IDEAS home Printed from https://ideas.repec.org/a/spr/infotm/v23y2022i4d10.1007_s10799-021-00350-8.html
   My bibliography  Save this article

Alerting patients via health information system considering trust-dependent patient adherence

Author

Listed:
  • Junbo Son

    (University of Delaware)

  • Yeongin Kim

    (Virginia Commonwealth University)

  • Shiyu Zhou

    (University of Wisconsin-Madison)

Abstract

The internet of things has ushered in a world of possibilities in chronic disease management. Connected to the health information network, a health device can monitor and provide intervention recommendations to patients in real time. However, this new health information system may face the risk of patients not following the system’s recommendations depending on their perception of the system. In this paper, we consider patients’ trust in the system a key factor driving their adherence to the system’s recommendation and develop an analytical model to design the optimal alerting strategy in the context of asthma management. Our method acknowledges that patient’s trust may change over time based on their experience of using the system, which may influence their future adherence behavior. We derive a set of structural properties of our solution and demonstrate that our approach can significantly improve patients’ quality of life compared to the current practice of asthma management. Furthermore, we investigate various real-world scenarios, such as the case that patients may have different level of tolerance for receiving alerts. Based on our findings, valuable insights can be shared with patients, healthcare practitioners, and companies in the technology-enabled healthcare business sector.

Suggested Citation

  • Junbo Son & Yeongin Kim & Shiyu Zhou, 2022. "Alerting patients via health information system considering trust-dependent patient adherence," Information Technology and Management, Springer, vol. 23(4), pages 245-269, December.
  • Handle: RePEc:spr:infotm:v:23:y:2022:i:4:d:10.1007_s10799-021-00350-8
    DOI: 10.1007/s10799-021-00350-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10799-021-00350-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10799-021-00350-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Turgay Ayer & Oguzhan Alagoz & Natasha K. Stout, 2012. "OR Forum---A POMDP Approach to Personalize Mammography Screening Decisions," Operations Research, INFORMS, vol. 60(5), pages 1019-1034, October.
    2. Barbara E. Kahn & Mary Frances Luce, 2003. "Understanding High-Stakes Consumer Decisions: Mammography Adherence Following False-Alarm Test Results," Marketing Science, INFORMS, vol. 22(3), pages 393-410, April.
    3. Donald Rosenfield, 1976. "Markovian Deterioration with Uncertain Information," Operations Research, INFORMS, vol. 24(1), pages 141-155, February.
    4. French, M.T. & Mauskopf, J.A., 1992. "A quality-of-life method for estimating the value of avoided morbidity," American Journal of Public Health, American Public Health Association, vol. 82(11), pages 1553-1555.
    5. Junbo Son & Patricia Flatley Brennan & Shiyu Zhou, 2016. "Rescue inhaler usage prediction in smart asthma management systems using joint mixed effects logistic regression model," IISE Transactions, Taylor & Francis Journals, vol. 48(4), pages 333-346, April.
    6. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    7. Richard D. Smallwood & Edward J. Sondik, 1973. "The Optimal Control of Partially Observable Markov Processes over a Finite Horizon," Operations Research, INFORMS, vol. 21(5), pages 1071-1088, October.
    8. Anthony Vance & Christophe M. Elie-Dit-Cosaque & Detmar W. Straub, 2008. "Examining Trust in Information Technology Artifacts: The Effects of System Quality and Culture," Post-Print halshs-00641137, HAL.
    9. Ohnishi, Masamitsu & Kawai, Hajime & Mine, Hisashi, 1986. "An optimal inspection and replacement policy under incomplete state information," European Journal of Operational Research, Elsevier, vol. 27(1), pages 117-128, October.
    10. Karlin, Samuel & Rinott, Yosef, 1980. "Classes of orderings of measures and related correlation inequalities II. Multivariate reverse rule distributions," Journal of Multivariate Analysis, Elsevier, vol. 10(4), pages 499-516, December.
    11. William S. Lovejoy, 1987. "Some Monotonicity Results for Partially Observed Markov Decision Processes," Operations Research, INFORMS, vol. 35(5), pages 736-743, October.
    12. Karlin, Samuel & Rinott, Yosef, 1980. "Classes of orderings of measures and related correlation inequalities. I. Multivariate totally positive distributions," Journal of Multivariate Analysis, Elsevier, vol. 10(4), pages 467-498, December.
    13. Lee, Yin-Yang & Lin, Julia L., 2009. "The effects of trust in physician on self-efficacy, adherence and diabetes outcomes," Social Science & Medicine, Elsevier, vol. 68(6), pages 1060-1068, March.
    14. Mehmet Eren Ahsen & Mehmet Ulvi Saygi Ayvaci & Srinivasan Raghunathan, 2019. "When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis," Service Science, INFORMS, vol. 30(1), pages 97-116, March.
    15. Donald Rosenfield, 1976. "Markovian Deterioration With Uncertain Information — A More General Model," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 23(3), pages 389-405, September.
    16. repec:dau:papers:123456789/2723 is not listed on IDEAS
    17. Georg Meyer & Gediminas Adomavicius & Paul E. Johnson & Mohamed Elidrisi & William A. Rush & JoAnn M. Sperl-Hillen & Patrick J. O'Connor, 2014. "A Machine Learning Approach to Improving Dynamic Decision Making," Information Systems Research, INFORMS, vol. 25(2), pages 239-263, June.
    18. Turgay Ayer & Oguzhan Alagoz & Natasha K. Stout & Elizabeth S. Burnside, 2016. "Heterogeneity in Women’s Adherence and Its Role in Optimal Breast Cancer Screening Policies," Management Science, INFORMS, vol. 62(5), pages 1339-1362, May.
    19. Indranil Bardhan & Jeong-ha (Cath) Oh & Zhiqiang (Eric) Zheng & Kirk Kirksey, 2015. "Predictive Analytics for Readmission of Patients with Congestive Heart Failure," Information Systems Research, INFORMS, vol. 26(1), pages 19-39, March.
    Full references (including those not matched with items on IDEAS)

    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. Chiel van Oosterom & Lisa M. Maillart & Jeffrey P. Kharoufeh, 2017. "Optimal maintenance policies for a safety‐critical system and its deteriorating sensor," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(5), pages 399-417, August.
    2. Hao Zhang & Weihua Zhang, 2023. "Analytical Solution to a Partially Observable Machine Maintenance Problem with Obvious Failures," Management Science, INFORMS, vol. 69(7), pages 3993-4015, July.
    3. Miehling, Erik & Teneketzis, Demosthenis, 2020. "Monotonicity properties for two-action partially observable Markov decision processes on partially ordered spaces," European Journal of Operational Research, Elsevier, vol. 282(3), pages 936-944.
    4. Saghafian, Soroush, 2018. "Ambiguous partially observable Markov decision processes: Structural results and applications," Journal of Economic Theory, Elsevier, vol. 178(C), pages 1-35.
    5. Deep, Akash & Zhou, Shiyu & Veeramani, Dharmaraj & Chen, Yong, 2023. "Partially observable Markov decision process-based optimal maintenance planning with time-dependent observations," European Journal of Operational Research, Elsevier, vol. 311(2), pages 533-544.
    6. Lisa M. Maillart & Ludmila Zheltova, 2007. "Structured maintenance policies on interior sample paths," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(6), pages 645-655, September.
    7. Jue Wang & Chi-Guhn Lee, 2015. "Multistate Bayesian Control Chart Over a Finite Horizon," Operations Research, INFORMS, vol. 63(4), pages 949-964, August.
    8. Wooseung Jang & J. George Shanthikumar, 2002. "Stochastic allocation of inspection capacity to competitive processes," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(1), pages 78-94, February.
    9. Vikram Krishnamurthy & Udit Pareek, 2015. "Myopic Bounds for Optimal Policy of POMDPs: An Extension of Lovejoy’s Structural Results," Operations Research, INFORMS, vol. 63(2), pages 428-434, April.
    10. Li, Weiyu & Denton, Brian T. & Morgan, Todd M., 2023. "Optimizing active surveillance for prostate cancer using partially observable Markov decision processes," European Journal of Operational Research, Elsevier, vol. 305(1), pages 386-399.
    11. Michael Jong Kim & Viliam Makis, 2013. "Joint Optimization of Sampling and Control of Partially Observable Failing Systems," Operations Research, INFORMS, vol. 61(3), pages 777-790, June.
    12. Lu Jin & Undarmaa Bayarsaikhan & Kazuyuki Suzuki, 2016. "Optimal control limit policy for age-dependent deteriorating systems under incomplete observations," Journal of Risk and Reliability, , vol. 230(1), pages 34-43, February.
    13. Jonathan E. Helm & Mariel S. Lavieri & Mark P. Van Oyen & Joshua D. Stein & David C. Musch, 2015. "Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support," Operations Research, INFORMS, vol. 63(5), pages 979-999, October.
    14. Burhaneddin Sandıkçı & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2013. "Alleviating the Patient's Price of Privacy Through a Partially Observable Waiting List," Management Science, INFORMS, vol. 59(8), pages 1836-1854, August.
    15. Armando Z. Milioni & Stanley R. Pliska, 1988. "Optimal inspection under semi‐markovian deterioration: Basic results," Naval Research Logistics (NRL), John Wiley & Sons, vol. 35(5), pages 373-392, October.
    16. Hossein Kamalzadeh & Vishal Ahuja & Michael Hahsler & Michael E. Bowen, 2021. "An Analytics‐Driven Approach for Optimal Individualized Diabetes Screening," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3161-3191, September.
    17. Stephen M. Gilbert & Hena M Bar, 1999. "The value of observing the condition of a deteriorating machine," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(7), pages 790-808, October.
    18. Nowak, Piotr Bolesław, 2016. "The MLE of the mean of the exponential distribution based on grouped data is stochastically increasing," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 49-54.
    19. Chi, Chang Koo & Murto, Pauli & Valimaki, Juuso, 2017. "All-Pay Auctions with Affiliated Values," MPRA Paper 80799, University Library of Munich, Germany.
    20. Sara Moussawi & Marios Koufaris & Raquel Benbunan-Fich, 2021. "How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 343-364, June.

    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:spr:infotm:v:23:y:2022:i:4:d:10.1007_s10799-021-00350-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.