The value of mHealth for managing chronic conditions

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.

[1]  J. Ralston,et al.  Improving BP control through electronic communications: an economic evaluation. , 2013, The American journal of managed care.

[2]  Douglas G. Woolford,et al.  A model for deceased-donor transplant queue waiting times , 2015, Queueing Syst. Theory Appl..

[3]  Maria Cristina Peñaloza-Ramos,et al.  The Importance of Model Structure in the Cost-Effectiveness Analysis of Primary Care Interventions for the Management of Hypertension. , 2017, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[4]  D. Williamson,et al.  Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence , 2010, Population health metrics.

[5]  M. Krahn,et al.  Acute appendicitis in young children: cost-effectiveness of US versus CT in diagnosis--a Markov decision analytic model. , 2009, Radiology.

[6]  C. L. Ventola.  Mobile devices and apps for health care professionals: uses and benefits. , 2014, P & T : a peer-reviewed journal for formulary management.

[7]  2. Classification and Diagnosis of Diabetes , 2014, Diabetes Care.

[8]  Amy Ming-Fang Yen,et al.  Stochastic models for multiple pathways of temporal natural history on co-morbidity of chronic disease , 2013, Comput. Stat. Data Anal..

[9]  P. Schulz,et al.  Mapping mHealth Research: A Decade of Evolution , 2013, Journal of medical Internet research.

[10]  Abraham Seidmann,et al.  Service Systems with Heterogeneous Customers: Investigating the Effect of Telemedicine on Chronic Care , 2019, Manag. Sci..

[11]  W. Elliott,et al.  The Economic Impact of Hypertension , 2003, Journal of clinical hypertension.

[12]  E. Topol,et al.  A call to action: lowering the cost of health care. , 2013, American journal of preventive medicine.

[13]  Ashlea Bennett Milburn,et al.  The value of remote monitoring systems for treatment of chronic disease , 2014 .

[14]  Amy M. Bauer,et al.  Aligning health information technologies with effective service delivery models to improve chronic disease care. , 2014, Preventive medicine.

[15]  Andrew J. Schaefer,et al.  Modeling Medical Treatment Using Markov Decision Processes , 2005 .

[16]  R Kay,et al.  A Markov model for analysing cancer markers and disease states in survival studies. , 1986, Biometrics.

[17]  I M Longini,et al.  The dynamics of CD4+ T-lymphocyte decline in HIV-infected individuals: a Markov modeling approach. , 1991, Journal of acquired immune deficiency syndromes.

[18]  Lorin M. Hitt,et al.  The Impact of E-Visits on Visit Frequencies and Patient Health: Evidence from Primary Care , 2017 .

[19]  Tony Hsiu-Hsi Chen,et al.  Assessing chronic disease progression using non‐homogeneous exponential regression Markov models: an illustration using a selective breast cancer screening in Taiwan , 2002, Statistics in medicine.

[20]  Saligrama R. Agnihothri,et al.  Mobile Health Monitoring: Development and Implementation of an App in a Diabetes and Hypertension Clinic , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[21]  P. Hogan,et al.  Economic Costs of Diabetes in the U , 2013 .

[22]  David Thompson.  Acute Appendicitis in Young Children: Cost-Effectiveness of US Versus CT in Diagnosis—A Markov Decision Analytic Model , 2009 .

[23]  R. Silverman,et al.  Mobile health applications: the patchwork of legal and liability issues suggests strategies to improve oversight. , 2014, Health affairs.

[24]  A. Valleron,et al.  Estimated timing of mother-to-child human immunodeficiency virus type 1 (HIV-1) transmission by use of a Markov model. The HIV Infection in Newborns French Collaborative Study Group. , 1995, American journal of epidemiology.

[25]  Muge Capan,et al.  A stochastic model of acute-care decisions based on patient and provider heterogeneity , 2015, Health Care Management Science.

[26]  B. Yaremko,et al.  Stereotactic body radiotherapy versus surgery for medically operable Stage I non-small-cell lung cancer: a Markov model-based decision analysis. , 2011, International journal of radiation oncology, biology, physics.

[27]  R. Holle,et al.  A Lifetime Markov Model for the Economic Evaluation of Chronic Obstructive Pulmonary Disease , 2012, PharmacoEconomics.

[28]  Ryusuke Inoue,et al.  The economic impact of the introduction of home blood pressure measurement for the diagnosis and treatment of hypertension , 2006, Blood pressure monitoring.

[29]  Neetika Garg,et al.  A content analysis of smartphone-based applications for hypertension management. , 2015, Journal of the American Society of Hypertension : JASH.

[30]  BOULIN,et al.  Classification and Diagnosis of Diabetes. , 2022, Primary care.

[31]  C. Mangione,et al.  Guidelines for improving the care of the older person with diabetes mellitus. , 2003, Journal of the American Geriatrics Society.

[32]  F. Ieva,et al.  A multi-state approach to patients affected by chronic heart failure , 2016, Health care management science.

[33]  J. Froom,et al.  The urgent need to improve hypertension care. , 2000, Archives of family medicine.

[34]  Jingshan Li,et al.  The impact of e-visits on patient access to primary care , 2017, Health Care Management Science.

[35]  T. Lindvall.  Lectures on the Coupling Method , 1992 .

[36]  A. Valleron,et al.  Quantification of fibrosis progression in patients with chronic hepatitis C using a Markov model , 2002, Journal of viral hepatitis.

[37]  James Allen Fill,et al.  The Passage Time Distribution for a Birth-and-Death Chain: Strong Stationary Duality Gives a First Stochastic Proof , 2007, 0707.4042.

[38]  M. Harris,et al.  Do people with existing chronic conditions benefit from telephone coaching? A rapid review. , 2013, Australian health review : a publication of the Australian Hospital Association.

[39]  Pelham Barton,et al.  A Systematic Review of Research Guidelines in Decision-Analytic Modeling. , 2015, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[40]  R. Cooper,et al.  Hypertension Treatment and Control in Five European Countries, Canada, and the United States , 2004, Hypertension.

[41]  Martha B. Adams,et al.  Economic evaluation of home blood pressure monitoring with or without telephonic behavioral self-management in patients with hypertension. , 2010, American journal of hypertension.

[42]  Kazuhiko Ohe,et al.  DialBetics: A Novel Smartphone-based Self-management Support System for Type 2 Diabetes Patients. , 2014, Journal of diabetes science and technology.

[43]  F. Ieva,et al.  Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure , 2017, Health care management science.

[44]  Plamen Nikolov,et al.  Economic Costs of Diabetes in the U.S. in 2002 , 2003, Diabetes care.

[45]  Yuncheol Kang,et al.  Modelling adherence behaviour for the treatment of obstructive sleep apnoea , 2016, Eur. J. Oper. Res..

[46]  Emma P Bray,et al.  Telemonitoring and self-management in the control of hypertension (TASMINH2): a cost-effectiveness analysis , 2014, European journal of preventive cardiology.

[47]  G. Parati,et al.  Home blood pressure telemonitoring in the 21st century , 2018, Journal of clinical hypertension.

[48]  Majd Alwan,et al.  Impact of blood pressure telemonitoring on hypertension outcomes: a literature review. , 2010, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[49]  F. Kirsch.  Economic Evaluations of Multicomponent Disease Management Programs with Markov Models: A Systematic Review. , 2016, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[50]  L. Tabár,et al.  Estimation of mean sojourn time in breast cancer screening using a Markov chain model of both entry to and exit from the preclinical detectable phase. , 1995, Statistics in medicine.

[51]  M. Brandeau,et al.  Optimizing patient treatment decisions in an era of rapid technological advances: the case of hepatitis C treatment , 2015, Health Care Management Science.

[52]  Xiao-Hua Zhou,et al.  A correlated random effects model for non-homogeneous Markov processes with nonignorable missingness , 2013, J. Multivar. Anal..

[53]  C. Lavie,et al.  The Role of Technology in Chronic Disease Care. , 2016, Progress in cardiovascular diseases.

[54]  Saligrama R. Agnihothri,et al.  Application of evidence-based management to chronic disease healthcare: a framework , 2018, Management Decision.

[55]  Andrew J. Schaefer,et al.  The Optimal Timing of Living-Donor Liver Transplantation , 2004, Manag. Sci..

[56]  S. Bonafini,et al.  Home blood pressure measurements: Advantages and disadvantages compared to office and ambulatory monitoring , 2015, Blood pressure.

[57]  L. Swartz,et al.  Scaling Up mHealth: Where Is the Evidence? , 2013, PLoS medicine.

[58]  Dimitris Bertsimas,et al.  Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening , 2016, Health care management science.