Development and assessment of an artificial intelligence-based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices. JAMA Netw Open 2021;4:e217249.
    OpenUrl
  24. 24.↵
    1. Rajpurkar P,
    2. Lungren MP
    . The current and future state of AI interpretation of medical images. N Engl J Med 2023;388:1981–90.
    OpenUrlCrossRefPubMed
  25. 25.↵
    1. Adler-Milstein J,
    2. Chen JH,
    3. Dhaliwal G
    . Next-generation artificial intelligence for diagnosis: from predicting diagnostic labels to “wayfinding.” Jama 2021;326:2467–8.
    OpenUrl
  26. 26.↵
    1. Rowe M
    . An introduction to machine learning for clinicians. Acad Med 2019;94:1433–6.
    OpenUrl
  27. 27.↵
    1. Narla A,
    2. Kuprel B,
    3. Sarin K,
    4. Novoa R,
    5. Ko J
    . Automated classification of skin lesions: from pixels to practice. J Invest Dermatol 2018;138:2108–10.
    OpenUrlCrossRefPubMed
  28. 28.↵
    1. Kulkarni PA,
    2. Singh H
    . Artificial intelligence in clinical diagnosis: opportunities, challenges, and hype. JAMA 2023;330:317–8.
    OpenUrl
  29. 29.↵
    1. Chen JH,
    2. Dhaliwal G,
    3. Yang D
    . Decoding artificial intelligence to achieve diagnostic excellence: learning from experts, examples, and experience. JAMA 2022;328:709–10.
    OpenUrl
  30. 30.↵
    1. Jing B,
    2. Boscardin WJ,
    3. Deardorff WJ,
    4. et al
    . Comparing machine learning to regression methods for mortality prediction using Veterans Affairs electronic health record clinical data. Med Care 2022;60:470–9.
    OpenUrl
  31. 31.↵
    1. Callahan A,
    2. Gombar S,
    3. Cahan EM,
    4. et al
    . Using aggregate patient data at the bedside via an on-demand consultation service. N Engl J Med Catalyst 2021;2.
  32. 32.↵
    1. Schmidt CMD
    . Anderson breaks with IBM Watson, raising questions about artificial intelligence in oncology. J Natl Cancer Inst 2017;109.
  33. 33.↵
    1. Strickland E
    . IBM Watson, heal thyself: how IBM overpromised and underdelivered on AI health care. IEEE Spectr 2019;56:24–31.
    OpenUrl
  34. 34.↵
    1. Detsky AS
    . Learning the art and science of diagnosis. JAMA 2022;327:1759–60.
    OpenUrl
  35. 35.↵
    1. Rajkomar A,
    2. Dean J,
    3. Kohane I
    . Machine learning in medicine. N Engl J Med 2019;380:1347–58.
    OpenUrlCrossRefPubMed
  36. 36.↵
    1. Wachter RM,
    2. Howell MD
    . Resolving the productivity paradox of health information technology: a time for optimism. JAMA 2018;320:25–6.
    OpenUrlPubMed
  37. 37.↵
    1. Davies N
    . The FDA and artificial intelligence. 2021;07-06-2021. Accessed Feb 13, 2023. Available at: https://www.thepharmaletter.com/article/the-fda-and-artificial-intelligence.
  38. 38.↵
    1. Beam AL,
    2. Manrai AK,
    3. Ghassemi M
    . Challenges to the reproducibility of machine learning models in health care. JAMA 2020;323:305–6.
    OpenUrlPubMed
  39. 39.↵
    1. Agha L,
    2. Skinner J,
    3. Chan D
    . Improving efficiency in medical diagnosis. Jama 2022;327:2189–90.
    OpenUrl
  40. 40.↵
    1. Bhatt S,
    2. Cohon A,
    3. Rose J,
    4. et al
    . Interpretable machine learning models for clinical decision-making in a high-need, value-based primary care setting. N Engl J Med Catalyst 2021;2.
  41. 41.↵
    1. Andaur Navarro CL,
    2. Damen JAA,
    3. Takada T,
    4. et al
    . Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ 2021;375:n2281.
    OpenUrlAbstract/FREE Full Text
  42. 42.↵
    1. Li Y,
    2. Sperrin M,
    3. Ashcroft DM,
    4. van Staa TP
    . Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar. BMJ 2020;371:m3919.
    OpenUrlAbstract/FREE Full Text
  43. 43.↵
    1. Rajkomar A,
    2. Kannan A,
    3. Chen K,
    4. et al
    . Automatically charting symptoms from patient-physician conversations using machine learning. JAMA Intern Med 2019;179:836–8.
    OpenUrl
  44. 44.↵
    1. Marso A,
    2. Waldren SE
    . Five administrative tasks technology could make easier for physicians. Fam Pract Manag 2022;29:5–8.
    OpenUrl
  45. 45.↵
    1. Rao SK,
    2. Kimball AB,
    3. Lehrhoff SR,
    4. et al
    . The impact of administrative burden on academic physicians: results of a hospital-wide physician survey. Acad Med 2017;92:237–43.
    OpenUrlCrossRefPubMed
  46. 46.↵
    1. Nayak A,
    2. Alkaitis MS,
    3. Nayak K,
    4. Nikolov M,
    5. Weinfurt KP,
    6. Schulman K
    . Comparison of history of present illness summaries generated by a chatbot and senior internal medicine residents. JAMA Intern Med 2023;183:1026–7.
    OpenUrl
  47. 47.↵
    1. Sahni NR,
    2. Carrus B
    . Artificial intelligence in U.S. health care delivery. N Engl J Med 2023;389:348–58.
    OpenUrlCrossRef
  48. 48.↵
    1. Fitzpatrick KK,
    2. Darcy A,
    3. Vierhile M
    . Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): a randomized controlled trial. JMIR Ment Health 2017;4:e19.
    OpenUrl
  49. 49.↵
    1. Piette JD,
    2. Newman S,
    3. Krein SL,
    4. et al
    . Patient-centered pain care using artificial intelligence and mobile health tools: a randomized comparative effectiveness trial. JAMA Intern Med 2022;182:975–83.
    OpenUrl
  50. 50.↵
    1. Iyer R,
    2. Nedeljkovic M,
    3. Meyer D
    . Using voice biomarkers to classify suicide risk in adult telehealth callers: retrospective observational study. JMIR Ment Health 2022;9:e39807.
    OpenUrl
  51. 51.↵
    1. Mehta A,
    2. Niles AN,
    3. Vargas JH,
    4. Marafon T,
    5. Couto DD,
    6. Gross JJ
    . Acceptability and effectiveness of artificial intelligence therapy for anxiety and depression (Youper): longitudinal observational study. J Med Internet Res 2021;23:e26771.
    OpenUrl
  52. 52.↵
    1. Chew HSJ,
    2. Ang WHD,
    3. Lau Y
    . The potential of artificial intelligence in enhancing adult weight loss: a scoping review. Public Health Nutr 2021;24:1993–2020.
    OpenUrl
  53. 53.↵
    1. Au-Yeung WM,
    2. Sahani AK,
    3. Isselbacher EM,
    4. Armoundas AA
    . Reduction of false alarms in the intensive care unit using an optimized machine learning based approach. NPJ Digit Med 2019;2:86.
    OpenUrl
  54. 54.↵
    1. Freeman K,
    2. Geppert J,
    3. Stinton C,
    4. et al
    . Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ 2021;374:n1872.
    OpenUrlAbstract/FREE Full Text
  55. 55.↵
    1. Li F,
    2. Du Y
    . From AlphaGo to power system AI. IEEE power and Energy Mag 2018;16:76–84. p.
    OpenUrl
  56. 56.↵
    1. Gupta S,
    2. Gupta A
    . Dealing with noise problem in machine learning data-sets: a systematic review. Procedia Computer Science 2019;161:466–74.
    OpenUrl
  57. 57.↵
    1. Sturmberg JP,
    2. Martin CM
    . Handbook of sytems and complexity in health. New York: Springer; 2013.
  58. 58.↵
    1. Goldstein BA,
    2. Navar AM,
    3. Pencina MJ
    . Risk prediction with electronic health records: the importance of model validation and clinical context. JAMA Cardiol 2016;1:976–7.
    OpenUrl
  59. 59.↵
    1. Yang TH,
    2. Ziemba R,
    3. Shehab N,
    4. et al
    . Assessment of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code assignment validity for case finding of medication-related hypoglycemia acute care visits among medicare beneficiaries. Med Care 2022;60:219–26.
    OpenUrl
  60. 60.↵
    1. Stein HD,
    2. Nadkarni P,
    3. Erdos J,
    4. Miller PL
    . Exploring the degree of concordance of coded and textual data in answering clinical queries from a clinical data repository. J Am Med Inform Assoc 2000;7:42–54.
    OpenUrlCrossRefPubMed
  61. 61.↵
    1. Vaghani V,
    2. Wei L,
    3. Mushtaq U,
    4. Sittig DF,
    5. Bradford A,
    6. Singh H
    . Validation of an electronic trigger to measure missed diagnosis of stroke in emergency departments. J Am Med Inform Assoc 2021;28:2202–11.
    OpenUrl
  62. 62.↵
    1. Peterson ED
    . Machine learning, predictive analytics, and clinical practice: can the past inform the present? JAMA 2019;322:2283–4.
    OpenUrlCrossRefPubMed
  63. 63.↵
    1. Shah ND,
    2. Steyerberg EW,
    3. Kent DM
    . Big data and predictive analytics: recalibrating expectations. JAMA 2018;320:27–8.
    OpenUrlCrossRefPubMed
  64. 64.↵
    1. Cooper Z,
    2. Craig SV,
    3. Gaynor M,
    4. Van Reenen J
    . The price ain't right? Hospital prices and health spending on the privately insured. Dec 2015. Accessed 1/23/2023. Available at: www.healthcarepricingproject.org/sites/default/files/pricing_variation_manuscript_0.pdf.
  65. 65.↵
    1. Cutler DM
    . What artificial intelligence means for health care. JAMA Health Forum 2023;4:e232652.
    OpenUrl
  66. 66.↵
    1. Ferrier D,
    2. Diver F,
    3. Corin S,
    4. McNair P,
    5. Cheng C
    . HealthLinks: incentivising better value chronic care in Victoria. International Journal of Integrated Care 2017;12:A129.
    OpenUrl
  67. 67.↵
    1. Ferrier D,
    2. Campbell D,
    3. Hamilton C,
    4. et al
    . Co-designing a new approach to delivering integrated services to chronically ill patients within existing funding constraints–Victoria's HealthLinks trial. Int J Integr Care 2019;19:322.
    OpenUrl
  68. 68.↵
    1. Martin C,
    2. Hinkley N,
    3. Stockman K,
    4. Campbell D
    . Capitated telehealth coaching hospital readmission service in Australia: pragmatic controlled evaluation. J Med Internet Res 2020;22:e18046.
    OpenUrl
  69. 69.↵
    1. Martin CM,
    2. Vogel C,
    3. Grady D,
    4. et al
    . Implementation of complex adaptive chronic care: the Patient Journey Record system (PaJR). Evaluation Clinical Practice 2012;18:1226–1234.
    OpenUrl
  70. 70.↵
    1. Gianfrancesco MA,
    2. Tamang S,
    3. Yazdany J,
    4. Schmajuk G
    . Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 2018;178:1544–1547.
    OpenUrl
  71. 71.↵
    1. Reyna MA,
    2. Nsoesie EO,
    3. Clifford GD
    . Rethinking algorithm performance metrics for artificial intelligence in diagnostic medicine. JAMA 2022;328:329–330.
    OpenUrlCrossRef
  72. 72.↵
    1. Ibrahim SA,
    2. Pronovost PJ
    . Diagnostic errors, health disparities, and artificial intelligence: a combination for health or harm? JAMA Health Forum 2021;2:e212430.
    OpenUrl
  73. 73.↵
    1. Noor P
    . Can we trust AI not to further embed racial bias and prejudice? BMJ 2020;368:m363.
    OpenUrlFREE Full Text
  74. 74.↵
    1. Lau BD,
    2. Haider AH,
    3. Streiff MB,
    4. et al
    . Eliminating health care disparities with mandatory clinical decision support: the venous thromboembolism (VTE) example. Med Care 2015;53:18–24.
    OpenUrlCrossRefPubMed
  75. 75.↵
    1. Parikh RB,
    2. Teeple S,
    3. Navathe AS
    . Addressing bias in artificial intelligence in health care. JAMA 2019;322:2377–2378.
    OpenUrlCrossRefPubMed
  76. 76.↵
    1. Parasuraman R,
    2. Manzey DH
    . Complacency and bias in human use of automation: an attentional integration. Hum Factors 2010;52:381–410.
    OpenUrlCrossRefPubMedWeb of Science
  77. 77.↵
    1. Lu J,
    2. Sattler A,
    3. Wang S,
    4. et al
    . Considerations in the reliability and fairness audits of predictive models for advance care planning. Front Digit Health 2022;4:943768.
    OpenUrl
  78. 78.↵
    1. Irvin JA,
    2. Kondrich AA,
    3. Ko M,
    4. et al
    . Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments. BMC Public Health 2020;20:608.
    OpenUrl
  79. 79.↵
    1. DeCamp M,
    2. Lindvall C
    . Latent bias and the implementation of artificial intelligence in medicine. J Am Med Inform Assoc 2020;27:2020–2023.
    OpenUrlCrossRefPubMed
  80. 80.↵
    1. Minssen T,
    2. Vayena E,
    3. Cohen IG
    . The challenges for regulating medical use of ChatGPT and other large language models. JAMA 2023;330:315–316.
    OpenUrlCrossRef
  81. 81.↵
    1. Yang Z,
    2. Silcox C,
    3. Sendak M,
    4. et al
    . Advancing primary care with artificial intelligence and machine learning. Healthcare 2022;10:100594.
    OpenUrl
  82. 82.↵
    1. Ellis GF
    . Top-down causation and emergence: some comments on mechanisms. Interface Focus 2012;2:126–140.
    OpenUrlCrossRefPubMed
  83. 83.↵
    1. Goldhahn J,
    2. Rampton V,
    3. Spinas GA
    . Could artificial intelligence make doctors obsolete? BMJ 2018;363:k4563.
    OpenUrlFREE Full Text
  84. 84.↵
    1. Sulmasy DP
    . Advance care planning and “The Love Song of J. Alfred Prufrock”. JAMA Intern Med 2020;180:813–814.
    OpenUrl
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