The Value of Remote Patient Monitoring (RPM) Physicians’ Perspectives

Study from Health Information Management System Society ( HIMSS) demonstrates positive results on how remotely monitoring patients enables early intervention and can reduce the escalation of a chronic conditions in order to avoid an emergency visit to the hospital. 

December 1, 2014 Source: HIMSS mHealth Physician Task Force by Alisa Niksch, M.D., Brian Rothman, M.D. , Natalie Hodge, M.D. , and Megan Ranney, M.D.

Evolution of RPM: Starting with Cardiology

Remote patient monitoring has been available for decades in the form of cardiac monitoring.  It initially involved external monitors (Holter monitors, event recorders), and since 2000 has included implantable pacemakers and defibrillators.  RPM was quickly incorporated into implantable cardiac devices for management of cardiac rhythm issues and heart failure. These systems, adopted several years ago by all four major cardiac device manufacturers, have produced significant changes in healthcare utilization. Using manufacturer-specific mobile units, active or passive detailed device data transmission can occur (1,5). Confirming earlier studies6, the CONNECT (Clinical Evaluation of Remote Notification to Reduce Time to Clinical Decision) trial in 2010 demonstrated that wireless remote monitoring allowed clinicians to make treatment decisions 17.4 days sooner than with in-office visits alone.  A statistically significant decrease in mean length of stay for cardiac related diagnoses from 4 days in the in-office group to 3.3 days was also demonstrated in the remote monitoring group (p<0.002)2.  Finally, the ALTITUDE Trial in 2010 showed a 50% reduction in mortality rates for wirelessly connected patients vs. those only seen in-office3.

New Biosensors, Improved RPM Systems

Biosensors are becoming less invasive, and when coupled with ubiquitous wireless technologies, they are transforming the way healthcare is delivered and the patient experience.  Biosensor measurements currently include heart rate, respiratory rate, blood oxygen levels, serum pH and electrolyte levels, toxin and drug levels, directly measuring muscular contraction, and detecting DNA mutations. Sensor integration with wireless technologies has enabled patient data transmission to mobile devices, including smartphones and tablets, where data is stored, analyzed, and transmitted. The sensors are increasingly mobile and “wearable.” Synthesizing these technologies with telehealth platforms has transformed the way caregivers interact with their patients.

Components of the ideal RPM system could consist of the following4:

1. An accurate, precise, and validated biosensor
2. Consistent wireless transmission of data to a HIPAA secure mobile platform
3. The intelligent storage, display, and trending of information for patient review, while avoiding unrestricted raw data sharing
4. Integrated, platform-based algorithms to appropriately filter and organize clinical data for caregivers and clinicians.
5. Thoughtfully designed alerts for clinicians with actionable results without overloading workflow with unlimited data
6. Mobile technology-based, bidirectional communication between the patient and provider to deliver instructions about disease intervention/management.
7. Smart analytics for clinicians to longitudinally track metrics for disease status and potentially extract data for prognostic evaluation.
8. Interoperability with other apps, database platforms, and EHR technologies.

Without well-developed platforms which motivate patient behavior change and facilitate appropriate treatment intervention, most technologies fall short of these practical and advanced levels of functioning.  Human factors also impact the effectiveness of RPM. We have already seen diminished fitness wearable usage (e.g. Nike FuelBand, Fitbit Flex, or Jawbone UP24). Limited incentives to use these devices have brought into question whether more permanently embedded, ingested, or even implanted sensors would provide more effective monitoring consistency for true clinical needs.

Regardless of this trend, as technology advances and evidence develops, motivation for using these advanced biosensors is expected to increase.

New Biosensors, Improved RPM System

Biosensors are becoming less invasive, and when coupled with ubiquitous wireless technologies, they are transforming the way healthcare is delivered and the patient experience.  Biosensor measurements currently include heart rate, respiratory rate, blood oxygen levels, serum pH and electrolyte levels, toxin and drug levels, directly measuring muscular contraction, and detecting DNA mutations. Sensor integration with wireless technologies has enabled patient data transmission to mobile devices, including smartphones and tablets, where data is stored, analyzed, and transmitted. The sensors are increasingly mobile and “wearable.” Synthesizing these technologies with telehealth platforms has transformed the way caregivers interact with their patients.

Components of the ideal RPM system could consist of the following4:

1. An accurate, precise, and validated biosensor
2. Consistent wireless transmission of data to a HIPAA secure mobile platform
3. The intelligent storage, display, and trending of information for patient review, while avoiding unrestricted raw data sharing
4. Integrated, platform-based algorithms to appropriately filter and organize clinical data for caregivers and clinicians.
5. Thoughtfully designed alerts for clinicians with actionable results without overloading workflow with unlimited data
6. Mobile technology-based, bidirectional communication between the patient and provider to deliver instructions about disease intervention/management.
7. Smart analytics for clinicians to longitudinally track metrics for disease status and potentially extract data for prognostic evaluation.
8. Interoperability with other apps, database platforms, and EHR technologies.

Without well-developed platforms which motivate patient behavior change and facilitate appropriate treatment intervention, most technologies fall short of these practical and advanced levels of functioning.  Human factors also impact the effectiveness of RPM. We have already seen diminished fitness wearable usage (e.g. Nike FuelBand, Fitbit Flex, or Jawbone UP24). Limited incentives to use these devices have brought into question whether more permanently embedded, ingested, or even implanted sensors would provide more effective monitoring consistency for true clinical needs.

Regardless of this trend, as technology advances and evidence develops, motivation for using these advanced biosensors is expected to increase.

Clinical Evidence for RPM

Multiple studies demonstrate the efficiency and improved outcomes using RPM. As mentioned previously, studies quantifying metrics from remote implantable cardiac devices transmissions have demonstrated improved time to interventions, decreased inpatient length of stay, and improved survival rates (2,3,9,10). Other remote patient monitoring application studies have been initiated that focus on the “Triple Aim”endpoints : improving the health of a defined population, enhancing the patient care experience, and reducing (or controlling) the per capita cost of care.

One example is Cyrcadia Health’s embedded heat sensors within a bra.  The technology is undergoing more extensive clinical studies, after early technology validation18, to detect abnormal cellular changes in breast tissue indicating early tumor growth.

Proteus Digital Health’s digital feedback system is an ingestible sensor integrated into pharmaceutical products paired with a wearable sensor. It has been used in compliance studies for tuberculosis19 and psychiatric conditions20 to demonstrate its feasibility to replace direct observation of therapy. The potential of Proteus Digital Health’s feedback system for tracking medication compliance may lead to improved disease management and decreased costs. Acceptance was high with 78% of their study participants stating that they wanted notifications on their phones if they forgot to take their medications20.

Omada Health’s Prevent program utilized social network principles, wireless scale, and pedometer and demonstrated favorable outcomes compared to other programs attempting to achieve the CDC Diabetes Prevention and Recognition Program goals11.

A study from Centura Health at Home in Denver, CO, using telehealth and RPM technologies to monitor 200 older adults, found that using the technology reduced 30-day readmissions for heart failure, diabetes, and COPD by 62%, and emergency department visits dropped to 21 from 283 during the same period the year before.

The data from these initial studies are impressive, but more mobile health technologies and remote patient monitoring validation needs to be performed to prove its value in benefitting population health. Human factors including compliance and behavioral modification, and system factors including hardware, wireless reliability, and privacy risks, will certainly have an impact on future studies.

Reimbursement and ROI

Reimbursement is one of the most important motivating factors for hospitals and physician groups. In 2006, the Centers for Medicare and Medicaid Services (CMS) approved structured billing codes for procedures related to analysis of data transmitted from implantable cardiac devices, encouraging further implementation of wireless remote monitoring4.  New CMS policies now include codes for non-face-to-face encounters for qualifying patients who have two or more chronic conditions. An additional monthly flat reimbursement fee to be implemented in 2015 aims to promote greater access to primary care for Medicare beneficiaries 5.

RPM has cost savings benefits outside of direct reimbursement models. CHRISTUS St. Michael Health System in Texarkana, TX conducted a 12-month RPM study with elderly patients who had preexisting chronic diseases. The 44 pilot subjects were given a tablet, wireless scale, blood pressure cuff and pulse oximeter.  Prior to enrolling in the RPM project the mean cost of care per subject was $12,937, but after completing the study, their mean cost of care was $1,23113.  This echoed the Geisinger Health Plan study using telemonitoring with heart failure patients over a 4-year period. The study demonstrated a  sustained 44% reduced probability of 30-day readmission, and delivered an 11% cost savings14.

RPM in the form of telehealth services has also gained traction; as of 2013 CMS approved a series of reimbursable telehealth services, including follow up hospital care services, end stage renal disease management, and smoking cessation counseling.  Services utilizing telecommunication to assess a patient’s health status that do not require the patient to be present (leveraging mobile health sensor and device capabilities) are also covered, as they would be on site6.  However, CMS has yet to recognize a patient’s home as a valid originating site for care (6,7), and state Medicaid coverage for remote patient monitoring and telehealth services is inconsistent.

RPM and Behavioral Change Models

Physicians, insurers, and health care organizations have long recognized that unhealthy behaviors, such as smoking and drinking, are the root cause of many illnesses.  We also know that positive behavior change, whether stopping a bad behavior or starting a good one, can reduce morbidity and mortality.

However, the quest to improve behaviors at an individual and population level is stymied by lack of resources, provider training, and provider time21.  Moreover, research suggests that longitudinal motivation is the most effective method to improve behavior, in contrast to the one-time office-based encounter that most physicians use22.

Mobile health technologies for remote patient monitoring (RPM) may overcome these resource and provider obstacles.  We are working to moving from identifying populations within our structured electronic health data, as dictated by Stage 2 Meaningful Use criteria, to the “intervention” phase of MU323. Digital health presents a key opportunity for health systems to achieve the so-called “triple aim” (improving individual patient experience; improving population health; and reducing per capita cost of care).

A growing body of research suggests that well-designed mobile health interventions can reduce risky behaviors and increase health-promoting behaviors among a wide variety of patients24.  Below, we outline the evidence behind three primary forms of mobile health (mHealth) for behavior change: text-messaging, apps, and wearables.

1. Text Messaging

Because text message enabled phones are nearly universally prevalent and are the most broadly understood communication format, the largest body of research on mHealth-enabled behavior change is through text messaging25. Text messages have effectively promoted a variety of behavior changes, including an increased rate of smoking cessation26,27, increased highly active antiretroviral therapy (HAART) adherence 28, and improved diabetes self-management29.

Studies have repeatedly demonstrated that tailored, two-way text messaging is more acceptable and efficacious than unidirectional or universal messaging25. Despite messaging being the “oldest” of the mHealth mediums, most studies are limited in scope and insufficiently powered to measure efficacy30.  More recent interventions fail to take full advantage of the medium (e.g. using unidirectional messages)

1. “Apps”

The second major category of mHealth for behavior change is the use of “apps,” or mobile platform applications.  mHealth apps can leverage many mobile phone capabilities, including accelerometry, GPS, self-tracking, and Bluetooth communication. Despite a proliferation of behavior change mobile phone applications, a minority are currently based on evidence(31,32) and many are only available on iOS platforms, which excludes about two-thirds of American smart phones that are Android-based. Some pilot studies have suggested that mobile applications can monitor signs of depression [Ginger.io] and enhance adherence to medication regimes [unpublished data from Medisafe]. However, self-management mobile apps for more complex medical conditions such as chronic pain and asthma are currently too simplistic and lack medical professional involvement.

Increasing evidence suggests that gamification, applying game thinking and mechanics to engage and motivate users in non-game contexts, can increase end-user uptake of intervention and behavior management mobile apps and effect actual behavior change.  Successful apps in this category that have increased patient participation and behavior change include disease specific monitoring36, medication administration adherence 33, depression symptoms management34.  Other work suggests that gamification may be applied to clinical decision support interventions to encourage provider behavior changes35.  Syandus is exploring this using treatment simulation software for conditions including Chronic Obstructive Pulmonary Disease (COPD) and Multiple Sclerosis.  While these results appear promising, further research is needed to examine the impact of these challenging technologies.

1. Social Sharing as a Tool for Patient Engagement

Utilizing social interaction is a key component of digital health patient engagement tools. Disease-specific digital communities have become exceedingly prevalent on social media sites (e.g., PatientsLikeMe), with many volunteering their clinical data with others.  This sharing often leads to support and advice from those with similar conditions.  Studies of Facebook pages that have incorporated an uploaded photo have been shown to draw 104% more user’s comments. However,  digital health management social interactions are complex.  While sharing of fitness data could be very motivational for some, the Pricewaterhouse Coopers Health Research Institute report from this year found that 43% of consumers did not want to share any information about themselves, and only 23% felt comfortable sharing health data with friends and family37. For text messaging services, apps, and wearable technologies to be accepted by the majority healthcare consumers, social interaction features that motivate some must be balanced with maintaining data privacy desired by others.

1. Integration of Wearable RPM Into Behavior Change

Increasingly, RPM is being incorporated into mobile interventions. For instance, a recent study by emergency physicians demonstrated that using “wearables” can detect patients’ use of opioids and cocaine in real-time.38   A possible future research opportunity could be to deliver targeted behavior change interventions when a relapse is identified. Ongoing studies are examining other RPM applications to decrease or prevent (through advanced predictive analytics) other types of high-risk behaviors and enable intelligent interventions by clinicians and other caregivers. Others are investigating RPM to increase health-centric behaviors, such as functional recovery from illnesses.39

Unanswered questions for the future include:

• For which conditions and which populations might these interventions be most effective?
• How can we best integrate text-messaging, app, social media and wearable data into EHRs?
• How can we best engage physicians in the development of these novel behavioral interventions?
• How can we increase uptake of mHealth technologies, particularly among high-risk populations?
• How can we appropriately translate existing, evidence-based preventive strategies into this novel medium?
• How cost-effective are RPM interventions?
• How do we maximize privacy and security of these interventions, given that text-messaging, apps, social media, and wearables are not inherently a secure medium?

Addressing these questions will require tight collaboration between device manufacturers, app developers, healthcare leaders from the public and private sectors (including physicians and other clinicians), and engaged patients. Overcoming these challenges will likely take years, and determining the outcomes related to these advancements will take even longer. Fortunately, this work has begun and can be embraced by the medical community.

Please note: Inclusion or exclusion of any vendor products within this or any HIMSS document does not imply any level of endorsement. These references are merely used for demonstrative purposes.

Additional Resources at HIMSS Mobile Health Privacy & Security page

References:

1.Crossely GH, Chen J, Choucair W, et al. Clinical benefits of remote versus transtelephonic monitoring of implanted pacemakers. J Am Coll Cardiol 2009; 54:2012–9.

2.Crossley GH, Boyle A, Vitense H, et al, CONNECT Investigators. The CONNECT (Clinical evaluation of remote notification to reduce time to clinical decision) trial. J Am Coll Cardiol 2011;57:1181–9.

3.Saxon LA, Hayes DL, Gilliam FR, et al. Long-term outcome after ICD and CRT implantation and influence of remote device follow-up: the ALTITUDE survival study. Circulation 2010;122:2359–67.

4.Fryback DG and Thornbury JR. The efficacy of diagnostic imaging. Medical Decision Making. 1991; 11(2): 88-94.

5.Kalahasty G, Alimohammad R, Mahajan R, Morjaria S, Ellenbogen KA. A brief history of remote cardiac monitoring. Card Electrophysiol Clin. Sept 2013; 5 (3): 275-282.

6.Proposed policy and payment changes to the Medicare Physician Fee Schedule for Calendar Year 2015. http://www.cms.gov; July 2014.

7.Medicare benefit policy manual, Part 15—Covered Medical and Other Health Services, 30-Physician Services. http://www.cms.hhs.gov/manuals/102 policy/bp102c15.pdf

8.State Medicaid Best Practice: Remote Patient Monitoring and Home Video Visits. American Telemedicine Association, July 2013.

9.Xiong WW, Benditt DG. Physiologic sensors in pacemakers: how do they work and how many do we need? Card Electrophysiol Clin. Sept 2013; 5 (3): 303-316.Chen J, Wilkoff B, Choucair W, Cohen TJ, Crossley GH, Johnson JB, Mongeon LR, Serwer GA, Sherfesee L. Design of the Pacemaker Remote Follow-up Evaluation and Review (PREFER) trial to assess the clinical value of the remote pacemaker interrogation in the management of pacemaker patients. J Am Coll Cardiol. 2009; 54(22):2012-2019.

10.Res CJ, Theuns DAMJ, Jordaens L: The role of remote monitoring in the reduction of inappropriate implantable cardioverter defibrillator therapies. Clin Res Cardiol 2006, 95: iii17-iii21.

11.Sepah SC, Jiang L, Peters AL. Translating the Diabetes Prevention Program into an Online Social Network: Validation against CDC standards.  Diabetes Educ. Apr 2014; 40 (4): 435-443.

12.2013. A LeadingAge CAST (Center for Aging Services Technologies) Report. Published 2013.

13.Bowman D. mHealth Summit2013: Remote Monitoring helps CHRISTUS Health slash patient costs. http://www.fiercemobilehealthcare.com/story/mhealth-summit-2013-remote-monitoring-helps-christus-health-slash-patient-c/2013-12-11. December 11, 2013.

14.Maeng DD, Starr AE, Tomcavage JF, Sciandra J, Salek D, Griffith D. Can telemonitoring reduce hospitalization and cost of care? A health plan’s experience in managing patients with heart failure. Population Health Management. Epub May 27, 2014.

15.Katalenich BL, Shi L, Liu S, et al. Evaluation of a remote monitoring system for diabetes control and medication adherence. Program and abstracts of the 74th American Diabetes Association Scientific Sessions, June 13-17, 2014; San Francisco, California. Abstract 852-P.

16.Bianchi W,Dugas AF,Hsieh YH,Saheed M,Hill P,Lindauer C,Terzis A,Rothman RE. Revitalizing a vital sign: improving detection of tachypnea at primary triage. Ann Emerg Med.2013 Jan;61(1):37-43

17.Dobkin BH, Xu X, Batalin M, Thomas S, Kaiser W. Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke. Stroke. June 2011; 42: 2246-50.

18.Farrar WB, Sexton PR, March W, Olsen J. An evaluation of a new objective method for early breast cancer screening. Ohio State Univ. clinical review; pub. May 2009.

19.Belknap R, Weis S, Brookens A, Au-Yeung KY, Moon G, DiCarlo L, Reves R. Feasibility of an ingestible sensor-based system for monitoring adherence to tuberculosis therapy. PLOS One. 2013; 8(1): e53373.

20.Kane JM, Perlis RH, DiCarlo LA, Au-Yeung KY, Duong J, Petrides G. First experience with a wireless system incorporating physiologic assessments and direct confirmation of digital tablet ingestions in ambulatory patients with schizophrenia or bipolar disorder. J Clin Psychiatry. Jun 2013; 74(6): e533-40.

21.http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447803/

22.http://www.cancer.gov/cancertopics/cancerlibrary/theory.pdf

23.Tang, P, et al. Draft Recommendations: Meaningful Use Stage 3. www.healthit.gov; August 2013.

24.Heron KE, Smyth JM. Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behavior treatments. Br J Health Psychol. 2010;15:1-39.

25.Head KJ, Noar SM, Iannarino NT, Grant Harrington N. Efficacy of text messaging-based interventions for health promotion: A meta-analysis. Social Science & Medicine. 2013;97:41-8.

26.Whitaker R, McRobbie H, Bullen C, Borland R, Rodgers A, Gu Y. Mobile phone based interventions for smoking cessation. Cochrane Database Syst Rev. Nov 14 2012; 11: CD006611.

27.Free C, et al. Smoking cessation support delivered via mobile phone text messaging (txt2stop): a single-blind, randomised trial. Lancet. July 2011; 378 (9785): 49-55.

28.Lester RT, et al. Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): a randomised trial. Lancet. Nov 27 2010; 376 (9755): 1838-45.

29.Arora S, Peters AL, Burner E, Lam CN, Menchine M. Trial to examine text-message based mHealth in emergency department patients with diabetes (TExT-MED): a randomized controlled trial. Ann Emerg Med. Jun 2014; 63(6): 745-54.

30.Vodopivek-Jamsek V, de Jongh T, Gurol-Uganci I, Atun R, Car J. Mobile phone messaging for preventive health care. Cochrane Database Syst Rev. Dec 12 2012; 12: CD007457.

31.IMS Institute for Medical Informatics. Patient Apps for Improved Healthcare: From Novelty to Mainstream. October 2013.

32.Abroms LC, et al. iPhone Apps for Smoking Cessation. Am J Prev Med. 2011; 40(3): 279-285.

33.Kato PM, et al. A video game improves behavioral outcomes in adolescents and young adults with cancer: a randomized trial. Pediatrics. Aug 2008; 122(2): e305-317.

34.http://annmarieroepke.files.wordpress.com/2013/08/superbetter-study-results-bulletin-8-15-13.pdf

35.http://medcitynews.com/2012/05/physician-education-gaming-software-expanding-to-patients-med-students/; May 7, 2012.

36.Cafazzo JA, et al. Design of an mHealth app for the self-management of adolescent Type I Diabetes: a pilot study. J Med Internet Res. May-Jun 2012; 14(3): e70.

37.Barnes, Kelly, et al. Health wearables: Early days. PwC Health Research Institute Report, 2014

38.Carreiro S, Smelson D, Ranney M, Horvath KJ, Picard RW, Boudreaux ED, Hayes R, Boyer EW. Real-Time Mobile Detection of Drug Use with Wearable Biosensors: A Pilot Study. J Med Toxicol. 2014 Oct 21. [Epub ahead of print]

39.Appelboom G, Yang AH, Christophe BR, Bruce EM, Slomian J, Bruyère O, Bruce SS,Zacharia BE, Reginster JY, Connolly ES Jr. The promise of wearable activity sensors to define patient recovery. J Clin Neurosci. 2014 Jul;21(7):1089-93.