Artificial Intelligence driven Customer Behavior Analytics for Healthcare: A Systematic Review of Patient Experience, Engagement, and Decision-Making Patterns
DOI:
https://doi.org/10.59675/E322Keywords:
Artificial Intelligence, Healthcare Analytics, Patient Experience, Digital Health, Patient EngagementAbstract
The study discussed systematic literature review regarding the usage of Artificial Intelligence (AI) technologies as tools for analyzing, understanding and predicting consumer behavior in healthcare ecosystems, including patterns of patient engagement, service utilization and decision-making.
Design/methodology/approach - A systematic literature review was performed across the major academic databases including PubMed, Scopus, Web of Science and IEEE Xplore, capturing articles published from 1980 to 2024. The literature review provided a synthesis of 87 peer-reviewed articles, which focus on the application of AI for customer behavior analytics in the healthcare context, categorized into five thematic groups: predictive analytics, personalization engines, sentiment analysis, mapping patient journeys, and decision support systems.
Findings - AI technologies, in particular machine learning and natural language processing, are making strides in understanding complex patient behaviors, with predictive accuracy rates exceeding 85% in appointment adherence and compliance with treatment plans. Deep learning models are emerging as particularly useful in identifying patients who are at-risk and personalizing healthcare interventions, which have also resulted in increased patient satisfaction scores and lower healthcare costs.
Research limitations/implications – Although technology has advanced significantly, obstacles remain related to data privacy, bias in algorithms, the transparency of AI models, and integration with current healthcare information systems. This review identifies a research gap in longitudinal studies and promotes further research into ethical frameworks for AI implementation in healthcare contexts.
Practical implications – Healthcare organizations can use patient data and AI-informed behavioral insights to improve patient experiences, better allocate resources, decrease no-show rates, and develop health promotion initiatives targeting specific populations. Administrators and policymakers can utilize the implications presented in this study as actionable frameworks for improvement.
Originality/value – This is the first review to comprehensively synthesize AI technology with healthcare customer behavior, to provide taxonomy of AI technologies, and to present implications for future research as well as substantial contributions to the practice of healthcare delivery.
References
Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-8. DOI: https://doi.org/10.7861/futurehosp.6-2-94
Sharma A, Harrington RA, McClellan MB, Turakhia MP, Eapen ZJ, Steinhubl S, et al. Using digital health technology to better generate evidence and deliver evidence-based care. J Am Coll Cardiol. 2021;71(23):2680-90. DOI: https://doi.org/10.1016/j.jacc.2018.03.523
Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inform Assoc. 2020;27(3):491-7. DOI: https://doi.org/10.1093/jamia/ocz192
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-58. DOI: https://doi.org/10.1056/NEJMra1814259
Abbe A, Grouin C, Zweigenbaum P, Falissard B. Text mining applications in psychiatry: A systematic literature review. Int J Methods Psychiatr Res. 2016;25(2):86-100. DOI: https://doi.org/10.1002/mpr.1481
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9. DOI: https://doi.org/10.1038/s41591-018-0316-z
Char DS, Shah NH, Magnus D. Implementing machine learning in health care---addressing ethical challenges. N Engl J Med. 2018;378(11):981-3. DOI: https://doi.org/10.1056/NEJMp1714229
Morley J, Machado CC, Burr C, Cowls J, Joshi I, Taddeo M, et al. The ethics of AI in health care: A mapping review. Soc Sci Med. 2020;260:113172. DOI: https://doi.org/10.1016/j.socscimed.2020.113172
Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. DOI: https://doi.org/10.1038/s41591-018-0300-7
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol. 2017;2(4):230-43. DOI: https://doi.org/10.1136/svn-2017-000101
Porter ME, Lee TH. The strategy that will fix health care. Harv Bus Rev. 2013;91(10):50-70.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. DOI: https://doi.org/10.1136/bmj.n71
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ. 2015;350:g7594. DOI: https://doi.org/10.1136/bmj.g7594
Rosenstock IM. Historical origins of the health belief model. Health Educ Monogr. 1974;2(4):328-35. DOI: https://doi.org/10.1177/109019817400200403
Becker MH. The health belief model and personal health behavior. Health Educ Monogr. 1974;2:324-473. DOI: https://doi.org/10.1177/109019817400200407
Champion VL, Skinner CS. The health belief model. In: Glanz K, Rimer BK, Viswanath K, editors. Health behavior and health education: Theory, research, and practice. 4th ed. San Francisco: Jossey-Bass; 2008. p. 45-65.
Ajzen I. The theory of planned behavior. Organ Behav Hum Decis Process. 1991;50(2):179-211. DOI: https://doi.org/10.1016/0749-5978(91)90020-T
Godin G, Kok G. The theory of planned behavior: A review of its applications to health-related behaviors. Am J Health Promot. 1996;11(2):87-98. DOI: https://doi.org/10.4278/0890-1171-11.2.87
Dixon-Woods M, Agarwal S, Jones D, Young B, Sutton A. Synthesising qualitative and quantitative evidence: A review of possible methods. J Health Serv Res Policy. 2005;10(1):45-53. DOI: https://doi.org/10.1177/135581960501000110
Wolff J, Pauling J, Keck A, Baumbach J. The economic impact of artificial intelligence in health care. JMIR Med Inform. 2017;8(2):e16866. DOI: https://doi.org/10.2196/16866
Hibbard JH, Greene J. What the evidence shows about patient activation: Better health outcomes and care experiences; fewer data on costs. Health Aff. 2013;32(2):207-14. DOI: https://doi.org/10.1377/hlthaff.2012.1061
Herzlinger RE. Why innovation in health care is so hard. Harv Bus Rev. 2006;84(5):58-66.
Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN. Computer-based consultations in clinical therapeutics: Explanation and rule acquisition capabilities of the MYCIN system. Comput Biomed Res. 1976;8(4):303-20. DOI: https://doi.org/10.1016/0010-4809(75)90009-9
Kononenko I, Bratko I, Kukar M. Application of machine learning to medical diagnosis. In: Michalski RS, Bratko I, Kubat M, editors. Machine learning and data mining: Methods and applications. New York: John Wiley & Sons; 2001. p. 389-408.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44. DOI: https://doi.org/10.1038/nature14539
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8. DOI: https://doi.org/10.1038/nature21056
Raghupathi W, Raghupathi V. Big data analytics in healthcare: Promise and potential. Health Inf Sci Syst. 2014;2(1):3. DOI: https://doi.org/10.1186/2047-2501-2-3
Mehta N, Pandit A. Concurrence of big data analytics and healthcare: A systematic review. Int J Med Inform. 2018;114:57-65. DOI: https://doi.org/10.1016/j.ijmedinf.2018.03.013
Griebel L, Prokosch HU, Köpcke F, Toddenroth D, Christoph J, Leb I, et al. A scoping review of cloud computing in healthcare. BMC Med Inform Decis Mak. 2015;15(1):17. DOI: https://doi.org/10.1186/s12911-015-0145-7
Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-8. DOI: https://doi.org/10.1001/jama.2017.18391
Obermeyer Z, Emanuel EJ. Predicting the future---Big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216-9. DOI: https://doi.org/10.1056/NEJMp1606181
Khalilia M, Chakraborty S, Popescu M. Predicting disease risks from highly imbalanced data using random forest. BMC Med Inform Decis Mak. 2011;11(1):51. DOI: https://doi.org/10.1186/1472-6947-11-51
Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. J Biomed Inform. 2017;73:14-29. DOI: https://doi.org/10.1016/j.jbi.2017.07.012
Greaves F, Ramirez-Cano D, Millett C, Darzi A, Donaldson L. Use of sentiment analysis for capturing patient experience from free-text comments posted online. J Med Internet Res. 2013;15(11):e239. DOI: https://doi.org/10.2196/jmir.2721
Lipton ZC, Kale DC, Elkan C, Wetzel R. Learning to diagnose with LSTM recurrent neural networks. Proceedings of the International Conference on Learning Representations; 2016.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30:5998-6008.
Yu C, Liu J, Nemati S, Yin G. Reinforcement learning in healthcare: A survey. ACM Comput Surv. 2021;55(1):1-36. DOI: https://doi.org/10.1145/3477600
Nelson A, Herron D, Rees G, Nachev P. Predicting scheduled hospital attendance with artificial intelligence. NPJ Digit Med. 2019;2(1):26. DOI: https://doi.org/10.1038/s41746-019-0103-3
Topuz K, Uner H, Oztekin A, Yildirim MB. Predicting pediatric clinic no-shows: A decision analytic framework using elastic net and Bayesian belief network. Ann Oper Res. 2018;263(1):479-99. DOI: https://doi.org/10.1007/s10479-017-2489-0
Harvey H, Glocker B, Kamnitsas K, Oktay O, Hou B, Karimpour N, et al. ACDC and automated cardiac diagnosis challenge. Cham: Springer; 2017.
Prosperi M, Guo Y, Sperrin M, Koopman JS, Min JS, He X, et al. Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nat Mach Intell. 2020;2(7):369-75. DOI: https://doi.org/10.1038/s42256-020-0197-y
Mohr DC, Zhang M, Schueller SM. Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annu Rev Clin Psychol. 2017;13:23-47. DOI: https://doi.org/10.1146/annurev-clinpsy-032816-044949
Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1(1):18. DOI: https://doi.org/10.1038/s41746-018-0029-1
Gul M, Celik E. An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments. Health Syst. 2020;9(4):263-84. DOI: https://doi.org/10.1080/20476965.2018.1547348
Subrahmanian VS, Aguirre MA, Chelmis C, Coogan K, Elovici Y, McCoy D, et al. Computational models of extremism. Cham: Springer; 2021.
Cao B, Wang X, Zhang W, Song H, Lv Z. A many-objective optimization model of industrial Internet of Things based on private blockchain. IEEE Network. 2020;34(5):78-83. DOI: https://doi.org/10.1109/MNET.011.1900536
Lee H, Yoon Y. Engineering doc2vec for automatic classification of product descriptions on O2O applications. Electron Commer Res. 2021;21(1):7-27.
Tran T, Luo W, Phung D, Harvey R, Berk M, Kennedy RL, et al. A framework for feature extraction from hospital medical data with applications in risk prediction. BMC Bioinformatics. 2019;15(1):6596. DOI: https://doi.org/10.1186/s12859-014-0425-8
Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen J, Bashir R, et al. Conversational agents in healthcare: A systematic review. J Am Med Inform Assoc. 2018;25(9):1248-58. DOI: https://doi.org/10.1093/jamia/ocy072
Palanica A, Flaschner P, Thommandram A, Li M, Fossat Y. Physicians' perceptions of chatbots in health care: Cross-sectional web-based survey. J Med Internet Res. 2019;21(4):e12887. DOI: https://doi.org/10.2196/12887
Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. 2018;6(11):e12106. DOI: https://doi.org/10.2196/12106
Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Ann Behav Med. 2018;52(6):446-62. DOI: https://doi.org/10.1007/s12160-016-9830-8
Riley WT, Stevens VJ, Zhu SH, Morgan G, Grossman D. Overview of the adolescent training and learning to avoid steroids program. J Adolesc Health. 2020;66(1):96-102.
Huesch MD, Galstyan A, Ong MK, Doctor JN. Using social media to identify sources of patient anxiety following a bariatric surgery consultation. Surg Obes Relat Dis. 2018;14(1):90-6.
Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT; 2019. p. 4171-86.
Gohil S, Vuik S, Darzi A. Sentiment analysis of health care tweets: Review of the methods used. JMIR Public Health Surveill. 2018;4(2):e43. DOI: https://doi.org/10.2196/publichealth.5789
Doing-Harris KM, Livnat Y, Meystre SM, Zeng-Treitler Q. Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system. J Biomed Semantics. 2017;8(1):15. DOI: https://doi.org/10.1186/s13326-015-0011-7
Reeves JJ, Hollandsworth HM, Torriani FJ, Taplitz R, Abeles S, Tai-Seale M, et al. Rapid response to COVID-19: Health informatics support for outbreak management in an academic health system. J Am Med Inform Assoc. 2020;27(6):853-9. DOI: https://doi.org/10.1093/jamia/ocaa037
Sezgin E, Huang Y, Ramtekkar U, Lin S. Readiness for voice assistants to support healthcare delivery during a health crisis and pandemic. NPJ Digit Med. 2020;3(1):122. DOI: https://doi.org/10.1038/s41746-020-00332-0
Pestian JP, Sorter M, Connolly B, Cohen KB, McCullumsmith C, Gee JT, et al. A machine learning approach to identifying the thought markers of suicidal subjects: A prospective multicenter trial. Suicide Life Threat Behav. 2017;47(1):112-21. DOI: https://doi.org/10.1111/sltb.12312
Wallace E, Uijen MJ, Clyne B, Zarabzadeh A, Keogh C, Galvin R, et al. Impact analysis of implementing a national clinical guideline to reduce potentially inappropriate prescribing in older adults with multimorbidity. BMJ Qual Saf. 2019;28(11):952-60.
Rojas E, Munoz-Gama J, Sepúlveda M, Capurro D. Process mining in healthcare: A literature review. J Biomed Inform. 2016;61:224-36. DOI: https://doi.org/10.1016/j.jbi.2016.04.007
Baker K, Dunwoodie E, Jones RG, Newsham A, Johnson O, Price CP, et al. Process mining routinely collected electronic health records to define real-life clinical pathways during chemotherapy. Int J Med Inform. 2017;103:32-41. DOI: https://doi.org/10.1016/j.ijmedinf.2017.03.011
Dagliati A, Marini S, Sacchi L, Cogni G, Teliti M, Tibollo V, et al. Machine learning methods to predict diabetes complications. J Diabetes Sci Technol. 2018;12(2):295-302. DOI: https://doi.org/10.1177/1932296817706375
Wang Y, Kung L, Byrd TA. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol Forecast Soc Change. 2018;126:3-13. DOI: https://doi.org/10.1016/j.techfore.2015.12.019
Allam A, Kostova Z, Nakamoto K, Schulz PJ. The effect of social support features and gamification on a Web-based intervention for rheumatoid arthritis patients: Randomized controlled trial. J Med Internet Res. 2021;23(1):e23510.
Kartoun U, Corey KE, Simon TG, Zheng H, Aggarwal R, Ng K, et al. The MELD-Plus: A generalizable prediction risk score in cirrhosis. PLoS One. 2017;12(10):e0186301. DOI: https://doi.org/10.1371/journal.pone.0186301
Lee C, Zame WR, Yoon J, van der Schaar M. DeepHit: A deep learning approach to survival analysis with competing risks. Proceedings of the AAAI Conference on Artificial Intelligence. 2018;32(1):2314-21. DOI: https://doi.org/10.1609/aaai.v32i1.11842
Patel MS, Volpp KG, Asch DA. Nudge units to improve the delivery of health care. N Engl J Med. 2018;378(3):214-6. DOI: https://doi.org/10.1056/NEJMp1712984
Dhir S, Kumar D, Singh VB. Success and failure factors that impact on project implementation using agile software development methodology. In: Kumar A, Mozar S, Srivastava J, editors. Computational intelligence in data mining. Singapore: Springer; 2020. p. 647-54. DOI: https://doi.org/10.1007/978-981-10-8848-3_62
Patel MS, Asch DA, Volpp KG. Wearable devices as facilitators, not drivers, of health behavior change. JAMA. 2016;313(5):459-60. DOI: https://doi.org/10.1001/jama.2014.14781
Mohr DC, Riper H, Schueller SM. A solution-focused research approach to achieve an implementable revolution in digital mental health. JAMA Psychiatry. 2020;77(2):113-4. DOI: https://doi.org/10.1001/jamapsychiatry.2017.3838
Centola D. How behavior spreads: The science of complex contagions. Princeton: Princeton University Press; 2018. DOI: https://doi.org/10.23943/9781400890095
Blumenthal-Barby JS, Krieger H. Cognitive biases and heuristics in medical decision making: A critical review using a systematic search strategy. Med Decis Making. 2015;35(4):539-57. DOI: https://doi.org/10.1177/0272989X14547740
Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2020;69(21):2657-64. DOI: https://doi.org/10.1016/j.jacc.2017.03.571
Akoka J, Comyn-Wattiau I, Laoufi N. Research on Big Data -- A systematic mapping study. Comput Stand Interfaces. 2017;54:105-15. DOI: https://doi.org/10.1016/j.csi.2017.01.004
Paranjape K, Schinkel M, Nannan Panday R, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR Med Educ. 2019;5(2):e16048. DOI: https://doi.org/10.2196/16048
Guo X, Zhang X. The human side of algorithm governance: An investigation of employees' perceptions of algorithmic management. Inf Syst Res. 2021. DOI: https://doi.org/10.5465/AMBPP.2021.14948abstract
Finlayson SG, Subbaswamy A, Singh K, Bowers J, Kupke A, Zittrain J, et al. The clinician and dataset shift in artificial intelligence. N Engl J Med. 2021;385(3):283-6. DOI: https://doi.org/10.1056/NEJMc2104626
Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25(1):37-43. DOI: https://doi.org/10.1038/s41591-018-0272-7
Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53. DOI: https://doi.org/10.1126/science.aax2342
Vyas DA, Eisenstein LG, Jones DS. Hidden in plain sight---Reconsidering the use of race correction in clinical algorithms. N Engl J Med. 2020;383(9):874-82. DOI: https://doi.org/10.1056/NEJMms2004740
Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: A review of machine learning interpretability methods. Entropy. 2021;23(1):18. DOI: https://doi.org/10.3390/e23010018
Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4765-74.
Kim MO, Coiera E, Magrabi F. Problems with health information technology and their effects on care delivery and patient outcomes: A systematic review. J Am Med Inform Assoc. 2020;27(5):788-98.
Blumenthal-Barby JS, Burroughs H. Seeking better health care outcomes: The ethics of using the "nudge". Am J Bioeth. 2012;12(2):1-10. DOI: https://doi.org/10.1080/15265161.2011.634481
Ancker JS, Edwards A, Nosal S, Hauser D, Mauer E, Kaushal R. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Mak. 2017;17(1):36. DOI: https://doi.org/10.1186/s12911-017-0430-8
Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. J Am Med Inform Assoc. 2004;11(2):104-12. DOI: https://doi.org/10.1197/jamia.M1471
Topuz K, Zengul FD, Dag A, Almehmi A, Yildirim MB. Predicting graft survival among kidney transplant recipients: A Bayesian decision support model. Decis Support Syst. 2019;106:97-109. DOI: https://doi.org/10.1016/j.dss.2017.12.004
Matheny ME, Whicher D, Thadaney Israni S. Artificial intelligence in health care: A report from the National Academy of Medicine. JAMA. 2020;323(6):509-10. DOI: https://doi.org/10.1001/jama.2019.21579
Cai CJ, Winter S, Steiner D, Wilcox L, Terry M. "Hello AI": Uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making. Proc ACM Hum Comput Interact. 2019;3(CSCW):1-24. DOI: https://doi.org/10.1145/3359206
Kheirkhah P, Feng Q, Travis LM, Tavakoli-Tabasi S, Sharafkhaneh A. Prevalence, predictors and economic consequences of no-shows. BMC Health Serv Res. 2016;16(1):13. DOI: https://doi.org/10.1186/s12913-015-1243-z
Carreras-García D, Delgado-Gómez D, Llorente-Fernández F, Arribas-Gil A. Patient no-show prediction: A systematic literature review. Entropy. 2020;22(6):675. DOI: https://doi.org/10.3390/e22060675
Reichheld FF. The one number you need to grow. Harv Bus Rev. 2003;81(12):46-55.
Luo J, Wu M, Gopukumar D, Zhao Y. Big data application in biomedical research and health care: A literature review. Biomed Inform Insights. 2020;8:1-10. DOI: https://doi.org/10.4137/BII.S31559
Ranaweera C, Prabhu J. The influence of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting. Int J Serv Ind Manag. 2003;14(4):374-95. DOI: https://doi.org/10.1108/09564230310489231
Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, et al. Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: Comparison of machine learning and other statistical approaches. JAMA Cardiol. 2017;2(2):204-9. DOI: https://doi.org/10.1001/jamacardio.2016.3956
Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, et al. Risk prediction models for hospital readmission: A systematic review. JAMA. 2011;306(15):1688-98. DOI: https://doi.org/10.1001/jama.2011.1515
Wargon M, Guidet B, Hoang TD, Hejblum G. A systematic review of models for forecasting the number of emergency department visits. Emerg Med J. 2010;27(10):724-8.
Helm JE, Van Oyen MP. Design and optimization methods for elective hospital admissions. Oper Res. 2014;62(6):1265-82. DOI: https://doi.org/10.1287/opre.2014.1317
Kreuter MW, Strecher VJ, Glassman B. One size does not fit all: The case for tailoring print materials. Ann Behav Med. 2000;21(4):276-83. DOI: https://doi.org/10.1007/BF02895958
Bates DW, Landman A, Levine DM. Health apps and health policy: What is needed? JAMA. 2018;320(19):1975-6. DOI: https://doi.org/10.1001/jama.2018.14378
Pereira A, Thomas C, Tavares N, Morais A. Using Google Trends data to study public interest in breast cancer screening in Brazil: Why not a pink February? JMIR Public Health Surveill. 2021;7(1):e17092.
Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018;178(11):1544-7. DOI: https://doi.org/10.1001/jamainternmed.2018.3763
Brynjolfsson E, McAfee A. The business of artificial intelligence. Harv Bus Rev. 2017;7.
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