The Economic Impact of Artificial Intelligence in Health Care: Systematic Review

Justus Wolff, Josch Pauling, Andreas Keck, Jan Baumbach, Justus Wolff, Josch Pauling, Andreas Keck, Jan Baumbach

Abstract

Background: Positive economic impact is a key decision factor in making the case for or against investing in an artificial intelligence (AI) solution in the health care industry. It is most relevant for the care provider and insurer as well as for the pharmaceutical and medical technology sectors. Although the broad economic impact of digital health solutions in general has been assessed many times in literature and the benefit for patients and society has also been analyzed, the specific economic impact of AI in health care has been addressed only sporadically.

Objective: This study aimed to systematically review and summarize the cost-effectiveness studies dedicated to AI in health care and to assess whether they meet the established quality criteria.

Methods: In a first step, the quality criteria for economic impact studies were defined based on the established and adapted criteria schemes for cost impact assessments. In a second step, a systematic literature review based on qualitative and quantitative inclusion and exclusion criteria was conducted to identify relevant publications for an in-depth analysis of the economic impact assessment. In a final step, the quality of the identified economic impact studies was evaluated based on the defined quality criteria for cost-effectiveness studies.

Results: Very few publications have thoroughly addressed the economic impact assessment, and the economic assessment quality of the reviewed publications on AI shows severe methodological deficits. Only 6 out of 66 publications could be included in the second step of the analysis based on the inclusion criteria. Out of these 6 studies, none comprised a methodologically complete cost impact analysis. There are two areas for improvement in future studies. First, the initial investment and operational costs for the AI infrastructure and service need to be included. Second, alternatives to achieve similar impact must be evaluated to provide a comprehensive comparison.

Conclusions: This systematic literature analysis proved that the existing impact assessments show methodological deficits and that upcoming evaluations require more comprehensive economic analyses to enable economic decisions for or against implementing AI technology in health care.

Keywords: artificial intelligence; cost-benefit analysis; machine learning; telemedicine.

Conflict of interest statement

Conflicts of Interest: None declared.

©Justus Wolff, Josch Pauling, Andreas Keck, Jan Baumbach. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.02.2020.

Figures

Figure 1
Figure 1
Study selection and identification flowchart.
Figure 2
Figure 2
Result of the literature review and improvement areas for economic impact assessment of artificial intelligence (AI) in health care.

References

    1. Phelps CE. Health Economics. Abingdon, Oxfordshire: Routledge; 2017.
    1. Statistisches Bundesamt. [2019-10-03]. Health Expenditure in 2017: +4.7% .
    1. Whitten PS, Mair FS, Haycox A, May CR, Williams TL, Hellmich S. Systematic review of cost effectiveness studies of telemedicine interventions. Br Med J. 2002 Jun 15;324(7351):1434–7. doi: 10.1136/bmj.324.7351.1434.
    1. Elbert N, van Os-Medendorp H, van Renselaar W, Ekeland A, Hakkaart-van Roijen L, Raat H, Nijsten T, Pasmans S. Effectiveness and cost-effectiveness of eHealth interventions in somatic diseases: a systematic review of systematic reviews and meta-analyses. J Med Internet Res. 2014 Apr 16;16(4):e110. doi: 10.2196/jmir.2790.
    1. Sanyal C, Stolee P, Juzwishin D, Husereau D. Economic evaluations of eHealth technologies: a systematic review. PLoS One. 2018;13(6):e0198112. doi: 10.1371/journal.pone.0198112.
    1. Buvik A, Bergmo TS, Bugge E, Smaabrekke A, Wilsgaard T, Olsen JA. Cost-effectiveness of Telemedicine in remote orthopedic consultations: randomized controlled trial. J Med Internet Res. 2019 Feb 19;21(2):e11330. doi: 10.2196/11330.
    1. Nordyke RJ, Appelbaum K, Berman MA. Estimating the impact of novel digital therapeutics in Type 2 diabetes and hypertension: health economic analysis. J Med Internet Res. 2019 Oct 9;21(10):e15814. doi: 10.2196/15814.
    1. Le LK, Sanci L, Chatterton ML, Kauer S, Buhagiar K, Mihalopoulos C. The cost-effectiveness of an internet intervention to facilitate mental health help-seeking by young adults: randomized controlled trial. J Med Internet Res. 2019 Jul 22;21(7):e13065. doi: 10.2196/13065.
    1. Google Trends. [2019-10-09]. Compare .
    1. Ekeland AG, Bowes A, Flottorp S. Effectiveness of telemedicine: a systematic review of reviews. Int J Med Inform. 2010 Nov;79(11):736–71. doi: 10.1016/j.ijmedinf.2010.08.006.
    1. Haycox A, Walley T. Pharmacoeconomics: evaluating the evaluators. Br J Clin Pharmacol. 1997 May;43(5):451–6. doi: 10.1046/j.1365-2125.1997.00575.x.
    1. Lee HK, Jin R, Feng Y, Bain PA, Goffinet J, Baker C, Li J. An analytical framework for TJR readmission prediction and cost-effective intervention. IEEE J Biomed Health Inform. 2019 Jul;23(4):1760–72. doi: 10.1109/JBHI.2018.2859581.
    1. Gönel A. Clinical biochemistry test eliminator providing cost-effectiveness with five algorithms. Acta Clin Belg. 2018 Dec 25;:1–5. doi: 10.1080/17843286.2018.1563324.
    1. Bremer V, Becker D, Kolovos S, Funk B, van Breda W, Hoogendoorn M, Riper H. Predicting therapy success and costs for personalized treatment recommendations using baseline characteristics: Data-driven analysis. J Med Internet Res. 2018 Aug 21;20(8):e10275. doi: 10.2196/10275.
    1. Grover D, Bauhoff S, Friedman J. Using supervised learning to select audit targets in performance-based financing in health: an example from Zambia. PLoS One. 2019;14(1):e0211262. doi: 10.1371/journal.pone.0211262.
    1. Lee I, Monahan S, Serban N, Griffin P, Tomar S. Estimating the cost savings of preventive dental services delivered to Medicaid-enrolled children in six southeastern states. Health Serv Res. 2018 Oct;53(5):3592–616. doi: 10.1111/1475-6773.12811.
    1. Golas SB, Shibahara T, Agboola S, Otaki H, Sato J, Nakae T, Hisamitsu T, Kojima G, Felsted J, Kakarmath S, Kvedar J, Jethwani K. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak. 2018 Jun 22;18(1):44. doi: 10.1186/s12911-018-0620-z.
    1. Spänig S, Emberger-Klein A, Sowa J, Canbay A, Menrad K, Heider D. The virtual doctor: an interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes. Artif Intell Med. 2019 Sep;100:101706. doi: 10.1016/j.artmed.2019.101706.
    1. Shen J, Zhang CJ, Jiang B, Chen J, Song J, Liu Z, He Z, Wong SY, Fang P, Ming W. Artificial Intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med Inform. 2019 Aug 16;7(3):e10010. doi: 10.2196/10010.
    1. Pillai M, Adapa K, Das SK, Mazur L, Dooley J, Marks LB, Thompson RF, Chera BS. Using Artificial Intelligence to improve the quality and safety of radiation therapy. J Am Coll Radiol. 2019 Sep;16(9 Pt B):1267–72. doi: 10.1016/j.jacr.2019.06.001.

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