Impact of Intensive Care Unit Telemedicine on Outcomes

Focusing on experts: Expectations of healthcare professionals regarding the use of telemedicine in intensive care units

Objectives

Telemedical applications are solutions to challenges in the healthcare system. However, it is unclear what intensive care unit healthcare professionals expect from such solutions. This study investigated the expectations and concerns of nurses and physicians when implementing telemedicine tools in intensive care units (tele-ICU).

Methods

The study was conducted in intensive care units in 2020 during the second wave of the COVID-19 pandemic. It used a mixed-methods approach targeted at physicians and nurses and involved 14 qualitative interviews and 63 quantitative questionnaires.

Results

The qualitative and quantitative data showed that both nurses and physicians were willing to use tele-ICU. Nurses recognised the advantages of real-time access to expertise offered by tele-ICU, but feared this would reduce physicians' on-site patient time. Physicians, in turn, were concerned that they would be expected to be continuously on call. The majority in both groups agreed that any tele-ICU solution must be simple to use and integrate easily into existing organisational structures, networks, and work routines. Additionally, COVID-19 significantly influenced expectations: those who reported having more personal health concerns during the pandemic were more predisposed to favour the use of tele-ICU.

Conclusions

Overall, tele-ICU supports better care, but a successful implementation depends on its ease of use and context-sensitive approaches. Effectively integrating tele-ICU solutions into daily clinical routines requires input from nurses and physicians and their involvement in the implementation process from the outset, as well as consideration of existing organisational structures. Such measures will vastly increase the chance of acceptance and successful adoption of telemedical solutions in clinical practice.

Descriptive Comparison of Two Models of Tele-Critical Care Delivery in a Large Multi-Hospital Health Care System

Introduction: The Society of Critical Care Medicine Tele-Critical Care (TCC) Committee has identified the need for rigorous comparative research of different TCC delivery models to support the development of best practices for staffing, application, and approaches to workflow. Our objective was to describe and compare outcomes between two TCC delivery models, TCC with 24/7 Bedside Intensivist (BI) compared with TCC with Private Daytime Attending Intensivist (PI) in relation to intensive care unit (ICU) and hospital mortality, ICU and hospital length of stay (LOS), cost, and complications across the spectrum of routine ICU standards of care. Methods: Observational cohort study at large health care system in 12 ICUs and included patients, ≥18, with Acute Physiology and Chronic Health Evaluation (APACHE) IVa scores and predictions (October 2016-June 2019). Results: Of the 19,519 ICU patients, 71.7% (n = 13,993) received TCC with 24/7 BI while 28.3% (n = 5,526) received TCC with PI. ICU and Hospital mortality (4.8% vs. 3.1%, p < 0.0001; 12.6% vs. 8.1%, p < 0.001); and ICU and Hospital LOS (3.2 vs. 2.4 days, p < 0.001; 9.8 vs. 7.2 days, p < 0.001) were significantly higher among 24/7 BI compared with PI. The APACHE observed/expected ratios (odds ratio [OR]; 95% confidence interval [CI]) for ICU mortality (0.62; 0.58-0.67) vs. (0.53; 0.46-0.61) and Hospital mortality (0.95; 0.57-1.48) vs. (0.77; 0.70-0.84) were significantly different for 24/7 BI compared with PI. Multivariate mixed models that adjusted for confounders demonstrated significantly greater odds of (OR; 95% CI) ICU mortality (1.58; 1.28-1.93), Hospital mortality (1.52; 1.33-1.73), complications (1.55; 1.18-2.04), ICU LOS [3.14 vs. 2.59 (1.25; 1.19-1.51)], and Hospital LOS [9.05 vs. 7.31 (1.23; 1.21-1.25)] among 24/7 BI when compared with PI. Sensitivity analyses adjusting for ICU admission within 24 h of hospital admission, receiving active ICU treatments, nighttime admission, sepsis, and highest third acute physiology score indicated significantly higher odds for 24/7 BI compared with PI. Conclusion: Our comparison demonstrated that TCC delivery model with PI provided high-quality care with significant positive effects on outcomes. This suggests that TCC delivery models have broad-ranging applicability and benefits in routine critical care, thus necessitating progressive research in this direction.

Remote monitoring in the use of extracorporeal membrane oxygenation and acute mechanical circulatory support

PURPOSE OF REVIEW

To provide an overview of the role of remote monitoring tools in management of critically-ill patients requiring acute mechanical circulatory support (MCS).

RECENT FINDINGS

Tele-critical care systems have received new interest during the COVID-19 pandemic, which has stretched the capacity of health systems everywhere. At the same time, utilization of MCS and extracorporeal membrane oxygenation (ECMO) technologies has increased during the pandemic. The opportunity for remote monitoring and clinical decision support for ECMO and acute MCS devices has been recognized by industry partners, with several major platforms implementing technology infrastructure for it in available products. Healthcare systems face challenges interfacing multiple devices from multiple manufacturers with each other and with their designated electronic health records. Furthermore, the availability of data must be combined with algorithms for alerting on clinical events and with implementation systems to act upon these alerts. Studies are not yet published validating remote monitoring platforms for ECMO and MCS in clinical care.

SUMMARY

Remote monitoring for MCS devices represents a major opportunity for further investigation to improve the utilization of these devices and better serve patients.

Telemedicine in Intensive Care Units: Scoping Review

BACKGROUND

The role of telemedicine in intensive care has been increasing steadily. Tele-intensive care unit (ICU) interventions are varied and can be used in different levels of treatment, often with direct implications for the intensive care processes. Although a substantial body of primary and secondary literature has been published on the topic, there is a need for broadening the understanding of the organizational factors influencing the effectiveness of telemedical interventions in the ICU.

OBJECTIVE

This scoping review aims to provide a map of existing evidence on tele-ICU interventions, focusing on the analysis of the implementation context and identifying areas for further technological research.

METHODS

A research protocol outlining the method has been published in JMIR Research Protocols. This review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). A core research team was assembled to provide feedback and discuss findings.

RESULTS

A total of 3019 results were retrieved. After screening, 25 studies were included in the final analysis. We were able to characterize the context of tele-ICU studies and identify three use cases for tele-ICU interventions. The first use case is extending coverage, which describes interventions aimed at extending the availability of intensive care capabilities. The second use case is improving compliance, which includes interventions targeted at improving patient safety, intensive care best practices, and quality of care. The third use case, facilitating transfer, describes telemedicine interventions targeted toward the management of patient transfers to or from the ICU.

CONCLUSIONS

The benefits of tele-ICU interventions have been well documented for centralized systems aimed at extending critical care capabilities in a community setting and improving care compliance in tertiary hospitals. No strong evidence has been found on the reduction of patient transfers following tele-ICU intervention.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/19695.

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

Artificial intelligence and computer simulation models in critical illness

Widespread implementation of electronic health records has led to the increased use of artificial intelligence (AI) and computer modeling in clinical medicine. The early recognition and treatment of critical illness are central to good outcomes but are made difficult by, among other things, the complexity of the environment and the often non-specific nature of the clinical presentation. Increasingly, AI applications are being proposed as decision supports for busy or distracted clinicians, to address this challenge. Data driven "associative" AI models are built from retrospective data registries with missing data and imprecise timing. Associative AI models lack transparency, often ignore causal mechanisms, and, while potentially useful in improved prognostication, have thus far had limited clinical applicability. To be clinically useful, AI tools need to provide bedside clinicians with actionable knowledge. Explicitly addressing causal mechanisms not only increases validity and replicability of the model, but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.