Assessing the Usability of a Novel Wearable Remote Patient Monitoring Device for the Early Detection of In-Hospital Patient Deterioration: Observational Study

Abstract

Background: Patients admitted to general wards are inherently at risk of deterioration. Thus, tools that can provide early detection of deterioration may be lifesaving. Frequent remote patient monitoring (RPM) has the potential to allow such early detection, leading to a timely intervention by health care providers.

Objective: This study aimed to assess the potential of a novel wearable RPM device to provide timely alerts in patients at high risk for deterioration.

Methods: This prospective observational study was conducted in two general wards of a large tertiary medical center. Patients determined to be at high risk to deteriorate upon admission and assigned to a telemetry bed were included. On top of the standard monitoring equipment, a wearable monitor was attached to each patient, and monitoring was conducted in parallel. The data gathered by the wearable monitors were analyzed retrospectively, with the medical staff being blinded to them in real time. Several early warning scores of the risk for deterioration were used, all calculated from frequent data collected by the wearable RPM device: these included (1) the National Early Warning Score (NEWS), (2) Airway, Breathing, Circulation, Neurology, and Other (ABCNO) score, and (3) deterioration criteria defined by the clinical team as a "wish list" score. In all three systems, the risk scores were calculated every 5 minutes using the data frequently collected by the wearable RPM device. Data generated by the early warning scores were compared with those obtained from the clinical records of actual deterioration among these patients.

Results: In total, 410 patients were recruited and 217 were included in the final analysis. The median age was 71 (IQR 62-78) years and 130 (59.9%) of them were male. Actual clinical deterioration occurred in 24 patients. The NEWS indicated high alert in 16 of these 24 (67%) patients, preceding actual clinical deterioration by 29 hours on average. The ABCNO score indicated high alert in 18 (75%) of these patients, preceding actual clinical deterioration by 38 hours on average. Early warning based on wish list scoring criteria was observed for all 24 patients 40 hours on average before clinical deterioration was detected by the medical staff. Importantly, early warning based on the wish list scoring criteria was also observed among all other patients who did not deteriorate.

Conclusions: Frequent remote patient monitoring has the potential for early detection of a high risk to deteriorate among hospitalized patients, using both grouped signal-based scores and algorithm-based prediction. In this study, we show the ability to formulate scores for early warning by using RPM. Nevertheless, early warning scores compiled on the basis of these data failed to deliver reasonable specificity. Further efforts should be directed at improving the specificity and sensitivity of such tools.

Trial registration: ClinicalTrials.gov NCT04220359; https://ichgcp.net/clinical-trials-registry/NCT04220359.

Keywords: clinical prediction; early warning score system; general ward; noninvasive monitoring; patient deterioration; remote patient monitoring; uHealth; wearable devices.

Conflict of interest statement

Conflicts of Interest: None declared.

©Edward Itelman, Gadi Shlomai, Avshalom Leibowitz, Shiri Weinstein, Maya Yakir, Idan Tamir, Michal Sagiv, Aia Muhsen, Maxim Perelman, Daniella Kant, Eyal Zilber, Gad Segal. Originally published in JMIR Formative Research (https://formative.jmir.org), 09.06.2022.

Figures

Figure 1

Trends of continuous data gathered by the monitoring platform. Sample of the monitoring data from a single patient, showing systolic blood pressure (mm Hg), heart rate (beats/min), respiratory rate (breaths/min), blood oxygen saturation (%), and markings of warnings and prediction. The black line indicates the time of actual clinical detection of deterioration by the medical staff. Red lines indicate times at which high-risk warnings were provided by the platform using the National Early Warning Score.