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The value of mHealth for managing chronic conditions

Author

Listed:
  • Saligrama Agnihothri

    (Binghamton University)

  • Leon Cui

    (Binghamton University)

  • Mohammad Delasay

    (Stony Brook University)

  • Balaraman Rajan

    (California State University East Bay)

Abstract

Chronic conditions place a high cost burden on the healthcare system and deplete the quality of life for millions of Americans. Digital innovations such as mobile health (mHealth) technology can be used to provide efficient and effective healthcare. In this research we explore the use of mobile technology to manage chronic conditions such as diabetes and hypertension. There is ample empirical evidence in the healthcare literature showing that patients who use mHealth observe improvement in their health. However, an analytical study that quantifies the benefit of using mHealth is lacking. The benefit of using mHealth depends on many factors such as the current health condition of the patient, pattern of disease progression, frequency of measurement and intervention, the effectiveness of intervention, and the cost of measuring. Stochastic modeling is a suitable approach to take these factors into consideration to evaluate the benefit of mHealth. In this paper, we model the disease progression with the help of a Markov chain and quantify the benefits of measuring and intervention taking into consideration the above-mentioned factors. We compare two different modes for measuring and intervention, mHealth mode and conventional office visit mode, and evaluate the impact of these factors on health outcome.

Suggested Citation

  • Saligrama Agnihothri & Leon Cui & Mohammad Delasay & Balaraman Rajan, 2020. "The value of mHealth for managing chronic conditions," Health Care Management Science, Springer, vol. 23(2), pages 185-202, June.
  • Handle: RePEc:kap:hcarem:v:23:y:2020:i:2:d:10.1007_s10729-018-9458-2
    DOI: 10.1007/s10729-018-9458-2
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    1. Dimitris Bertsimas & John Silberholz & Thomas Trikalinos, 2018. "Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening," Health Care Management Science, Springer, vol. 21(1), pages 105-118, March.
    2. Chen, Baojiang & Zhou, Xiao-Hua, 2013. "A correlated random effects model for non-homogeneous Markov processes with nonignorable missingness," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 1-13.
    3. Muge Capan & Julie S. Ivy & James R. Wilson & Jeanne M. Huddleston, 2017. "A stochastic model of acute-care decisions based on patient and provider heterogeneity," Health Care Management Science, Springer, vol. 20(2), pages 187-206, June.
    4. Petra Menn & Reiner Leidl & Rolf Holle, 2012. "A Lifetime Markov Model for the Economic Evaluation of Chronic Obstructive Pulmonary Disease," PharmacoEconomics, Springer, vol. 30(9), pages 825-840, September.
    5. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2004. "The Optimal Timing of Living-Donor Liver Transplantation," Management Science, INFORMS, vol. 50(10), pages 1420-1430, October.
    6. Hessam Bavafa & Lorin M. Hitt & Christian Terwiesch, 2018. "The Impact of E-Visits on Visit Frequencies and Patient Health: Evidence from Primary Care," Management Science, INFORMS, vol. 64(12), pages 5461-5480, December.
    7. Shan Liu & Margaret L. Brandeau & Jeremy D. Goldhaber-Fiebert, 2017. "Optimizing patient treatment decisions in an era of rapid technological advances: the case of hepatitis C treatment," Health Care Management Science, Springer, vol. 20(1), pages 16-32, March.
    8. Francesca Ieva & Anna Maria Paganoni & Teresa Pietrabissa, 2017. "Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure," Health Care Management Science, Springer, vol. 20(3), pages 353-364, September.
    9. Yen, Amy Ming-Fang & Chen, Hsiu-Hsi, 2013. "Stochastic models for multiple pathways of temporal natural history on co-morbidity of chronic disease," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 570-588.
    10. Kang, Yuncheol & Sawyer, Amy M. & Griffin, Paul M. & Prabhu, Vittaldas V., 2016. "Modelling adherence behaviour for the treatment of obstructive sleep apnoea," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1005-1013.
    11. Francesco Grossetti & Francesca Ieva & Anna Maria Paganoni, 2018. "A multi-state approach to patients affected by chronic heart failure," Health Care Management Science, Springer, vol. 21(2), pages 281-291, June.
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    2. Kraus, Sascha & Schiavone, Francesco & Pluzhnikova, Anna & Invernizzi, Anna Chiara, 2021. "Digital transformation in healthcare: Analyzing the current state-of-research," Journal of Business Research, Elsevier, vol. 123(C), pages 557-567.

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