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Incorporating the advanced practice provider (APP) in the delivery of tele critical care medicine (teleCCM) addresses the critical care provider shortage. However, the current literature lacks details of potential workflows, deployment difficulties and implementation outcomes while suggesting that expanding teleCCM service may be difficult. Here, we demonstrate the implementation of a telemedicine APP (eAPP) pilot service within an existing teleCCM program with the objective of determining the feasibility and ease of deployment. The goal is to augment an existing tele-ICU system with a balanced APP service to assess the feasibility and potential impact on the ICU performance in several hospitals affiliated within a large academic center. A REDCap survey was used to assess eAPP workflows, expediency of interventions, duration of tasks, and types of assignments within different service locations. Between 02/01/2021 and 08/31/2021, 204 interventions (across 133 12-h shift) were recorded by eAPP (nroutine = 109 (53.4%); nurgent = 82 (40.2%); nemergent = 13 (6.4%). The average task duration was 10.9 ± 6.22 min, but there was a significant difference based on the expediency of the task (F [2; 202] = 3.89; p < 0.022) and type of tasks (F [7; 220] = 6.69; p < 0.001). Furthermore, the eAPP task type and expediency varied depending upon the unit engaged and timeframe since implementation. The eAPP interventions were effectively communicated with bedside staff with only 0.5% of suggestions rejected. Only in 2% cases did the eAPP report distress. In summary, the eAPP can be rapidly deployed in existing teleCCM settings, providing adaptable and valuable care that addresses the specific needs of different ICUs while simultaneously enhancing the delivery of ICU care. Further studies are needed to quantify the input more robustly. [ABSTRACT FROM AUTHOR]

Objectives: To compare the mean per-episode unit cost for a direct-to-consumer (DTC) telemedicine service for medical center employees (OnDemand) with that of in-person care and to estimate whether the offered service increased the use of care., Study Design: Propensity score-matched retrospective cohort study of adult employees and dependents of a large academic health system between July 7, 2017, and December 31, 2019., Methods: To estimate differences in per-episode unit costs within 7 days, we compared costs between OnDemand encounters and conventional in-person encounters (primary care, urgent care, and emergency department) for any similar condition using a generalized linear model. We used interrupted time series analyses limited to the top 10 clinical conditions managed by OnDemand to estimate the effect of OnDemand's availability on the trends for overall employee per-month encounters., Results: A total of 10,826 encounters among 7793 beneficiaries were included (mean [SD] age, 38.5 [10.9] years; 81.6% were women). The mean (SE) 7-day per-episode cost among employees and beneficiaries was lower for OnDemand encounters at $379.76 ($19.83) relative to non-OnDemand encounters at $493.49 ($25.53), a mean per-episode savings of $113.73 (95% CI, $50.36-$177.10; P < .001). After the introduction of OnDemand, among employees with encounters for the top 10 clinical conditions managed by OnDemand, the trend for encounter rates per 100 employees per month increased marginally (0.03; 95% CI, 0.00-0.05; P = .03)., Conclusions: These results suggest that DTC telemedicine staffed by an academic health system and offered directly to employees reduced the per-episode unit costs and only marginally increased utilization, suggesting lower cost overall.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Laudanski, Huffenberger, Scott, Williams, Wain, Jablonski and Hanson.)

Both provider- and protocol-driven electrolyte replacement have been linked to the over-prescription of ubiquitous electrolytes. Here, we describe the development and retrospective validation of a data-driven clinical decision support tool that uses reinforcement learning (RL) algorithms to recommend patient-tailored electrolyte replacement policies for ICU patients. We used electronic health records (EHR) data that originated from two institutions (UPHS; MIMIC-IV). The tool uses a set of patient characteristics, such as their physiological and pharmacological state, a pre-defined set of possible repletion actions, and a set of clinical goals to present clinicians with a recommendation for the route and dose of an electrolyte. RL-driven electrolyte repletion substantially reduces the frequency of magnesium and potassium replacements (up to 60%), adjusts the timing of interventions in all three electrolytes considered (potassium, magnesium, and phosphate), and shifts them towards orally administered repletion over intravenous replacement. This shift in recommended treatment limits risk of the potentially harmful effects of over-repletion and implies monetary savings. Overall, the RL-driven electrolyte repletion recommendations reduce excess electrolyte replacements and improve the safety, precision, efficacy, and cost of each electrolyte repletion event, while showing robust performance across patient cohorts and hospital systems.

A 24/7 telemedicine respiratory therapist (eRT) service was set up as part of the established University of Pennsylvania teleICU (PENN E-LERT ® ) service during the COVID-19 pandemic, serving five hospitals and 320 critical care beds to deliver effective remote care in lieu of a unit-based RT. The eRT interventions were components of an evidence-based care bundle and included ventilator liberation protocols, low tidal volume protocols, tube patency, and an extubation checklist. In addition, the proactive rounding of patients, including ventilator checks, was included. A standardized data collection sheet was used to facilitate the review of medical records, direct audio-visual inspection, or direct interactions with staff. In May 2020, a total of 1548 interventions took place, 93.86% of which were coded as "routine" based on established workflows, 4.71% as "urgent", 0.26% "emergent", and 1.17% were missing descriptors. Based on the number of coded interventions, we tracked the number of COVID-19 patients in the system. The average intervention took 6.1 ± 3.79 min. In 16% of all the interactions, no communication with the bedside team took place. The eRT connected with the in-house respiratory therapist (RT) in 66.6% of all the interventions, followed by house staff (9.8%), advanced practice providers (APP; 2.8%), and RN (2.6%). Most of the interaction took place over the telephone (88%), secure text message (16%), or audio-video telemedicine ICU platform (1.7%). A total of 5115 minutes were spent on tasks that a bedside clinician would have otherwise executed, reducing their exposure to COVID-19. The eRT service was instrumental in several emergent and urgent critical interventions. This study shows that an eRT service can support the bedside RT providers, effectively monitor best practice bundles, and carry out patient-ventilator assessments. It was effective in certain emergent situations and reduced the exposure of RTs to COVID-19. We plan to continue the service as part of an integrated RT service and hope to provide a framework for developing similar services in other facilities.

Biosensors represent one of the numerous promising technologies envisioned to extend healthcare delivery. In perioperative care, the healthcare delivery system can use biosensors to remotely supervise patients who would otherwise be admitted to a hospital. This novel technology has gained a foothold in healthcare with significant acceleration due to the COVID-19 pandemic. However, few studies have attempted to narrate, or systematically analyze, the process of their implementation. We performed an observational study of biosensor implementation. The data accuracy provided by the commercially available biosensors was compared to those offered by standard clinical monitoring on patients admitted to the intensive care unit/perioperative unit. Surveys were also conducted to examine the acceptance of technology by patients and medical staff. We demonstrated a significant difference in vital signs between sensors and standard monitoring which was very dependent on the measured variables. Sensors seemed to integrate into the workflow relatively quickly, with almost no reported problems. The acceptance of the biosensors was high by patients and slightly less by nurses directly involved in the patients' care. The staff forecast a broad implementation of biosensors in approximately three to five years, yet are eager to learn more about them. Reliability considerations proved particularly troublesome in our implementation trial. Careful evaluation of sensor readiness is most likely necessary prior to system-wide implementation by each hospital to assess for data accuracy and acceptance by the staff.

The COVID-19 pandemic has accelerated the demand for virtual healthcare delivery and highlighted the scarcity of telehealth medical student curricula, particularly tele-critical care. In partnership with the Penn E-lert program and the Department of Anesthesiology and Critical Care, the Perelman School of Medicine (PSOM) established a tele-ICU rotation to support the care of patients diagnosed with COVID-19 in the Intensive Care Unit (ICU). The four-week course had seven elements: (1) 60 h of clinical engagement; (2) multiple-choice pretest; (3) faculty-supervised, student-led case and topic presentations; (4) faculty-led debriefing sessions; (5) evidence-based-medicine discussion forum; (6) multiple-choice post-test; and (7) final reflection. Five third- and fourth-year medical students completed 300 h of supervised clinical engagement, following 16 patients over three weeks and documenting 70 clinical interventions. Knowledge of critical care and telehealth was demonstrated through improvement between pre-test and post-test scores. Professional development was demonstrated through post-course preceptor and learner feedback. This tele-ICU rotation allowed students to gain telemedicine exposure and participate in the care of COVID patients in a safe environment.

Objectives: This study explored what smartphone health applications (apps) are used by patients, how they learn about health apps, and how information about health apps is shared., Methods: Patients seeking care in an academic ED were surveyed about the following regarding their health apps: use, knowledge, sharing, and desired app features. Demographics and health information were characterized by summary statistics., Results: Of 300 participants, 212 (71%) owned smartphones, 201 (95%) had apps, and 94 (44%) had health apps. The most frequently downloaded health apps categories were exercise 46 (49%), brain teasers 30 (32%), and diet 23 (24%). The frequency of use of apps varied as six (6%) of health apps were downloaded but never used, 37 (39%) apps were used only a few times, and 40 (43%) health apps were used once per month. Only five apps (2%) were suggested to participants by health care providers, and many participants used health apps intermittently (55% of apps ≤ once a month). Participants indicated sharing information from 64 (59%) health apps, mostly within social networks (27 apps, 29%) and less often with health care providers (16 apps, 17%)., Conclusions: While mobile health has experienced tremendous growth over the past few years, use of health apps among our sample was low. The most commonly used apps were those that had broad functionality, while the most frequently used health apps encompassed the topics of exercise, diet, and brain teasers. While participants most often shared information about health apps within their social networks, information was less frequently shared with providers, and physician recommendation played a small role in influencing patient use of health apps., (© 2015 by the Society for Academic Emergency Medicine.)

Objectives: Voluntary patient uptake and use of electronic health record (EHR) features have been low. It is unknown whether EHRs fully meet needs of providers or patients with chronic diseases., Study Design: To explore in-depth user experiences, we conducted 6 focus groups: 3 of patients followed by 3 of providers discussing 2 key EHR components: the after-visit summary (AVS) and the patient portal (PP). Focus groups were audio-recorded, transcribed, and analyzed by 3 independent coders., Methods: Participants with moderate-to-severe asthma and prevalent comorbidities were recruited from 4 primary care and 2 asthma clinics serving low-income urban neighborhoods. Participants discussed their expectations and experience using the AVS and PP, and responded to prototype formats of these features. Additionally, one-on-one interviews were conducted with 10 patients without PP experience to assess their ability to use the system., Results: The 21 patient and 13 provider perspectives differed regarding AVS features and use. Patients wanted a unified view of their medical issues and health management tools, while providers wanted to focus on recommendations from 1 visit at a time. Both groups advocated improving the AVS format and content. Lack of awareness and knowledge about the PP was patients' largest barrier, and was traced back to providers' lack of PP training., Conclusions: Our results underscore the importance of user-centered design when constructing the content and features of the EHR. As technology evolves, an ongoing understanding of patient and provider experiences will be critical to improve uptake, increase use, and ensure engagement, optimizing the potential of EHRs.

Background: On Tuesday, January 12, 2010 at 16:53 local time, a magnitude 7.0 M(w) earthquake struck Haiti. The global humanitarian attempt to respond was swift, but poor infrastructure and emergency preparedness limited many efforts. Rapid, successful deployment of emergency medical care teams was accomplished by organizations with experience in mass disaster casualty response. Well-intentioned, but unprepared, medical teams also responded. In this report, we describe the preparation and planning process used at an academic university department of anesthesiology with no preexisting international disaster response program, after a call from an American-based nongovernmental organization operating in Haiti requested medical support. The focus of this article is the pre-deployment readiness process, and is not a post-deployment report describing the medical care provided in Haiti., Methods: A real-time qualitative assessment and systematic review of the Hospital of the University of Pennsylvania's communications and actions relevant to the Haiti earthquake were performed. Team meetings, conference calls, and electronic mail communication pertaining to planning, decision support, equipment procurement, and actions and steps up to the day of deployment were reviewed and abstracted. Timing of key events was compiled and a response timeline for this process was developed. Interviews with returning anesthesiology members were conducted., Results: Four days after the Haiti earthquake, Partners in Health, a nonprofit, nongovernmental organization based in Boston, Massachusetts, with >20 years of experience providing medical care in Haiti contacted the University of Pennsylvania Health System to request medical team support. The departments of anesthesiology, surgery, orthopedics, and nursing responded to this request with a volunteer selection process, vaccination program, and systematic development of equipment lists. World Health Organization and Centers for Disease Control guidelines, the American Society of Anesthesiology Committee on Trauma and Emergency Preparedness, published articles, and in-country contacts were used to guide the preparatory process., Conclusion: An organized strategic response to medical needs after an international natural disaster emergency can be accomplished safely and effectively within 6 to 12 days by an academic anesthesiology department, with medical system support, in a center with no previously established response system. The value and timeliness of this response will be determined with further study. Institutions with limited experience in putting an emergency medical team into the field may be able to quickly do so when such efforts are executed in a systematic manner in coordination with a health care organization that already has support infrastructure at the site of the disaster.

Unlabelled: The purpose of this study was to determine if rates of telemetry events differ between patients whose monitoring is appropriately "indicated" versus "not indicated" by systematically applying rigorous criteria for appropriateness of electrocardiogram (ECG) telemetry usage. We performed a retrospective cohort study on 1097 telemetry admissions between January 1, 2000 and March 31, 2000. A convenience sample of 218 patients generated 236 telemetry admissions. One-hundred-sixty-two arrhythmic events were detected during 400 "indicated" telemetry days. Nine arrhythmic events were detected during 345 "not indicated" telemetry days. The relative rate for arrhythmic events was significantly different, at P < 0.0001, with the incidence rate ratio of 15 indicating a very large effect size. Consequently, current use of ECG telemetry may not be optimal, and a prospective analysis of the application of rigorous indications for ECG telemetry needs to be undertaken., Implications: The application of standard criteria to electrocardiogram telemetry admissions found that the majority of abnormal heart rhythms were found when patients met appropriate criteria.