Intensive care unit telemedicine: review and consensus recommendations.

ABSTRACTThe gut microbiome of an individual can shape the local environmental surface microbiome. We sought to determine how the intensive care unit (ICU) patient gut microbiome shapes the ICU room surface microbiome, focusing on vancomycin-resistant Enterococcus (VRE), a common ICU pathogen. This was an ICU-based prospective cohort study. Rectal swabs were performed in adult ICU patients immediately at the time of ICU admission and environmental surface swabs were performed at five predetermined time points. All swabs underwent 16S rRNA gene sequencing and culture for VRE. 304 ICU patients and 24 ICU rooms were sampled (5 longitudinal samples per ICU room). Spatially adjacent ICU rooms were no more microbially similar than nonadjacent rooms. Microbial signatures within rooms diverged rapidly over time: in 14 days, ICU rooms were as similar to other ICU rooms as they were to their prior selves. This divergence over time was more pronounced in rooms with higher patient turnover. Examining VRE status by culture, patient VRE gut colonization had modest agreement with room surface VRE (kappa statistic 0.36). There were no ICU rooms that consistently cultured positive for VRE, including those that housed VRE positive patients. Individual ICU patients had a limited impact on ICU room surface microbiome, and rooms diverged similarly over time regardless of patients. Patient VRE gut colonization may have a modest influence on room surface VRE but there were no “bad rooms” that consistently cultured positive for VRE. These results may be useful in planning infection control measures.IMPORTANCE This study found that intensive care unit (ICU) room microbial signatures diverged from their baseline quickly: within 2 weeks, individual ICU rooms had lost distinguishing characteristics and were as similar to other ICU rooms as they were to their former selves. Patient turnover within rooms accelerated this drift. Patient gut colonization with vancomycin-resistant Enterococcus (VRE) was associated with ICU room surface contamination with VRE; again, within 2 weeks, this association was substantially diminished. These results provide dynamic information regarding how patients control the microbiota on local hospital room surfaces and may facilitate decision making for infection prevention and control measures targeting VRE or other organisms.

Early implementations of intensive care unit (ICU) telemedicine, or tele-ICU, focused on using remote intensivists to alleviate intensivist staffing shortages and to meet national ICU staffing standards. With decreasing technology acquisition and implementation costs and improved technological capabilities, the use cases for ICU telemedicine consultations have expanded. Traditional tele-ICU consultation includes ICU patient triage for improved hospital system resource allocation and continuous monitoring of critically ill patients to promote early interventions that prevent patient deterioration. With the widespread adoption of electronic medical records, integration of medical devices, and sensorization of the environment, the boundaries of ICU tele-consulting will rapidly expand. This expansion should allow a wide array of specialist consultation for patients within an ICU while also extending the ability of intensivists to tele-consult on patients not located in the ICU. Such activities have the potential to expand the reach of ICU care beyond the walls of the ICU, leading to improved care of patients who are trending toward critical illness and thus preventing the need for ICU admission. The barriers to more universal use of telemedicine for critical care consultations include staffing needs, existing work processes, and the attitudes and perceptions of ICU staff. Models for telemedicine implementations are highly variable and depend on the resources and use cases of individual healthcare systems. At this time there is no single optimal model of ICU telemedicine. As models continue to mature and evolve, the value that telemedicine brings to critically ill patients, providers, and healthcare systems will be better established.

Effects of Intensive Care Unit (ICU) telemedicine on patient and staff outcomes are mixed. Variation in utilization is potentially driving these differences. ICU telemedicine utilization is understudied, with existing research focusing on telemedicine staff. We assess ICU telemedicine utilization from the perspective of the end user-ICU staff-to better understand how telemedicine use is conceptualized and practiced at the bedside. We conducted a thematic content analysis of semistructured interviews with bedside ICU staff. Staff were interviewed at seven ICUs in six Veterans Health Administration facilities, representing varying ICU complexities and points in time (2 and 12 months postimplementation of ICU telemedicine). Fifty-eight bedside ICU staff described instances of telemedicine use, which were categorized into three types: Urgent ICU Patient Care, Clinical Decision-Making and Support, and General ICU Patient Care. The most commonly described use was General ICU Patient Care and the least common was Urgent ICU Patient Care. ICU staff from lower complexity ICUs had fewer descriptions of use compared to staff at higher complexity ICUs. At 12 months postimplementation, staff recounted more instances of all three utilization types. It is important to understand how telemedicine is being used within ICUs to evaluate its impact. The presence of three types of use, variability in use by ICU complexity, and change in use over time suggest the need for comprehensive measures of utilization to evaluate effectiveness. ICU telemedicine needs to develop an agreed upon typology for documenting ICU telemedicine utilization and incorporate these measures into models of its effect on clinical outcomes.

POINT: Should Computerized Protocols Replace Physicians for Managing Mechanical Ventilation? Yes

MORE THAN 25 YEARS HAVE PASSED SINCE THE original description of intensive care unit (ICU) telemedicine, a technological strategy to improve critical care outcomes by expanding the reach and availability of intensivist clinicians. Since then, the understanding of evidence-based practice and the role of information technology in the ICU have substantially increased. Multiple commercial applications of ICU telemedicine now exist, and telemedicine is widely touted as an all-encompassing strategy to improve ICU outcomes. Yet even after 25 years, the optimal role of telemedicine in the ICU remains uncertain. A large, multicenter study published recently showed no demonstrable clinical benefit, and a recent meta-analysis found no beneficial association between ICU telemedicine and in-hospital mortality. These results have left clinicians, hospital administrators, and policy makers wondering how to best use this technology, if at all. In this issue of JAMA, Lilly and colleagues report a single academic center investigation that is of similar size and scope as the previous multicenter study, but has dramatically different results. Lilly et al examined the outcomes associated with implementation of ICU telemedicine among 6290 adult patients admitted to 7 ICUs using a stepped-wedge time series design. Tele-intensivists acted in complement with local clinicians to enforce daily goals, review adherence to evidence-based practices, and respond to bedside alarms. Admission to an ICU operating under the telemedicine model was associated with increased receipt of evidence-based preventive strategies and lower rates of ICU-acquired complications. Hospital mortality, ICU length of stay, and duration of mechanical ventilation also decreased. Importantly, telemedicine was associated with lower mortality both within ICUs over time and across ICUs during the same periods, strengthening the inference that the results are not due purely to time trends. The current study by Lilly et al and the previous study by Thomas et al included roughly the same number of ICUs and hospitals, used the same proprietary telemedicine system, and were both directed by academic leaders in the field. So how can these discordant results be reconciled? In the study by Thomas et al, there was limited physician buy-in and less than one-third of the patients received the full discretion of the telemedicine team for clinical care. In contrast, the telemedicine team in the study by Lilly and colleagues was allowed full discretion for all patients. Increased discretion may have led to more interventions directed at improving outcomes. Perhaps more importantly, the telemedicine program in the study by Thomas et al was not linked to any specific quality improvement programs. Instead, the teleintensivists provided mostly remote monitoring, intervening only when necessary—such that care via telemedicine was reactive rather than proactive. In contrast, the telemedicine program in the study by Lilly et al was tightly linked to specific quality improvement activities. The teleintensivists conducted real-time best-practice audits for selected evidence-based practices, reviewed care plans and daily goal sheets, and ensured adherence with the local intensivists’ plans. These interventions worked, as evidenced by the patients in the telemedicine program being more likely to receive evidence-based practices for prevention of ICU complications, demonstrating a plausible mechanism for the decrease in mortality. For this reason, the study by Lilly et al provides the first convincing evidence that ICU telemedicine can be an effective complement to bedside care in some settings. However, this study is not without limitations. The size of the adjusted mortality reduction is implausibly large, leaving open the possibility that some of the apparent benefit is due to differences in how severity of illness was measured between the 2 study groups. Moreover, the 7 ICUs in this study are part of one relatively well-resourced academic medical center that has a strong culture of quality improvement. It is unclear if these results could be replicated in hospitals with fewer resources to devote toward ICU quality. Additionally, all of the telemedicine physicians also worked in the target ICUs, which may have served to increase buy-in among local practitioners. These

As our population ages and the demand for high-level intensive care unit (ICU) services increase, the ICU physician supply continues to lag. In addition, hospitals, physician groups, and patients are demanding rapid access for the highest level of expertise in the care of critically ill patients. Telemedicine in the ICU combined with remote patient monitoring has been increasingly touted as a model of care to increase efficiencies and quality of care. Telemedicine in the ICU provides the potential to connect critically ill patients to sophisticated specialty care on a 24/7 basis, even for those hospitalized in rural locations where access to timely specialty consultations are uncommon. Research on the use of telemedicine in the ICU has suggested improved outcomes, such as reductions in mortality, reductions in length of stay, and greater adherence to evidence-based guidelines. Although the clinical footprint of telemedicine in ICU has grown over the past 20 years, there has been a relative slowing of implementation. This review examines the clinical evidence supporting the use of telemedicine in the ICU and discusses the impact on clinical efficacy and costs of care. Additionally, we review the current hurdles to more rapid adoption, including the significant financial investment, different models of care affecting the return on investment, and the varied cultural attitudes that impact the success and acceptance of care models using telemedicine in the ICU.

IntroductionClinicians and specialty societies often emphasize the potential importance of natural light for quality care of critically ill patients, but few studies have examined patient outcomes associated with exposure to natural light. We hypothesized that receiving care in an intensive care unit (ICU) room with a window might improve outcomes for critically ill patients with acute brain injury.MethodsThis was a secondary analysis of a prospective cohort study. Seven ICU rooms had windows, and five ICU rooms did not. Admission to a room was based solely on availability.We analyzed data from 789 patients with subarachnoid hemorrhage (SAH) admitted to the neurological ICU at our hospital from August 1997 to April 2006. Patient information was recorded prospectively at the time of admission, and patients were followed up to 1 year to assess mortality and functional status, stratified by whether care was received in an ICU room with a window.ResultsOf 789 SAH patients, 455 (57.7%) received care in a window room and 334 (42.3%) received care in a nonwindow room. The two groups were balanced with regard to all patient and clinical characteristics. There was no statistical difference in modified Rankin Scale (mRS) score at hospital discharge, 3 months or 1 year (44.8% with mRS scores of 0 to 3 with window rooms at hospital discharge versus 47.2% with the same scores in nonwindow rooms at hospital discharge; adjusted odds ratio (aOR) 1.01, 95% confidence interval (95% CI) 0.67 to 1.50, P = 0.98; 62.7% versus 63.8% at 3 months, aOR 0.85, 95% CI 0.58 to 1.26, P = 0.42; 73.6% versus 72.5% at 1 year, aOR 0.78, 95% CI 0.51 to 1.19, P = 0.25). There were also no differences in any secondary outcomes, including length of mechanical ventilation, time until the patient was able to follow commands in the ICU, need for percutaneous gastrostomy tube or tracheotomy, ICU and hospital length of stay, and hospital, 3-month and 1-year mortality.ConclusionsThe presence of a window in an ICU room did not improve outcomes for critically ill patients with SAH admitted to the ICU. Further studies are needed to determine whether other groups of critically ill patients, particularly those without acute brain injury, derive benefit from natural light.

Intensive care unit (ICU) telemedicine is an increasingly common strategy for improving the outcome of critical care, but its overall impact is uncertain. To determine the effectiveness of ICU telemedicine in a national sample of hospitals and quantify variation in effectiveness across hospitals. We performed a multicenter retrospective case-control study using 2001-2010 Medicare claims data linked to a national survey identifying US hospitals adopting ICU telemedicine. We matched each adopting hospital (cases) to up to 3 nonadopting hospitals (controls) based on size, case-mix, and geographic proximity during the year of adoption. Using ICU admissions from 2 years before and after the adoption date, we compared outcomes between case and control hospitals using a difference-in-differences approach. A total of 132 adopting case hospitals were matched to 389 similar nonadopting control hospitals. The preadoption and postadoption unadjusted 90-day mortality was similar in both case hospitals (24.0% vs. 24.3%, P=0.07) and control hospitals (23.5% vs. 23.7%, P<0.01). In the difference-in-differences analysis, ICU telemedicine adoption was associated with a small relative reduction in 90-day mortality (ratio of odds ratios=0.96; 95% CI, 0.95-0.98; P<0.001). However, there was wide variation in the ICU telemedicine effect across individual hospitals (median ratio of odds ratios=1.01; interquartile range, 0.85-1.12; range, 0.45-2.54). Only 16 case hospitals (12.2%) experienced statistically significant mortality reductions postadoption. Hospitals with a significant mortality reduction were more likely to have large annual admission volumes (P<0.001) and be located in urban areas (P=0.04) compared with other hospitals. Although ICU telemedicine adoption resulted in a small relative overall mortality reduction, there was heterogeneity in effect across adopting hospitals, with large-volume urban hospitals experiencing the greatest mortality reductions.

Évaluation d’une liste de contrôle du matériel médical avant ouverture de chambre en réanimation

The chapter highlights established that implementation of an Intensive care Unit (ICU) telemedicine program can simultaneously improve outcomes and reduce costs at an academic medical center. During the pre-pandemic period of formal ICU telemedicine program growth there was wide recognition that access to high quality specialty medical care was particularly limited among the geographically isolated people served by the health resources and services administration and that the ability of ICU telemedicine programs to improve access to specialty care in these settings was worthy of fiscal support. Equipoise with regard to the ability of ICU telemedicine programs to standardize adult critical care delivery practices and observe clinically important changes in outcomes was driven in no small part by early reports from tertiary centers that failed both to replicate the model. ICU telemedicine critical care best practice review, communication, and remediation achieved significantly higher rates of adherence than daily ICU bedside team review of a reminder list alone.

SummaryBackgroundUltraviolet- C (UV–C) light is effective for reducing environmental bioburden in hospitals, and the use of robots to deliver it may be advantageous.AimTo evaluate the feasibility and clinical efficacy of an autonomous programmable UV-C robot in surgical and intensive care unit (ICU) rooms of a tertiary hospital.MethodDuring ten consecutive months, the device was used in six theatres where cardiac, colorectal and orthopaedic surgeries were performed, and in the rooms previously occupied by patients subjected to contact precautions of a 14-bed ICU. Surgical site infection (SSI) rates of procedures performed in the UV-cleaned theatres were compared with those of the previous year. Incidence in clinical samples of ICU-acquired multiple-drug resistant (MDR) microorganisms was compared with that of the same period of the previous year. An UV-C exposure study done by semi-quantitative dosimeters and a survey of the bioburden on surfaces were carried out.FindingsSSI rates in the pre- and post-intervention periods were 8.67% (80/922) and 7.5% (61/813), respectively (p=0.37). Incidence of target microorganisms in clinical samples remained unchanged (38.4 vs. 39.4 per 10,000 patient-days, p=0.94). All the dosimeters exposed to ≤1 meter received ≥500 mJ/cm2. The bacterial load on surfaces decreased after the intervention, particularly in ICU rooms (from 4.57±7.4 CFU to 0.27±0.8 CFU, p<0.0001).ConclusionDeployment of an UV-C robot in surgical and ICU rooms is feasible, ensures adequate delivery of germicidal UV-C light and reduces the environmental bacterial burden. Rates of surgical site infections or acquisition of MDR in clinical samples of critically-ill patients remained unchanged.

The early detection of the acute deterioration of escalating illness severity is crucial for effective patient management and can significantly impact patient outcomes. Ambient sensing technology, such as computer vision, may provide real-time information that could impact early recognition and response. This study aimed to develop a computer vision model to quantify the number and type (clinician vs. visitor) of people in an intensive care unit (ICU) room, study the trajectory of their movement, and preliminarily explore its relationship with delirium as a marker of illness severity. To quantify the number of people present, we implemented a counting-by-detection supervised strategy using images from ICU rooms. This was accomplished through developing three methods: single-frame, multi-frame, and tracking-to-count. We then explored how the type of person and distribution in the room corresponded to the presence of delirium. Our designed pipeline was tested with a different set of detection models. We report model performance statistics and preliminary insights into the relationship between the number and type of persons in the ICU room and delirium. We evaluated our method and compared it with other approaches, including density estimation, counting by detection, regression methods, and their adaptability to ICU environments.

Hospitals rely on pagers and ordinary telephones to reach staff members in emergency situations. New telecommunication technologies such as General Packet Radio Service (GPRS), the third generation mobile phone system Universal Mobile Telecommunications System (UMTS), and Wireless Local Area Network (WLAN) might be able to replace hospital pagers if they are electromagnetically compatible with medical devices. In this study, we sought to determine if GPRS, UMTS (Wideband Code Division Multiple Access-Frequency Division Duplex [WCDMA FDD]), and WLAN (IEEE 802.11b) transmitted signals interfere with life-supporting equipment in the intensive care and operating room environment. According to United States standard, ANSI C63.18-1997, laboratory tests were performed on 76 medical devices. In addition, clinical tests during 11 operations and 100 h of intensive care were performed. UMTS and WLAN signals caused little interference. Devices using these technologies can be used safely in critical care areas and during operations, but direct contact between medical devices and wireless communication devices ought to be avoided. In the case of GPRS, at a distance of 50 cm, it caused an older infusion pump to alarm and stop infusing; the pump had to be reset. Also, 10 cases of interference with device displays occurred. GPRS can be used safely at a distance of 1 m. Terminals/cellular phones using these technologies should be allowed without restriction in public areas because the risk of interference is minimal.

BackgroundModel-informed precision dosing (MIPD) can serve as a powerful tool during therapeutic drug monitoring (TDM) to help individualize dosing in populations with large pharmacokinetic variation. Yet, adoption of MIPD in the clinical setting has been limited. Overcoming technologic hurdles that allow access to MIPD at the point-of-care and placing it in the hands of clinical specialists focused on medication dosing may encourage adoption.ObjectiveTo describe the hospital implementation and usage of a MIPD clinical decision support (CDS) tool for vancomycin in a pediatric population.MethodsWithin an academic children’s hospital, MIPD for vancomycin was implemented via a commercial cloud-based CDS tool that utilized Bayesian forecasting. Clinical pharmacists were recognized as local champions to facilitate adoption of the tool and operated as end-users. Integration within the electronic health record (EHR) and automatic transmission of patient data to the tool were identified as important requirements. A web-link icon was developed within the EHR which when clicked sends users and needed patient-level clinical data to the CDS platform. Individualized pharmacokinetic predictions and exposure metrics for vancomycin are then presented in the form of a web-based dashboard. Use of the CDS tool as part of TDM was tracked and users were surveyed on their experience.ResultsAfter a successful pilot phase in the neonatal intensive care unit, implementation of MIPD was expanded to the pediatric intensive care unit, followed by availability to the entire hospital. During the first 2+ years since implementation, a total of 853 patient-courses (n = 96 neonates, n = 757 children) and 2,148 TDM levels were evaluated using the CDS tool. For the most recent 6 months, the CDS tool was utilized to support 79% (181/230) of patient-courses in which TDM was performed. Of 26 users surveyed, > 96% agreed or strongly agreed that automatic transmission of patient data to the tool was a feature that helped them complete tasks more efficiently; 81% agreed or strongly agreed that they were satisfied with the CDS tool.ConclusionsIntegration of a vancomycin CDS tool within the EHR, along with leveraging the expertise of clinical pharmacists, allowed for successful adoption of MIPD in clinical care.

Do cost savings from reductions in nosocomial infections justify additional costs of single-bed rooms in intensive care units? A simulation case study