scholarly journals Barriers to obstetric patient utilization of remote patient monitoring for blood pressure

2022 ◽  
Vol 226 (1) ◽  
pp. S275-S276
Author(s):  
Jennifer Kidd ◽  
Elizabeth Patberg ◽  
Agata Kantorowska ◽  
Dajana Alku ◽  
Meredith Akerman ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Patient Monitoring ◽  
Remote Patient Monitoring ◽  
Obstetric Patient ◽  
Remote Patient

Abstract P156: Improvement In Blood Pressure Using Remote Patient Monitoring During COVID19

Hypertension ◽  
2020 ◽  
Vol 76 (Suppl_1) ◽  
Author(s):  
Nadia A Liyanage-Don ◽  
Joseph E Schwartz ◽  
Nathalie Moise ◽  
Kelsey B Bryant ◽  
Adina Bono ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Patient Monitoring ◽  
Healthcare Access ◽  
Vital Signs ◽  
Primary Care Doctor ◽  
Remote Patient Monitoring ◽  
Nursing Support ◽  
Before And After ◽  
Remote Patient ◽  
The Impact

Introduction: The coronavirus disease 2019 (COVID19) pandemic required strict social distancing to curb transmission. Unfortunately, these measures severely limited healthcare access and chronic disease management. In response, many health organizations rapidly developed or expanded telemedicine to provide care directly to patients at home. Little has been reported about the impact of such interventions on clinical outcomes during COVID19. We examined whether enrollment in a remote patient monitoring (RPM) program for hypertension (HTN) prior to COVID19 was associated with improved blood pressure during the pandemic. Methods: We developed an RPM program that tracked vital signs, medication side effects, and treatment adherence patterns outside of the clinic. Patients were referred by their primary care doctor for uncontrolled HTN or suspected white coat HTN. Patients received a two-way tablet, blood pressure cuff, and virtual nursing support via scheduled video visits. Those referred for uncontrolled HTN who had at least two weeks of data both before and after the onset of COVID19 (defined as the first two weeks of March 2020) were included in the study. A mixed-models analysis that adjusted for serial autocorrelation was used to compare mean systolic blood pressure (SBP) and mean diastolic blood pressure (DBP) in the pre-/post-COVID19 periods. Results: Of 94 patients enrolled in the RPM program to date, 46 had at least two weeks of data both pre-COVID19 and post-COVID19. Mean age was 69.0 ± 10.9 years, 69.6% (32 of 46) were women, 78.3% (36 of 46) were Hispanic, and 63.0% (29 of 46) were Spanish-speaking. Pre-COVID, mean SBP was 132.31 ± 13.99 mmHg, mean DBP was 77.10 ± 9.87 mmHg, and 70% (32 of 46) of patients had uncontrolled BP (>130/80 mmHg per AHA guidelines). Post-COVID, mean SBP was 129.57 ± 13.29 mmHg, mean DBP was 76.00 ± 9.16 mmHg, and 57% (26 of 46) of patients had uncontrolled BP. There was a significant reduction in both mean SBP (β = –2.74, 95% CI –5.21, –0.26, p = 0.03) and mean DBP (β = –1.10, 95% CI –2.22, 0.02, p = 0.05) post-COVID vs. pre-COVID. Discussion: Despite the stress and social isolation associated with COVID19, participation in an RPM program that combines home BP monitoring with virtual nursing support can help maintain and even mildly decrease BP.

scholarly journals Patient perceptions, opinions and satisfaction of telehealth with remote blood pressure monitoring postpartum

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nicole A. Thomas ◽  
Anna Drewry ◽  
Susan Racine Passmore ◽  
Nadia Assad ◽  
Kara K. Hoppe
Keyword(s):  
Blood Pressure ◽  
Patient Monitoring ◽  
Blood Pressure Monitoring ◽  
Study Endpoint ◽  
Free Text ◽  
Remote Patient Monitoring ◽  
Pressure Monitoring ◽  
Link Type ◽  
Remote Patient

Abstract Background Our aim was to conduct a post participation survey of respondent experiences with in-home remote patient monitoring via telehealth for blood pressure monitoring of women with postpartum hypertension. We hypothesized that the in-home remote patient monitoring application will be implemented with strong fidelity and have positive patient acceptability. Methods This analysis was a planned secondary analysis of a non-randomized controlled trial of telehealth with remote blood pressure patient monitoring for postpartum hypertension compared to standard outpatient monitoring in women with a hypertension-related diagnosis during pregnancy. In collaboration with survey experts, we developed a 41-item web-based survey to assess 1) perception of quality of care received, 2) ease of use/ease to learn the telehealth program, 3) effective orientation of equipment, 4) level of perceived security/privacy utilizing telehealth and 5) problems encountered. The survey included multiple question formats including Likert scale responses, dichotomous Yes/No responses, and free text. We performed a descriptive analysis on all responses and then performed regression analysis on a subset of questions most relevant to the domains of interest. The qualitative data collected through open ended responses was analyzed to determine relevant categories. Intervention participants who completed the study received the survey at the 6-week study endpoint. Results Sixty six percent of respondents completed the survey. The majority of women found the technology fit easily into their lifestyle. Privacy concerns were minimal and factors that influenced this included age, BMI, marital status, and readmissions. 95% of women preferred remote care for postpartum follow-up, in which hypertensive type, medication use and ethnicity were found to be significant factors in influencing location of follow-up. Most women were satisfied with the devices, but rates varied by hypertensive type, infant discharge rates and BMI. Conclusions Postpartum women perceived the telehealth remote intervention was a safe, easy to use method that represented an acceptable burden of care and an overall satisfying method for postpartum blood pressure monitoring. Trial registration ClinicalTrials.gov identification number: NCT03111095 Date of registration: April 12, 2017.

Abstract P120: Feasibility Of An Asynchronous, Semi-Automated Remote Patient Monitoring Blood Pressure Management System

Hypertension ◽  
2021 ◽  
Vol 78 (Suppl_1) ◽  
Author(s):  
Ashish Sarraju ◽  
Meg Babakhanian ◽  
Irvin Szeto ◽  
Clark Seninger ◽  
Tara I Chang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Pilot Study ◽  
Patient Monitoring ◽  
The United States ◽  
User Feedback ◽  
Mobile App ◽  
Remote Patient Monitoring ◽  
Rpm System ◽  
Remote Patient ◽  
High Engagement

Introduction: While remote patient monitoring (RPM) for hypertension (HTN) continues to grow in the United States, most systems are third party, employer-directed, or do not directly lead to changes in medication management. Systems that address these issues may reduce therapeutic inertia and lead to more rapid control of blood pressure (BP). We developed a clinician-facing HTN RPM system with evidence-based customizable medication titration protocols that integrate with a patient mobile app and Bluetooth®-connected BP cuff. We report interim results from the pilot implementation of this system. Hypothesis: In a pilot study, an RPM system with patient and clinician-facing platforms and a semi-automated protocol will achieve high engagement with actionable user feedback. Methods: We performed a single arm, single center study with five clinicians from primary care (3) and cardiology (2). Eligible patients had essential hypertension (BP >130/80 mmHg) and a smartphone (iPhone, Android). Patients used a Bluetooth®-connected cuff that sent readings to a patient app and a clinician dashboard. Based on BP and comorbidities, a protocol provided medication titration recommendations for clinicians. In this 12-week study, we assessed feasibility through user feedback, user engagement (defined as the number of BP measurements), and changes in systolic (SBP, mmHg) and diastolic BP (DBP, mmHg). Results: We enrolled 18 patients (age 51 + 11y; 94% male; 29% White). Baseline SBP was 133 + 7.8 and DBP was 87 + 7.1. At a mean follow-up of 4.7 weeks, there were 15 + 11 weekly BP measurements per patient. Mean per-patient decreases in SBP and DBP were 12 (95% CI 5.8-18, p<0.001) and 7.1 (95% CI 3.1-11, p = 0.002), respectively. A total of 77.8% (14/18) patients continued BP measurements without attrition. Key feedback included improved cuff-mobile app connectivity (patients) and increased medication choices in protocols (clinicians). Conclusions: In interim results of a pilot study, an RPM HTN system was implemented with high engagement, evidence of BP reduction, and actionable feedback. Complete results including medication and BP changes are anticipated by September 2020 and will guide a planned, funded, large, multicenter cluster randomized trial.

scholarly journals Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Radio Frequency Identification ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Remote Patient Monitoring ◽  
Rfid Technology ◽  
Rpm System ◽  
Frequency Identification ◽  
Remote Patient

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.

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