scholarly journals Patient Generated Health Data Earn a Seat at the Table:Clinical Adoption During the Covid-19 Transition to Telemedicine

JAMIA Open ◽  
2021 ◽  
Author(s):  
Benjamin I Rosner ◽  
Joseph C Kvedar ◽  
Julia Adler-Milstein
Keyword(s):  
Vital Sign ◽  
Clinical Care ◽  
Healthcare Providers ◽  
Vital Signs ◽  
Application Programming Interface ◽  
Health Data ◽  
Health Concern ◽  
Clinical Use ◽  
Interrupted Time Series Analysis ◽  
Potential Value

Abstract Background Patient generated health data (PGHD) have not achieved widespread clinical adoption. However, the COVID-induced shift to telemedicine may have created opportunities for PGHD as surrogates for vital signs collected in-person. We assessed whether this shift was associated with greater ambulatory care PGHD use. Methods We conducted an interrupted time series analysis of physician enrollment in, and patient-initiated vital sign transmission of non-COVID associated PGHD through, a national PGHD platform (Validic). Results Ten health systems, 4,695 physicians, and 51,320 patients were included. We found a significant increase in physician enrollment (slope change of 0.86/week, P=.02). Platform application programming interface calls continued their pre-COVID upward trend, despite large reductions in overall encounters. Discussion These findings suggest significantly greater pandemic-associated clinical demand for PGHD, and patient supply disproportionate to encounter rates. Conclusion Increasing clinical use and ongoing efforts to reduce barriers, could help seize current adoption momentum to realize PGHD’s potential value. Lay Summary Patient generated health data (PGHD) - health-related data created and recorded by or from patients outside of the clinical setting to help address a health concern—have not yet achieved widespread adoption in routine clinical care. The COVID-19 pandemic precipitated a rapid transition of outpatient encounters to telemedicine in which healthcare providers lacked access to vital signs routinely collected during in-person visits. We conducted an analysis to determine whether the transition to telemedicine increased patient transmission of, and provider adoption of vital sign-related PGHD as surrogates for their in-person equivalents. We found that the number of healthcare providers enrolling on a national PGHD platform increased significantly following the transition to telemedicine, and that the amount of PGHD transmission continued the upward trajectory that it was already experiencing, substantially outpacing the dramatic decline in overall encounters that occurred early in the pandemic. While adoption challenges persist, including questions about accuracy of PGHD, liability, reimbursement, and the potential for exacerbating disparities, these findings suggest an increasing willingness of patients and healthcare providers to use vital sign-related PGHD to supplement telemedicine encounters. Increasing clinical use and ongoing efforts to reduce barriers, could help seize current adoption momentum to realize PGHD’s potential value.

Digital Connectivity: The Sixth Vital Sign

2021 ◽  
pp. 193229682110152
Author(s):  
David C. Klonoff ◽  
Trisha Shang ◽  
Jennifer Y. Zhang ◽  
Eda Cengiz ◽  
Chhavi Mehta ◽  
...  
Keyword(s):  
Vital Sign ◽  
Pressure Pulse ◽  
Digital Health ◽  
Clinical Care ◽  
Vital Signs ◽  
Personal Data ◽  
Skills Training ◽  
Digital Data ◽  
Digital Monitoring ◽  
Continuous Glucose Monitors

Digital health and telehealth connectivity have become important aspects of clinical care. Connected devices, including continuous glucose monitors and automated insulin delivery systems for diabetes, are being used increasingly to support personalized clinical decisions based on automatically collected data. Furthermore, the development, demand, and coverage for telehealth have all recently expanded, as a result of the COVID-19 pandemic. Medical care, and especially diabetes care, are therefore becoming more digital through the use of both connected digital health devices and telehealth communication. It has therefore become necessary to integrate digital data into the electronic health record and maintain personal data confidentiality, integrity, and availability. Connected digital monitoring combined with telehealth communication is known as virtual health. For this virtual care paradigm to be successful, patients must have proper skills, training, and equipment. We propose that along with the five current vital signs of blood pressure, pulse, respiratory rate, temperature, and pain, at this time, digital connectivity should be considered as the sixth vital sign. In this article, we present a scale to assess digital connectivity.

scholarly journals Physiological deterioration in the Emergency Department: The SNAP40-ED study

2021 ◽  
Vol 17 (3) ◽  
Author(s):  
Matthew J. Reed ◽  
Rachel O'Brien ◽  
Polly L. Black ◽  
Steff Lewis ◽  
Hannah Ensor ◽  
...  
Keyword(s):  
Emergency Department ◽  
Vital Sign ◽  
Clinical Care ◽  
Ambulatory Monitoring ◽  
Vital Signs ◽  
Clinical Staff ◽  
The Novel ◽  
Open Label ◽  
Novel Device ◽  
Time To Detection

Continuous novel ambulatory monitoring may detect deterioration in Emergency Department (ED) patients more rapidly, prompting treatment and preventing adverse events. Single-centre, open-label, prospective, observational cohort study recruiting high/medium acuity (Manchester triage category 2 and 3) participants, aged over 16 years, presenting to ED. Participants were fitted with a novel wearable monitoring device alongside standard clinical care (wired monitoring and/or manual clinical staff vital sign recording) and observed for up to 4 hours in the ED. Primary outcome was time to detection of deterioration. Two-hundred and fifty (250) patients were enrolled. In 82 patients (32.8%) with standard monitoring (wired monitoring and/or manual clinical staff vital sign recording), deterioration in at least one vital sign was noted during their four-hour ED stay. Overall, the novel device detected deterioration a median of 34 minutes earlier than wired monitoring (Q1, Q3 67,194; n=73, mean difference 39.48, p<0.0001). The novel device detected deterioration a median of 24 minutes (Q1, Q3 2,43; n=42) earlier than wired monitoring and 65 minutes (Q1, Q3 28,114; n=31) earlier than manual vital signs. Deterioration in physiology was common in ED patients. ED staff spent a significant amount of time performing observations and responding to alarms, with many not escalated. The novel device detected deterioration significantly earlier than standard care.

scholarly journals Accuracy of Vital Signs Measurements by a Smartwatch and a Portable Health Device: Validation Study

10.2196/16811 ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. e16811 ◽  
Author(s):  
Christina Hahnen ◽  
Cecilia G Freeman ◽  
Nilanjan Haldar ◽  
Jacquelyn N Hamati ◽  
Dylan M Bard ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Vital Sign ◽  
Vital Signs ◽  
Final Analysis ◽  
University Hospital ◽  
Health Concern ◽  
Absolute Difference ◽  
Mean Absolute Difference ◽  
The Mean ◽  
Performance Monitor

Background New consumer health devices are being developed to easily monitor multiple physiological parameters on a regular basis. Many of these vital sign measurement devices have yet to be formally studied in a clinical setting but have already spread widely throughout the consumer market. Objective The aim of this study was to investigate the accuracy and precision of heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and oxygen saturation (SpO2) measurements of 2 novel all-in-one monitoring devices, the BodiMetrics Performance Monitor and the Everlast smartwatch. Methods We enrolled 127 patients (>18 years) from the Thomas Jefferson University Hospital Preadmission Testing Center. SBP and HR were measured by both investigational devices. In addition, the Everlast watch was utilized to measure DBP, and the BodiMetrics Performance Monitor was utilized to measure SpO2. After 5 min of quiet sitting, four hospital-grade standard and three investigational vital sign measurements were taken, with 60 seconds in between each measurement. The reference vital sign measurements were calculated by determining the average of the two standard measurements that bounded each investigational measurement. Using this method, we determined three comparison pairs for each investigational device in each subject. After excluding data from 42 individuals because of excessive variation in sequential standard measurements per prespecified dropping rules, data from 85 subjects were used for final analysis. Results Of 85 participants, 36 (42%) were women, and the mean age was 53 (SD 21) years. The accuracy guidelines were only met for the HR measurements in both devices. SBP measurements deviated 16.9 (SD 13.5) mm Hg and 5.3 (SD 4.7) mm Hg from the reference values for the Everlast and BodiMetrics devices, respectively. The mean absolute difference in DBP measurements for the Everlast smartwatch was 8.3 (SD 6.1) mm Hg. The mean absolute difference between BodiMetrics and reference SpO2 measurements was 3.02%. Conclusions Both devices we investigated met accuracy guidelines for HR measurements, but they failed to meet the predefined accuracy guidelines for other vital sign measurements. Continued sale of consumer physiological monitors without prior validation and approval procedures is a public health concern.

scholarly journals Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Viktor Tóth ◽  
Marsha Meytlis ◽  
Douglas P. Barnaby ◽  
Kevin R. Bock ◽  
Michael I. Oppenheim ◽  
...  
Keyword(s):  
Vital Sign ◽  
Characteristic Curve ◽  
Healthcare Providers ◽  
Vital Signs ◽  
Early Warning Score ◽  
Hospital Patients ◽  
Small Set ◽  
Using Data ◽  
Impaired Sleep ◽  
Deep Learning Model

AbstractImpaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers. Previous efforts to forgo overnight vital sign measurements and improve patient sleep used providers’ subjective stability assessment or utilized an expanded, thus harder to retrieve, set of vitals and laboratory results to predict overnight clinical risk. Here, we present a model that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. Using data obtained from a multi-hospital health system between 2012 and 2019, a recurrent deep neural network was trained and evaluated using ~2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. We achieved an area under the receiver operating characteristic curve of 0.966 (95% confidence interval [CI] 0.956–0.967) on the retrospective testing set, and 0.971 (95% CI 0.965–0.974) on the prospective set to predict overnight patient stability. The model enables safe avoidance of overnight monitoring for ~50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable. Our approach is straightforward to deploy, only requires regularly obtained vital signs, and delivers easily actionable clinical predictions for a peaceful sleep in hospitals.

scholarly journals Patient-Generated Health Data (PGHD) Interoperability: An Integrative Perspective

2021 ◽  
Author(s):  
Rada Hussein ◽  
Rik Crutzen ◽  
Johanna Gutenberg ◽  
Stefan Tino Kulnik ◽  
Mahdi Sareban ◽  
...  
Keyword(s):  
Health Outcome ◽  
Clinical Practice ◽  
Digital Health ◽  
Clinical Care ◽  
Healthcare Providers ◽  
Health Data ◽  
Chronic Illnesses ◽  
Functional Health ◽  
Health Record ◽  
Full Utilization

With advances in Digital Health (DH) tools, it has become much easier to collect, use, and share patient-generated health data (PGHD). This wealth of data could be efficiently used in monitoring and controlling chronic illnesses as well as predicting health outcome. Although integrating PGHD into clinical practice is currently in a promising stage, there are several technical challenges and usage barriers that hinder the full utilization of the PGHD potential in clinical care and research. This paper aims to address PGHD opportunities and challenges while developing the DH-Convener project to integrate PGHD into the Electronic Health Record in Austria (ELGA). Accordingly, it provides an integrative technical-clinical-user approach for developing a fully functional health ecosystem for exchanging integrated data among patients, healthcare providers, and researchers.

scholarly journals Diagnosis and Management of Osteoporosis During COVID-19: Systematic Review and Practical Guidance

2021 ◽  
Author(s):  
G. Hampson ◽  
M. Stone ◽  
J. R. Lindsay ◽  
R. K. Crowley ◽  
S. H. Ralston
Keyword(s):  
Clinical Care ◽  
Fragility Fractures ◽  
Osteoporosis Treatment ◽  
Healthcare Services ◽  
Urgent Care ◽  
Health Concern ◽  
Care Pathways ◽  
Long Term Conditions ◽  
Treatment Pathways ◽  
Diagnosis And Management

AbstractIt is acknowledged that the COVID-19 pandemic has caused profound disruption to the delivery of healthcare services globally. This has affected the management of many long-term conditions including osteoporosis as resources are diverted to cover urgent care. Osteoporosis is a public health concern worldwide and treatment is required for the prevention of further bone loss, deterioration of skeletal micro-architecture, and fragility fractures. This review provides information on how the COVID-19 pandemic has impacted the diagnosis and management of osteoporosis. We also provide clinical recommendations on the adaptation of care pathways based on experience from five referral centres to ensure that patients with osteoporosis are still treated and to reduce the risk of fractures both for the individual patient and on a societal basis. We address the use of the FRAX tool for risk stratification and initiation of osteoporosis treatment and discuss the potential adaptations to treatment pathways in view of limitations on the availability of DXA. We focus on the issues surrounding initiation and maintenance of treatment for patients on parenteral therapies such as zoledronate, denosumab, teriparatide, and romosozumab during the pandemic. The design of these innovative care pathways for the management of patients with osteoporosis may also provide a platform for future improvement to osteoporosis services when routine clinical care resumes.

scholarly journals Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 285
Author(s):  
Chuchart Pintavirooj ◽  
Tanapon Keatsamarn ◽  
Treesukon Treebupachatsakul
Keyword(s):  
Real Time ◽  
Vital Sign ◽  
Vital Signs ◽  
Mobile Network ◽  
Cloud Platform ◽  
Telemedicine System ◽  
Web Based ◽  
The Real ◽  
Real Time Application ◽  
Commercial Platform

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

Mobile Health Applications to Support COVID-19 Self-management: Review and Evaluation

2021 ◽  
Author(s):  
Sahar Khenarinezhad ◽  
Ehsan Ghazanfari Savadkoohi ◽  
Leila Shahmoradi
Keyword(s):  
Mobile Health ◽  
Mobile Apps ◽  
Healthcare Providers ◽  
Vital Signs ◽  
Complete Information ◽  
Self Management ◽  
Management Measures ◽  
Management Procedures ◽  
Mobile Health Applications ◽  
Health Applications

Aim: During the epidemic and with an increase in coronavirus (COVID-19) disease prevalence, emergency care is essential to help people stay informed and undertake self-management measures to protect their health. One of these self-management procedures is the use of mobile apps in health. Mobile health (mHealth) applications include mobile devices in collecting clinical health data, sharing healthcare information for practitioners and patients, real-time monitoring of patient vital signs, and the direct provision of care (via mobile telemedicine). Mobile apps are increasing to improve health, but before healthcare providers can recommend these applications to patients, they need to be sure the apps will help change patients' lifestyles. Method: A search was conducted systematically using the keywords "Covid-19," "Coronavirus," "Covid-19, and Self-management" at the "Apple App Store". Then we evaluated the apps according to MARS criteria in May 2020. Results: A total of 145 apps for COVID-19 self-management were identified, but only 32 apps met our inclusion criteria after being assessed. The overall mean MARS score was 2.9 out of 5, and more than half of the apps had a minimum acceptability score (range 2.5-3.9). The "who academy" app received the highest functionality score. Who Academy, Corona-Care and First Responder COVID-19 Guide had the highest scores for behavior change. Conclusion: Our findings showed that few apps meet the quality, content, and functionality criteria for Covid-19 self-management. Therefore, developers should use evidence-based medical guidelines in creating mobile health applications so that, they can provide comprehensive and complete information to both patients and healthcare provider.

RF Vital Sign Sensing under Free Body Movement

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Jian Gong ◽  
Xinyu Zhang ◽  
Kaixin Lin ◽  
Ju Ren ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Vital Sign ◽  
Estimation Error ◽  
Body Movement ◽  
Vital Signs ◽  
Motion Artifacts ◽  
Body Parts ◽  
Training Scheme ◽  
Pattern Movement ◽  
Free Body ◽  
Average Estimation Error

Radio frequency (RF) sensors such as radar are instrumental for continuous, contactless sensing of vital signs, especially heart rate (HR) and respiration rate (RR). However, decades of related research mainly focused on static subjects, because the motion artifacts from other body parts may easily overwhelm the weak reflections from vital signs. This paper marks a first step in enabling RF vital sign sensing under ambulant daily living conditions. Our solution is inspired by existing physiological research that revealed the correlation between vital signs and body movement. Specifically, we propose to combine direct RF sensing for static instances and indirect vital sign prediction based on movement power estimation. We design customized machine learning models to capture the sophisticated correlation between RF signal pattern, movement power, and vital signs. We further design an instant calibration and adaptive training scheme to enable cross-subjects generalization, without any explicit data labeling from unknown subjects. We prototype and evaluate the framework using a commodity radar sensor. Under a variety of moving conditions, our solution demonstrates an average estimation error of 5.57 bpm for HR and 3.32 bpm for RR across multiple subjects, which largely outperforms state-of-the-art systems.

Development of a breast cancer-specific prognostic tool using CancerLinQ Discovery.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 275-275
Author(s):  
Emily Miller Ray ◽  
Xinyi Zhang ◽  
Lisette Dunham ◽  
Xianming Tan ◽  
Jennifer Elston Lafata ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Metastatic Breast Cancer ◽  
High Risk ◽  
Performance Status ◽  
Vital Signs ◽  
Metastatic Breast ◽  
Clinical Use ◽  
Final Model ◽  
Prognostic Tool

275 Background: Oncologists often struggle to know which patients are near end of life to enable a timely transition to supportive care. We developed a breast cancer-specific prognostic tool, using electronic health record data from CancerLinQ Discovery (CLQD), to help identify patients at high risk of near-term death. We created multiple candidate models with varying thresholds for defining high risk that will be considered for future clinical use. Methods: We included patients with breast cancer diagnosed between 1/1/2000 to 6/1/2020 who had at least one encounter with vital signs and evidence of metastatic breast cancer (MBC). All encounters from 1/1/2000 to 7/5/2020 were included. We used multiple imputation (MI) to impute missing numeric variables and treated missing values as a new level for categorical variables. We sampled one encounter per patient and oversampled within 30 days of death, so that the event rate (death within 30 days of encounter) was about 10%. We randomly divided these patients into training (70%) and test datasets (30%). We evaluated candidate predictors of the event using logistic regression with forward variable selection. Candidate predictors included age, vital signs, laboratory values, performance status, pain score, time since chemotherapy, and ER/PR/HER2 receptor status, and change from baseline and change rate of numeric variables. We obtained a single final model by combining resulted logistic regression model from 10 MI training sets. We evaluated this final model on the MI test sets. We varied the alert threshold (i.e., high-risk proportion) from 5% to 40%. Results: We identified 9,270 patients, representing 586,801 encounters. Significant predictors of mortality were: increased age, decreased age at diagnosis, negative change in body mass index, low albumin, high ALP, high AST, high WBC, low sodium, high creatinine, worse performance status, low pulse oximetry, increased age with increased creatinine, high pain score with no opiates, increased pulse rate, unknown/missing PR, opiate use in past 3 months, and prior chemotherapy in past 1 year but not past 30 days. Candidate models had prediction accuracy of 70-89% and positive predictive value of 31-77%. Conclusions: Demographic and clinical variables can be used to predict risk of death within 30 days of a clinical encounter for patients with MBC. Next steps include selection of a preferred model for clinical use, balancing performance characteristics and acceptability, followed by implementation and evaluation of the prognostic tool in the clinic. Candidate models, varying by threshold or percentage of patients assumed to be at high risk, for the outcome of death within 30 days among patients with metastatic breast cancer.[Table: see text]

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