Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study

Abstract

Background Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development. Objective This study focused on the satisfaction of ICU staff with current patient monitoring and their suggestions for future improvements. We aimed to identify aspects of monitoring that interrupt patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff members are willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further aimed to identify differences in the responses of different professional groups. Methods This survey study was performed with ICU staff from 4 ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire, by analyzing a preceding qualitative interview study with ICU staff, about the clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and chi-square tests to compare the distributions of item responses of the professional groups. Results In total, 86 of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated they felt confident using the patient monitoring equipment, but that high rates of false-positive alarms and the many sensor cables interrupted patient care. Regarding future improvements, respondents asked for wireless sensors, a reduction in the number of false-positive alarms, and hospital standard operating procedures for alarm management. Responses to the display devices proposed for remote patient monitoring were divided. Most respondents indicated it would be useful for earlier alerting or when they were responsible for multiple wards. AI for ICUs would be useful for early detection of complications and an increased risk of mortality; in addition, the AI could propose guidelines for therapy and diagnostics. Transparency, interoperability, usability, and staff training were essential to promote the use of AI. The majority wanted to learn more about new technologies for the ICU and required more time for learning. Physicians had fewer reservations than nurses about AI-based intelligent alarm management and using mobile phones for remote monitoring. Conclusions This survey study of ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm standard operating procedures, introducing wireless sensors, preparing for the use of AI, and enhancing the digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine. Trial Registration ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173

Proposal for an Implementation Framework for Digital Health Technology in the Intensive Care Unit: A Qualitative Study

“…Adaptability and user-centered design have been identi ed as key facilitators of digital health implementation in other settings [8,46,47]. To create the respective conditions, developers and providers of digital health technologies should actively participate in the implementation processes by taking advantage of the valuable feedback from clinical stakeholders and adapting their products in the spirit of user-centered design [48][49][50]. Therefore, our proposed implementation framework suggests several strategies to enhance the involvement of clinical stakeholders directly (Organize clinician implementation team meetings, Inform local opinion leaders, Identify and prepare champions), in line with proposed strategies for other implementation settings [51,52].…”

Über die Zukunft des maschinellen Lernens in der Anästhesiologie

“…In den letzten Jahren sind viele Veröffentlichungen erschienen, die einen erfolgreichen Einsatz diverser intelligenter Algorithmen in verschiedenen medizinischen Bereichen versprechen [5][6][7][8][9]…”

SARS-CoV, MERS-CoV and SARS-CoV-2: A Diagnostic Challenge

“…Antiviral vaccines are currently under development [22] , [23] , [24] . These new viruses pose major problems to global health in general and primarily to infection control and public health services [25] , [26] , [27] . In the current scenario, extensive awareness of in-depth education about SARS-CoV-2, SARS-CoV and MERS-CoV and multi-directional care between healthcare professionals and hospitalized patients infected with the above-mentioned viruses can help reduce the spread of these viruses [28] , [29] , [30] .…”

“…The community spread of the viral infection emphasizes the significance of the clinical diagnosis of illness caused by SARS-CoV-2, SARS-CoV and MERS-CoV. In compliance with the guidelines provided by the Centers for Disease Control and Prevention, the clinicians must organize screening tests for viral infections in patients with nearby health professionals [26] , [27] , [28] , [29] , [30] . Traditionally, real-time reverse transcription-polymerase chain reaction (RT-PCR) is the most extensively employed diagnostic tool for diagnosing viral infection caused by SARS-CoV-2, SARS-CoV and MERS-CoV, while viral cultures are not approved [27] , [28] , [29] , [30] .…”

“…In compliance with the guidelines provided by the Centers for Disease Control and Prevention, the clinicians must organize screening tests for viral infections in patients with nearby health professionals [26] , [27] , [28] , [29] , [30] . Traditionally, real-time reverse transcription-polymerase chain reaction (RT-PCR) is the most extensively employed diagnostic tool for diagnosing viral infection caused by SARS-CoV-2, SARS-CoV and MERS-CoV, while viral cultures are not approved [27] , [28] , [29] , [30] . In addition, the viral infection can be identified from a combined effect of symptoms, health issues and a chest computed tomography (CT) scan displaying the characteristics of pneumonia [32] , [33] , [34] , [35] .…”