IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0212356.html
   My bibliography  Save this article

Applications of artificial neural networks in health care organizational decision-making: A scoping review

Author

Listed:
  • Nida Shahid
  • Tim Rappon
  • Whitney Berta

Abstract

Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997–2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique.

Suggested Citation

  • Nida Shahid & Tim Rappon & Whitney Berta, 2019. "Applications of artificial neural networks in health care organizational decision-making: A scoping review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    • Handle: RePEc:plo:pone00:0212356
    DOI: 10.1371/journal.pone.0212356
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212356
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0212356&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0212356?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mohsen Sangi & Khin Than Win & Farid Shirvani & Mohammad-Reza Namazi-Rad & Nagesh Shukla, 2015. "Applying a Novel Combination of Techniques to Develop a Predictive Model for Diabetes Complications," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-22, April.
    2. Thomas Chesney & Kay Penny & Peter Oakley & Simon Davies & David Chesney & Nicola Maffulli & John Templeton, 2006. "Data Mining Medical Information: Should Artificial Neural Networks Be Used to Analyse Trauma Audit Data?," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 1(2), pages 51-64, April.
    3. Paolo Massimo Buscema & Lara Gitto & Simone Russo & Andrea Marcellusi & Federico Fiori & Guido Maurelli & Giulia Massini & Francesco Saverio Mennini, 2017. "The perception of corruption in health: AutoCM methods for an international comparison," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(1), pages 459-477, January.
    4. Shaoyan Zhang & Christos Tjortjis & Xiaojun Zeng & Hong Qiao & Iain Buchan & John Keane, 2009. "Comparing data mining methods with logistic regression in childhood obesity prediction," Information Systems Frontiers, Springer, vol. 11(4), pages 449-460, September.
    5. Ching-Feng Lin & Aera LeBoulluec & Li Zeng & Victoria Chen & Robert Gatchel, 2014. "A decision-making framework for adaptive pain management," Health Care Management Science, Springer, vol. 17(3), pages 270-283, September.
    6. Enzo Grossi & Angelo Compare & Massimo Buscema, 2014. "The concept of individual semantic maps in clinical psychology: a feasibility study on a new paradigm," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 15-35, January.
    7. Yee-Loong Chong, Alain & Liu, Martin J. & Luo, Jun & Keng-Boon, Ooi, 2015. "Predicting RFID adoption in healthcare supply chain from the perspectives of users," International Journal of Production Economics, Elsevier, vol. 159(C), pages 66-75.
    8. Catherine L. Anderson & Ritu Agarwal, 2011. "The Digitization of Healthcare: Boundary Risks, Emotion, and Consumer Willingness to Disclose Personal Health Information," Information Systems Research, INFORMS, vol. 22(3), pages 469-490, September.
    9. Will Penny & David Frost, 1996. "Neural Networks in Clinical Medicine," Medical Decision Making, , vol. 16(4), pages 386-398, October.
    10. Hussein Atoui & David Télisson & Jocelyne Fyan & Paul Rubel, 2008. "Ambient Intelligence and Pervasive Architecture Designed within the EPI-MEDICS Personal ECG Monitor," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 3(4), pages 68-80, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yiğit Kazançoğlu & Muhittin Sağnak & Çisem Lafcı & Sunil Luthra & Anil Kumar & Caner Taçoğlu, 2021. "Big Data-Enabled Solutions Framework to Overcoming the Barriers to Circular Economy Initiatives in Healthcare Sector," IJERPH, MDPI, vol. 18(14), pages 1-21, July.
    2. Saurabh Shukla & Mohd Fadzil Hassan & Muhammad Khalid Khan & Low Tang Jung & Azlan Awang, 2019. "An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-31, November.
    3. Sabir, Zulqurnain & Said, Salem Ben & Baleanu, Dumitru, 2022. "Swarming optimization to analyze the fractional derivatives and perturbation factors for the novel singular model," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    4. Abreu, Paulo & Santos, Daniel & Barbosa-Povoa, Ana, 2023. "Data-driven forecasting for operational planning of emergency medical services," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    5. Yasmin MOBASHER, 2022. "The Importance Of Implementing Integrated Information Systems In Hospitals," Business Excellence and Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 12(5), pages 5-21, October.
    6. Çaparoğlu, Ömer Faruk & Ok, Yeşim & Tutam, Mahmut, 2021. "To restrict or not to restrict? Use of artificial neural network to evaluate the effectiveness of mitigation policies: A case study of Turkey," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    7. Pumplun, Luisa & Fecho, Mariska & Islam, Nihal & Buxmann, Peter, 2021. "Machine Learning Systems in Clinics – How Mature Is the Adoption Process in Medical Diagnostics?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124660, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    8. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.
    9. Sudatta Kar & Arpan Kumar Kar & Manmohan Prasad Gupta, 2021. "Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(4), pages 217-238, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ritu Agarwal & Michelle Dugas & Guodong (Gordon) Gao & P. K. Kannan, 2020. "Emerging technologies and analytics for a new era of value-centered marketing in healthcare," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 9-23, January.
    2. Hu, Li & Ma, Hoi-Lam & Wang, Li & Liu, Yang, 2023. "Hiding or disclosing? Information discrimination in member-only discounts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    3. Talukder, Md. Shamim & Sorwar, Golam & Bao, Yukun & Ahmed, Jashim Uddin & Palash, Md. Abu Saeed, 2020. "Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
    4. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang & Lee, Cheng fang, 2022. "Adoption model of healthcare wearable devices," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Hoon S. Choi & Darrell Carpenter & Myung S. Ko, 2022. "Risk Taking Behaviors Using Public Wi-Fi™," Information Systems Frontiers, Springer, vol. 24(3), pages 965-982, June.
    6. Zhenhui (Jack) Jiang & Cheng Suang Heng & Ben C. F. Choi, 2013. "Research Note —Privacy Concerns and Privacy-Protective Behavior in Synchronous Online Social Interactions," Information Systems Research, INFORMS, vol. 24(3), pages 579-595, September.
    7. Tian , Xiaoguang & Prybutok, Victor & Mirzaei, Fouad & Dinulescu, Catalin C., 2020. "Millennials Acceptance of Insurance Telematics: An Integrative Empirical Study," American Business Review, Pompea College of Business, University of New Haven, vol. 23(1), pages 156-181, May.
    8. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    9. Pope, Leah G. & Warnock, Amanda & Perry, Tyler H. & Langlois, Stephanie & Anderson, Simone & Boswell, Tehya & Appelbaum, Paul & Dixon, Lisa & Watson, Amy & Compton, Michael T., 2022. "Information sharing across mental health service providers and criminal legal system stakeholders: Perspectives of people with serious mental illnesses and their family members," Social Science & Medicine, Elsevier, vol. 307(C).
    10. Simona Sternad Zabukovšek & Samo Bobek & Uroš Zabukovšek & Zoran Kalinić & Polona Tominc, 2022. "Enhancing PLS-SEM-Enabled Research with ANN and IPMA: Research Study of Enterprise Resource Planning (ERP) Systems’ Acceptance Based on the Technology Acceptance Model (TAM)," Mathematics, MDPI, vol. 10(9), pages 1-28, April.
    11. Leonie Kuen & Fiona Schürmann & Daniel Westmattelmann & Sophie Hartwig & Shay Tzafrir & Gerhard Schewe, 2023. "Trust transfer effects and associated risks in telemedicine adoption," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    12. Sun, Shiwei & Zhang, Jin & Zhu, Yiwei & Jiang, Mian & Chen, Shuhui, 2022. "Exploring users' willingness to disclose personal information in online healthcare communities: The role of satisfaction," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    13. Ming Yang & Jinglu Jiang & Melody Kiang & Fangyun Yuan, 2022. "Re-Examining the Impact of Multidimensional Trust on Patients’ Online Medical Consultation Service Continuance Decision," Information Systems Frontiers, Springer, vol. 24(3), pages 983-1007, June.
    14. Renata Benigna Gonçalves & Júlio César Bastos Figueiredo, 2022. "Effects of perceived risks and benefits in the formation of the consumption privacy paradox: a study of the use of wearables in people practicing physical activities," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1485-1499, September.
    15. Lui, Ariel K.H. & Lo, Chris K.Y. & Ngai, Eric W.T. & Yeung, Andy C.L., 2023. "A tough pill to swallow? The lessons learned from mandatory RFID adoption," International Journal of Production Economics, Elsevier, vol. 258(C).
    16. Idris Adjerid & Alessandro Acquisti & Rahul Telang & Rema Padman & Julia Adler-Milstein, 2016. "The Impact of Privacy Regulation and Technology Incentives: The Case of Health Information Exchanges," Management Science, INFORMS, vol. 62(4), pages 1042-1063, April.
    17. Cristian Morosan, 2021. "An affective approach to modelling intentions to use technologies for social distancing in hotels," Information Technology & Tourism, Springer, vol. 23(4), pages 549-573, December.
    18. Liébana-Cabanillas, Francisco & Marinkovic, Veljko & Ramos de Luna, Iviane & Kalinic, Zoran, 2018. "Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 117-130.
    19. Chi-Wen Chen & Marios Koufaris, 2015. "The impact of decision support system features on user overconfidence and risky behavior," European Journal of Information Systems, Taylor & Francis Journals, vol. 24(6), pages 607-623, November.
    20. Paul S. Heckerling & Ben S. Gerber & Thomas G. Tape & Robert S. Wigton, 2003. "Prediction of Community-Acquired Pneumonia Using Artificial Neural Networks," Medical Decision Making, , vol. 23(2), pages 112-121, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0212356. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.