Tell-a provider about tele: Reducing overuse of telemetry across 10 hospitals in a safety net system.

BACKGROUND: Telemetry is often a scarce resource at hospitals and is important for arrhythmia and myocardial ischemia detection. Overuse of telemetry monitoring leads to alarm fatigue resulting in failure to respond to arrhythmias, patient harm, and possible unnecessary testing. METHODS: This quality improvement initiative was implemented across NYC Health and Hospitals, an 11-hospital urban safety net system. The electronic health record intervention involved the addition of a mandatory indication in the telemetry order and a best practice advisory (BPA) that would fire after the recommended time period for reassessment had passed. RESULTS: The average telemetry hours per patient encounter went from 60.1 preintervention to 48.4 postintervention, a 19.5% reduction (p < .001). When stratified by the 11 hospitals, decreases ranged from 9% to 30%. The BPA had a 53% accept rate and fired 52,682 times, with 27,938 "discontinue telemetry" orders placed. The true accept rate was 50.4%, as there was a 2.6% 24-h reorder rate. There was variation based on clinician specialty and clinician type (attending, fellow, resident, physician associate, nurse practitioner). CONCLUSION: We successfully reduced telemetry monitoring across a multisite safety net system using solely an electronic health record (EHR) intervention. This expands on previous telemetry monitoring reduction initiatives using EHR interventions at single academic sites. Further study is needed to investigate variation across clinician type, specialty, and post-acute sites.

Effectiveness of an Analytics-Based Intervention for Reducing Sleep Interruption in Hospitalized Patients: A Randomized Clinical Trial.

IMPORTANCE: Sleep has major consequences for physical and emotional well-being. Hospitalized patients experience frequent iatrogenic sleep interruptions and there is evidence that such interruptions can be safely reduced. OBJECTIVE: To determine whether a clinical decision support tool, powered by real-time patient data and a trained prediction algorithm, can help physicians identify clinically stable patients and safely discontinue their overnight vital sign checks. DESIGN, SETTING, AND PARTICIPANTS: A randomized clinical trial, with inpatient encounters randomized 1:1 to intervention vs usual care, was conducted from March 11 to November 24, 2019. Participants included physicians serving on the primary team of 1699 patients on the general medical service (not in the intensive care unit) of a tertiary care academic medical center. INTERVENTIONS: A clinical decision support notification informed the physician if the patient had a high likelihood of nighttime vital signs within the reference ranges based on a logistic regression model that used real-time patient data as input. The notification provided the physician an opportunity to discontinue measure of nighttime vital signs, dismiss the notification for 1 hour, or dismiss the notification for that day. MAIN OUTCOMES AND MEASURES: The primary outcome was delirium, as determined by bedside nurse assessment of Nursing Delirium Screening Scale scores, a standardized delirium screening tool (delirium diagnosed with score ≥2). Secondary outcomes included mean nighttime vital sign checks. Potential harms included intensive care unit transfers and code blue alarms. All analyses were conducted on the basis of intention-to-treat. RESULTS: A total of 1930 inpatient encounters in 1699 patients (intervention encounters: 566 of 966 [59%] men; mean [SD] age, 53 [15] years) were randomized. In the intervention vs control arm, there was a significant decrease in the mean (SD) number of nighttime vital sign checks (0.97 [0.95] vs 1.41 [0.86]; P < .001) with no increase in intensive care unit transfers (49 [5%] vs 47 [5%]; P = .92) or code blue alarms (2 [0.2%] vs 9 [0.9%]; P = .07). The incidence of delirium was not significantly reduced (108 [11%] vs 123 [13%]; P = .32). CONCLUSIONS AND RELEVANCE: While this randomized clinical trial found no difference between groups in the primary outcome, delirium incidence, the secondary findings indicate that a real-time prediction algorithm embedded within a clinical decision support tool in the electronic health record can help physicians identify clinically stable patients who can forgo routine vital sign checks, safely giving them greater opportunity to sleep. Other aspects of hospital care that depend on clinical stability, such as level of care or cardiac monitoring, may be amenable to a similar intervention. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04046458.

A Methodology to Generate Longitudinally Updated Acute-On-Chronic Liver Failure Prognostication Scores From Electronic Health Record Data.

Queries of electronic health record (EHR) data repositories allow for automated data collection. These techniques have not been used in hepatology due to the inability to capture hepatic encephalopathy (HE) grades, which are inputs for acute-on-chronic liver failure (ACLF) models. Here, we describe a methodology to use EHR data to calculate rolling ACLF scores. We examined 239 patient admissions with end-stage liver disease from July 2014 to June 2019. We mapped EHR flowsheet data to determine HE grades and calculated two longitudinally updated ACLF scores. We validated HE grades and ACLF diagnoses by chart review and calculated sensitivity, specificity, and Cohen's kappa. Of 239 patient admissions analyzed, 37% were women, 46% were non-Hispanic white, median age was 60 years, and the median Model for End-Stage Liver Disease-Na score at admission was 25. Of the 239, 7% were diagnosed with ACLF as defined by the North American Consortium for the Study of End-Stage Liver Disease (NACSELD) diagnostic criteria at admission, 27% during the hospitalization, and 9% at discharge. Forty percent were diagnosed with ACLF by the European Association for the Study of the Liver- Chronic Liver Failure Consortium (CLIF-C) diagnostic criteria at admission, 51% during the hospitalization, and 34% at discharge. From the chart review of 51 admissions, we found sensitivities and specificities for any HE (grades 1-4) were 92%-97% and 76%-95%, respectively; for severe HE (grades 3-4), sensitivities and specificities were 100% and 78%-98%, respectively. Cohen's kappa between flowsheet and chart review of HE grades ranged from 0.55 to 0.72. Sensitivities and specificities for NACSELD-ACLF diagnoses were 75%-100% and 96%-100%, respectively; for CLIF-C-ACLF diagnoses, these were 91%-100% and 96-100%, respectively. We generated approximately 28 unique ACLF scores per patient per admission day. Conclusion: We developed an informatics-based methodology to calculate longitudinally updated ACLF scores. This opens new analytic potentials, such as big data methods, to develop electronic phenotypes for patients with ACLF.

Applying Machine Learning Across Sites: External Validation of a Surgical Site Infection Detection Algorithm.

BACKGROUND: Surgical complications have tremendous consequences and costs. Complication detection is important for quality improvement, but traditional manual chart review is burdensome. Automated mechanisms are needed to make this more efficient. To understand the generalizability of a machine learning algorithm between sites, automated surgical site infection (SSI) detection algorithms developed at one center were tested at another distinct center. STUDY DESIGN: NSQIP patients had electronic health record (EHR) data extracted at one center (University of Minnesota Medical Center, Site A) over a 4-year period for model development and internal validation, and at a second center (University of California San Francisco, Site B) over a subsequent 2-year period for external validation. Models for automated NSQIP SSI detection of superficial, organ space, and total SSI within 30 days postoperatively were validated using area under the curve (AUC) scores and corresponding 95% confidence intervals. RESULTS: For the 8,883 patients (Site A) and 1,473 patients (Site B), AUC scores were not statistically different for any outcome including superficial (external 0.804, internal [0.784, 0.874] AUC); organ/space (external 0.905, internal [0.867, 0.941] AUC); and total (external 0.855, internal [0.854, 0.908] AUC) SSI. False negative rates decreased with increasing case review volume and would be amenable to a strategy in which cases with low predicted probabilities of SSI could be excluded from chart review. CONCLUSIONS: Our findings demonstrated that SSI detection machine learning algorithms developed at 1 site were generalizable to another institution. SSI detection models are practically applicable to accelerate and focus chart review.

Evaluation of a novel metric for personalized opioid prescribing after hospitalization.

BACKGROUND: The duration of an opioid prescribed at hospital discharge does not intrinsically account for opioid needs during a hospitalization. This discrepancy may lead to patients receiving much larger supplies of opioids on discharge than they truly require. OBJECTIVE: Assess a novel discharge opioid supply metric that adjusts for opioid use during hospitalization, compared to the conventional discharge prescription signature. DESIGN, SETTING, & PARTICIPANTS: Retrospective study using electronic health record data from June 2012 to November 2018 of adults who received opioids while hospitalized and after discharge from a single academic medical center. MEASURES & ANALYSIS: We ascertained inpatient opioids received and milligrams of opioids supplied after discharge, then determined days of opioids supplied after discharge by the conventional prescription signature opioid-days ("conventional days") and novel hospital-adjusted opioid-days ("adjusted days") metrics. We calculated descriptive statistics, within-subject difference between measurements, and fold difference between measures. We used multiple linear regression to determine patient-level predictors associated with high difference in days prescribed between measures. RESULTS: The adjusted days metric demonstrates a 2.4 day median increase in prescription duration as compared to the conventional days metric (9.4 vs. 7.0 days; P<0.001). 95% of all adjusted days measurements fall within a 0.19 to 6.90-fold difference as compared to conventional days measurements, with a maximum absolute difference of 640 days. Receiving a liquid opioid prescription accounted for an increased prescription duration of 135.6% by the adjusted days metric (95% CI 39.1-299.0%; P = 0.001). Of patients who were not on opioids prior to admission and required opioids during hospitalization but not in the last 24 hours, 325 (8.6%) were discharged with an opioid prescription. CONCLUSIONS: The adjusted days metric, based on inpatient opioid use, demonstrates that patients are often prescribed a supply lasting longer than the prescription signature suggests, though with marked variability for some patients that suggests potential under-prescribing as well. Adjusted days is more patient-centered, reflecting the reality of how patients will take their prescription rather than providers' intended prescription duration.

Clinical features, diagnostics, and outcomes of patients presenting with acute respiratory illness: A retrospective cohort study of patients with and without COVID-19.

BACKGROUND: Most data on the clinical presentation, diagnostics, and outcomes of patients with COVID-19 have been presented as case series without comparison to patients with other acute respiratory illnesses. METHODS: We examined emergency department patients between February 3 and March 31, 2020 with an acute respiratory illness who were tested for SARS-CoV-2. We determined COVID-19 status by PCR and metagenomic next generation sequencing (mNGS). We compared clinical presentation, diagnostics, treatment, and outcomes. FINDINGS: Among 316 patients, 33 tested positive for SARS-CoV-2; 31 without COVID-19 tested positive for another respiratory virus. Among patients with additional viral testing (27/33), no SARS-CoV-2 co-infections were identified. Compared to those who tested negative, patients with COVID-19 reported longer symptoms duration (median 7d vs. 3d, p < 0.001). Patients with COVID-19 were more often hospitalized (79% vs. 56%, p = 0.014). When hospitalized, patients with COVID-19 had longer hospitalizations (median 10.7d vs. 4.7d, p < 0.001) and more often developed ARDS (23% vs. 3%, p < 0.001). Most comorbidities, medications, symptoms, vital signs, laboratories, treatments, and outcomes did not differ by COVID-19 status. INTERPRETATION: While we found differences in clinical features of COVID-19 compared to other acute respiratory illnesses, there was significant overlap in presentation and comorbidities. Patients with COVID-19 were more likely to be admitted to the hospital, have longer hospitalizations and develop ARDS, and were unlikely to have co-existent viral infections. FUNDING: National Center for Advancing Translational Sciences, National Heart Lung Blood Institute, National Institute of Allergy and Infectious Diseases, Chan Zuckerberg Biohub, Chan Zuckerberg Initiative.

Clinical features, diagnostics, and outcomes of patients presenting with acute respiratory illness: a comparison of patients with and without COVID-19.

BACKGROUND: Emerging data on the clinical presentation, diagnostics, and outcomes of patients with COVID-19 have largely been presented as case series. Few studies have compared these clinical features and outcomes of COVID-19 to other acute respiratory illnesses. METHODS: We examined all patients presenting to an emergency department in San Francisco, California between February 3 and March 31, 2020 with an acute respiratory illness who were tested for SARS-CoV-2. We determined COVID-19 status by PCR and metagenomic next generation sequencing (mNGS). We compared demographics, comorbidities, symptoms, vital signs, and laboratory results including viral diagnostics using PCR and mNGS. Among those hospitalized, we determined differences in treatment (antibiotics, antivirals, respiratory support) and outcomes (ICU admission, ICU interventions, acute respiratory distress syndrome, cardiac injury). FINDINGS: In a cohort of 316 patients, 33 (10%) tested positive for SARS-CoV-2; 31 patients, all without COVID-19, tested positive for another respiratory virus (16%). Among patients with additional viral testing, no co-infections with SARS-CoV-2 were identified by PCR or mNGS. Patients with COVID-19 reported longer symptoms duration (median 7 vs. 3 days), and were more likely to report fever (82% vs. 44%), fatigue (85% vs. 50%), and myalgias (61% vs 27%); p<0.001 for all comparisons. Lymphopenia (55% vs 34%, p=0.018) and bilateral opacities on initial chest radiograph (55% vs. 24%, p=0.001) were more common in patients with COVID-19. Patients with COVID-19 were more often hospitalized (79% vs. 56%, p=0.014). Of 186 hospitalized patients, patients with COVID-19 had longer hospitalizations (median 10.7d vs. 4.7d, p<0.001) and were more likely to develop ARDS (23% vs. 3%, p<0.001). Most comorbidities, home medications, signs and symptoms, vital signs, laboratory results, treatment, and outcomes did not differ by COVID-19 status. INTERPRETATION: While we found differences in clinical features of COVID-19 compared to other acute respiratory illnesses, there was significant overlap in presentation and comorbidities. Patients with COVID-19 were more likely to be admitted to the hospital, have longer hospitalizations and develop ARDS, and were unlikely to have co-existent viral infections. These findings enhance understanding of the clinical characteristics of COVID-19 in comparison to other acute respiratory illnesses. .

Randomized Controlled Trials of Electronic Health Record Interventions: Design, Conduct, and Reporting Considerations.

Electronic health record (EHR) systems can be configured to deliver novel EHR interventions that influence clinical decision making and to support efficient randomized controlled trials (RCTs) designed to evaluate the effectiveness, safety, and costs of those interventions. In designing RCTs of EHR interventions, one should carefully consider the unit of randomization (for example, patient, encounter, clinician, or clinical unit), balancing concerns about contamination of an intervention across randomization units within clusters (for example, patients within clinical units) against the superior control of measured and unmeasured confounders that comes with randomizing a larger number of units. One should also consider whether the key computational assessment components of the EHR intervention, such as a predictive algorithm used to target a subgroup for decision support, should occur before randomization (so that only 1 subgroup is randomized) or after randomization (including all subgroups). When these components are applied after randomization, one must consider expected heterogeneity in the effect of the differential decision support across subgroups, which has implications for overall impact potential, analytic approach, and sample size planning. Trials of EHR interventions should be reviewed by an institutional review board, but may not require patient-level informed consent when the interventions being tested can be considered minimal risk or quality improvement, and when clinical decision making is supported, rather than controlled, by an EHR intervention. Data and safety monitoring for RCTs of EHR interventions should be conducted to guide institutional pragmatic decision making about implementation and ensure that continuing randomization remains justified. Reporting should follow the CONSORT (Consolidated Standards of Reporting Trials) Statement, with extensions for pragmatic trials and cluster RCTs when applicable, and should include detailed materials to enhance reproducibility.

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.

Importance: Laboratory testing is an important target for high-value care initiatives, constituting the highest volume of medical procedures. Prior studies have found that up to half of all inpatient laboratory tests may be medically unnecessary, but a systematic method to identify these unnecessary tests in individual cases is lacking. Objective: To systematically identify low-yield inpatient laboratory testing through personalized predictions. Design, Setting, and Participants: In this retrospective diagnostic study with multivariable prediction models, 116 637 inpatients treated at Stanford University Hospital from January 1, 2008, to December 31, 2017, a total of 60 929 inpatients treated at University of Michigan from January 1, 2015, to December 31, 2018, and 13 940 inpatients treated at the University of California, San Francisco from January 1 to December 31, 2018, were assessed. Main Outcomes and Measures: Diagnostic accuracy measures, including sensitivity, specificity, negative predictive values (NPVs), positive predictive values (PPVs), and area under the receiver operating characteristic curve (AUROC), of machine learning models when predicting whether inpatient laboratory tests yield a normal result as defined by local laboratory reference ranges. Results: In the recent data sets (July 1, 2014, to June 30, 2017) from Stanford University Hospital (including 22 664 female inpatients with a mean [SD] age of 58.8 [19.0] years and 22 016 male inpatients with a mean [SD] age of 59.0 [18.1] years), among the top 20 highest-volume tests, 792 397 were repeats of orders within 24 hours, including tests that are physiologically unlikely to yield new information that quickly (eg, white blood cell differential, glycated hemoglobin, and serum albumin level). The best-performing machine learning models predicted normal results with an AUROC of 0.90 or greater for 12 stand-alone laboratory tests (eg, sodium AUROC, 0.92 [95% CI, 0.91-0.93]; sensitivity, 98%; specificity, 35%; PPV, 66%; NPV, 93%; lactate dehydrogenase AUROC, 0.93 [95% CI, 0.93-0.94]; sensitivity, 96%; specificity, 65%; PPV, 71%; NPV, 95%; and troponin I AUROC, 0.92 [95% CI, 0.91-0.93]; sensitivity, 88%; specificity, 79%; PPV, 67%; NPV, 93%) and 10 common laboratory test components (eg, hemoglobin AUROC, 0.94 [95% CI, 0.92-0.95]; sensitivity, 99%; specificity, 17%; PPV, 90%; NPV, 81%; creatinine AUROC, 0.96 [95% CI, 0.96-0.97]; sensitivity, 93%; specificity, 83%; PPV, 79%; NPV, 94%; and urea nitrogen AUROC, 0.95 [95% CI, 0.94, 0.96]; sensitivity, 87%; specificity, 89%; PPV, 77%; NPV 94%). Conclusions and Relevance: The findings suggest that low-yield diagnostic testing is common and can be systematically identified through data-driven methods and patient context-aware predictions. Implementing machine learning models appear to be able to quantify the level of uncertainty and expected information gained from diagnostic tests explicitly, with the potential to encourage useful testing and discourage low-value testing that incurs direct costs and indirect harms.

Reducing Unnecessary Treatment of Asymptomatic Elevated Blood Pressure with Intravenous Medications on the General Internal Medicine Wards: A Quality Improvement Initiative.

BACKGROUND: Asymptomatic elevated blood pressure (BP) is common in the hospital. There is no evidence supporting the use of intravenous (IV) antihypertensives in this setting. OBJECTIVE: To determine the prevalence and effects of treating asymptomatic elevated BP with IV antihypertensives and to investigate the efficacy of a quality improvement (QI) initiative aimed at reducing utilization of these medications. DESIGN: Retrospective cohort study. SETTING: Urban academic hospital. PATIENTS: Patients admitted to the general medicine service, including the intensive care unit (ICU), with ≥1 episode of asymptomatic elevated BP (>160/90 mm Hg) during hospitalization. INTERVENTION: A two-tiered, QI initiative. MEASUREMENTS: The primary outcome was the monthly proportion of patients with asymptomatic elevated BP treated with IV labetalol or hydralazine. We also analyzed median BP and rates of balancing outcomes (ICU transfers, rapid responses, cardiopulmonary arrests). RESULTS: We identified 2,306 patients with ≥1 episode of asymptomatic elevated BP during the 10-month preintervention period, of which 251 (11%) received IV antihypertensives. In the four-month postintervention period, 70 of 934 (7%) were treated. The odds of being treated were 38% lower in the postintervention period after adjustment for baseline characteristics, including length of stay and illness severity (OR = 0.62; 95% CI 0.47-0.83; P = .001). Median SBP was similar between pre- and postintervention (167 vs 168 mm Hg; P = .78), as were the adjusted proportions of balancing outcomes. CONCLUSIONS: Hospitalized patients with asymptomatic elevated BP are commonly treated with IV antihypertensives, despite the lack of evidence. A QI initiative was successful at reducing utilization of these medications.

Assessment of a Targeted Electronic Health Record Intervention to Reduce Telemetry Duration: A Cluster-Randomized Clinical Trial.

Importance: Physicians frequently use cardiac monitoring, or telemetry, beyond the duration recommended by published practice standards, resulting in "alarm fatigue" and excess cost. Prior studies have demonstrated an association between multicomponent quality improvement interventions and safe reduction of telemetry duration. Objective: To determine if a single-component intervention, a targeted electronic health record (EHR) alert, could achieve similar gains to multicomponent interventions and safely reduce unnecessary monitoring. Design, Setting, and Participants: This cluster-randomized clinical trial was conducted between November 2016 and May 2017 on the general medicine service of the Division of Hospital Medicine at the University of California, San Francisco Medical Center and included physicians of 12 inpatient medical teams (6 intervention, 6 control). Interventions: The EHR alert was randomized to half of the teams on the general medicine service. The alert displayed during daytime hours when physicians attempted to place an order for patients not in the intensive care unit whose telemetry order duration exceeded the recommended duration for a given indication. Main Outcomes and Measures: The primary outcome was telemetry monitoring hours per hospitalization, which was measured using time-stamped orders data from the EHR database. Physician responses to the alert were collected using EHR reporting tools. The potential adverse outcomes of rapid-response calls and medical emergency events were measured by counting the notes documenting these events in the EHR. Results: Of the 1021 patients included in this study, in the intervention arm, there was a mean (SD) age of 64.5 (18.9) and 215 (45%) were women; in the control arm, there was a mean (SD) age of 63.8 (19.1) and 249 (46%) were women. The 12 teams were stratified to 8 house-staff teams and 4 hospitalist teams, with 499 hospitalizations analyzed in the intervention arm and 567 hospitalizations analyzed in the control arm. The alert prompted a significant reduction in telemetry monitoring duration (-8.7 hours per hospitalization; 95% CI, -14.1 to -3.5 hours; P = .001) with no significant change in rapid-response calls or medical emergency events. The most common physician response to the alert was to discontinue telemetry monitoring (62% of 200 alerts). Conclusions and Relevance: A targeted EHR alert can safely and successfully reduce cardiac monitoring by prompting discontinuation when appropriate. This single-component electronic intervention is less resource intensive than typical multicomponent interventions that include human resources. Trial Registration: ClinicalTrials.gov identifier: NCT02529176.

Electronic Order Volume as a Meaningful Component in Estimating Patient Complexity and Resident Physician Workload.

BACKGROUND: Though patient census has been used to describe resident physician workload, this fails to account for variations in patient complexity. Changes in clinical orders captured through electronic health records may provide a complementary window into workload. We aimed to determine whether electronic order volume correlated with measures of patient complexity and whether higher order volume was associated with quality metrics. METHODS: In this retrospective study of admissions to the internal medicine teaching service of an academic medical center in a 13-month period, we tested the relationship between electronic order volume and patient level of care and severity of illness category. We used multivariable logistic regression to examine the association between daily team orders and two discharge-related quality metrics (receipt of a high-quality patient after-visit summary (AVS) and timely discharge summary), adjusted for team census, patient severity of illness, and patient demographics. RESULTS: Our study included 5,032 inpatient admissions for whom 929,153 orders were entered. Mean daily order volume was significantly higher for patients in the intensive care unit than in step-down units and general medical wards (40 vs. 24 vs. 19, P < .001). Order volume was also significantly correlated with severity of illness (P < .001). Patients were 12% less likely to receive a timely discharge summary for every 100 additional team orders placed on the day prior to discharge (OR 0.88; 95% CI 0.82-0.95). CONCLUSIONS: Electronic order volume is significantly associated with patient complexity and may provide valuable additional information in measuring resident physician workload.

It All Just Clicks: Development of an Inpatient E-Consult Program.

Although the use of electronic consultations (e-consults) in the outpatient setting is commonplace, there is little evidence of their use in the inpatient setting. Often, the only choice hospitalists have is between requesting a time-consuming in-person consultation or requesting an informal, undocumented "curbside" consultation. For a new, remote hospital in our healthcare system, we developed an e-consult protocol that can be used to address simple consultation questions. In the first year of the program, 143 e-consults occurred; the top 5 consultants were infectious disease, hematology, endocrinology, nephrology, and cardiology. Over the first 4 months, no safety issues were identified in chart review audits; to date, no safety issues have been identified through the hospital's incident reporting system. In surveys, hospitalists were universally pleased with the quality of e-consult recommendations, though only 43% of consultantsagreed. With appropriate care for patient selection, e-consults can be used to safely and efficiently provide subspecialty expertise to a remote inpatient site Journal of Hospital Medicine 2017;12:332-334.

Standardized Attending Rounds to Improve the Patient Experience: A Pragmatic Cluster Randomized Controlled Trial.

BACKGROUND: At academic medical centers, attending rounds (AR) serve to coordinate patient care and educate trainees, yet variably involve patients. OBJECTIVE: To determine the impact of standardized bedside AR on patient satisfaction with rounds. DESIGN: Cluster randomized controlled trial. SETTING: 500-bed urban, quaternary care hospital. PATIENTS: 1200 patients admitted to the medicine service. INTERVENTION: Teams in the intervention arm received training to adhere to 5 AR practices: 1) pre-rounds huddle; 2) bedside rounds; 3) nurse integration; 4) real-time order entry; 5) whiteboard updates. Control arm teams continued usual rounding practices. MEASUREMENTS: Trained observers audited rounds to assess adherence to recommended AR practices and surveyed patients following AR. The primary outcome was patient satisfaction with AR. Secondary outcomes were perceived and actual AR duration, and attending and trainee satisfaction. RESULTS: We observed 241 (70.1%) and 264 (76.7%) AR in the intervention and control arms, respectively, which included 1855 and 1903 patient rounding encounters. Using a 5-point Likert scale, patients in the intervention arm reported increased satisfaction with AR (4.49 vs 4.25; P = 0.01) and felt more cared for by their medicine team (4.54 vs 4.36; P = 0.03). Although the intervention shortened the duration of AR by 8 minutes on average (143 vs 151 minutes; P = 0.052), trainees perceived intervention AR as lasting longer and reported lower satisfaction with intervention AR. CONCLUSIONS: Medicine teams can adopt a standardized, patient-centered, time-saving rounding model that leads to increased patient satisfaction with AR and the perception that patients are more cared for by their medicine team. Journal of Hospital Medicine 2017;12:143-149.

Improving attending rounds: Qualitative reflections from multidisciplinary providers.

BACKGROUND: Attending rounds, the time for the attending physician and the team to discuss the team's patients, take place at teaching hospitals every day, often with little standardization. OBJECTIVE: This hypothesis-generating qualitative study sought to solicit improvement recommendations for standardizing attending rounds from the perspective of a multi-disciplinary group of providers. METHODS: Attending physicians, housestaff (residents and interns), medical students, nurses and pharmacists at an academic medical center participated in a quality improvement initiative between January and April 2013. Participants completed an individual or focus group interview or an e-mail survey with three open-ended questions: (1) What are poor or ineffective practices for attending rounds? (2) How would you change attending rounds structure and function? (3) What do you consider best practices for attending rounds? We undertook content analysis to summarize each clinical stakeholder group's improvement recommendations. RESULTS: Sixty stakeholders participated in our study including 23 attending hospitalists, 24 housestaff, 7 medical students, 2 pharmacists and 4 nurses. Key improvement recommendations included (1) performing a pre-rounds huddle, (2) planning of the visit schedule based on illness or pending discharge, (3) real-time order writing, (4) patient involvement in rounds with shared decision-making, (5) bedside nurse inclusion and (6) minimizing interruption of intern or student presentations. CONCLUSIONS: The practice improvement recommendations identified in this study will require deliberate systems changes and training to implement, and they warrant rigorous evaluation to determine their impact on the clinical and educational goals of rounds.

A wireless and battery-less miniature intracardiac pressure sensor: early implantation studies.

Permanently implantable hemodynamic monitors show great promise in providing personalized and cost-efficient care to heart failure patients by providing timely intracardiac pressure data under ambulatory conditions. The data may be used to titrate maintenance therapies and to monitor health status so that more intensive interventions can be planned and performed under optimal conditions. In this pilot study, we present the results of the implantation of a novel wireless, battery-less pressure sensor into the apex of the left ventricle of four dogs for a period of 8 weeks. All animals recovered to a normal state and did not show any clinical signs of cardiac insufficiency or any complications suggestive of thromboembolism. All sensors functioned throughout the implantation period and provided detailed waveforms of ventricular pressure.