Outcomes Associated with ICU Telemedicine and Other Risk Factors in a Multi-Hospital Critical Care System: A Retrospective, Cohort Study for 30-Day In-Hospital Mortality.

Introduction: Intensive care unit telemedicine (ICU-TM) is expanding due to increasing demands for critical care, but impact on outcomes remains controversial. This study evaluated the association of ICU-TM and other clinical factors with 30-day, in-hospital mortality. Methods: This retrospective, cohort study included 151,780 consecutive ICU patients admitted to nine hospitals in the Cleveland Clinic Health System from 2010 to 2020. Patients were identified from an institutional datamart and Acute Physiology and Chronic Health Evaluation IV (APACHE IV) registry. Primary outcome was 30-day in-hospital mortality. Analyses included multivariate logistic regression modeling, and survival analysis. Results: Overall, unadjusted 30-day, in-hospital mortality incidence was significantly different with (5.6%) or without ICU-TM (7.2%), and risk ratio was 0.78 (95% confidence interval [CI] 0.75-0.81) (p < 0.0001). Mortality rate for ICU-TM and no ICU-TM was 2.4/1,000 versus 3.2/1,000 patient days, respectively (p < 0.0001). Multivariate logistic regression showed that ICU-TM was associated with reduced 30-day mortality (odds ratio 0.78, 95% CI 0.72-0.83). Increased risk was seen with cardiac arrest admissions, males, acute stroke, weekend admission, emergency admission, race (non-white), sepsis, APACHE IV score, ICU length of stay (LOS), and the interaction term, emergency surgical admissions. Reduced risk was associated with hospital LOS, surgical admission, and the interaction terms (weekend admissions with ICU-TM and after-hour admissions with ICU-TM). The model c-statistic was 0.77. Median ICU and hospital lengths of stay were significantly reduced with ICU-TM, with no difference in 48-h mortality or 48-h mortality rate. Conclusion: ICU telemedicine exposure appears to be one of several operational and clinical factors associated with reduced 30-day, in-hospital mortality.

Beyond the Nuts and Bolts: Tele-Critical Care Patients, Workflows, and Activity Patterns.

Background: Tele-critical care (TCC) adoption has been slow since its emergence in the early 2000s. The COVID-19 pandemic has renewed interest in telemedicine and may spur expansion or development of new TCC programs. This narrative addresses the Cleveland Clinic TCC service, (eHospital) to promote exchange of ideas to continually optimize the practice for current and future users. Methods: A descriptive narrative methodology is used in this report. Results: Cleveland Clinic's eHospital was established in 2014 to support nighttime critical care across system hospitals. It encompasses a tiered system of two-way audiovisual communication, telemetry, software platform that integrates the electronic health record, and a proprietary risk stratification algorithm for targeted electronic surveillance. The TCC team includes intensivists, advanced care providers, and registered nurses. Three coverage models evolved depending on onsite clinician availability. More than 133,000 patients have been served by eHospital to date, and span the typical spectrum of critical illness. Along with universal monitoring, ∼18% of patients received active interventions, the most common of which are categorized. Patterns of activity, typical workflows, and adaptations of bedside best practices are also described. Bookending the work shift are sign outs focused on pending critical issues, unstable patients, and those who can be triaged out of the intensive care unit. In between, TCC teams round periodically and interact with bedside teams. Conclusions: TCC adoption has proceeded slowly. Some acceleration is anticipated in a post-COVID-19 pandemic world. Our experience highlights operational practices that can facilitate successful TCC practice.

Association between limiting the number of open records in a tele-critical care setting and retract-reorder errors.

BACKGROUND: Wrong patient selection errors may be tracked by retract-reorder (RAR) events. The aim of this quality improvement study was to assess the impact of reducing the number of concurrently open electronic health records from 4 to 2 on RAR errors generated by a tele-critical care service. METHODS: The study encompassed 32 months before and 21 months after restriction. Chi-Square test of proportions and T statistical process control chart for rare events were used. RESULTS: There were 156 318 orders with 57 RAR errors (36.5/100 000 orders) before restriction, and 122 587 orders with 34 errors (27.7/100 000 orders) after. Rates were not statistically different (P = .20), but analysis was underpowered. When plotted on a T control chart, random variation was detected between RAR errors. CONCLUSION: We found no significant difference in RAR errors in the tele-critical care setting after open record limitation. Other strategies should be studied to reduce wrong patient selection errors.

Association of Unit-Level Telecritical Care Intensity of Service and Length of Stay in the Intensive Care Unit.

Background:Telecritical care (TCC) has been shown to improve outcomes in the intensive care unit (ICU). A TCC was developed and implemented a nocturnal TCC across 10 ICUs in our Health System. TCC coverage patterns and level of involvement vary among ICUs. We identified an opportunity to determine the impact of TCC involvement on the ICU length of stay (LOS).Objective:The primary objective of this study was to assess if intensity of service provided by TCC impacts ICU LOS.Methods:This retrospective review was conducted for all patients admitted to covered ICUs during a 2-year period. ICUs were stratified by the coverage model provided by the TCC and the count of orders placed by the TCC served as a surrogate for intensity of service. Confounding variables were abstracted from the Acute Physiology and Chronic Health Evaluation (APACHE) databases. Spearman's rank correlation coefficient was used to measure the strength of the relationship between ICU LOS and TCC order volume. A linear regression model was used to describe the relationship between order volume and ICU LOS, while adjusting for confounding variables.Results:There is a strong negative relationship between TCC order volume and ICU LOS, as shown by the Spearman rank correlation coefficient of -0.818. The associated p-value of 0.0038 supports the strength of this relationship.Conclusion:Our results demonstrate the impact of nocturnal TCC involvement in patient care. As TCC order volume per ICU admission increases, ICU LOS decreases. We interpret this as an indication for deeper involvement between the TCC team and any on-site providers.

Limiting the number of open charts does not impact wrong patient order entry in the emergency department.

OBJECTIVE: We sought to examine the impact of limiting the number of open active charts on wrong patient order entry events among 13 emergency departments (EDs) in a large integrated health system. METHODS: A retrospective chart review of all orders placed between September 2017 and September 2019 was conducted. The rate of retract and reorder events was analyzed with no overlap in both the period pre- and post-intervention period. Secondary analysis of error rate by clinician type, clinician patient load, and time of day was performed. RESULTS: The order retraction rate was not improved pre- and post-intervention. Retraction rates varied by clinician type with residents retracting more often than physicians (odds ratio [OR] = 1.443 [1.349, 1.545]). Advanced practice providers also showed a slightly higher rate than physicians (OR = 1.114 [1.071, 1.160]). Pharmacists showed very low rates compared to physicians (OR = 0.191 [0.048, 0.764]). Time of day and staffing ratios appear to be a factor with wrong patient order entry rates slightly lower during the night (1900-0700) than the day (OR 0.958 [0.923, 0.995]), and increasing slightly with every additional patient per provider (OR 1.019 [1.005, 1.032]). The Academic Medical Center had more retractions that the other EDs. OR for the various ED types compared to the Academic Medical Center included Community (OR 0.908 [0.859, 0.959]), Teaching Hospitals (OR 0.850 [0.802, 0.900]), and Freestanding (OR 0.932 [0.864, 1.006]). CONCLUSIONS: Limiting the number of open active charts from 4 to 2 did not significantly reduce the incidence of wrong patient order entry. Further investigation into other factors contributing to order entry errors is warranted.

Bedside Nurse Acceptance of Intensive Care Unit Telemedicine Presence.

Since 2010, health care organizations have rapidly adopted telemedicine as part of their health care delivery system to inpatients and outpatients. The application of telemedicine in the intensive care unit is often referred to as tele-ICU In telemedicine, nurses, nurse practitioners, physicians, and other health care professionals provide patient monitoring and intervention from a remote location. Tele-ICU presence has demonstrated positive outcomes such as increased adherence to evidence-based care and improved perception of support at the bedside. Despite the successes, acceptance of tele-ICU varies. Known barriers to acceptance include perceptions of intrusiveness and invasion of privacy.

Telemedicine/Virtual ICU: Where Are We and Where Are We Going?

Intensive care unit telemedicine (tele-ICU) is technology enabled care delivered from off-site locations that was developed to address the increasing complexity of patients and insufficient supply of intensivists. Although tele-ICU deployment is increasing, it continues to cover only a small proportion of ICU patients. This is primarily due to expense, with first-year costs exceeding $50,000 per bed. Meta-analyses of outcomes indicate survival benefits and quality improvements, albeit with significant heterogeneity. Depending on the context, a wide range of estimated incremental cost-effectiveness ratios reflects variable effects on cost and outcomes, such as mortality or length of stay. Tele-ICUs may fit within a hybrid model of care to complement high-intensity ICU staff coverage. However, more research is required to foster consensus and determine best practices. This review summarizes data on tele-ICU structure, operations, outcomes, and costs. Evidence was extracted from meta-analyses, with secondary data from Cleveland Clinic's tele-ICU experience.

Critical Care Nurses' Perceived Need for Guidance in Addressing Spirituality in Critically Ill Patients.

BACKGROUND: The term spirituality is highly subjective. No common or universally accepted definition for the term exists. Without a clear definition, each nurse must reconcile his or her own beliefs within a framework mutually suitable for both nurse and patient. OBJECTIVES: To examine individual critical care nurses' definition of spirituality, their comfort in providing spiritual care to patients, and their perceived need for education in providing this care. METHODS: Individual interviews with 30 nurses who worked in a critical care unit at a large Midwestern teaching hospital. RESULTS: Nurses generally feel comfortable providing spiritual care to critically ill patients but need further education about multicultural considerations. Nurses identified opportunities to address spiritual needs throughout a patient's stay but noted that these needs are usually not addressed until the end of life. CONCLUSIONS: A working definition for spirituality in health care was developed: That part of person that gives meaning and purpose to the person's life. Belief in a higher power that may inspire hope, seek resolution, and transcend physical and conscious constraints.

Building a novel inpatient diabetes management mentor program: a blueprint for success.

The intent of this project was to create a formalized educational program for bedside nurses responsible for inpatient diabetes management. Bedside nurses are recruited to serve as diabetes management mentors. The mentors receive advanced education concerning teaching and learning principles, the AADE7™ Self-Care Behaviors, and diabetes management strategies. They teach their peers, advocate for patients, and facilitate referrals for outpatient Diabetes Self-Management Education (DSME) programs. The focus of these ongoing educational activities is to foster the development of diabetes management mentors and to create teaching tools that mentors can use with peers to address practice gaps or skill deficiencies. The diabetes management mentor is integral in enhancing the care of patients with diabetes in the hospital. The empowerment of bedside nurses as mentors for their peers and their patients is an invaluable asset that helps nurses take ownership of their practice. This role could be applied to other complex disease entities, helping nurses to develop specific management skills to improve patient outcomes and enhance patient satisfaction.

A diabetes management mentor program: outcomes of a clinical nurse specialist initiative to empower staff nurses.

PURPOSE: The purpose of this project was to enhance the knowledge of the bedside nurse in diabetes management. A forum for ongoing support and exploration of clinical problems, along with the distribution of educational tools were the components of this program. BACKGROUND: Diabetes accounts for 30% of patients admitted to the hospital. It has become more challenging to manage as the treatment choices have increased. There are a number of researchers who have identified nurse and physician knowledge of diabetes management principles as suboptimal. DESCRIPTION OF THE INNOVATION: Staff nurses are educated for a role as a Diabetes Management Mentor and are expected to educate/dialogue with peers monthly, model advocacy and diabetes patient education skills, facilitate referrals for diabetes education, and direct staff to resources for diabetes management. OUTCOMES: Diabetes Management Mentors feel more confident in their knowledge of diabetes and their ability to resolve clinical issues as they arise. CONCLUSION: The Diabetes Management Mentor role is another avenue for nurses to refine their clinical knowledge base and acquire skills to share with colleagues while remaining at the bedside. IMPLICATIONS: The clinical nurse specialist is expertly prepared to foster the professional development of bedside nurses while simultaneously making a positive impact on disease management. Opportunity for future investigation includes efficacy of teaching tools on diabetes mastery, the effect of clinical nurse specialist mentoring on a select group of bedside nurses, and the Diabetes Management Mentor's impact on prevention of near-miss events.