AI-driven Optimization in Healthcare: the Diagnostic Process
Purpose: Process optimization in healthcare using artificial intelligence (AI) is still in its infancy. In this study, we address the research question “To what extent can an AI-driven chatbot help to optimize the diagnostic process?”
Design / Method / Approach: First, we developed a mathematical model for the utility (i.e., total satisfaction received from consuming a good or service) resulting from the diagnostic process in primary healthcare. We calculated this model using MS Excel. Second, after identifying the main pain points for optimization (e.g., waiting time in the queue), we ran a small experiment (n = 25) in which we looked at time to diagnosis, average waiting time, and their standard deviations. In addition, we used a questionnaire to examine patient perceptions of the interaction with an AI-driven chatbot.
Findings: Our results show that scheduling is the main factor causing issues in a physician’s work. An AI-driven chatbot may help to optimize waiting time as well as provide data for faster and more accurate diagnosis. We found that patients trust AI-driven solutions primarily when a real (not virtual) physician is also involved in the diagnostic process.
Practical Implications: AI-driven chatbots may indeed help to optimize diagnostic processes. Nevertheless, physicians need to remain involved in the process in order to establish patient trust in the diagnosis.
Originality / Value: We analyze the utility to physicians and patients of a diagnostic process and show that, while scheduling may reduce the overall process utility, AI-based solutions may increase the overall process utility.
Research Limitations / Future Research: First, our simulation includes a number of assumptions with regard to the distribution of mean times for encounter and treatment. Second, the data we used for our model were obtained from different papers, and thus from different healthcare systems. Third, our experimental study has a very small sample size and only one test-physician.
Paper type: Empirical
Advisor, I. G. (2018, July 24). Global Views on Healthcare in 2018. Ipsos Global Advisor. Retrieved from https://www.ipsos.com/en-be/global-views-healthcare.
Ahmadi-Javid, A., Jalali, Z., & Klassen, K. J. (2017). Outpatient appointment systems in healthcare: A review of optimization studies. European Journal of Operational Research, 258(1): 3–34. https://doi.org/10.1016/j.ejor.2016.06.064.
Alexopoulos, C., Goldsman, D., Fontanesi, J., Kopald, D., & Wilson, J. R. (2008). Modeling patient arrivals in community clinics. Omega, 36(1), 33–43. https://doi.org/10.1016/j.omega.2005.07.013.
Allon, G., & Kremer, M. (2018). Behavioral Foundations of Queueing Systems. The Handbook of Behavioral Operations, 323–366. https://doi.org/10.1002/9781119138341.ch9.
Bhavnani, S. P., Narula, J., & Sengupta, P. P. (2016). Mobile technology and the digitization of healthcare. European Heart Journal, 37(18), 1428–1438. https://doi.org/10.1093/eurheartj/ehv770.
Bogodistov, Y. (2017). Example of an individual report on Lean Six Sigma. no. 2017-1, ProcessLab. Retrieved from https://www.researchgate.net/profile/Yevgen-Bogodistov/publication/320298556_Example_of_an_individual_report_on_Lean_Six_Sigma/links/59dc8384458515e9ab4c67d2/Example-of-an-individual-report-on-Lean-Six-Sigma.pdf.
Bogodistov, Y., & Moormann, J. (2019). Theorizing on Operational Excellence: A Capability-Based Approach. Academy of Management Proceedings, 2019(1), 12174. doi:10.5465/ambpp.2019.12174abstract.
Bogodistov, Y., Moormann, J., & Sibbel, R. (2018). Beyond Health Care Reform: How Process Management Can Alter Patients’ Experience. Academy of Management Proceedings, 2018(1), 12544. https://doi.org/10.5465/ambpp.2018.12544abstract.
Bogodistov, Y., Moormann, J., Sibbel, R., Krupskyi, O. P., & Hromtseva, O. (2021). Process maturity and patient orientation in times of a health system reform. Business Process Management Journal, ahead-of-print (ahead-of-print). https://doi.org/10.1108/BPMJ-09-2020-0428.
Bohr, A., & Memarzadeh, K. (2020). Current healthcare, big data, and machine learning. Artificial Intelligence in Healthcare, 1–24. https://doi.org/10.1016/b978-0-12-818438-7.00001-0.
Kamel Boulos, M. N., Brewer, A. C., Karimkhani, C., Buller, D. B., & Dellavalle, R. P. (2014). Mobile medical and health apps: state of the art, concerns, regulatory control and certification. Online Journal of Public Health Informatics, 5(3). e229. https://doi.org/10.5210/ojphi.v5i3.4814.
Brennan, V. K., & Dixon, S. (2013). Incorporating Process Utility into Quality Adjusted Life Years: A Systematic Review of Empirical Studies. PharmacoEconomics, 31(8), 677–691. https://doi.org/10.1007/s40273-013-0066-1.
Carayon, P., & Hoonakker, P. (2019). Human Factors and Usability for Health Information Technology: Old and New Challenges. Yearbook of Medical Informatics, 28(01), 071–077. https://doi.org/10.1055/s-0039-1677907.
Carlucci, D., Renna, P., & Schiuma, G. (2012). Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network. Health Care Management Science, 16(1), 37–44. https://doi.org/10.1007/s10729-012-9211-1.
Coleman, S. Y. (2012). Six Sigma in Healthcare. Statistical Methods in Healthcare, 286–308. https://doi.org/10.1002/9781119940012.ch14.
Corn, J. B. (2009). Six sigma in health care. Radiologic technology, 81(1), 92-95.
Coulter, A., & Jenkinson, C. 2005. European patients’ views on the responsiveness of health systems and healthcare providers. European Journal of Public Health, 15(4), 355–360. https://doi.org/10.1093/eurpub/cki004.
Cutler, D. M. (2007). The lifetime costs and benefits of medical technology. Journal of Health Economics, 26(6), 1081–1100. https://doi.org/10.1016/j.jhealeco.2007.09.003.
Dayer, L., Heldenbrand, S., Anderson, P., Gubbins, P. O., & Martin, B. C. (2013). Smartphone medication adherence apps: Potential benefits to patients and providers: Response to Aungst. Journal of the American Pharmacists Association, 53(4), 345. https://doi.org/10.1331/japha.2013.13121.
Ettinger, W. H. (1998). Consumer-Perceived Value: The Key to a Successful Business Strategy in the Healthcare Marketplace. Journal of the American Geriatrics Society, 46(1), 111–113. https://doi.org/10.1111/j.1532-5415.1998.tb01024.x.
Fuchs, V. R. (1996). Economics, values, and health care reform. American Economic Review, 86(1), 1–24. Retrieved from https://web.stanford.edu/~jay/health_class/Readings/Lecture01/fuchs_health_survey.pdf.
George, D., & Mallery, P. (2019). IBM SPSS statistics 26 step by step: A simple guide and reference (6th Edition). New York, NY: Routledge. Retrieved from https://www.taylorfrancis.com/books/mono/10.4324/9780429056765/ibm-spss-statistics-26-step-step-darren-george-paul-mallery.
George, M., Maxey, J., Rowlands, D., & Upton, M. (2005). The Lean Six Sigma Pocket Toolbook: A Quick Reference Guide to Nearly 100 Tools for Improving Process Quality, Speed, and Complexity. New Yor, NY: McGraw Hill. Retrieved from https://play.google.com/store/books/details?id=s5abSW-exREC.
Graber, M. L. (2013). The incidence of diagnostic error in medicine. BMJ Quality & Safety, 22(Suppl 2), ii21–ii27. https://doi.org/10.1136/bmjqs-2012-001615.
Greene, M. G., Adelman, R. D., Friedmann, E., & Charon, R. (1994). Older patient satisfaction with communication during an initial medical encounter. Social Science & Medicine, 38(9), 1279–1288. https://doi.org/10.1016/0277-9536(94)90191-0.
Haimi, M., Brammli-Greenberg, S., Waisman, Y., & Baron-Epel, O. (2018). Physicians’ experiences, attitudes and challenges in a Pediatric Telemedicine Service. Pediatric Research, 84(5), 650–656. https://doi.org/10.1038/s41390-018-0117-6.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (Eds.). (2010). Multivariate data analysis (7th ed). Upper Saddle River, NJ: Prentice Hall.
Hall, A. M., Ferreira, P. H., Maher, C. G., Latimer, J., & Ferreira, M. L. (2010). The Influence of the Therapist-Patient Relationship on Treatment Outcome in Physical Rehabilitation: A Systematic Review. Physical Therapy, 90(8), 1099–1110. https://doi.org/10.2522/ptj.20090245.
Hassin, R., & Haviv, M. (2003). To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems. https://doi.org/10.1007/978-1-4615-0359-0.
Herzlinger, R. E. (2006). Why innovation in health care is so hard. Harvard business review, 84(5), 58-66. Retrieved from https://www.hse.ie/eng/about/who/healthbusinessservices/hbs-news-and-events/why-innovation-in-health-care-is-so-hard.pdf.
Hudak, P. L., Hogg-Johnson, S., Bombardier, C., McKeever, P. D., & Wright, J. G. (2004). Testing a New Theory of Patient Satisfaction With Treatment Outcome. Medical Care, 42(8), 726–739. https://doi.org/10.1097/01.mlr.0000132394.09032.81.
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial Intelligence in Precision Cardiovascular Medicine. Journal of the American College of Cardiology, 69(21), 2657–2664.
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial Intelligence in Precision Cardiovascular Medicine. Journal of the American College of Cardiology, 69(21), 2657–2664. https://doi.org/10.1016/j.jacc.2017.03.571.
Lehmann, C. A., Mintz, N., & Giacini, J. M. (2006). Impact of Telehealth on Healthcare Utilization by Congestive Heart Failure Patients. Disease Management & Health Outcomes, 14(3), 163–169. https://doi.org/10.2165/00115677-200614030-00005.
Levine, R., Shore, K., Lubalin, J., Garfinkel, S., Hurtado, M., & Carman, K. (2012). Comparing physician and patient perceptions of quality in ambulatory care. International Journal for Quality in Health Care, 24(4), 348–356. https://doi.org/10.1093/intqhc/mzs023.
Like, R., & Zyzanski, S. J. (1987). Patient satisfaction with the clinical encounter: Social psychological determinants. Social Science & Medicine, 24(4), 351–357. https://doi.org/10.1016/0277-9536(87)90153-5.
Marmor, T., & Wendt, C. (2012). Conceptual frameworks for comparing healthcare politics and policy. Health Policy, 107(1), 11–20. https://doi.org/10.1016/j.healthpol.2012.06.003.
MathWave Technologies. (2019). EasyFit - Software für Verteilungsanpassung. Retrieved from http://www.mathwave.com/de/home.html.
Miller, D. D., & Brown, E. W. (2018). Artificial Intelligence in Medical Practice: The Question to the Answer? The American Journal of Medicine, 131(2), 129–133. https://doi.org/10.1016/j.amjmed.2017.10.035.
Moormann, J., Antony, J., Chakraborty, A., Bogodistov, Y., & Does, R. (2017). Lean Six Sigma in the Financial Services Industry: Germany. https://doi.org/10.13140/RG.2.2.27000.55043.
Moreira, M. W. L., Rodrigues, J. J. P. C., Korotaev, V., Al-Muhtadi, J., & Kumar, N. (2019). A Comprehensive Review on Smart Decision Support Systems for Health Care. IEEE Systems Journal, 13(3), 3536–3545. https://doi.org/10.1109/jsyst.2018.2890121.
Peprah, A. A. (2013). Healthcare Delivery in Sub-Saharan Africa: Patients’ Satisfaction and Perceived Service Quality, A Case Study of Sunyani Regional Hospital in Ghana. LAP Lambert Academic Publishing. Retrieved from https://books.google.com/books/about/Healthcare_Delivery_in_Sub_Saharan_Afric.html?hl=&id=_wO4ngEACAAJ.
Plsek, P. E., & Greenhalgh, T. (2001). Complexity science: The challenge of complexity in health care. BMJ, 323(7313), 625–628. https://doi.org/10.1136/bmj.323.7313.625.
Polisena, J., Tran, K., Cimon, K., Hutton, B., McGill, S., & Palmer, K. (2009). Home telehealth for diabetes management: a systematic review and meta-analysis. Diabetes, Obesity and Metabolism, 11(10), 913–930. https://doi.org/10.1111/j.1463-1326.2009.01057.x.
Polisena, J., Tran, K., Cimon, K., Hutton, B., McGill, S., Palmer, K., & Scott, R. E. (2010). Home telehealth for chronic obstructive pulmonary disease: a systematic review and meta-analysis. Journal of Telemedicine and Telecare, 16(3), 120–127. https://doi.org/10.1258/jtt.2009.090812.
Proudlove, N., Moxham, C., & Boaden, R. 2008. Lessons for Lean in Healthcare from Using Six Sigma in the NHS. Public Money & Management, 28(1): 27–34.
Rau, C.-L., Tsai, P.-F. J., Liang, S.-F. M., Tan, J.-C., Syu, H.-C., Jheng, Y.-L., … Jaw, F.-S. (2013). Using discrete-event simulation in strategic capacity planning for an outpatient physical therapy service. Health Care Management Science, 16(4), 352–365. https://doi.org/10.1007/s10729-013-9234-2.
Rubel, J. A., Bar-Kalifa, E., Atzil-Slonim, D., Schmidt, S., & Lutz, W. (2018). Congruence of therapeutic bond perceptions and its relation to treatment outcome: Within- and between-dyad effects. Journal of Consulting and Clinical Psychology, 86(4), 341–353. https://doi.org/10.1037/ccp0000280.
Saltman, R. B., & Figueras, J. (1997). European health care reform: analysis of current strategies. WHO Regional Publications. European Series, 72, 5–38.
Semigran, H. L., Linder, J. A., Gidengil, C., & Mehrotra, A. (2015). Evaluation of symptom checkers for self diagnosis and triage: audit study. BMJ, h3480. https://doi.org/10.1136/bmj.h3480.
Singh, H., Meyer, A. N. D., & Thomas, E. J. (2014). The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Quality & Safety, 23(9), 727–731. https://doi.org/10.1136/bmjqs-2013-002627.
Sinsky, C., Colligan, L., Li, L., Prgomet, M., Reynolds, S., Goeders, L., … Blike, G. (2016). Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Annals of Internal Medicine, 165(11), 753-761. https://doi.org/10.7326/m16-0961.
Teke, A., Cengiz, E., Çetin, M., Demir, C., Kirkbir, F., & Fedai, T. (2010). Analysis of the Multi-Item Dimensionality of Patients’ Perceived Value in Hospital Services. Journal of Medical Systems, 36(3), 1301–1307. https://doi.org/10.1007/s10916-010-9590-0.
Thompson, D. A., Yarnold, P. R., Williams, D. R., & Adams, S. L. (1996). Effects of Actual Waiting Time, Perceived Waiting Time, Information Delivery, and Expressive Quality on Patient Satisfaction in the Emergency Department. Annals of Emergency Medicine, 28(6), 657–665. https://doi.org/10.1016/s0196-0644(96)70090-2.
Varkevisser, M., van der Geest, S. A., & Schut, F. T. (2012). Do patients choose hospitals with high quality ratings? Empirical evidence from the market for angioplasty in the Netherlands. Journal of Health Economics, 31(2), 371–378. https://doi.org/10.1016/j.jhealeco.2012.02.001.
Williams, E. S., Manwell, L. B., Konrad, T. R., & Linzer, M. (2007). The relationship of organizational culture, stress, satisfaction, and burnout with physician-reported error and suboptimal patient care. Health Care Management Review, 32(3), 203–212. https://doi.org/10.1097/01.hmr.0000281626.28363.59.
The authors agree with the following conditions:
1. Authors retain copyright and grant the journal right of first publication (Download agreement) with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
2. Authors have the right to complete individual additional agreements for the non-exclusive spreading of the journal’s published version of the work (for example, to post work in the electronic repository of the institution or to publish it as part of a monograph), with the reference to the first publication of the work in this journal.
3. Journal’s politics allows and encourages the placement on the Internet (for example, in the repositories of institutions, personal websites, SSRN, ResearchGate, MPRA, SSOAR, etc.) manuscript of the work by the authors, before and during the process of viewing it by this journal, because it can lead to a productive research discussion and positively affect the efficiency and dynamics of citing the published work (see The Effect of Open Access).