Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards

Xu, Haoran; Li, Peiyao; Yang, Zhicheng; Liu, Xiaoli; Wang, Zhao; Yan, Wei; He, Maoqing; Chu, Wenya; She, Yingjia; Li, Yuzhu; Cao, Desen; Yan, Muyang; Zhang, Zhengbo

doi:10.1007/s10916-020-01653-z

ScienceOpen: research and publishing network

For Publishers

For Researchers

Blog
About

Search
Advanced search

views

recommends

Record: found
Abstract: found
Article: not found

Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards

research-article

Author(s): Haoran Xu ¹ ^, ² , Peiyao Li ³ ^, ⁴ , Zhicheng Yang ⁵ , Xiaoli Liu ⁶ , Zhao Wang ¹ , Wei Yan ⁷ , Maoqing He ⁸ , Wenya Chu ⁸ , Yingjia She ⁹ , Yuzhu Li ¹⁰ , Desen Cao ³ , Muyang Yan ⁷ ^, , Zhengbo Zhang ³ ^, ¹¹ ^,

Publication date (Electronic): 4 September 2020

Journal: Journal of Medical Systems

Publisher: Springer US

Keywords: Wearable technology, Physiological signals, Wireless monitoring system, Electronic health records, Machine learning applications

Read this article at

ScienceOpenPublisher PMC

Bookmark

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Physiological signals can contain abundant personalized information and indicate health status and disease deterioration. However, in current medical practice, clinicians working in the general wards are usually lack of plentiful means and tools to continuously monitor the physiological signals of the inpatients. To address this problem, we here presented a medical-grade wireless monitoring system based on wearable and artificial intelligence technology. The system consists of a multi-sensor wearable device, database servers and user interfaces. It can monitor physiological signals such as electrocardiography and respiration and transmit data wirelessly. We highly integrated the system with the existing hospital information system and explored a set of processes of physiological signal acquisition, storage, analysis, and combination with electronic health records. Multi-scale information extracted from physiological signals and related to the deterioration or abnormality of patients could be shown on the user interfaces, while a variety of reports could be provided daily based on time-series signal processing technology and machine learning to make more information accessible to clinicians. Apart from an initial attempt to implement the system in a realistic clinical environment, we also conducted a preliminary validation of the core processes in the workflow. The heart rate veracity validation of 22 patient volunteers showed that the system had a great consistency with ECG Holter, and bias for heart rate was 0.04 (95% confidence interval: −7.34 to 7.42) beats per minute. The Bland-Altman analysis showed that 98.52% of the points were located between Mean ± 1.96SD. This system has been deployed in the general wards of the Hyperbaric Oxygen Department and Respiratory Medicine Department and has collected more than 1000 cases from the clinic. The whole system will continue to be updated based on clinical feedback. It has been demonstrated that this system can provide reliable physiological monitoring for patients in general wards and has the potential to generate more personalized pathophysiological information related to disease diagnosis and treatment from the continuously monitored physiological data.

Related collections

Most cited references 21

Record: found
Abstract: found
Article: not found

Clinical antecedents to in-hospital cardiopulmonary arrest.

Roland Schein, Nelson Hazday, María Fernanda Peña … (1990)

While the outcome of in-hospital cardiopulmonary arrest has been studied extensively, the clinical antecedents of arrest are less well defined. We studied a group of consecutive general hospital ward patients developing cardiopulmonary arrest. Prospectively determined definitions of underlying pathophysiology, severity of underlying disease, patient complaints, and clinical observations were used to determine common clinical features. Sixty-four patients arrested 161 +/- 26 hours following hospital admission. Pathophysiologic alterations preceding arrest were classified as respiratory in 24 patients (38 percent), metabolic in 7 (11 percent), cardiac in 6 (9 percent), neurologic in 4 (6 percent), multiple in 17 (27 percent), and unclassified in 6 (9 percent). Patients with multiple disturbances had mainly respiratory (39 percent) and metabolic (44 percent) disorders. Fifty-four patients (84 percent) had documented observations of clinical deterioration or new complaints within eight hours of arrest. Seventy percent of all patients had either deterioration of respiratory or mental function observed during this time. Routine laboratory tests obtained before arrest showed no consistent abnormalities, but vital signs showed a mean respiratory rate of 29 +/- 1 breaths per minute. The prognoses of patients' underlying diseases were classified as ultimately fatal in 26 (41 percent), nonfatal in 23 (36 percent), and rapidly fatal in 15 (23 percent). Five patients (8 percent) survived to hospital discharge. Patients developing arrest on the general hospital ward services have predominantly respiratory and metabolic derangements immediately preceding their arrests. Their underlying diseases are generally not rapidly fatal. Arrest is frequently preceded by a clinical deterioration involving either respiratory or mental function. These features and the high mortality associated with arrest suggest that efforts to predict and prevent arrest might prove beneficial.

0 comments Cited 174 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Wearable flexible sweat sensors for healthcare monitoring: a review

Michael Chung, Giuseppino Fortunato, Norbert Radacsi (2019)

The state-of-the-art in wearable flexible sensors (WFSs) for sweat analyte detection was investigated. Recent advances show the development of integrated, mechanically flexible and multiplexed sensor systems with on-site circuitry for signal processing and wireless data transmission. When compared with single-analyte sensors, such devices provide an opportunity to more accurately analyse analytes that are dependent on other parameters (such as sweat rate and pH) by improving calibration from in situ real-time analysis, while maintaining a lightweight and wearable design. Important health conditions can be monitored and on-demand regulating drugs can be delivered using integrated wearable systems but require correlation verification between sweat and blood measurements using in vivo validation tests before any clinical application can be considered. Improvements are necessary for device sensitivity, accuracy and repeatability to provide more reliable and personalized continuous measurements. With rapid recent development, it can be concluded that non-invasive WFSs for sweat analysis have only skimmed the surface of their health monitoring potential and further significant advancement is sure to be made in the medical field.

0 comments Cited 93 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions

Catherine Dinh-Le, Rachel Chuang, Sara Chokshi … (2019)

Background Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology. Although wearables have the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and patient data overload pose a challenge to the adoption of wearables by providers. Objective This study aimed to review the landscape of wearable health technology and data integration to provider EHRs, specifically Epic, because of its prevalence among health systems. The objectives of the study were to (1) identify the current innovations and new directions in the field across start-ups, health systems, and insurance companies and (2) understand the associated challenges to inform future wearable health technology projects at other health organizations. Methods We used a scoping process to survey existing efforts through Epic’s Web-based hub and discussion forum, UserWeb, and on the general Web, PubMed, and Google Scholar. We contacted Epic, because of their position as the largest commercial EHR system, for information on published client work in the integration of patient-collected data. Results from our searches had to meet criteria such as publication date and matching relevant search terms. Results Numerous health institutions have started to integrate device data into patient portals. We identified the following 10 start-up organizations that have developed, or are in the process of developing, technology to enhance wearable health technology and enable EHR integration for health systems: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of the meaningful use of device data and streamlining provider workflows. We also found 4 insurance companies that encourage the growth and uptake of wearables through health tracking and incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock. Conclusions The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges. Although this study does not provide a full comprehensive catalog of all wearable health technology initiatives, it is representative of trends and implications for the integration of patient data into the EHR. Our work serves as an initial foundation to provide resources on implementation and workflows around wearable health technology for organizations across the health care industry.

0 comments Cited 93 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Muyang Yan: yanmy301@sina.com

Zhengbo Zhang:

ORCID: http://orcid.org/0000-0001-9218-5644

zhengbozhang@126.com

Journal

Journal ID (nlm-ta): J Med Syst

Journal ID (iso-abbrev): J Med Syst

Title: Journal of Medical Systems

Publisher: Springer US (New York )

ISSN (Print): 0148-5598

ISSN (Electronic): 1573-689X

Publication date (Electronic): 4 September 2020

Publication date (Print): 2020

Volume: 44

Issue: 10

Electronic Location Identifier: 182

Affiliations

[1 ]GRID grid.488137.1, ISNI 0000 0001 2267 2324, Medical School of Chinese PLA, ; Beijing, China

[2 ]Medical Sergeant School, Army Medical University, Hebei, China

[3 ]GRID grid.414252.4, ISNI 0000 0004 1761 8894, Department of Biomedical Engineering, , Chinese PLA General Hospital, ; Beijing, China

[4 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Department of Computer Science and Technology, , Tsinghua University, ; Beijing, China

[5 ]PAII Inc, Palo Alto, CA USA

[6 ]GRID grid.64939.31, ISNI 0000 0000 9999 1211, School of Biological Science and Medical Engineering, , Beihang University, ; Beijing, China

[7 ]GRID grid.414252.4, ISNI 0000 0004 1761 8894, Department of Hyperbaric Oxygen Therapy, the First Medical Center, , Chinese PLA General Hospital, ; Beijing, China

[8 ]Beijing Haisi Ruige Science & Technology Co., Ltd, Beijing, China

[9 ]GRID grid.414252.4, ISNI 0000 0004 1761 8894, Department of Pulmonary & Critical Care Medicine, the First Medical Center, , Chinese PLA General Hospital, ; Beijing, China

[10 ]Department of Pulmonary & Critical Care Medicine, Hainan Hospital of PLA General Hospital, Sanya, Hainan China

[11 ]GRID grid.414252.4, ISNI 0000 0004 1761 8894, Center for Artificial Intelligence in Medicine, , Chinese PLA General Hospital, ; Beijing, China

Author information

Zhengbo Zhang http://orcid.org/0000-0001-9218-5644

Article

Publisher ID: 1653

DOI: 10.1007/s10916-020-01653-z

PMC ID: 7471584

PubMed ID: 32885290

SO-VID: 7c15c334-1be0-4445-a837-e6aba3d5e5ad

License:

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

History

Date received : 7 July 2020

Date accepted : 25 August 2020