scholarly journals CMS Signals Support for Remote Patient Monitoring with New Reimbursement Incentives

Author(s):  
Carrie Nixon ◽  
Rebecca E. Gwilt

Two final rules issued by The Centers for Medicare & Medicaid Services (CMS) in November 2017 gave physicians and other healthcare providers ringing in the New Year another reason to celebrate. The Centers for Medicare & Medicaid Services has opened entirely new avenues for reimbursement of Remote Patient Monitoring (RPM) services in 2018, creating the potential for better patient outcomes and a boost to a healthcare providers’ bottom lines.

2021 ◽  
Author(s):  
Sachin Shailendra Shah ◽  
Afsana Safa ◽  
Kuldhir Johal ◽  
Dillon Obika ◽  
Sophie Valenine

Abstract Background COVID-19 has placed unprecedented strain on healthcare providers, in particular, primary care services. GP practices have to effectively manage patients remotely preserving social distancing. We aim to assess an app-based remote patient monitoring solution in reducing the workload of a clinician. Primary care COVID patients in West London deemed medium risk where recruited into the virtual ward. Patients were monitored for 14 days by telephone or by both the Huma app and telephone. Information on number of phone calls, duration of phone calls and duration of time spent reviewing the app data recorded.Results The amount of time spent reviewing one patient on the telephone only arm of the study was 490 minutes, compared with 280 minutes spent reviewing one patient who was monitored via both the Huma app and telephone. Based on employed clinicians monitoring patients, this equates to a 0.04 reduction of full-time equivalent staffing I.e. for every 100 patients, it would require 4 less personal to remotely monitor them. There was no difference in mortality or adverse events between the two groups.Conclusion App-based remote patient monitoring clearly holds large economic benefit to COVID-19 patients. In wake of further waves or future pandemics, and even in routine care, app-based remote monitoring patients could free up vital resources in terms of clinical team’s time, allowing a better reallocation of services.


2021 ◽  
Vol 10 (3) ◽  
pp. e001496
Author(s):  
Rebecca Steinberg ◽  
Bjorn Anderson ◽  
Ziyue Hu ◽  
Theodore M Johnson ◽  
James B O’Keefe ◽  
...  

ObjectiveTo assess whether engagement in a COVID-19 remote patient monitoring (RPM) programme or telemedicine programme improves patient outcomes.MethodsThis is a retrospective cohort study analysing patient responsiveness to our RPM survey or telemedicine visits and outcomes during the COVID-19 pandemic. Daily text message surveys and telemedicine consultations were offered to all patients who tested positive for SARS-CoV-2 at our institutional screening centres. Survey respondents with alarm responses were contacted by a nurse. We assessed the relationship between virtual engagement (telemedicine or RPM survey response) and clinical outcomes using multivariable logistic regression.ResultsBetween 10 July 2020 and 2 January 2021, 6822 patients tested positive, with 1230 (18%) responding to at least one survey. Compared with non-responders, responders were younger (49 vs 53 years) and more likely to be white (40% vs 33%) and female (65% vs 55%) and had fewer comorbidities. After adjustment, individuals who engaged virtually were less likely to experience an emergency department visit, hospital admission or intensive care unit–level care.ConclusionTelemedicine and RPM programme engagement (vs no engagement) were associated with better outcomes, but this was likely due to differences in groups at baseline rather than the efficacy of our intervention alone.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sachin Shailendra Shah ◽  
Afsana Safa ◽  
Kuldhir Johal ◽  
Dillon Obika ◽  
Sophie Valentine

Abstract Background The novel coronavirus disease in 2019 (COVID-19) has placed unprecedented strain on healthcare providers, in particular, primary care services. General practitioners (GP) have to effectively manage patients remotely preserving social distancing. We aim to assess an app-based remote patient monitoring solution in reducing the workload of a clinician and reflect this as time-saved in an economic context. Primary care COVID patients in West London deemed medium risk were recruited into the virtual ward. Patients were monitored for 14 days by telephone or by both the Huma app and telephone. Information on number of phone calls, duration of phone calls and duration of time spent reviewing the app data was recorded. Results The amount of time spent reviewing one patient in the telephone only arm of the study was 490 min, compared with 280 min spent reviewing one patient who was monitored via both the Huma app and telephone. Based on employed clinicians monitoring patients, this equates to a 0.04 reduction of full-time equivalent staffing I.e. for every 100 patients, it would require 4 less personnel to remotely monitor them. There was no difference in mortality or adverse events between the two groups. Conclusion App-based remote patient monitoring potentially holds large economic benefit to COVID-19 patients. In wake of further waves or future pandemics, and even in routine care, app-based remote monitoring patients could free up vital resources in terms of clinical team’s time, allowing a better reallocation of services.


Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.


2021 ◽  
Vol 46 (5) ◽  
pp. 100800
Author(s):  
Abdulaziz Joury ◽  
Tamunoinemi Bob-Manuel ◽  
Alexandra Sanchez ◽  
Fnu Srinithya ◽  
Amber Sleem ◽  
...  

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