Optimizing Tele-ICU Operational Efficiency Through Workflow Process Modeling and Restructuring

Best Practices in Telecritical Care: Expert Consensus Recommendations From the Telecritical Care Collaborative Network

OBJECTIVES

Telecritical care (TCC) refers to the delivery of critical care using telehealth technologies. Despite increasing utilization, significant practice variation exists and literature regarding efficacy remains sparse. The Telecritical Care Collaborative Network sought to provide expert, consensus-based best practice recommendations for the design and delivery of TCC.

DESIGN

We used a modified Delphi methodology. Following literature review, an oversight panel identified core domains and developed declarative statements for review by an expert voting panel. During three voting rounds, voters agreed or disagreed with statements and provided open-ended feedback, which the oversight panel used to revise statements. Statements met criteria for consensus when accepted by greater than or equal to 85% of voters.

SETTING/SUBJECTS

The oversight panel included 18 multidisciplinary members of the TCC Collaborative Network, and the voting panel included 32 invited experts in TCC, emphasizing diversity of discipline, care delivery models, and geography.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We identified ten core domains: definitions/terminology; care delivery models; staffing and coverage models; technological considerations; ergonomics and workplace safety; licensing, credentialing, and certification; trust and relationship building; quality, safety, and efficiency, research agenda; and advocacy, leading to 79 practice statements. Of 79 original statements, 67 were accepted in round 1. After revision, nine were accepted in round 2 and two in round 3 (two statements were merged). In total, 78 practice statements achieved expert consensus.

CONCLUSIONS

These expert consensus recommendations cover a broad range of topics relevant to delivery of TCC. Experts agreed that TCC is most effective when delivered by care teams with specific expertise and by programs with explicit protocols focusing on effective communication, technical reliability, and real-time availability. Interventions should be tailored to local conditions. Although further research is needed to guide future best practice statements, these results provide valuable and actionable recommendations for the delivery of high-quality TCC.

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.

Operation analysis of the tele-critical care service demonstrates value delivery, service adaptation over time, and distress among tele-providers

Background

Our study addresses the gaps in knowledge of the characterizations of operations by remote tele-critical care medicine (tele-CCM) service providers interacting with the bedside team. The duration of engagements, the evolution of the tele-CCM service over time, and the distress during interactions with the bedside team have not been characterized systematically. These characteristics are critical for planning the deployment of teleICU services and preventing burnout among remote teleICU providers.

Methods

REDCap self-reported activity logs collected engagement duration, triggers (emergency button, tele-CCM software platform, autonomous algorithm, asymmetrical communication platform, phone), expediency, nature (proactive rounding, predetermined task, response to medical needs), communication modes, and acceptance. Seven hospitals with 16 ICUs were overseen between 9/2020 and 9/2021 by teams consisting of telemedicine medical doctors (eMD), telemedicine registered nurses (eRN), and telemedicine respiratory therapists (eRT).

Results

39,915 total engagements were registered. eMDs had a significantly higher percentage of emergent and urgent engagements (31.9%) vs. eRN (9.8%) or eRT (1.7%). The average tele-CCM intervention took 16.1 ± 10.39 min for eMD, 18.1 ± 16.23 for eRN, and 8.2 ± 4.98 min for eRT, significantly varied between engagement, and expediency, hospitals, and ICUs types. During the observation period, there was a shift in intervention triggers with an increase in autonomous algorithmic ARDS detection concomitant with predominant utilization of asynchronous communication, phone engagements, and the tele-CCM module of electronic medical records at the expense of the share of proactive rounding. eRT communicated more frequently with bedside staff (% MD = 37.8%; % RN = 36.8, % RT = 49.0%) but mostly with other eRTs. In contrast, the eMD communicated with all ICU stakeholders while the eRN communicated chiefly with other RN and house staff at the patient's bedside. The rate of distress reported by tele-CCM staff was 2% among all interactions, with the entity hospital being the dominant factor.

Conclusions

Delivery of tele-CCM services has to be tailored to the specific beneficiary of tele-CCM services to optimize care delivery and minimize distress. In addition, the duration of the average intervention must be considered while creating an efficient workflow.

A Survey of Tele-Critical Care State and Needs in 2019 and 2020 Conducted among the Members of the Society of Critical Care Medicine

The study’s objective was to assess facilitators and barriers of Tele-Critical Care (TCC) perceived by SCCM members. By utilizing a survey distributed to SCCM members, a cross-sectional study was developed to analyze survey results from December 2019 and July 2020. SCCM members responded to the survey (n = 15,502) with a 1.9% response rate for the first distribution and a 2.54% response rate for the second survey (n = 9985). Participants (n = 286 and n = 254) were almost equally distributed between non-users, providers, users, and potential users of TCC services. The care delivery models for TCC were similar across most participants. Some consumers of TCC services preferred algorithmic coverage and scheduled rounds, while reactive and on-demand models were less utilized. The surveys revealed that outcome-driven measures were the principal form of TCC performance evaluation. A 1:100 (provider: patients) ratio was reported to be optimal. Factors related to costs, perceived lack of need for services, and workflow challenges were described by those who terminated TCC services. Barriers to implementation revolved around lack of reimbursement and adequate training. Interpersonal communication was identified as an essential TCC provider skill. The second survey introduced after the onset pandemic demonstrated more frequent use of advanced practice providers and focus on performance measures. Priorities for effective TCC deployment include communication, knowledge, optimal operationalization, and outcomes measurement at the organizational level. The potential effect of COVID-19 during the early stages of the pandemic on survey responses was limited and focused on the need to demonstrate TCC value.

Communication and role clarity inform TeleICU use: a qualitative analysis of opportunities and barriers in an established program using AACN framework

Background

Understanding the use of tele-intensive care unit (ICU) services is an essential component in evaluating current practice and informing future use as the adoption and application of teleICU services expands. We sought to explore if novel ways to utilize teleICU services can emerge within an established, consulting-style teleICU model considering the program’s flexible, provider-driven operation.

Methods

This was a qualitative study of one teleICU/hospital dyad using semi-structured interviews from a convenience sample of ICU (n = 19) and teleICU (n = 13) nurses. Interviews were analyzed using directed content analysis to identify themes that describe their experiences with teleICU using a deductive codebook developed from an expert consensus (American Association of Critical Care Nurses) AACN statement on teleICU nursing.

Results

Three themes were identified through the qualitative content analysis: [1] nurses described unique teleICU knowledge, including systems thinking and technological skills, [2] the teleICU partnership supported quality improvement initiatives, and [3] elements of the work environment influenced perceptions of teleICU and its use. When elements of the work environment, such as effective communication and role clarity, were not present, teleICU use was variable.

Conclusions

Flexible, provider-driven approaches for integrating teleICU services into daily practice may help define the future use of the teleICU model’s applicability. Future work should focus on the importance of effective communication and role clarity in integrating the emerging teleICU services into teleICU/ICU practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06287-6.

Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges

Supplemental Digital Content is available in the text.

Objectives:

As the demand for critical care beds rises each year, hospitals must be able to adapt. Delayed transfer of care reduces available critical care capacity and increases occupancy. The use of mathematic modeling within healthcare systems has the ability to aid planning of resources. Discrete-event simulation models can determine the optimal number of critical care beds required and simulate different what-if scenarios.

Design:

Complex discrete-event simulation model was developed using a warm-up period of 30 days and ran for 30 trials against a 2-year period with the mean calculated for the runs. A variety of different scenarios were investigated to determine the effects of increasing capacity, increasing demand, and reduction of proportion and length of delayed transfer of care out of the ICU.

Setting:

Combined data from two ICUs in United Kingdom.

Patients:

The model was developed using 1,728 patient records and was validated against an independent dataset of 2,650 patients.

Interventions:

None.

Measurements and Main Results:

During model validation, the average bed utilization and admittance rate were equal to the real-world data. In the what-if scenarios, we found that increasing bed numbers from 23 to 28 keeping the arrival rate stable reduces the average occupancy rate to 70%. We found that the projected 4% yearly increase in admissions could overwhelm even the 28-bedded unit, without change in the delayed transfer of care episodes. Reduction in the proportion of patients experiencing delayed transfer of care had the biggest effect on occupancy rates, time spent at full capacity, and average bed utilization.

Conclusions:

Using discrete-event simulation of commonly available baseline patient flow and patient care data produces reproducible models. Reducing the proportion of patients with delayed transfer of care had a greater effect in reducing occupancy levels than simply increasing bed numbers even when demand is increased.