Closing the Loop in AI, EMR and Provider Partnerships: The Key to Improved Population Health Management?


  • Alexis Kurek Spectrum Health
  • David Langholz Spectrum Health
  • Aiesha Ahmed Spectrum Health



The capabilities of and interest in artificial intelligence (AI) in healthcare, and more specifically, population health, has grown exponentially over the past decade. The vast volume of digital data or “big data” in the form of images generated by an aging population, with an ever-increasing demand for imaging, amassed by radiology departments, provides ample opportunity for AI application and has allowed radiology to become a service line leader of AI in the medical field. The screening and detection capabilities of AI make it a valuable tool in population health management, as organizations work to shift their services to early identification and intervention, especially as it relates to chronic disease. In this paper, the clinical, technological, and operational workflows that were developed and integrated within each other to support the adoption of AI algorithms aimed at detecting subclinical osteoporosis and coronary artery disease are described. The benefits of AI are reviewed and weighed against potential drawbacks within the context of population health management and risk contract arrangements. Mitigation tactics are discussed, as well as the anticipated outcomes in terms of cost-avoidance, physician use of evidence-based clinical pathways, and reduction in major patient events (e.g., stroke, hip fracture). The plan for data collection and analysis is also described for program evaluation.


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Manning C. Artificial intelligence definitions. Stanford University Human-Centered Artificial Intelligence; 2020. Available from: [cited 10 July 2022].

Roux C, Briot K. The crisis of inadequate treatment in osteoporosis. Lancet Rheumatol 2020 Feb 1; 2(2): e110–19. doi: 10.1016/S2665-9913(19)30136-5

Bessette L, Ste-Marie LG, Jean S, Davison KS, Beaulieu M, Baranci M, et al. The care gap in diagnosis and treatment of women with a fragility fracture. Osteoporos Int 2008 Jan; 19(1): 79–86. doi: 10.1007/s00198-007-0426-9

Kearns AE. Osteoporosis secondary fracture prevention: a united voice. Endocr Pract 2020 May 1; 26(5): 571–2. doi: 10.4158/EP-2020-0068

Lagerweij GR, de Wit GA, Moons KG, van der Schouw YT, Verschuren WM, Dorresteijn JA, et al. A new selection method to increase the health benefits of CVD prevention strategies. Eur J Prevent Cardiol 2018 Apr 1; 25(6): 642–50. doi: 10.1177/2047487317752948

Emberson J, Whincup P, Morris R, Walker M, Ebrahim S. Evaluating the impact of population and high-risk strategies for the primary prevention of cardiovascular disease. Eur Heart J 2004 Mar 1; 25(6): 484–91. doi: 10.1016/j.ehj.2003.11.012

Chen MM, Golding LP, Nicola GN. Who will pay for AI? Radiology 2021 May; 3(3): e210030. doi: 10.1148/ryai.2021210030

Patel MJ, de Lemos JA, McGuire DK, See R, Lindsey JB, Murphy SA, et al. Evaluation of coronary artery calcium screening strategies focused on risk categories: the Dallas Heart Study. Am Heart J 2009 Jun 1; 157(6): 1001–9. doi: 10.1016/j.ahj.2009.03.018

Gurupur V, Wan TT. Inherent bias in artificial intelligence-based decision support systems for healthcare. Medicine 2020 Mar 20; 56(3): 141. doi: 10.3390/medicina56030141

Geis JR, Brady AP, Wu CC, Spencer J, Ranschaert E, Jaremko JL, et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Can Assoc Radiol J 2019 Nov; 70(4): 329–34. doi: 10.1016/j.carj.2019.08.010

Guo Y, Hao Z, Zhao S, Gong J, Yang F. Artificial intelligence in health care: bibliometric analysis. J Med Internet Res 2020 Jul 29; 22(7): e18228. doi: 10.2196/18228

Perrault R, Shoham Y, Brynjolfsson E, Clark J, Etchemendy J, Grosz B, et al. The AI index 2019 annual report. Stanford, CA: AI Index Steering Committee, Human-Centered AI Institute, Stanford University; 2019.

Zhu S, Gilbert M, Chetty I, Siddiqui F. The 2021 landscape of FDA-approved artificial intelligence/machine learning-enabled medical devices: an analysis of the characteristics and intended use. Int J Med Inform 2022 Sep 1; 165: 104828. doi: 10.1016/j.ijmedinf.2022.104828

Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, Brink J. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 2018 Mar 1; 15(3): 504–8. doi: 10.1016/j.jacr.2017

Desai AN. Artificial intelligence: promise, pitfalls, and perspective. JAMA 2020 Jun 23; 323(24): 2448–9. doi: 10.1001/jama.2020.8737

Tadavarthi Y, Vey B, Krupinski E, Prater A, Gichoya J, Safdar N, et al. The state of radiology AI: considerations for purchase decisions and current market offerings. Radiology 2020 Nov; 2(6): e200004. doi: 10.1148/ryai.2020200004

Golding LP, Nicola GN. A business case for artificial intelligence tools: the currency of improved quality and reduced cost. J Am Coll Radiol 2019 Sep 1; 16(9): 1357–61. doi: 10.1016/j.jacr.2019.05.004

Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Bohr A, Memarzadeh K, eds. Artificial Intelligence in Healthcare. Academic Press, 2020; pp. 25–60.

Shadmi R, Mazo V, Bregman-Amitai O, Elnekave E. Fully-automatic deep learning based system for agatston score prediction from any non-contrast chest CT. Eur J Radiol 2021 Jan; 134: 109420. doi: 10.1016/j.ejrad.2

Subramanian M, Wojtusciszyn A, Favre L, Boughorbel S, Shan J, Letaief KB, et al. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med 2020 Dec; 18(1): 1–2. doi: 10.1186/s12967-020-02658-5

Farouk A. Rethinking how physicians learn to prevent, manage chronic disease. American Medical Association; 2016. Available from: [cited 10 July 2022].

Yates SW. Physician stress and burnout. Am J Med 2020 Feb 1; 133(2): 160–4. doi: 10.1016/j.amjmed.2019.08.034

Arndt BG, Beasley JW, Watkinson MD, Temte JL, Tuan WJ, Sinsky CA, et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med 2017 Sep 1; 15(5): 419–26. doi: 10.1370/afm.2121

Forsyth R. New report finds 90 percent of hospitals have an AI strategy; up 37 percent from 2019. PRNewswire 2020 March 9. Available from: [cited 10 July 2022].

Strohm L, Hehakaya C, Ranschaert ER, Boon WP, Moors EH. Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. Eur Radiol 2020 Oct; 30(10): 5525–32. doi: 10.1007/s00330-020-06946-y

Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, et al. Heart disease and stroke statistics – 2022 update: a report from the American Heart Association. Circulation 2022 Feb 22; 145(8): e153–639.

De Smedt D, Kotseva K, De Bacquer D, Wood D, De Backer G, Dallongeville J, et al. Cost-effectiveness of optimizing prevention in patients with coronary heart disease: the EUROASPIRE III health economics project. Eur Heart J 2012 Nov 1; 33(22): 2865–72. doi: 10.1093/eurheartj/ehs210

Ibanez B, Fernández-Ortiz A, Fernández-Friera L, García-Lunar I, Andrés V, Fuster V. Progression of early subclinical atherosclerosis (PESA) study: JACC focus seminar 7/8. J Am Coll Cardiol 2021 Jul 13; 78(2): 156–79. doi: 10.1016/j.jacc.2021.05.011



How to Cite

Kurek, A., Langholz, D. ., & Ahmed, A. (2022). Closing the Loop in AI, EMR and Provider Partnerships: The Key to Improved Population Health Management?. Telehealth and Medicine Today, 7(5).



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