Remote patient monitoring
Remote patient monitoring (RPM) is a technology to enable monitoring of patients outside of conventional clinical settings, such as in the home or in a remote area, which may increase access to care and decrease healthcare delivery costs.
Incorporating RPM in chronic-disease management may significantly improve an individual's quality of life, by allowing patients to maintain independence, prevent complications, and to minimize personal costs. RPM facilitates these goals by delivering care through telecommunications. This form of patient monitoring can be particularly important when patients are managing complex self-care processes such as home hemodialysis. Key features of RPM, like remote monitoring and trend analysis of physiological parameters, enable early detection of deterioration; thereby reducing emergency department visits, hospitalizations, and the duration of hospital stays.
The diverse applications of RPM lead to numerous variations of RPM technology architecture. However, most RPM technologies follow a general architecture that consists of four components.:
- Sensors on a device that is enabled by wireless communications to measure physiological parameters.
- Local data storage at patients’ site that interfaces between sensors and other centralized data repository and/or healthcare providers.
- Centralized repository to store data sent from sensors, local data storage, diagnostic applications, and/or healthcare providers.
- Diagnostic application software that develops treatment recommendations and intervention alerts based on the analysis of collected data.
Physiological data such as blood pressure and subjective patient data are collected by sensors on peripheral devices. Examples of peripheral devices are: blood pressure cuff, pulse oximeter, and glucometer. The data are transmitted to healthcare providers or third parties via wireless telecommunication devices. The data are evaluated for potential problems by a healthcare professional or via a clinical decision support algorithm, and patient, caregivers, and health providers are immediately alerted if a problem is detected. As a result, timely intervention ensures positive patient outcomes. The newer applications also provide education, test and medication reminder alerts, and a means of communication between the patient and the provider. The following section illustrates examples of RPM applications, but RPM is not limited to those disease states.
Dementia and falls
For patients with dementia that are at risk for falls, RPM technology promotes safety and prevents harm through continuous surveillance. RPM sensors can be affixed to the individual or their assistive mobility devices such as canes and walkers. The sensors monitor an individual’s location, gait, linear acceleration and angular velocity, and utilize a mathematical algorithm to predict the likelihood for falls, detect movement changes, and alert caregivers if the individual has fallen. Furthermore, tracking capabilities via Wi-Fi, global positioning system (GPS) or radio frequency enables caregivers to locate wandering elders.
Diabetes management requires control of multiple parameters: blood pressure, weight, and blood glucose. The real-time delivery of blood glucose and blood pressure readings enables immediate alerts for patient and healthcare providers to intervene when needed. There is evidence to show that daily diabetes management involving RPM is just as effective as usual clinic visit every 3 months.
Congestive heart failure
A systematic review of the literature on home monitoring for heart failure patients indicates that RPM improves quality of life, improves patient-provider relationships, shortens duration of stay in hospitals, decreases mortality rate, and reduces costs to the healthcare system.
A recent study of a remote patient monitoring solution for infertility demonstrated that for appropriately screened patients who had been seeking In-Vitro Fertilization (IVF) treatment, a six-month remote monitoring program had the same pregnancy rate as a cycle of IVF. The remote patient monitoring product and service used had a cost-per-patient of $800, compared to the average cost of a cycle of IVF of $15,000, suggesting a 95% reduction in the cost of care for the same outcome.
Telemedicine in prison systems
A forerunner to RPM, Florida first experimented with "primitive" telemedicine use in its prisons during the latter 1980s. Working with Doctors Oscar W. Boultinghouse and Michael J. Davis, from the early 1990s to 2007, Glenn G. Hammack led the University of Texas Medical Branch's development of a pioneering telehealth program in Texas state prisons.
Veterans Health Administration
The Veterans Health Administration (VHA), United States’ largest integrated healthcare system, is an early adopter which became highly involved in the implementation and evaluation of RPM technologies. It has expanded use of RPM beyond common chronic disease applications, to post-traumatic stress disorder, cancer and palliative care. VHA’s findings indicate improvements in a wide range of metrics, including decrease in emergency department visits, hospitalizations, and nursing home admissions. Findings from the VHA Care Coordination/Home Telehealth program show that RPM deployment resulted in significant savings to the organization.
Whole System Demonstrator Trial in UK
The UK’s Department of Health’s Whole System Demonstrator (WSD) launched in May 2008. It is the largest randomised control trial of telehealth and telecare in the world, involving 6191 patients and 238 GP practices across three sites, Newham, Kent and Cornwall. The trials were evaluated by: City University London, University of Oxford, University of Manchester, Nuffield Trust, Imperial College London and London School of Economics.
- 45% reduction in mortality rates
- 20% reduction in emergency admissions
- 15% reduction in A&E visits
- 14% reduction in elective admissions
- 14% reduction in bed days
- 8% reduction in tariff costs
RPM is highly dependent on the individual’s motivation to manage their health. Without the patient’s willingness to be an active participant in their care, RPM implementation will likely fail. Cost is also a barrier to its widespread use. There is a lack of reimbursement guidelines for RPM services, which may deter its incorporation into clinical practice. The shift of accountability associated with RPM brings up liability issues. There are no clear guidelines in respect to whether clinicians have to intervene every time they receive an alert regardless of the urgency. The continuous flow of patient data requires a dedicated team of health care providers to handle the information, which may, in fact, increase the workload. Although technology is introduced with the intent to increase efficiency, it can become a barrier to some healthcare providers that are not technological. There are common obstacles that health informatics technologies encounter that applies to RPM. Depending on the comorbidities monitored, RPM involves a diverse selection of devices in its implementation. Standardization is required for data exchange and interoperability among multiple components. Furthermore, RPM deployment is highly dependent on an extensive wireless telecommunications infrastructure, which may not be available or feasible in rural areas. Since RPM involves transmission of sensitive patient data across telecommunication networks, information security is a concern.
Published by the New England Journal of Medicine, a randomized controlled trial involving congestive heart failure patients concluded that the use of telemonitoring failed to provide a benefit over usual care. The telemonitoring patient group was instructed to call a designated number daily, and answer a series of questions about their symptoms using a keypad. Clearly, the process described by Chaudhry et al. (2010) differs from the RPM methodology illustrated in the overview, which involves actual collection and transmission of physiological data through point-of-care devices. With articles from Forbes associating RPM with the negative findings by Chaudhry et al. (2010), it may be difficult to clear the misconception that telemonitoring is synonymous with remote patient monitoring. The lack of standardization of RPM nomenclature and definition makes it difficult to differentiate between different forms of patient monitoring involving technology.
- Bayliss, E.; Steiner, J.F.; Fernald, D.H.; Crane, L.A.; Main, D.S. (2003). "Descriptions of barriers to self-care by persons with comorbid chronic diseases". Ann Fam Med. 1 (1): 15–21. doi:10.1370/afm.4. PMC 1466563. PMID 15043175.
- [Cafazzo, J.A., Leonard, K., Easty, A.C., Rossos, P.G., & Chan, C.T. (2009). Bridging the self-care deficit gap: remote patient monitoring and hospital at home. In Electronic Healthcare First International Conference, eHealth 2008. doi:10.1007/978-3-642-00413-1_8]
- Center for Technology and Aging. (2010, April). Technologies for remote patient monitoring in older adults: Position paper. Retrieved from http://www.phi.org/uploads/application/files/mjr85izva3yk7v3ah3yqtf5bt1phgzywg67a7zlsv7xcy9h85w.pdf
- O'Donoghue, John; Herbert, John (2012). "Data Management within mHealth Environments: Patient Sensors, Mobile Devices, and Databases". J. Data and Information Quality. 4: 1–20. doi:10.1145/2378016.2378021.
- Coye, M.; Haskelkorn, A.; Demello, S. (2009). "Remote patient management: technology-enabled innovation and evolving business models for chronic disease care". Health Affairs. 28 (1): 126–135. doi:10.1377/hlthaff.28.1.126. PMID 19124862.
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- Smith, T. & Sweeney, R. (2010, September). Fusion trends and opportunities. Medical devices and communications. Retrieved from http://www.nerac.com/nerac_insights.php?category=reports&id=279
- Chase, H.P.; Pearson, J.A.; Wightman, C.; Roberts, M.D.; Oderberg, A.D.; Garg, S.K. (2003). "Modem transmission of glucose values reduces the costs and need for clinic visits". Diabetes Care. 26 (5): 1475–1479. doi:10.2337/diacare.26.5.1475. PMID 12716807.
- Martínez, A.; Everss, E.; Rojo-Alvarez, J.L.; Figal, D.P.; García-Alberola, A. (2006). "A systematic review of the literature on home monitoring for patients with heart failure". J Telemed Telecare. 12 (5): 234–41. doi:10.1258/135763306777889109. PMID 16848935.
- Chausiaux, O.; Hayes, J.; Long, C.; Morris, S.; Williams, G.; Husheer, S. (2011). "Pregnancy Prognosis in Infertile Couples on the DuoFertility Programme Compared with In Vitro Fertilisation/Intracytoplasmic Sperm Injection". European Obstetrics & Gynaecology. 6 (2): 92–4.
- Illove, Michael (January 21, 2016). "State Prisons Turn to Telemedicine to Improve Health and Save Money". The PEW Charitable Trusts. Retrieved 2019-10-03.
- FREUDENHEIM, Milt (May 29, 2010). "The Doctor Will See You Now. Please Log On". Retrieved 2019-10-03.
- Darkins, A.; Ryan, P.; Kobb, R.; Foster, L; Edmonson, E.; Wakefield, B.; Lancaster, A.E. (2008). "Care coordination/home Telehealth: the systematic implementation of health informatics, home Telehealth, and disease management to support the care of Veteran patients with chronic conditions". Telemed J E-Health. 14 (10): 1118–1126. doi:10.1089/tmj.2008.0021. PMID 19119835.
- Whole Systems Demonstrators: An Overview of Telecare and Telehealth
- 3 Million Lives Announcement
- Chaudhry, S.I.; Mattera, J.A.; Curtis, J.P.; Spertus, J.A.; Herrin, J.; Lin, Z.; Phillips, C.O.; Hodshon, B.V.; Coopers, L.S.; Krumholz, H.M. (2010). "Telemonitoring in patients with heart failure". N Engl J Med. 363 (24): 2301–2309. doi:10.1056/nejmoa1010029. PMC 3237394. PMID 21080835.
- Langreth, R. (2010, November 18). "Why remote patient monitoring is overhyped". Forbes. Retrieved from https://www.forbes.com/sites/robertlangreth/2010/11/18/why-telemedicine-is-overhyped/2/
- Krumholz, H. (2010, November 19). "A double whammy for remote patient monitoring". Forbes. Retrieved from https://www.forbes.com/sites/sciencebiz/2010/11/19/a-double-whammy-for-remote-patient-monitoring/