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Background: A recent ICU telemedicine research consensus conference identified the need for reliable methods of measuring structural features and processes of critical care delivery in the domains of organizational context and characteristics of ICU teams, ICUs, hospitals, and of the communities supported by an ICU.Methods: The American College of Chest Physicians Critical Care Institute developed and conducted a survey of ICU telemedicine practices. A 32-item survey was delivered electronically to leaders of 311 ICUs, and 11 domains were identified using principal components analysis. Survey reliability was judged by intraclass correlation among raters, and validity was measured for items for which independent assessment was available.Results: Complete survey information was obtained for 170 of 311 ICUs sent invitations. Analysis of a subset of surveys from 45 ICUs with complete data from more than one rater indicated that the survey reliability was in the excellent to nearly perfect range. Coefficients for measures of external validation ranged from 0.63 to 1.0. Analyses of the survey revealed substantial variation in the practice of ICU telemedicine, including ICU telemedicine center staffing patterns; qualifications of providers; case sign-out, ICU staffing models, leadership, and governance; intensivist review for new patients; adherence to best practices; use of quality and safety information; and ICU physician sign out for their patients.Conclusions: The American College of Chest Physicians ICU telemedicine survey is a reliable tool for measuring variation among ICUs with regard to staffing, structure, processes of care, and ICU telemedicine practices. [ABSTRACT FROM AUTHOR]

This prospective study examined whether the integration of acute care nurse practitioners (ACNP) in a 'semiclosed' surgical intensive care unit (SICU) model increased compliance with clinical practice guidelines (CPG). Patients were admitted to critical care services with a (a) 'semiclosed'/ACNP team or (b) 'mandatory consultation'/non-ACNP team. CPG compliance was significantly higher (P < .05) on the 'semiclosed'/ACNP team for all 3 CPGs examined in the study. [ABSTRACT FROM AUTHOR]

Purpose: Medication nonadherence is a persistent and costly problem across health care. Measures of medication adherence are ineffective. Methods such as self-report, prescription claims data, or smart pill bottles have been used to monitor medication adherence, but these are subject to recall bias, lack real-time feedback, and are often expensive., Methods: We proposed a method for monitoring medication adherence using a commercially available wearable device. Passively collected motion data were analyzed on the basis of the Movelet algorithm, a dictionary learning framework that builds person-specific chapters of movements from short frames of elemental activities within the movements. We adapted and extended the Movelet method to construct a within-patient prediction model that identifies medication-taking behaviors., Results: Using 15 activity features recorded from wrist-worn wearable devices of 10 patients with breast cancer on endocrine therapy, we demonstrated that medication-taking behavior can be predicted in a controlled clinical environment with a median accuracy of 85%., Conclusion: These results in a patient-specific population are exemplar of the potential to measure real-time medication adherence using a wrist-worn commercially available wearable device.

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.

Importance: Serious illness conversations (SICs) between oncology clinicians and patients are associated with improved quality of life and may reduce aggressive end-of-life care. However, most patients with cancer die without a documented SIC., Objective: To test the impact of behavioral nudges to clinicians to prompt SICs on the SIC rate and end-of-life outcomes among patients at high risk of death within 180 days (high-risk patients) as identified by a machine learning algorithm., Design, Setting, and Participants: This prespecified 40-week analysis of a stepped-wedge randomized clinical trial conducted between June 17, 2019, and April 20, 2020 (including 16 weeks of intervention rollout and 24 weeks of follow-up), included 20 506 patients with cancer representing 41 021 encounters at 9 tertiary or community-based medical oncology clinics in a large academic health system. The current analyses were conducted from June 1, 2021, to May 31, 2022., Intervention: High-risk patients were identified using a validated electronic health record machine learning algorithm to predict 6-month mortality. The intervention consisted of (1) weekly emails to clinicians comparing their SIC rates for all patients against peers' rates, (2) weekly lists of high-risk patients, and (3) opt-out text messages to prompt SICs before encounters with high-risk patients., Main Outcomes and Measures: The primary outcome was SIC rates for all and high-risk patient encounters; secondary end-of-life outcomes among decedents included inpatient death, hospice enrollment and length of stay, and intensive care unit admission and systemic therapy close to death. Intention-to-treat analyses were adjusted for clinic and wedge fixed effects and clustered at the oncologist level., Results: The study included 20 506 patients (mean [SD] age, 60.0 [14.0] years) and 41 021 patient encounters: 22 259 (54%) encounters with female patients, 28 907 (70.5%) with non-Hispanic White patients, and 5520 (13.5%) with high-risk patients; 1417 patients (6.9%) died by the end of follow-up. There were no meaningful differences in demographic characteristics in the control and intervention periods. Among high-risk patient encounters, the unadjusted SIC rates were 3.4% (59 of 1754 encounters) in the control period and 13.5% (510 of 3765 encounters) in the intervention period. In adjusted analyses, the intervention was associated with increased SICs for all patients (adjusted odds ratio, 2.09 [95% CI, 1.53-2.87]; P < .001) and decreased end-of-life systemic therapy (7.5% [72 of 957 patients] vs 10.4% [24 of 231 patients]; adjusted odds ratio, 0.25 [95% CI, 0.11-0.57]; P = .001) relative to controls, but there was no effect on hospice enrollment or length of stay, inpatient death, or end-of-life ICU use., Conclusions and Relevance: In this randomized clinical trial, a machine learning-based behavioral intervention and behavioral nudges to clinicans led to an increase in SICs and reduction in end-of-life systemic therapy but no changes in other end-of-life outcomes among outpatients with cancer. These results suggest that machine learning and behavioral nudges can lead to long-lasting improvements in cancer care delivery., Trial Registration: ClinicalTrials.gov Identifier: NCT03984773.

Expansive growth in the use of health information technology (HIT) has dramatically altered medicine without translating to fully realized improvements in healthcare delivery. Bridging this divide will require healthcare professionals with all levels of expertise in clinical informatics. However, due to scarce opportunities for exposure and training in informatics, medical students remain an underdeveloped source of potential informaticists. To address this gap, our institution developed and implemented a 5-tiered clinical informatics curriculum at the undergraduate medical education level: (1) a practical orientation to HIT for rising clerkship students; (2) an elective for junior students; (3) an elective for senior students; (4) a longitudinal area of concentration; and (5) a yearlong predoctoral fellowship in operational informatics at the health system level. Most students found these offerings valuable for their training and professional development. We share lessons and recommendations for medical schools and health systems looking to implement similar opportunities., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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.)

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 (n routine  = 109 (53.4%); n urgent  = 82 (40.2%); n emergent  = 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., (© 2022. The Author(s).)

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.

Background: Clinical workflows require the ability to synthesize and act on existing and emerging patient information. While offering multiple benefits, in many circumstances electronic health records (EHRs) do not adequately support these needs., Objectives: We sought to design, build, and implement an EHR-connected rounding and handoff tool with real-time data that supports care plan organization and team-based care. This article first describes our process, from ideation and development through implementation; and second, the research findings of objective use, efficacy, and efficiency, along with qualitative assessments of user experience., Methods: Guided by user-centered design and Agile development methodologies, our interdisciplinary team designed and built Carelign as a responsive web application, accessible from any mobile or desktop device, that gathers and integrates data from a health care institution's information systems. Implementation and iterative improvements spanned January to July 2016. We assessed acceptance via usage metrics, user observations, time-motion studies, and user surveys., Results: By July 2016, Carelign was implemented on 152 of 169 total inpatient services across three hospitals staffing 1,616 hospital beds. Acceptance was near-immediate: in July 2016, 3,275 average unique weekly users generated 26,981 average weekly access sessions; these metrics remained steady over the following 4 years. In 2016 and 2018 surveys, users positively rated Carelign's workflow integration, support of clinical activities, and overall impact on work life., Conclusion: User-focused design, multidisciplinary development teams, and rapid iteration enabled creation, adoption, and sustained use of a patient-centered digital workflow tool that supports diverse users' and teams' evolving care plan organization needs., Competing Interests: The application described in this manuscript was designed, developed, and implemented by an internal group of clinicians and clinical application developers at Penn Medicine. There were no outside funds used to design, build, implement, or study the application as described in this manuscript. In 2018—after the design, development, and implementation of this application described in the manuscript—the project leader and the last author of the paper, Dr. Subha Airan-Javia, and the Penn Center for Innovation launched a start-up company to bring the application into other health systems (TrekIT Health Inc. d/b/a CareAlign). Both Dr. Airan-Javia and the Board of Trustees of the University of Pennsylvania own equity in the company and receive royalty payments on an annual basis from sales of the company. Dr. Airan-Javia is a full-time salaried employee and CEO of the company, as well as a member of the Board of Directors. No other authors have any involvement in this company, financial or otherwise. The other authors declare that they have no conflicts of interest related to this work. Dr. Airan-Javia reports no salary from TrekIT Health Inc. during the conduct of the study; but as stated above, has received salary from TrekIT Health Inc. outside the submitted work., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

The Covid-19 pandemic required rapid scale of telemedicine as well as other digital workflows to maintain access to care while reducing infection risk. Both patients and clinicians who hadn't used telemedicine before were suddenly faced with a multi-step setup process to log into a virtual meeting. Unlike in-person examination rooms, locking a virtual meeting room was more error-prone and posed a risk of multiple patients joining the same online session. There was administrative burden on the practice staff who were generating and manually sending links to patients, and educating patients on device set up was time-consuming and unsustainable. A solution had to be deployed rapidly system-wide, without the usual roll out across months. Our answer was to design and implement a novel EHR-integrated web application called the Switchboard, in just two weeks. The Switchboard leverages a commercial, cloud-based video meeting platform and facilitates an end-to-end virtual care encounter workflow, from pre-visit reminders to post-visit SMS text message-based measurement of patient experience, with tools to extend contact-less workflows to in-person appointments. Over the first 11 months of the pandemic, the in-house platform has been adopted across 6 hospitals and >200 practices, scaled to 8,800 clinicians who at their peak conducted an average of 30,000 telemedicine appointments/week, and enabled over 10,000-20,000 text messages/day to be exchanged through the platform. Furthermore, it enabled our organization to convert from an average of 75% of telehealth visits being conducted via telephone to 75% conducted via video within weeks., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: The coronavirus disease 2019 (COVID-19) pandemic continues to surge in the United States and globally., Objective: To describe the epidemiology of COVID-19-related critical illness, including trends in outcomes and care delivery., Design: Single-health system, multihospital retrospective cohort study., Setting: 5 hospitals within the University of Pennsylvania Health System., Patients: Adults with COVID-19-related critical illness who were admitted to an intensive care unit (ICU) with acute respiratory failure or shock during the initial surge of the pandemic., Measurements: The primary exposure for outcomes and care delivery trend analyses was longitudinal time during the pandemic. The primary outcome was all-cause 28-day in-hospital mortality. Secondary outcomes were all-cause death at any time, receipt of mechanical ventilation (MV), and readmissions., Results: Among 468 patients with COVID-19-related critical illness, 319 (68.2%) were treated with MV and 121 (25.9%) with vasopressors. Outcomes were notable for an all-cause 28-day in-hospital mortality rate of 29.9%, a median ICU stay of 8 days (interquartile range [IQR], 3 to 17 days), a median hospital stay of 13 days (IQR, 7 to 25 days), and an all-cause 30-day readmission rate (among nonhospice survivors) of 10.8%. Mortality decreased over time, from 43.5% (95% CI, 31.3% to 53.8%) to 19.2% (CI, 11.6% to 26.7%) between the first and last 15-day periods in the core adjusted model, whereas patient acuity and other factors did not change., Limitations: Single-health system study; use of, or highly dynamic trends in, other clinical interventions were not evaluated, nor were complications., Conclusion: Among patients with COVID-19-related critical illness admitted to ICUs of a learning health system in the United States, mortality seemed to decrease over time despite stable patient characteristics. Further studies are necessary to confirm this result and to investigate causal mechanisms., Primary Funding Source: Agency for Healthcare Research and Quality.

Background:  The electronic health record (EHR) has become increasingly ubiquitous. At the same time, health professionals have been turning to this resource for access to data that is needed for the delivery of health care and for clinical research. There is little doubt that the EHR has made both of these functions easier than earlier days when we relied on paper-based clinical records. Coupled with modern database and data warehouse systems, high-speed networks, and the ability to share clinical data with others are large number of challenges that arguably limit the optimal use of the EHR OBJECTIVES:  Our goal was to provide an exhaustive reference for those who use the EHR in clinical and research contexts, but also for health information systems professionals as they design, implement, and maintain EHR systems., Methods:  This study includes a panel of 24 biomedical informatics researchers, information technology professionals, and clinicians, all of whom have extensive experience in design, implementation, and maintenance of EHR systems, or in using the EHR as clinicians or researchers. All members of the panel are affiliated with Penn Medicine at the University of Pennsylvania and have experience with a variety of different EHR platforms and systems and how they have evolved over time., Results:  Each of the authors has shared their knowledge and experience in using the EHR in a suite of 20 short essays, each representing a specific challenge and classified according to a functional hierarchy of interlocking facets such as usability and usefulness, data quality, standards, governance, data integration, clinical care, and clinical research., Conclusion:  We provide here a set of perspectives on the challenges posed by the EHR to clinical and research users., Competing Interests: T.S.C. reports grants from ACRIN, NIH, ACR, and RSNA, as well as royalties from the Osler Institute for lectures in 2013, outside the submitted work. D.S.H. reports grants and nonfinancial support from Roche Diagnostics, outside the submitted work. R.A.H. reports grants from Johnson & Johnson, Merck, and Pfizer, outside the submitted work. Q.L. reports grants from NIH, during the conduct of the study; grants from Pfizer and Bayer, outside the submitted work., (Thieme. All rights reserved.)

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.

1: Most large employers self-insure their employee health benefits, creating a motivation for employers to improve health care's value. 2: Employers who are also health care providers can aim for value through the direct provision of clinical services, not just through wellness programs or the design of insurance products. 3: Innovation and design methods can be systematically applied to health care problems to guide decisions about solutions which should or should not be scaled. 4: A virtual, on-demand urgent care service provided by a health care provider organization to its employees has the potential to reduce unnecessary emergency department visits and decrease the total cost of care., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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.

Importance: Serious illness conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Interventions that increase the rate of SICs between oncology clinicians and patients may improve goal-concordant care and patient outcomes., Objective: To determine the effect of a clinician-directed intervention integrating machine learning mortality predictions with behavioral nudges on motivating clinician-patient SICs., Design, Setting, and Participants: This stepped-wedge cluster randomized clinical trial was conducted across 20 weeks (from June 17 to November 1, 2019) at 9 medical oncology clinics (8 subspecialty oncology and 1 general oncology clinics) within a large academic health system in Pennsylvania. Clinicians at the 2 smallest subspecialty clinics were grouped together, resulting in 8 clinic groups randomly assigned to the 4 intervention wedge periods. Included participants in the intention-to-treat analyses were 78 oncology clinicians who received SIC training and their patients (N = 14 607) who had an outpatient oncology encounter during the study period., Interventions: (1) Weekly emails to oncology clinicians with SIC performance feedback and peer comparisons; (2) a list of up to 6 high-risk patients (≥10% predicted risk of 180-day mortality) scheduled for the next week, estimated using a validated machine learning algorithm; and (3) opt-out text message prompts to clinicians on the patient's appointment day to consider an SIC. Clinicians in the control group received usual care consisting of weekly emails with cumulative SIC performance., Main Outcomes and Measures: Percentage of patient encounters with an SIC in the intervention group vs the usual care (control) group., Results: The sample consisted of 78 clinicians and 14 607 patients. The mean (SD) age of patients was 61.9 (14.2) years, 53.7% were female, and 70.4% were White. For all encounters, SICs were conducted among 1.3% in the control group and 4.6% in the intervention group, a significant difference (adjusted difference in percentage points, 3.3; 95% CI, 2.3-4.5; P < .001). Among 4124 high-risk patient encounters, SICs were conducted among 3.6% in the control group and 15.2% in the intervention group, a significant difference (adjusted difference in percentage points, 11.6; 95% CI, 8.2-12.5; P < .001)., Conclusions and Relevance: In this stepped-wedge cluster randomized clinical trial, an intervention that delivered machine learning mortality predictions with behavioral nudges to oncology clinicians significantly increased the rate of SICs among all patients and among patients with high mortality risk who were targeted by the intervention. Behavioral nudges combined with machine learning mortality predictions can positively influence clinician behavior and may be applied more broadly to improve care near the end of life., Trial Registration: ClinicalTrials.gov Identifier: NCT03984773.

Importance: The coronavirus disease 2019 (COVID-19) pandemic has required a shift in health care delivery platforms, necessitating a new reliance on telemedicine., Objective: To evaluate whether inequities are present in telemedicine use and video visit use for telemedicine visits during the COVID-19 pandemic., Design, Setting, and Participants: In this cohort study, a retrospective medical record review was conducted from March 16 to May 11, 2020, of all patients scheduled for telemedicine visits in primary care and specialty ambulatory clinics at a large academic health system. Age, race/ethnicity, sex, language, median household income, and insurance type were all identified from the electronic medical record., Main Outcomes and Measures: A successfully completed telemedicine visit and video (vs telephone) visit for a telemedicine encounter. Multivariable models were used to assess the association between sociodemographic factors, including sex, race/ethnicity, socioeconomic status, and language, and the use of telemedicine visits, as well as video use specifically., Results: A total of 148 402 unique patients (86 055 women [58.0%]; mean [SD] age, 56.5 [17.7] years) had scheduled telemedicine visits during the study period; 80 780 patients (54.4%) completed visits. Of 78 539 patients with completed visits in which visit modality was specified, 35 824 (45.6%) were conducted via video, whereas 24 025 (56.9%) had a telephone visit. In multivariable models, older age (adjusted odds ratio [aOR], 0.85 [95% CI, 0.83-0.88] for those aged 55-64 years; aOR, 0.75 [95% CI, 0.72-0.78] for those aged 65-74 years; aOR, 0.67 [95% CI, 0.64-0.70] for those aged ≥75 years), Asian race (aOR, 0.69 [95% CI, 0.66-0.73]), non-English language as the patient's preferred language (aOR, 0.84 [95% CI, 0.78-0.90]), and Medicaid insurance (aOR, 0.93 [95% CI, 0.89-0.97]) were independently associated with fewer completed telemedicine visits. Older age (aOR, 0.79 [95% CI, 0.76-0.82] for those aged 55-64 years; aOR, 0.78 [95% CI, 0.74-0.83] for those aged 65-74 years; aOR, 0.49 [95% CI, 0.46-0.53] for those aged ≥75 years), female sex (aOR, 0.92 [95% CI, 0.90-0.95]), Black race (aOR, 0.65 [95% CI, 0.62-0.68]), Latinx ethnicity (aOR, 0.90 [95% CI, 0.83-0.97]), and lower household income (aOR, 0.57 [95% CI, 0.54-0.60] for income <$50 000; aOR, 0.89 [95% CI, 0.85-0.92], for $50 000-$100 000) were associated with less video use for telemedicine visits. These results were similar across medical specialties., Conclusions and Relevance: In this cohort study of patients scheduled for primary care and medical specialty ambulatory telemedicine visits at a large academic health system during the early phase of the COVID-19 pandemic, older patients, Asian patients, and non-English-speaking patients had lower rates of telemedicine use, while older patients, female patients, Black, Latinx, and poorer patients had less video use. Inequities in accessing telemedicine care are present, which warrant further attention.

The coronavirus disease 2019 (COVID-19) public health emergency necessitated changes in health care delivery that will have lasting implications. The University of Pennsylvania Health System is a large multihospital system with an academic medical center at its core. To continue to care for patients with and without COVID-19, the system had to rapidly deploy telemedicine. We describe the challenges faced with the existing telemedicine infrastructures, the central mechanisms created to facilitate the necessary conversions, and the workflow changes instituted to support both inpatient and outpatient activities for thousands of providers, many of whom had little or no experience with telemedicine. We also discuss innovations that occurred as a result of this transition and the future of telemedicine at our institution., Competing Interests: S.A. is a member of the EPIC cardiology steering board. The others declared no competing financial interests., (© Lawrence R. Wechsler et al., 2020; Published by Mary Ann Liebert, Inc.)

Importance: Machine learning (ML) algorithms can identify patients with cancer at risk of short-term mortality to inform treatment and advance care planning. However, no ML mortality risk prediction algorithm has been prospectively validated in oncology or compared with routinely used prognostic indices., Objective: To validate an electronic health record-embedded ML algorithm that generated real-time predictions of 180-day mortality risk in a general oncology cohort., Design, Setting, and Participants: This prognostic study comprised a prospective cohort of patients with outpatient oncology encounters between March 1, 2019, and April 30, 2019. An ML algorithm, trained on retrospective data from a subset of practices, predicted 180-day mortality risk between 4 and 8 days before a patient's encounter. Patient encounters took place in 18 medical or gynecologic oncology practices, including 1 tertiary practice and 17 general oncology practices, within a large US academic health care system. Patients aged 18 years or older with outpatient oncology or hematology and oncology encounters were included in the analysis. Patients were excluded if their appointment was scheduled after weekly predictions were generated and if they were only evaluated in benign hematology, palliative care, or rehabilitation practices., Exposures: Gradient-boosting ML binary classifier., Main Outcomes and Measures: The primary outcome was the patients' 180-day mortality from the index encounter. The primary performance metric was the area under the receiver operating characteristic curve (AUC)., Results: Among 24 582 patients, 1022 (4.2%) died within 180 days of their index encounter. Their median (interquartile range) age was 64.6 (53.6-73.2) years, 15 319 (62.3%) were women, 18 015 (76.0%) were White, and 10 658 (43.4%) were seen in the tertiary practice. The AUC was 0.89 (95% CI, 0.88-0.90) for the full cohort. The AUC varied across disease-specific groups within the tertiary practice (AUC ranging from 0.74 to 0.96) but was similar between the tertiary and general oncology practices. At a prespecified 40% mortality risk threshold used to differentiate high- vs low-risk patients, observed 180-day mortality was 45.2% (95% CI, 41.3%-49.1%) in the high-risk group vs 3.1% (95% CI, 2.9%-3.3%) in the low-risk group. Integrating the algorithm into the Eastern Cooperative Oncology Group and Elixhauser comorbidity index-based classifiers resulted in favorable reclassification (net reclassification index, 0.09 [95% CI, 0.04-0.14] and 0.23 [95% CI, 0.20-0.27], respectively)., Conclusions and Relevance: In this prognostic study, an ML algorithm was feasibly integrated into the electronic health record to generate real-time, accurate predictions of short-term mortality for patients with cancer and outperformed routinely used prognostic indices. This algorithm may be used to inform behavioral interventions and prompt earlier conversations about goals of care and end-of-life preferences among patients with cancer.

Background: The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations., Objective: To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated., Design: Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle., Setting: 3 hospitals in an academic health system., Patients: All people living in the greater Philadelphia region., Measurements: The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators., Results: Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators., Limitations: Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction., Conclusion: Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic., Primary Funding Source: University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.

Objectives: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes., Design: Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation., Setting: Tertiary teaching hospital system in Philadelphia, PA., Patients: All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184)., Interventions: A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction., Measurement and Main Result: Patients identified for training the algorithm were required to have International Classification of Diseases, 9th Edition codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate greater than 2.2 mmol/L or a systolic blood pressure less than 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and IV fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer., Conclusions: Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool and optimizing algorithm design and delivery.

Background: Focusing on high-value delivery of health care, we describe our implementation of telephone postoperative visits as alternatives to in-person follow-up after routine, low-risk surgery in an urban setting. Our pilot program assessed telephone postoperative visit feasibility as well as patient satisfaction and clinical outcomes., Methods: We offered telephone postoperative visits to all clinically eligible, in-state patients scheduled for appropriate low-risk operations. An advanced practitioner conducted the telephone postoperative visit within 2 weeks of the operation and discharged patients from routine follow-up if recovery was satisfactory. We reviewed the medical records to identify encounters and adverse events in the 30-day postoperative period., Results: Telephone postoperative visits were opted for by 92/94 (98%) clinically eligible, in-state patients. Most patients cited convenience (55%), travel (34%), and time (22%) as their main motivations. The average patient opting in was 55 ± 16 years old (range 23-88, 8% > 65) and lived 22 ± 26 miles from our clinic (range 0.9-124). Of 50 patients completing telephone postoperative visits, 48 (96%, 2 were not asked) were satisfied with the telephone postoperative visit as their sole postoperative visit, 44 (88%) of whom required no additional follow-up. On average, telephone postoperative visits lasted 8.6 ± 3.9 minutes, compared with the 82.8 ± 33.4 minutes for preintervention, postoperative visit time. Adding travel times, we estimate each patient saved an average of 139-199 minutes or 94-96% of the time they would have spent coming to clinic. No instances of major morbidity or mortality were identified on chart review., Conclusion: Many patients find telephone postoperative visits more convenient than in-clinic visits. Moreover, estimates of time saved are compelling. Amid changing regulations and reimbursement, our findings support the growing use of telehealth for postoperative care of routine, low risk operations., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Importance: Many health systems are considering increasing price transparency at the time of order entry. However, evidence of its impact on clinician ordering behavior is inconsistent and limited to single-site evaluations of shorter duration., Objective: To test the effect of displaying Medicare allowable fees for inpatient laboratory tests on clinician ordering behavior over 1 year., Design, Setting, and Participants: The Pragmatic Randomized Introduction of Cost data through the electronic health record (PRICE) trial was a randomized clinical trial comparing a 1-year intervention to a 1-year preintervention period, and adjusting for time trends and patient characteristics. The trial took place at 3 hospitals in Philadelphia between April 2014 and April 2016 and included 98 529 patients comprising 142 921 hospital admissions., Interventions: Inpatient laboratory test groups were randomly assigned to display Medicare allowable fees (30 in intervention) or not (30 in control) in the electronic health record., Main Outcomes and Measures: Primary outcome was the number of tests ordered per patient-day. Secondary outcomes were tests performed per patient-day and Medicare associated fees., Results: The sample included 142 921 hospital admissions representing patients who were 51.9% white (74 165), 38.9% black (55 526), and 56.9% female (81 291) with a mean (SD) age of 54.7 (19.0) years. Preintervention trends of order rates among the intervention and control groups were similar. In adjusted analyses of the intervention group compared with the control group over time, there were no significant changes in overall test ordering behavior (0.05 tests ordered per patient-day; 95% CI, -0.002 to 0.09; P = .06) or associated fees ($0.24 per patient-day; 95% CI, -$0.42 to $0.91; P = .47). Exploratory subset analyses found small but significant differences in tests ordered per patient-day based on patient intensive care unit (ICU) stay (patients with ICU stay: -0.16; 95% CI, -0.31 to -0.01; P = .04; patients without ICU stay: 0.13; 95% CI, 0.08-0.17; P < .001) and the magnitude of associated fees (top quartile of tests based on fee value: -0.01; 95% CI, -0.02 to -0.01; P = .04; bottom quartile: 0.03; 95% CI, 0.002-0.06; P = .04). Adjusted analyses of tests that were performed found a small but significant overall increase in the intervention group relative to the control group over time (0.08 tests performed per patient day, 95% CI, 0.03-0.12; P < .001)., Conclusions and Relevance: Displaying Medicare allowable fees for inpatient laboratory tests did not lead to a significant change in overall clinician ordering behavior or associated fees., Trial Registration: clinicaltrials.gov Identifier: NCT02355496.

Background: In hospitals, effective and efficient communication among care providers is critical to the provision of high-quality patient care. Yet, major problems impede communications including the frequent use of interruptive and one-way communication paradigms. This is especially frustrating for frontline providers given the dynamic nature of hospital care teams in an environment that is in constant flux., Methods: We conducted a pre-post evaluation of a commercially available secured messaging mobile application on 4 hospital units at a single institution for over one year. We included care providers on these units: residents, hospitalists, fellows, nurses, social workers, and pharmacists. Utilization metrics and survey responses on clinician perceptions were collected and analyzed using descriptive statistics, the Kruskal-Wallis test, and Mann-Whitney U test where appropriate., Results: Between May 2013 and June 2014, 1,021 providers sent a total of 708,456 messages. About 85.5% of total threads were between two providers and the remaining were group messages. Residents and social workers/clinical resource coordinators were the largest per person users of this communication system, sending 9 (IQR 2-20) and 9 (IQR 2-22) messages per person per day, and receiving 18 (IQR 5-36) and 14 (IQR 5-29) messages per person per day, respectively (p=0.0001). More than half of the messages received by hospitalists, residents, and nurses were read within a minute. Communicating using secured messaging was found to be statistically significantly less disruptive to workflow by both nursing and physician survey respondents (p<0.001 for each comparison)., Conclusions: Routine adoption of secured messaging improved perceived efficiency among providers on 4 hospital units. Our study suggests that a mobile application can improve communication and workflow efficiency among providers in a hospital. New technology has the potential to improve communication among care providers in hospitals., Competing Interests: The authors report no competing financial interests exist, including financial compensation from Cureatr for the use of its system. The views and opinions expressed herein do not reflect those of Cureatr or its affiliates, and no Cureatr official has contributed to the conceptualization or content of this manuscript. The authors report no conflict of interests in the research.

Background: Changes in the medium of communication from paging to mobile secure text messaging may change clinical care, but the effects of these changes on patient outcomes have not been well examined., Objective: To evaluate the association between inpatient medicine service adoption of mobile secure text messaging and patient length of stay and readmissions., Design: Observational study., Participants: Patients admitted to medicine services at the Hospital of the University of Pennsylvania (intervention site; n = 8995 admissions of 6484 patients) and Penn Presbyterian Medical Center (control site; n = 6799 admissions of 4977 patients) between May 1, 2012, and April 30, 2014., Intervention: Mobile secure text messaging., Main Measures: Change in length of stay and 30-day readmissions, comparing patients at the intervention site to the control site before (May 1, 2012 to April 30, 2013) and after (May 1, 2013 to April 30, 2014) the intervention, adjusting for time trends and patient demographics, comorbidities, insurance, and disposition., Key Results: During the pre-intervention period, the mean length of stay ranged from 4.0 to 5.0 days at the control site and from 5.2 to 6.7 days at the intervention site, but trends were similar. In the first month after the intervention, the mean length of stay was unchanged at the control site (4.7 to 4.7 days) but declined at the intervention site (6.0 to 5.4 days). Trends were mostly similar during the rest of the post-intervention period, ranging from 4.4 to 5.6 days at the control site and from 5.4 to 6.5 days at the intervention site. Readmission rates varied significantly within sites before and after the intervention, but overall trends were similar. In adjusted analyses, there was a significant decrease in length of stay for the intervention site relative to the control site during the post-intervention period compared to the pre-intervention period (-0.77 days ; 95 % CI, -1.14, -0.40; P < 0.001). There was no significant difference in the odds of readmission (OR, 0.97; 95 % CI: 0.81, 1.17; P = 0.77). These findings were supported by multiple sensitivity analyses., Conclusions: Compared to a control group over time, hospitalized medical patients on inpatient services whose care providers and staff were offered mobile secure text messaging showed a relative decrease in length of stay and no change in readmissions.

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: Identification of patients at high risk for readmission is a crucial step toward improving care and reducing readmissions. The adoption of electronic health records (EHR) may prove important to strategies designed to risk stratify patients and introduce targeted interventions., Objective: To develop and implement an automated prediction model integrated into our health system's EHR that identifies on admission patients at high risk for readmission within 30 days of discharge., Design: Retrospective and prospective cohort., Setting: Healthcare system consisting of 3 hospitals., Patients: All adult patients admitted from August 2009 to September 2012., Interventions: An automated readmission risk flag integrated into the EHR., Measures: Thirty-day all-cause and 7-day unplanned healthcare system readmissions., Results: Using retrospective data, a single risk factor, ≥ 2 inpatient admissions in the past 12 months, was found to have the best balance of sensitivity (40%), positive predictive value (31%), and proportion of patients flagged (18%), with a C statistic of 0.62. Sensitivity (39%), positive predictive value (30%), proportion of patients flagged (18%), and C statistic (0.61) during the 12-month period after implementation of the risk flag were similar. There was no evidence for an effect of the intervention on 30-day all-cause and 7-day unplanned readmission rates in the 12-month period after implementation., Conclusions: An automated prediction model was effectively integrated into an existing EHR and identified patients on admission who were at risk for readmission within 30 days of discharge., (© 2013 Society of Hospital Medicine.)

We conducted a retrospective, observational study of patient outcomes in two intensive care units in the same hospital. The surgical ICU (SICU) implemented telemedicine and electronic medical records, while the medical ICU (MICU) did not. Medical charts were reviewed for a one-year period before telemedicine and a one-year period afterwards. In the SICU, records were obtained for 246 patients before and 1499 patients after implementation; in the MICU, records were obtained for 220 patients and 285 patients in the same periods. The outcomes of interest were ICU length of stay and mortality, and hospital length of stay and mortality. Outcome variables were severity-adjusted using APACHE scoring. A bootstrap method, with 1000 replicates, was used to assess stability of the findings. The adjusted ICU length of stay, ICU mortality, and hospital mortality for the SICU patients all decreased significantly after the implementation of telemedicine. There was no change in adjusted outcome variables in the MICU patients. Implementation of telemedicine and electronic records in the surgical ICU was associated with a profound reduction in severity-adjusted ICU length of stay, ICU mortality, and hospital mortality. However, it is not possible to conclude definitively that the observed associations seen in the SICU were due to the intervention.

Background: Intensive care unit telemedicine is an innovative approach to providing critical care services for a broad geographic area, but its success may depend on acceptance by bedside providers., Objectives: To determine critical care nurses' attitudes toward and perceptions about the use of telemedicine in critical care., Methods: A total of 179 nurses in 3 critical care units in 2 university-affiliated academic hospitals that use telemedicine intensivists and nurses were surveyed via the Internet about their practice and perceptions of telemedicine., Results: Among the 93 respondents (response rate, 52%), 72 worked at least 1 night shift and therefore had experience with the telemedicine unit. Reported contact with the telemedicine unit was relatively infrequent: 31% reported being called by the unit 3 or more times in the preceding 6 months. A total of 44% reported regularly incorporating interventions suggested by the telemedicine staff. A majority (72%) thought that telemedicine increases patients' survival, but fewer thought that telemedicine prevents medical errors (47%) or improves the satisfaction of patients' families (42%). Some respondents thought that telemedicine interrupted work flow (9%), was intrusive (11%), or resulted in a feeling of being spied upon (13%). Most nurses thought that personally knowing the telemedicine physician was important (79%), and nurses were more likely to contact the telemedicine unit if they knew the physician on call (61%)., Conclusions: Practicing bedside nurses with experience in telemedicine generally support its use, but concerns about privacy issues and the desire to personally know the telemedicine physician may hinder broader application of the technology.

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.

Volatile compounds from human breath are a potential source of information for disease diagnosis. Breath may include volatile organic compounds (VOCs) originating in the nasal sinuses. If the sinuses are infected, disease-specific volatiles may enter exhaled air. Sinus infections are commonly caused by several known bacteria. We examined the volatiles characteristic of infectious bacteria in culture using solid-phase microextraction to collect and gas chromatography-mass spectrometry as well as gas chromatography with flame photometric detection to separate and analyze the resulting VOCs. Infected sinus mucus samples were also collected and their VOCs examined. Similar characteristic volatiles were seen from both cultures of individual "pure" bacteria and several mucus samples. However, the relative amounts of characteristic VOCs from individual bacteria differ greatly between cultures and sinus mucus. New compounds, not seen in culture were also seen in some mucus samples. Our results suggest an important role for growth substrate and environment. Our data further suggests that in some sinus mucus samples identification of bacteria-specific volatiles is possible and can suggest the identity of an infecting organism to physicians. Knowledge of these bacteria-related volatiles is necessary to create electronic nose-based, volatile-specific sensors for non-invasive examination for suspected sinus infection.