Clinical UM Guideline

Remote therapeutic monitoring (RTM) refers to the remote monitoring and management of therapy services, for example, monitoring of respiratory or musculoskeletal status, and medication and therapy adherence and response. RTM involves remote managing and collection of non-physiological individual data.

Remote physiologic monitoring (RPM) refers to the monitoring of physiological data, for example, weight, blood pressure (BP), pulse oximetry, respiratory flow rate, as well as associated physiologic monitoring treatment management services.

Please see the following related documents for additional information:

• DME.00047 Rehabilitative Devices with Remote Monitoring
• DME.00050 Remote Devices for Intermittent Monitoring of Intraocular Pressure
• MED.00101 Physiologic Recording of Tremor using Accelerometer(s) and Gyroscope(s)
• MED.00130 Surface Electromyography and Electrodermal Activity Sensor Devices for Seizure Monitoring
• MED.00131 Electronic Home Visual Field Monitoring
• MED.00134 Non-invasive Cardiac Management and Monitoring Systems
• SURG.00166 Implantable Devices for Remote Monitoring of Heart Failure

Note: For a high-level overview of this document, please see “Summary for Members and Families” below. 

Medically Necessary:

Remote therapeutic monitoring (RTM) in a non-healthcare setting is considered medically necessary when clinical records document the rationale for monitoring including ALL of the following:

1. RTM is clinically appropriate, in terms of type, quantity, frequency, extent, site and duration and is considered effective for the individual’s illness, injury or disease and in accordance with generally accepted standards of medical practice*; and
2. RTM data is being regularly assessed to detect acute changes in clinical status and prompt intervention; and
3. RTM is not primarily for the convenience of the individual, physician, caregiver, or other health care provider; and
4. The individual is at risk of clinically significant changes in medical status which warrant enhanced monitoring based on current status and instability of the underlying clinical condition; and
5. The individual is unable to access regularly scheduled outpatient clinical care or therapeutic monitoring is required between visits due to potential changes in medical status; and
6. Monitoring is reasonably likely to prevent avoidable deterioration in the clinical condition and/or other adverse events relating to the underlying clinical condition.

Remote physiologic monitoring (RPM), in a non-healthcare setting is considered medically necessary when clinical records document the rationale for monitoring including ALL of the following:

1. RPM involves an FDA-recognized medical device that directly measures member physiologic data (for example, sphygmomanometer, pulse oximeter, heart rate monitor, glucometer, thermometer, weight scale, respiratory flow rate monitor) used to develop and manage a treatment plan related to a chronic and/or acute health illness or condition; and
2. RPM is clinically appropriate, in terms of type, quantity, frequency, extent, site and duration and is considered appropriate for the individual’s illness, injury or disease and in accordance with generally accepted standards of medical practice*; and
3. RPM data is being assessed to detect acute changes in clinical status and prompt intervention; and
4. RPM is not primarily for the convenience of the individual, physician, caregiver, or other health care provider; and
5. The individual is at risk of clinically significant changes in medical status which warrant enhanced monitoring based on current status and instability of the underlying clinical condition; and
6. The individual is unable to access regularly scheduled outpatient clinical care or physiological monitoring is required between visits due to potential changes in medical status; and
7. Monitoring is reasonably likely to prevent avoidable deterioration in the clinical condition and/or other adverse events relating to the underlying clinical condition.

Not Medically Necessary:

RTM or RPM is considered not medically necessary when similar services are being provided concurrently, for example, home health services.

RTM or RPM is considered not medically necessary when the criteria above have not been met.

*Generally accepted standards of medical practice means standards that are based on credible scientific evidence published in peer-reviewed medical literature generally recognized by the relevant medical community, physician specialty society recommendations and the views of physicians practicing in relevant clinical settings.

This document describes clinical studies and expert recommendations, and explains whether remote therapeutic and physiologic monitoring services are clinically appropriate. The following summary does not replace the medical necessity criteria or other information in this document. The summary may not contain all of the relevant criteria or information. This summary is not medical advice. Please check with your healthcare provider for any advice about your health.

Key Information

Remote monitoring is a way for healthcare providers to track an individual’s health while they are at home. Remote therapeutic monitoring (RTM) focuses on non-physical data, such as how well someone follows their treatment or how they feel during therapy. Remote physiologic monitoring (RPM) tracks physical signs like blood pressure (BP), weight, or heart rate (HR) using approved medical devices. RTM and RPM may help people with chronic conditions manage their health better between office visits. Both use secure systems to send information to a provider who can adjust care if needed. These tools can help avoid hospital visits, but they may not always improve long-term health. RTM and RPM are not helpful for everyone and should only be used when regular office care is hard to access or when close monitoring is needed due to changes in health.

What the Studies Show

RTM helps providers manage care by tracking symptoms and treatment responses, especially for people with issues like breathing or joint problems. RPM devices collect health data, such as BP or HR, and can alert providers when something is wrong. These tools can make care more useful and improve how people follow their treatment plans. Some studies showed that RPM can lower hospital readmissions or reduce time spent in the hospital, especially for people at high risk or with heart failure (HF). However, not all studies showed clear benefits. In one study, RPM didn’t improve health outcomes compared to regular care. Another study found benefits during use, but these stopped after the monitoring ended. Some programs, such as home ultrasounds during pregnancy or smart knee implants, may help collect useful data, but these have not been proven to improve health. Many experts agree that more research is needed to know which people benefit most from remote monitoring.

When is Remote Monitoring Clinically Appropriate?

Remote monitoring may be appropriate in these situations:

• RTM may be used when all of the following apply:
  • It is appropriate for the individual’s health problem.
  • The provider reviews data often to spot any health changes.
  • It is not used for convenience.
  • The individual has a condition that may change quickly and needs watching.
  • The individual cannot go to regular office visits or needs care between visits.
  • Monitoring can help avoid serious health problems.
• RPM may be used when all of the following apply:
  • The individual uses a medical device approved by the United States Food and Drug Administration (FDA) to track their health data.
  • It is appropriate for the individual’s health problem.
  • The provider checks the data to find any problems and act quickly.
  • It is not used for convenience.
  • The individual has a condition that may change quickly and needs watching.
  • The individual cannot go to regular office visits or needs care between visits.
  • Monitoring can help avoid serious health problems.

When is this not Clinically Appropriate?

Remote monitoring is not appropriate in these situations:

• When an individual is already getting similar services, such as home health care, at the same time.
• When the above criteria are not met.

RTM and RPM are not clinically appropriate in scenarios other than those listed above.

(Return to Description)

The following codes for treatments and procedures applicable to this guideline are included below for informational purposes. Inclusion or exclusion of a procedure, diagnosis or device code(s) does not constitute or imply member coverage or provider reimbursement policy. Please refer to the member's contract benefits in effect at the time of service to determine coverage or non-coverage of these services as it applies to an individual member.

When services may be Medically Necessary when criteria are met:

Associated Coding

When services may also be Medically Necessary for associated add-on codes when criteria are met for the related monitoring codes listed above:

When services are Not Medically Necessary:
For the procedure codes listed above when criteria are not met or for situations designated in the Clinical Indications section as not medically necessary.

Summary

Remote therapeutic monitoring (RTM) and remote physiological monitoring (RPM) can be used to manage and support individuals with chronic conditions through technology. RTM is implemented under a physician’s order and focuses on collecting non-physiological data, such as medication adherence and therapy response, to manage chronic conditions and potentially avoid hospital readmissions. In contrast, RPM uses devices capable of transmitting clinical data to healthcare providers and focuses on physiological data. The aim is to enable more immediate treatment adjustments, and improve treatment adherence and clinical outcomes. Both RTM and RPM utilize United States Food and Drug Administration (FDA) approved medical devices and require Health Insurance Portability and Accountability Act (HIPAA) compliant communication systems. RTM and RPM systems have been evaluated in a number of studies and have been found to provide clinical utility in selected situations.

Discussion

RTM treatment management services are provided when a physician or other qualified healthcare professional uses the results of RTM to manage an individual’s chronic condition under a specific treatment plan. The service must be ordered by a physician or other qualified healthcare professional. RTM services involve “general medicine” collection of data (that is: non-physiological individual data), for example monitoring of medication and therapy adherence, or respiratory or musculoskeletal status. RTM has the potential to prevent avoidable deterioration in the clinical condition in individuals at risk of clinically significant changes in medical status, thereby preventing rehospitalizations, or urgent care and emergency room visits.

In contrast, RPM involves monitoring of physiological data only. RPM services involve data from monitoring devices which have the capability to transmit clinical data for physician review and for the intended use of managing the individual’s condition using these results under a specific treatment plan. This enables the clinician, the individual being treated, or both, to respond and adjust treatment regimens in a more immediate way than would be possible with, for example, routine clinic visits. Some RPM systems may be designed with automated voice response software to give instructions to the monitored individual; others may alert health professionals and/or the individual being monitored to clinical values outside an acceptable range and, in other systems, a health professional may respond immediately. Home-based technologies enable healthcare professionals to monitor physiological (for example, blood pressure [BP]) and psychological (for example, depression and mood) variables more routinely than is possible through face-to-face office visits. It has been reported that ambulatory BP monitoring is more predictive of clinical outcomes than office BPs, and its use leads to improved BP control. These technologies change the communication channel between the provider and the treated individual, in order to minimize barriers to care and improve delivery of medical services. The increased surveillance, support, and enhanced communication afforded by remote technology have significant potential to improve the individual’s attention to, and adherence with, disease treatment and to facilitate communication between the individual and the provider.

Telerehabilitation provides rehabilitation services (for example, physical therapy, occupational therapy) remotely using telehealth tools. It is often combined with some form of monitoring (apps, wearables, video check-ins, etc.). Although both RPM and RTM may be incorporated into telerehabilitation, RTM is more commonly utilized while RPM is used selectively.

A large and diverse number of monitoring devices are currently cleared by the FDA and on the market with remote technology monitoring capabilities. The device used must be a medical device as defined by the FDA. Remote technology services require a live, interactive communication between the physician and the monitored individual or caregiver. Data transmission must be accomplished using a HIPAA compliant network, with sufficient bandwidth and screen resolution to permit adequate interaction with the individual being treated and assessment of behavioral and physical features. The system must maintain a log of communication, with time, date, and duration. The applicable codes are specific to the initial device set-up, the individual’s education on its use, daily recordings and the professional’s time in communication with the monitored individual. RTM currently deals with musculoskeletal and respiratory systems (for system status, therapy adherence [such as inhaler use], and therapy response). Additional indications for this type of monitoring are anticipated in the future (for example for hypertension [HTN], heart failure [HF], and diabetes mellitus [DM]). It is purported that RTM can help physicians to protect individuals at risk of heart attack or stroke; improve BP management; and identify hypertensive crisis and HF exacerbations, which may enable early intervention. Home monitoring devices automatically upload readings to the online portal for the provider to monitor between office visits while the individual is out of the office thereby enabling faster response times when an abnormal value is picked up by the monitoring device.

The Agency for Healthcare Research and Quality (AHRQ) conducted a research project between September 13, 2007 and August 31, 2011 entitled Digital Healthcare Research using Health Information Technology to Improve Ambulatory Chronic Disease Care. This project was designed to test strategies for clinician use of health information technology (HIT) in ambulatory settings to improve outcomes through more effective clinical decision support, medication management, or care delivery. The initiative encouraged consideration of the role of workflow and effective use of clinical alerts and reminders, with an emphasis on prevention and chronic illness management. Medication management was a particular focus, as medication therapy is a significant source of medical errors, cost, and missed opportunities for health care coordination, and HIT can be a potent intervention to address these issues.

The study took place in the primary care practices of the Department of Family and Community Medicine (FCM) and the Division of General Internal Medicine (GIM) of the Department of Internal Medicine at the University of Missouri Health System (UMHC). This project sought to leverage collaborative efforts between the University of Missouri (MU) Department of Family and Community Medicine and its electronic medical record (EMR) vendor, the Cerner Corporation, to create new tools and functionalities to improve chronic disease care.

In 2005, FCM Department leaders began to collaborate with the Cerner Corporation to develop an enhanced ambulatory HIT system to support chronic disease care. Of multiple proposed components, several were anticipated within the time frame of the proposed evaluation including: 

• Condition summary screens which were specially designed dashboards, accessible from a tab within the electronic record, that included key information needed for managing that condition, such as blood pressure (BP) readings in diabetes mellitus (DM), as well as indicators of whether quality metrics were being achieved for that particular individual;
• Easily accessible condition algorithms outlining standard care management;
• Electronic templates for creating visit notes, that would facilitate data acquisition for performance reports; and
• Performance reports on chronic condition quality indicators (for example, having a glycohemoglobin during the past year in subjects with DM) for those participants assigned to individual providers as well as the entire practice, including a list of subjects with out-of-range values.

Additionally, tied to this effort, the Health System planned introduction of a web-based participant interface, IQ Health, to enhance connectivity and secure communication between trial participants and clinicians. It was anticipated to enable individuals to access information in their electronic health record, to upload clinical data and to verify medications. It was anticipated that “smart” devices that could directly upload readings, such as BP and blood glucose, would interface with IQ Health to upload data directly into the electronic health record. For those without internet access, the “smart” devices would be able to upload data over an ordinary phone line. Diabetes performance reports were phased in at 10 UMHC primary care practices. In 3 practices, a portal for secure communications was implemented. A trial of home monitoring of blood glucose and BP occurred in 108 participants.

Multiple studies included: a usability study of a diabetes dashboard; a quasi-experimental study of two kinds of performance reports distributed in a factorial design for 1 year; a qualitative analysis of differences between clinics with different patterns of performance; surveys of interest and experience with the participant web portal; testing accuracy and response to individuals electronically reporting medication inconsistencies; and a randomized trial of 3-months of home monitoring of BP and BP with electronic reporting. Results showed that the diabetes dashboard was efficient and improved accuracy. A composite measure improved in practices able to access performance information in the electronic record. Practices improving in the second year showed strong leadership, sharing of information, and exhibited adaptive reserve. Initial use of the participant portal was relatively limited; however, physicians felt better about its impact after use. In-home medication reconciliation was potentially limited by incomplete information from trial participants and failure to update records by providers. Home monitoring did not improve outcomes, but qualitative findings pointed to important implementation principles. The investigators concluded that the effectiveness study of use of remote monitoring did not demonstrate an impact on clinical outcomes but did lead to the identification of important themes that will inform practices who are considering a remote monitoring intervention for individuals with chronic illness. Such practices need to understand the capabilities and limitations of the technology. Additionally, they should seek independent references to evaluate the vendor’s performance on technical troubleshooting. Practices should design and understand the workflow and consider protocols for the flow of information. Additionally, the human side of the equation, relationships between the individual and the provider, remained a crucial component of working with remote monitoring data. Buy-in by all participants appears important. Lastly, integration of the data transmission system with the EMR and electronic personal health record is key to the intervention’s sustainability in real practices (Mehr, 2011).

Additional indications for RTM and RPM have been reported in limited studies. In 2011, Koehler and colleagues enrolled 710 participants with stable chronic HF in New York Heart Association (NYHA) functional class II or III HF with a left ventricular ejection fraction (LVEF) of ≤ 35% and a history of HF decompensation within the previous 2 years or with a LVEF ≤ 25%. Trial participants were randomly assigned (1:1) to remote monitoring or usual care. Remote telemedical management used portable devices for electrocardiogram (ECG), BP, and body weight measurements connected to a personal digital assistant that sent automated encrypted transmission via cell phones to the telemedical centers. The primary end point was death from any cause. The first secondary end point was a composite of cardiovascular death and hospitalization for HF. Baseline characteristics were similar between the RTM (n=354) and control (n=356) groups. Of those participants assigned to RTM, 287 (81%) were at least 70% compliant with daily data transfers and no break for over 30 days (except during hospitalizations). The median follow-up was 26 months (minimum 12) and was 99.9% complete. The authors concluded that, compared with usual care, RTM had no significant effect on all-cause mortality (hazard ratio [HR], 0.97; 95% confidence interval [CI], 0.67 to 1.41; p=0.87) or on cardiovascular death or HF hospitalization (HR, 0.89; 95% CI, 0.67 to 1.19; p=0.44).

Extended results of the above trial (the telemedical interventional management in individuals with HF II [TIM-HF2] randomized trial) were reported by Koehler and colleagues in 2020. TIM-HF2 was a prospective, randomized, multicenter trial done in 43 hospitals, 60 cardiology practices, and 87 general practitioners’ offices in Germany. Trial participants included those with HF, in NYHA functional class II or III HF who had been hospitalized for HF within 12 months before randomization. Trial participants were randomly assigned to either the RPM intervention or usual care. At the final study visit (main trial), the RPM intervention was stopped and the 1-year extended follow-up period started, which lasted 1 year. The primary outcome was percentage of days lost due to unplanned cardiovascular hospitalizations and all-cause mortality. Analyses were done using the intention-to-treat principle. Results at 1 year post RPM intervention showed that, compared with usual care, a structured RPM intervention done over 12-months reduced the percentage of days lost, due to unplanned cardiovascular hospitalizations and all-cause death. However, when data from the main trial and the extended follow-up period were combined, the percentage of days lost due to unplanned cardiovascular hospitalization or all-cause death was significantly less in those allocated to the RPM group (382 [50%] of 765; weighted mean 9.28%; 95% CI, 7.76-10.81) than in the usual care group (398 [51%] of 773; 11.78%; 95% CI, 10.08-13.49; ratio of weighted average 0.79; 95% CI, 0.62-1.00; p=0.0486). The positive effect of RPM intervention on morbidity and mortality over the course of the main trial was no longer observed 1 year after stopping the RPM intervention. However, because the TIM-HF2 trial was not powered to show significance during the extended follow-up period, these results are considered preliminary and require further research.

In 2021, Dawson and colleagues conducted a prospective, randomized controlled trial (RCT) to assess whether home 30-day telemonitoring after discharge for individuals at high risk of readmission would reduce readmissions or mortality. A total of 1380 participants (mean [standard deviation SD] age, 66 [14] years; 722 [52.3%] men and 658 [47.7%] women) participated in this study; participants were defined as high risk for readmission based on criteria assessed during hospitalization, including payer source, poor health literacy, lack of social support or the inability to self-care, an admission within the previous 12 months, emergent admission, a hospitalization of greater than  5 days, or history of a major medical comorbid condition (DM, myocardial infarction, stroke, peripheral artery disease, congestive heart failure (CHF), chronic obstructive pulmonary disease, substance abuse, depression, acute delirium, receiving dialysis, previous or active cancer, end-stage liver disease, or human immunodeficiency virus [HIV]). They compared 30-day readmission rates and mortality for those who received home telemonitoring compared to standard care between November 1, 2014, and November 30, 2018, in 2 tertiary care hospitals. The intervention group received home-installed equipment to measure BP, heart rate (HR), pulse oximetry, weight if HF was present, and glucose if DM was present. Results were transmitted daily and reviewed by a nurse; changes in vital signs outside a preset range, determined by a standard protocol provided by the device company, triggered an alert for the nurse. Both groups received standard care. Using a modified intention-to-treat analysis, the risk of readmission or death within 30 days among those at high readmission risk was 23.7% (137/578) in the control group and 18.2% (87/477) in the telemonitoring group (absolute risk difference, -5.5% [95% CI, -10.4 to -0.6%]; relative risk [RR], 0.77 [95% CI, 0.61 to 0.98]; p=0.03). Emergency department visits occurred within 30 days after discharge in 14.2% (81/570) in the control group and 8.6% (40/464) in the telemonitoring group (absolute risk difference, -5.6% [95% CI, -9.4 to -1.8%]; RR, 0.61 [95% CI, 0.42 to 0.87]; p=0.005). The authors concluded that 30 days of post discharge telemonitoring may reduce readmissions of high-risk individuals but further study is needed.

In a 2023 randomized trial by Patel and colleagues, the authors collected data regarding readmission rates 30 days following hospital discharge. Approximately 20% of individuals discharged from the hospital will be readmitted within 30 days. The primary conditions for admission included acute myocardial infarction or coronary heart disease, chronic obstructive pulmonary disease, CHF, DM, and pneumonia. In this study, primary outcome was hospital readmission or death within 30 days of discharge. For those assigned to the smartphone arm, the application tracked physical activity patterns. Participants in the wearable device arm wore the device which tracked physical activity patterns in addition to sleep patterns. The authors evaluated whether there were differences based on the type of RPM device used and participants were randomly assigned to gathering data via smartphone (n=250) or via wearable device (n=250). For those in the smartphone group, there were 46 participants (18.4%) readmitted to the hospital and 1 participant death (0.4%). In the wearable device arm, there were 33 participants (13.2%) readmitted to the hospital and 2 participant (0.8%) deaths. The authors concluded that prediction of 30-day hospital readmission improved with RPM on activity patterns after hospital discharge.

More recent studies and recommendations highlight the benefits of remote therapeutic and physiologic monitoring for individuals with diabetes, HF and other health conditions. Siedner (2025) reported on a randomized controlled trial that evaluated a home-based model of HTN care in rural South Africa and demonstrated that integrating individual self-monitoring, community health worker (CHW) support, and remote, nurse-led management can significantly improve BP outcomes compared to standard clinic-based care. Participants included 774 adults with uncontrolled HTN receiving home-based interventions. Those with enhanced digital monitoring, experienced substantially greater reductions in systolic BP (approximately 8-10 mmHg more than standard care) and higher rates of HTN control at both 6 and 12 months (76.9% and 82.8% vs. 57.6% in standard care, respectively). These improvements were achieved with high retention in care and no increase in adverse events, suggesting the model is both effective and safe. The findings highlight the potential of decentralized, technology-supported care to overcome structural barriers in low-resource settings and improve chronic disease management outcomes. Limitations included that the study was conducted in only two rural clinics in one region of South Africa and the single-disease focus.

In 2025, Ra examined the emerging role of RTM and RPM in chronic pain management, highlighting their potential to enhance individual engagement, enable continuous data collection, and improve clinical decision-making. RTM focuses on individual-reported, non-physiological data such as pain levels, function, and therapy adherence, while RPM captures objective physiological metrics (for example, HR, activity, sleep) via wearable devices, often transmitted automatically to providers. Together, these technologies allow for real-time pain assessment, better tracking of treatment responses, and more personalized care, with studies showing improvements in pain, function, and mental health outcomes, as well as potential reductions in rehospitalization and enhanced adherence through features like reminders and gamification. However, implementation challenges remain, including data privacy and security risks, technological literacy barriers, and inequities in digital access, particularly among older or underserved populations. The review concludes that while RTM and RPM offer promising tools to extend care beyond the clinic and provide more objective, continuous insights into chronic pain, their successful integration depends on addressing ethical, regulatory, and infrastructure considerations.

Several RTM platforms are commercially available, including mymobility^® and Persona IQ^® The Smart Knee™ (Zimmer Biomet, Warsaw, IN). In summary, these programs are not considered in accordance with generally accepted standards of medical practice.

Mymobility (paired with an Apple Watch) is a program designed to assist individuals before and after joint replacement surgeries, particularly hip and knee replacements. The wearable technology provides educational and exercise content. Passive data is collected and can be communicated with a provider’s office via text messaging-like application. A 2021 study by Crawford and colleagues (2021a) reported results of a multicenter RCT to evaluate the use of a smartphone-based system for primary total knee arthroplasty and partial knee arthroplasty. The objective was to determine the non-inferiority of the smartphone system compared to a traditional in-person rehabilitation model. There were 244 participants in the control group and 208 participants in the treatment group. Outcomes were assessed using 90-day knee range of movement, EuroQoL five-dimension five-level score, Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOO S JR) score, 30-day single leg stance (SLS) time, Time up and Go (TUG) time, and need for manipulation under anaesthesia (MUA). For the 90-day range of movement, there were no significant differences between the control group and the treatment group (121° [SD 11.7°]) and 121°; p=0.559, respectively. The 90-day mean SLS time was 22.7 seconds in the control group and 24.3 seconds in the treatment group. Mean KOOS JR scores were 73.6 (SD 13.4) in the control group and 70.4 (SD 12.6) in the treatment group. Mean TUG time in the control group was 10.1 seconds and 9.3 seconds in the treatment group. There were 9 participants in the control group and 4 participants in the treatment group who required MUA. The authors conclude the smartphone-based system demonstrated non-inferiority when compared to traditional care models. However, lack of standardization of traditional care and short-term follow-up of 90 days may not allow for generalizability.

Another study by Crawford and colleagues (2021b) evaluated outcomes of a smartphone-based exercise management system after total hip arthroplasty. In this randomized, multicenter study, the authors report on 198 participants who received standard of care following total hip arthroplasty and 167 participants who received the smartphone-based management system following total hip arthroplasty. Outcomes included 90-day hip range of motion, the Hip disability and Osteoarthritis Outcome Score (HOOS, JR), health-related quality-of-life EuroQol five-dimension five-level score (EQ-5D-5L), SLS test, and the TUG test. At the 90-day assessment, there were no significant differences in mean hip flexion between the control group (101° [SD 10.8°]) and the treatment group (100° [SD 11.3°]). Mean HOOS, JR scores revealed no significant differences between the control group (73.0 [SD 13.8) and the treatment group (73.6 [SD 13.0]). The mean 30-day SLS time was 22.9 seconds in the control group and 20.7 seconds in the treatment group. Mean TUG time was 11.8 seconds in the control group and 11.9 seconds in the treatment group. Physical therapy use data was available for 125 participants. For those who did not attend physical therapy, 84.5% (71/84) had ≥ 75% compliance (reported exercises performed on days for which it was assigned). For those who attended physical therapy, 63.4% (26/41) had ≥ 75% compliance (reported exercises performed on days when it was assigned). Lack of information regarding preoperative functional testing could lead to influence about postoperative functional assessments. Also, variations in standard of care across multiple centers and variations involving surgical protocols, operative approaches, implant usage and postoperative care pathways may not allow for generalizability.

The Persona IQ is a knee implant which captures kinematic data metrics including functional knee range of motion, step count, and sampled average walking speed. This is accomplished by a specialized tibial stem extension which is attached to the tibial base plate of the Persona Knee System. The implant provides functional capacity data which is transmitted through the mymobility platform. A 2023 analysis by Yocum and colleagues assessed and reported the correlations between gait kinematics, participant-reported outcomes, and knee range of motion. There were 130 participants who received the Persona IQ total knee implant. Participant-reported outcomes were assessed at baseline and 6 weeks postoperatively. Gait kinematics were recorded daily using the sensor in the knee implant. There were 5 participants who required additional intervention postoperatively which included cortisone injections and manipulation under anesthesia due to poor range of motion. There were 98 (75.4%) participants with Veterans RAND 12 (VR-12) physical scores and/or KOOS Jr. data available at both baseline and 6-weeks. The KOOS Jr. scores improved from baseline (48 ± 13.3) to 6-weeks (66.5 ± 12.1) postoperatively. The postoperative VR-12 physical health scores (38.7 ± 8) were higher compared to baseline (35.7 ± 9). The VR-12 mental health scores were decreased 6 weeks after surgery (53.8 ± 10.2), compared to baseline (56.1 ± 9.2, p=0.023). Baseline preoperative extension (6.1° ± 8.1°) and flexion (94.7° ± 15.6°) range of motion improved to 0.5° ± 1.6° and 122.7° ± 9.4°, respectively. Of the 130 participants, 6 (4.6%) did not have qualified step count data and 3 (2.3%) did not have walking speed, cadence, stride length, tibial range of motion or knee range of motion data available during the gait analysis period. The average qualified step count during this time was 2800 ± 2380 steps/day, with an average walking speed of 0.58 ± 0.14 m/s. Participants had an average cadence of 87.5 ± 9.7 steps/min and stride length of 0.70 ± 0.17 m. These results are based on a single center, single surgeon utilizing one specific implant, in one geographic area which may limit generalizability across multiple populations. Further study is necessary.

A 2025 study by Rocque and colleagues evaluated remote monitoring of the symptoms of individuals who were being treated for cancer. Participants were enrolled in a web-based electronic participant-reported outcome reporting platform (Carevive, Health Catalyst) that can be used on computers or smartphones. Participants received weekly symptom surveys for 6 months, and responses to the surveys were integrated into their EMRs. When participants reported moderate to severe symptoms, alerts were sent to their care team. Outcomes were compared to those in historical controls. A total of 1392 individuals were enrolled in the study and there were 4557 historical controls. The primary outcomes were hospitalization 3 and 6 months after enrollment in the program. In adjusted analysis, the risk of hospitalization was significantly lower in individuals participating in the intervention at 3 months (RR, 0.81; 95% CI, 0.73-0.91) and 6 months (RR, 0.87; 95% CI, 0.80-0.96) compared with historical controls. However, the risk of intensive-care unit (ICU) admission and emergency department (ED) visits were not statistically different between groups at 3 or 6 months. The study was not randomized and did not use concurrent controls and thus is subject to confounding.

An RPM device that is commercially available is the Pulsenmore™ Home Ultrasound (Pulsenmore Ltd., Darom, Israel). The Pulsenmore ES has been authorized by the FDA as a prescription device under De Novo authorization. This home-use ultrasound imaging system enables pregnant individuals to perform ultrasound scans at home for clinician interpretation.

Le Vance (2025) presented a single-center randomized feasibility study that evaluated the acceptability and technical performance of home cardiotocography (CTG) and home ultrasound monitoring in 15 high-risk pregnant women (≥32 weeks’ gestation) allocated to ultrasound alone, CTG alone, or both devices in addition to routine antenatal care. Across 24 remote ultrasounds and 59 remote CTGs, most monitoring episodes were clinically interpretable: fetal heartbeat, movements, and liquor volume were identified in 92%, 83%, and 100% of ultrasound scans respectively, with 79% meeting all 3 validated criteria, and 75% of CTGs achieving ≥20 minutes of continuous interpretable fetal heart rate (FHR) tracing. Adherence to scheduled monitoring was high (median delays 5-11 minutes), real-time review occurred in 95% of CTGs with appropriate same-day escalation when needed, and no maternal-fetal adverse events were attributed to device use. Acceptability ratings were favorable across domains including reassurance and convenience. Limitations include the small sample size, single-center design, short monitoring duration, exclusion of BMI ≥35 and non-English speakers, incomplete ultrasound heartbeat detection in all scans, substantial need for clinician phone support during CTG recordings, limited assessment of oligohydramnios and earlier gestations, and absence of clinical outcome endpoints. Overall, the Pulsenmore home ultrasound platform demonstrated promising feasibility, acceptability, and basic fetal wellbeing assessment capability in high-risk pregnancies. Thus, while findings suggest home CTG and ultrasound are feasible and acceptable in selected high-risk pregnancies, larger adequately powered trials are needed to confirm safety, reliability, and implementation pathways.

Pardo (2025) conducted a prospective study that evaluated the Pulsenmore ES, which allows pregnant women to self-scan at home so clinicians can later measure FHR and amniotic fluid volume using the maximal vertical pocket (MVP). In a group of 28 pregnant women, self-scans performed either with app-based instructions or with live clinician guidance were compared with standard in-clinic ultrasound measurements. The study found that FHR measurements from home scans were generally close to in-clinic results, especially when a clinician guided the scan, and MVP measurements showed high specificity and good sensitivity for identifying normal compared to abnormal amniotic fluid levels. These findings suggest the system may support remote fetal monitoring in selected situations. Limitations include a small sample size, a single study site, and the use of experienced clinicians to review scans, which may limit how well the results apply to routine practice. Accuracy was lower in app-guided scans than clinician-guided scans, and measuring amniotic fluid remains variable even with standard ultrasound, particularly when fluid levels are abnormal. The study also excluded people with very high body mass index, multiple pregnancies, or fetal anomalies, and it did not evaluate whether home monitoring improves pregnancy outcomes or replaces in-clinic care.

Shufaro (2025) conducted a prospective, single-center study that evaluated whether women undergoing ovarian stimulation for in vitro fertilization or fertility preservation could reliably monitor their treatment at home using a self-operated vaginal ultrasound device, compared with standard in-clinic ultrasounds. Among 34 participants who completed the study, home self-scans produced images of adequate quality and showed strong agreement with in-clinic scans for key measures used to guide treatment, including the number and size of ovarian follicles, identification of the leading follicle, and endometrial thickness. Measurements from home scans were also well correlated with treatment outcomes, such as the number of total and mature eggs retrieved, and most participants reported high satisfaction and interest in future use. Limitations include a small sample size, a single study site, and a non-randomized design in which home scans were compared with in-clinic scans in the same people rather than in separate groups. The study excluded women with higher body mass index, abnormal pelvic anatomy, or certain medical conditions, which limits how well the findings apply to broader or higher-risk populations. About 1 in 5 participants could not complete training or found the device unsuitable, suggesting this approach may not work for everyone. In addition, clinical decisions were still based on in-clinic scans, and the study did not evaluate whether home monitoring improves pregnancy outcomes or can safely replace in-clinic monitoring.

Table 1. Examples of RTM and RPM platforms/devices addressed in this document (not an all-inclusive list)

Interactive communication: A real-time, two-way communication between physician and the individual being monitored and treated.

Female: Refers to sex assignment at birth. The gender descriptions used in this document, for example, ‘female’, ‘woman’, and ‘women’, refer to the reproductive capacity of the individual, regardless of gender identity or expression.

Male: Refers to sex assignment at birth. The gender descriptions used in this document, for example, ‘male’, ‘man’, and ‘men’, refer to the reproductive capacity of the individual, regardless of gender identity or expression.

Non-healthcare setting: Refers to the location of healthcare delivery as occurring outside of a hospital, clinic, or equivalent reimbursement setting, for example, “Hospital Care at Home” programs are considered a healthcare setting.

Remote physiologic monitoring (RPM): Refers to the monitoring of physiological data only. Data may derive from devices used to collect clinical data and transmit the data to the physician for interpretation and management of the individual’s condition (examples are weight scales, BP machines, pulse oximetry devices). RPM refers to use of these monitoring devices for longer than 16 days.

Remote therapeutic monitoring (RTM): Refers to the remote monitoring and management of therapy services, for example, monitoring of respiratory or musculoskeletal status, and medication and therapy adherence and response. RTM involves remote managing and collection of non-physiological individual data.

Telehealth: A broad term that refers to the use of digital and communication technologies to deliver or support healthcare services, education, and public health remotely.

Telemedicine: The delivery of clinical healthcare services at a distance using telecommunications technology. It allows providers to evaluate, diagnose, treat, and monitor individuals remotely via tools like video visits, phone calls, remote monitoring devices, and secure messaging.

Telerehabilitation: A subtype of telemedicine that focuses specifically on rehabilitation services delivered remotely. It enables individuals to receive physical therapy, occupational therapy, speech therapy, or other rehab interventions through video sessions, guided exercise programs, wearable sensors, and digital platforms.

Peer Reviewed Publications:

1. Chang A, Ragupathi L, Whellan D. Disease management and telemedicine in heart failure. In: Felker G, Mann D. Heart Failure: a companion to Braunwald's Heart Disease. 4th ed. Elsevier; 2020:674-686.
2. Crawford DA, Duwelius PJ, Sneller MA, et al. 2021 Mark Coventry Award: use of a smartphone-based care platform after primary partial and total knee arthroplasty: a prospective randomized controlled trial. Bone Joint J. 2021a; 103-B(6 Supple A):3-12.
3. Crawford DA, Lombardi AV, Berend KR, et al. Early outcomes of primary total hip arthroplasty with use of a smartphone-based care platform: a prospective randomized controlled trial. Bone Joint J. 2021b; 103-B(7 Supple B):91-97.
4. Dawson NL, Hull BP, Vijapura P, et al. Home telemonitoring to reduce readmission of high-risk patients: a modified intention-to-treat randomized clinical trial. Gen Intern Med. 2021; 36(11):3395-3401.
5. Koehler F, Koehler K, Prescher S, et al. Mortality and morbidity 1 year after stopping a remote patient management intervention: extended follow-up results from the telemedical interventional management in patients with heart failure II (TIM-HF2) randomized trial. Lancet Digit Health. 2020; 2(1):e16-e24.
6. Koehler F, Winkler S, Schieber M, et al. Impact of remote telemedical management on mortality and hospitalizations in ambulatory patients with chronic heart failure: the telemedical interventional monitoring in heart failure study. Circulation. 2011; 123(17):1873-1880.
7. Le Vance J, Emms A, Hodgetts Morton V, et al. Remote maternal-fetal telemedicine monitoring for high-risk pregnancy care: a feasibility study. PLoS One. 2025; 20(11):e0336797.
8. Pardo A, Nahum Fridland S, Rak OL, et al. Fetal heart rate and amniotic fluid volume measurements with a home ultrasound device. Am J Perinatol. 2025; 42(9):1192-1199.
9. Patel MS, Volpp KG, Small DS, et al. Using remotely monitored patient activity patterns after hospital discharge to predict 30 day hospital readmission: a randomized trial. Sci Rep. 2023; 13(1):8258.
10. Ra OH, Sou BS, Streid J, et al. Reviewing the advances in remote therapeutic and physiological monitoring and applicability for chronic pain management. J Pain Res. 2025; 18:3757-3764.
11. Rocque GB, Franks JA, Deng L, et al. Remote symptom monitoring with electronic patient-reported outcomes in clinical cancer populations. JAMA Netw Open. 2025; 8(5):e259852.
12. Shufaro Y, Cohen M, Wertheimer A, et al. Monitoring ovarian stimulation for assisted reproduction with patient self-scans using a home vaginal ultrasound device: a single-center interventional, prospective study. J Med Internet Res. 2025; 27:e72607.
13. Siedner MJ, Magula N, Mazibuko L, et al. Home-based care for hypertension in rural South Africa. N Engl J Med. 2025; 393(13):1304-1314.
14. Yocum D, Elashoff B, Verta P, et al. Patient reported outcomes do not correlate to functional knee recovery and range of motion in total knee arthroplasty. J Orthop. 2023; 43:36-40.
15. Zhang K, Schatman ME, Hascalovici J. Remote therapeutic monitoring in musculoskeletal pain medicine: a systematic review and comparison with remote physiologic monitoring. J Pain Res. 2026; 19:588752.

Government Agency, Medical Society, and Other Authoritative Publications:

1. Mehr DR, Wakefield D, Caligiuri F, et al. Using health information technology (HIT) to improve ambulatory chronic disease care. Agency for Healthcare Research and Quality (AHRQ) Grant ID: HS017035. Inclusive Dates: 9/13-2007- 8/31/11. Available at: https://digital.ahrq.gov/sites/default/files/docs/publication/r18hs017035-mehr-final-report-2011.pdf. Accessed on July 30, 2026.
2. Stegmann T, Koehler K, Wachter R, et al. Heart failure patients with atrial fibrillation benefit from remote patient management: insights from the TIM-HF2 trial. ESC Heart Fail. 2020; 7(5):2516-2526.

1. U.S. Department of Health and Human Services (HHS). Introduction to telehealth and remote patient monitoring. Last updated August 28, 2024. Available at: https://telehealth.hhs.gov/providers/best-practice-guides/telehealth-and-remote-patient-monitoring. Accessed on July 30, 2026.

Remote physiologic monitoring (RPM)
Remote therapeutic monitoring (RTM)
Telehealth
Telemedicine

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