A bit of medical paternalism? A qualitative study on power relations between women and healthcare providers when deciding on mode of birth in five public maternity wards of Argentina.

BACKGROUND: Whether women should be able to decide on mode of birth in healthcare settings has been a topic of debate in the last few decades. In the context of a marked increase in global caesarean section rates, a central dilemma is whether pregnant women should be able to request this procedure without medical indication. Since 2015, Law 25,929 of Humanised Birth is in place in Argentina. This study aims at understanding the power relations between healthcare providers, pregnant women, and labour companions regarding decision-making on mode of birth in this new legal context. To do so, central concepts of power theory are used. METHODS: This study uses a qualitative design. Twenty-six semi-structured interviews with healthcare providers were conducted in five maternity wards in different regions of Argentina. Participants were purposively selected using heterogeneity sampling and included obstetrician/gynaecologists (heads of department, specialists working in 24-h shifts, and residents) and midwives where available. Reflexive thematic analysis was used to inductively develop themes and categories. RESULTS: Three themes were developed: (1) Healthcare providers reconceptualize decision-making processes of mode of birth to make women's voices matter; (2) Healthcare providers feel powerless against women's request to choose mode of birth; (3) Healthcare providers struggle to redirect women's decision regarding mode of birth. An overarching theme was built to explain the power relations between healthcare providers, women and labour companions: Healthcare providers' loss of beneficial power in decision-making on mode of birth. CONCLUSIONS: Our analysis highlights the complexity of the healthcare provider-woman interaction in a context in which women are, in practice, allowed to choose mode of birth. Even though healthcare providers claim to welcome women being an active part of the decision-making processes, they feel powerless when women make autonomous decisions regarding mode of birth. They perceive themselves to be losing beneficial power in the eyes of patients and consider fruitful communication on risks and benefits of each mode of birth to not always be possible. At the same time, providers perform an increasing number of CSs without medical indication when it is convenient for them, which suggests that paternalistic practices are still in place.

In the last few decades, there has been a debate on whether women should be able to choose if they haver a vaginal birth or a caesarean section. This debate has been framed by the fact that an increasing number of caesarean sections are being performed. Since 2015, Argentina has a Law of Humanised Birth. We conducted a study to understand the power relations between healthcare providers, pregnant women and labour companions in decision making on mode of birth in this new legal context. To do so, we used central concepts of power theory. We conducted 26 semi-structured interviews with healthcare providers in five maternity wards of Argentina. The interviewees were obstetrician/gynaecologists (heads of department, specialists working in 24-h shifts, and residents) and midwives where available. We used thematic analysis to build themes from the data. We discovered that healthcare providers perceive themselves to be losing beneficial power in decision-making on mode of birth. Even though they claim to want women to make autonomous decisions, they feel frustrated when this happens. They also perceive it to be more difficult to communicate with patients regarding the risks and benefits of vaginal birth and caesarean section. At the same time, providers carry out an increasing number of CSs without medical indication when it is convenient for them, which suggests that paternalistic practices are still in place.

Pilot of rapid implementation of the advanced practice provider in the workflow of an existing tele-critical care program.

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.

Characteristics, Outcomes, and Trends of Patients With COVID-19-Related Critical Illness at a Learning Health System in the United States.

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.

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic.

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.

A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

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.

Trends in caesarean section rates between 2007 and 2013 in obstetric risk groups inspired by the Robson classification: results from population-based surveys in a low-resource setting.

OBJECTIVE: To describe caesarean section rates and neonatal mortality to assess change in access to life-saving interventions in a rural low-resource setting between 2007 and 2013. DESIGN: Population-based cross-sectional study. SETTING: Southern Tanzania. POPULATION: A total of 34 063 women from 384 549 households who gave birth in the previous year. METHODS: Using data collected in two geo-referenced household surveys conducted in 2007 and 2013 in the context of two cluster-randomized controlled trials, we describe trends in caesarean section and neonatal mortality in obstetric risk groups inspired by the 10-group Robson classification. MAIN OUTCOME MEASURES: Rates of self-reported birth by caesarean section and neonatal mortality. RESULTS: Population-based caesarean section rates increased from 4.0% in 2007 to 6.4% in 2013. In 2013, the lowest caesarean section rate was found in multipara whose labour was not induced or augmented [4.4%, 95% confidence interval (CI) 3.9-4.9], a group that showed a rate increase of over 50% from 2007 [adjusted prevalence ratio 1.57 (95% CI 1.34-1.82)]. Nullipara whose labour was not induced or augmented had rates of 6.2% in 2007 and 8.5% in 2013. Caesarean rates in multiple pregnancies were low at 8.1% (95% CI 5.6-10.5) in 2007, and 14.6% (95% CI 9.4-19.8) in 2013. Overall neonatal mortality was high: 3.5% in 2007 and 3.2% in 2013, with rates being lowest in multiparous women whose labour was not induced or augmented: 2.4% (95% CI 2.2-2.7) and 1.7% (95% CI 1.4-2.0) in 2007 and 2013, respectively. CONCLUSION: Although use of caesarean section remains insufficient, and higher rates do not necessarily imply better quality of care, our analysis highlights improvements in reaching women with caesarean section. Rates in multiple birth remained low compared with high-income settings. TWEETABLE ABSTRACT: In Southern Tanzania caesarean section rates increased over time, but the rate in high-risk births remained alarmingly low.

'What gets measured gets managed': revisiting the indicators for maternal and newborn health programmes.

BACKGROUND: The health of women and children are critical for global development. The Sustainable Development Goals (SDG) agenda and the Global Strategy for Women's, Children's, and Adolescent's Health 2016-2030 aim to reduce maternal and newborn deaths, disability, and enhancement of well-being. However, information and data on measuring countries' progress are limited given the variety of methodological challenges of measuring care around the time of birth, when most maternal and neonatal deaths and morbidities occur. MAIN BODY: In 2015, the World Health Organization launched Mother and Newborn Information for Tracking Outcomes and Results (MoNITOR), a technical advisory group to WHO. MoNITOR comprises 14 independent global experts from a variety of disciplines selected in a competitive process for their technical expertise and regional representation. MoNITOR will provide technical guidance to WHO to ensure harmonized guidance, messages, and tools so that countries can collect useful data to track progress toward achieving the Sustainable Development Goals. SHORT CONCLUSION: Ultimately, MoNITOR will provide technical guidance to WHO to ensure harmonized guidance, messages, and tools so that countries can collect useful data to track progress toward achieving the Sustainable Development Goals.

Do dental undergraduates think that Thiel-embalmed cadavers are a more realistic model for teaching exodontia?

INTRODUCTION: Teaching exodontia to novice undergraduates requires a realistic model. Thiel-embalmed cadavers retain the flexibility of the soft tissues and could be used to teach exodontia. OBJECTIVE: The objective was to determine whether Thiel-embalmed cadavers were perceived to be a more realistic model by undergraduates in comparison with mannequins. MATERIALS AND METHODS: Over a period of 4 years (2011-2014), students were randomly assigned into two groups: those taught exodontia on mannequins only (NT) and those who also experienced cadaveric teaching (T). This was followed by an assessment. RESULTS: There were 174 students in the T group and 108 in the NT group. Sixty-five per cent of the T group and 69% of the NT group provided feedback. Ninety-eight per cent (98%) felt that they had been advantaged by being included in the group compared with 95% in the NT who felt disadvantaged. The majority (98%) thought that using the cadavers was advantageous and gave a realistic feel for soft tissue management (89%) and that it was similar to managing a patient (81%). Self-reported confidence in undertaking an extraction was not different between the two groups (P=.078), and performance in the extraction assessment was not significantly different between the two groups over the 4 years (P=.8). CONCLUSION: The Thiel-embalmed cadavers were well received by the students who found it a more realistic model for exodontia than a mannequin, even though this did not impact on their performance in a following assessment. Future work on these cadavers may be expanded to include surgical procedures.

Computational discovery of transcription factors associated with drug response.

This study integrates gene expression, genotype and drug response data in lymphoblastoid cell lines with transcription factor (TF)-binding sites from ENCODE (Encyclopedia of Genomic Elements) in a novel methodology that elucidates regulatory contexts associated with cytotoxicity. The method, GENMi (Gene Expression iN the Middle), postulates that single-nucleotide polymorphisms within TF-binding sites putatively modulate its regulatory activity, and the resulting variation in gene expression leads to variation in drug response. Analysis of 161 TFs and 24 treatments revealed 334 significantly associated TF-treatment pairs. Investigation of 20 selected pairs yielded literature support for 13 of these associations, often from studies where perturbation of the TF expression changes drug response. Experimental validation of significant GENMi associations in taxanes and anthracyclines across two triple-negative breast cancer cell lines corroborates our findings. The method is shown to be more sensitive than an alternative, genome-wide association study-based approach that does not use gene expression. These results demonstrate the utility of GENMi in identifying TFs that influence drug response and provide a number of candidates for further testing.

Change In Length of Stay and Readmissions among Hospitalized Medical Patients after Inpatient Medicine Service Adoption of Mobile Secure Text Messaging.

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.

Perceptions of Autonomous Shuttles for Adults With Spinal Cord Injuries.

Individuals with a spinal cord injury (SCI) have challenges using transportation. Autonomous shuttles (ASs), if accessible, may support their transportation needs. This study quantified the perceptions of AS for adults with and without SCI, before and after riding in the AS. We hypothesized that the perceptions of AS for individuals with SCI would improve, by the greatest magnitude, after riding in the AS. This mixed-method quasi-experimental design included 16 adults with SCI and 16 age-matched controls. While there were no differences between the groups, both groups reported having fewer perceived barriers to using AS after riding in the AS (p = .025). After riding in the AS, both groups stated that the AS must be available, accessible, and affordable if they are to use AS. In conclusion, adults with SCI should experience AS if they are to accept and adopt this mode of transportation.

Economics of a health system's direct-to-consumer telemedicine for its employees.

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.

Detection of Medication Taking Using a Wrist-Worn Commercially Available Wearable Device.

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.

Long-term Effect of Machine Learning-Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer: A Randomized Clinical Trial.

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.

Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach.

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.

Training digital natives to transform healthcare: a 5-tiered approach for integrating clinical informatics into undergraduate medical education.

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.

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

Background: Our study addresses the gaps in knowledge of the characterizations of operations by remote tele-critical care medicine (tele-CCM) service providers interacting with the bedside team. The duration of engagements, the evolution of the tele-CCM service over time, and the distress during interactions with the bedside team have not been characterized systematically. These characteristics are critical for planning the deployment of teleICU services and preventing burnout among remote teleICU providers. Methods: REDCap self-reported activity logs collected engagement duration, triggers (emergency button, tele-CCM software platform, autonomous algorithm, asymmetrical communication platform, phone), expediency, nature (proactive rounding, predetermined task, response to medical needs), communication modes, and acceptance. Seven hospitals with 16 ICUs were overseen between 9/2020 and 9/2021 by teams consisting of telemedicine medical doctors (eMD), telemedicine registered nurses (eRN), and telemedicine respiratory therapists (eRT). Results: 39,915 total engagements were registered. eMDs had a significantly higher percentage of emergent and urgent engagements (31.9%) vs. eRN (9.8%) or eRT (1.7%). The average tele-CCM intervention took 16.1 ± 10.39 min for eMD, 18.1 ± 16.23 for eRN, and 8.2 ± 4.98 min for eRT, significantly varied between engagement, and expediency, hospitals, and ICUs types. During the observation period, there was a shift in intervention triggers with an increase in autonomous algorithmic ARDS detection concomitant with predominant utilization of asynchronous communication, phone engagements, and the tele-CCM module of electronic medical records at the expense of the share of proactive rounding. eRT communicated more frequently with bedside staff (% MD = 37.8%; % RN = 36.8, % RT = 49.0%) but mostly with other eRTs. In contrast, the eMD communicated with all ICU stakeholders while the eRN communicated chiefly with other RN and house staff at the patient's bedside. The rate of distress reported by tele-CCM staff was 2% among all interactions, with the entity hospital being the dominant factor. Conclusions: Delivery of tele-CCM services has to be tailored to the specific beneficiary of tele-CCM services to optimize care delivery and minimize distress. In addition, the duration of the average intervention must be considered while creating an efficient workflow.