Virtual Obstetric Hospitalist Support for Obstetric Emergencies and Deliveries: The Mayo Clinic Experience.

Objective: To implement use of obstetric (OB) hospitalist telemedicine services (TeleOB) to support clinicians facing OB emergencies in low-resource hospital settings. Methods: TeleOB was staffed by OB hospitalists working at a tertiary maternity center. The service was available via real-time high-definition audio/video technology for providers at 17 outlying hospitals across a health system spanning two states. The initial 25 service activations are described. Results: TeleOB supported 17 deliveries, two postpartum emergency department (ED) consultations, and four antenatal ED consultations. In 10 of 17 (59%) deliveries, teleneonatology was jointly activated to support neonatal resuscitation. Sixteen (94%) deliveries occurred in multiparas, and five (29%) resulted from spontaneous preterm labor. Eighty percent (20/25) of activations occurred in facilities without maternity services. Conclusions: A TeleOB service staffed by OB hospitalists successfully supports hospitals in an integrated health care system. TeleOB is feasible for support of hospitals with no delivery facilities or with limited maternity care resources.

Association of limited English proficiency with emergency department irregular departures and return visits: A cross-sectional cohort study in the Upper Midwest between January 2018 and December 2021.

OBJECTIVES: Patients with limited English proficiency (LEP) have been shown to experience disparities in emergency department (ED) care. The objectives of this study were to examine the associations between LEP and irregular ED departures and return ED visits. METHODS: We conducted a multicenter cross-sectional analysis of 18 EDs within an integrated health system in the upper Midwest from January 1, 2018, to December 31, 2021. ED visits of pediatric and adult patients who were discharged on the index visit were included for analysis. We analyzed the association of LEP with irregular departures, 72-h and 7-day return visits, and ED disposition at the time of that return visit. Multivariable model associations were calculated using generalized estimating equations and reported as odds ratios (OR) with 95% confidence intervals (CIs). RESULTS: A total of 745,464 total ED visits were analyzed, including 27,906 (3.7%) visits among patients with LEP. The most common preferred languages among patients with LEP were Spanish (12,759; 45.7%), Somali (4978; 17.8%), and Arabic (3185; 11.4%). After multivariable adjustment there were no differences in proportions of irregular departures (OR 1.09, 95% CI 0.99-1.21), 72-h returns (OR 0.99, 95% CI 0.92-1.06), or 7-day returns (OR 0.99, 95% CI 0.93-1.05) between patients with LEP or English proficiency. Patients with LEP returning within 72 h (OR 1.19, 95% CI 1.01-1.40) and 7 days (OR 1.15, 95% CI 1.01-1.33) were more likely to be admitted to the hospital. CONCLUSIONS: After multivariable adjustment, we did not find an increased frequency of irregular ED departures or 72-h or 7-day returns among patients with LEP compared with people proficient in English. However, we did find that higher proportions of patients with LEP were admitted to the hospital at the time of the return ED visit.

A Clinician's Guide to Running Custom Machine-Learning Models in an Electronic Health Record Environment.

We recently brought an internally developed machine-learning model for predicting which patients in the emergency department would require hospital admission into the live electronic health record environment. Doing so involved navigating several engineering challenges that required the expertise of multiple parties across our institution. Our team of physician data scientists developed, validated, and implemented the model. We recognize a broad interest and need to adopt machine-learning models into clinical practice and seek to share our experience to enable other clinician-led initiatives. This Brief Report covers the entire model deployment process, starting once a team has trained and validated a model they wish to deploy in live clinical operations.

Comparison of clinical note quality between an automated digital intake tool and the standard note in the emergency department.

BACKGROUND: Medical encounters require an efficient and focused history of present illness (HPI) to create differential diagnoses and guide diagnostic testing and treatment. Our aim was to compare the HPI of notes created by an automated digital intake tool versus standard medical notes created by clinicians. METHODS: Prospective trial in a quaternary academic Emergency Department (ED). Notes were compared using the 5-point Physician Documentation Quality Instrument (PDQI-9) scale and the Centers for Medicare & Medicaid Services (CMS) level of complexity index. Reviewers were board certified emergency medicine physicians blinded to note origin. Reviewers received training and calibration prior to note assessments. A difference of 1 point was considered clinically significant. Analysis included McNemar's (binary), Wilcoxon-rank (Likert), and agreement with Cohen's Kappa. RESULTS: A total of 148 ED medical encounters were charted by both digital note and standard clinical note. The ability to capture patient information was assessed through comparison of note content across paired charts (digital-standard note on the same patient), as well as scores given by the reviewers. Reviewer agreement was kappa 0.56 (CI 0.49-0.64), indicating moderate level of agreement between reviewers scoring the same patient chart. Considering all 18 questions across PDQI-9 and CMS scales, the average agreement between standard clinical note and digital note was 54.3% (IQR 44.4-66.7%). There was a moderate level of agreement between content of standard and digital notes (kappa 0.54, 95%CI 0.49-0.60). The quality of the digital note was within the 1 point clinically significant difference for all of the attributes, except for conciseness. Digital notes had a higher frequency of CMS severity elements identified. CONCLUSION: Digitally generated clinical notes had moderate agreement compared to standard clinical notes and within the one point clinically significant difference except for the conciseness attribute. Digital notes more reliably documented billing components of severity. The use of automated notes should be further explored to evaluate its utility in facilitating documentation of patient encounters.

Improving Timely Antibiotic Administration for Pediatric Oncology Patients With Neutropenic Fever Seen in the Emergency Department.

Objective: To improve the care for pediatric oncology patients with neutropenic fever who present to the emergency department (ED) by administering appropriate empiric antibiotics within 60 minutes of arrival. Patients and Methods: We focused on improving the care for pediatric oncology patients at risk of neutropenia who presented to the ED with concern for fever. Our baseline adherence to the administration of empiric antibiotics within 60 minutes for this population was 53% (76/144) from January 1, 2010, to December 21, 2014. During 2015, we reviewed data monthly, finding 73% adherence. We used the Lean methodology to identify the process waste, completed a value-stream map with input from multidisciplinary stakeholders, and convened a root cause analysis to identify causes for delay. The 4 causes were as follows: (1) lack of staff awareness; (2) missing patient information in electronic medical record; (3) practice variation; and 4) lack of clear prioritization of laboratory draws. We initiated Plan-Do-Study-Act cycles to achieve our goal of 80% of patients receiving appropriate empiric antibiotics within 60 minutes of arrival in the ED. Results: Five Plan-Do-Study-Act cycles were completed, focusing on the following: (1) timely identification of patients by utilizing the electronic medical record to initiate a page to the care team; (2) creation of a streamlined intravascular access process; (3) practice standardization; (4) convenient access to appropriate antibiotics; and (5) care team education. Timely antibiotic administration increased from 73%-95% of patients by 2018. More importantly, the adherence was sustained to greater than 90% through 2021. Conclusion: A structured and multifaceted approach using quality improvement methodologies can achieve and sustain improved patient care outcomes in the ED.

Assessing the Generalizability of a Clinical Machine Learning Model Across Multiple Emergency Departments.

Objective: To assess the generalizability of a clinical machine learning algorithm across multiple emergency departments (EDs). Patients and Methods: We obtained data on all ED visits at our health care system's largest ED from May 5, 2018, to December 31, 2019. We also obtained data from 3 satellite EDs and 1 distant-hub ED from May 1, 2018, to December 31, 2018. A gradient-boosted machine model was trained on pooled data from the included EDs. To prevent the effect of differing training set sizes, the data were randomly downsampled to match those of our smallest ED. A second model was trained on this downsampled, pooled data. The model's performance was compared using area under the receiver operating characteristic (AUC). Finally, site-specific models were trained and tested across all the sites, and the importance of features was examined to understand the reasons for differing generalizability. Results: The training data sets contained 1918-64,161 ED visits. The AUC for the pooled model ranged from 0.84 to 0.94 across the sites; the performance decreased slightly when Ns were downsampled to match those of our smallest ED site. When site-specific models were trained and tested across all the sites, the AUCs ranged more widely from 0.71 to 0.93. Within a single ED site, the performance of the 5 site-specific models was most variable for our largest and smallest EDs. Finally, when the importance of features was examined, several features were common to all site-specific models; however, the weight of these features differed. Conclusion: A machine learning model for predicting hospital admission from the ED will generalize fairly well within the health care system but will still have significant differences in AUC performance across sites because of site-specific factors.

Outcomes of COVID-19 With the Mayo Clinic Model of Care and Research.

OBJECTIVE: To report the Mayo Clinic experience with coronavirus disease 2019 (COVID-19) related to patient outcomes. METHODS: We conducted a retrospective chart review of patients with COVID-19 diagnosed between March 1, 2020, and July 31, 2020, at any of the Mayo Clinic sites. We abstracted pertinent comorbid conditions such as age, sex, body mass index, Charlson Comorbidity Index variables, and treatments received. Factors associated with hospitalization and mortality were assessed in univariate and multivariate models. RESULTS: A total of 7891 patients with confirmed COVID-19 infection with research authorization on file received care across the Mayo Clinic sites during the study period. Of these, 7217 patients were adults 18 years or older who were analyzed further. A total of 897 (11.4%) patients required hospitalization, and 354 (4.9%) received care in the intensive care unit (ICU). All hospitalized patients were reviewed by a COVID-19 Treatment Review Panel, and 77.5% (695 of 897) of inpatients received a COVID-19-directed therapy. Overall mortality was 1.2% (94 of 7891), with 7.1% (64 of 897) mortality in hospitalized patients and 11.3% (40 of 354) in patients requiring ICU care. CONCLUSION: Mayo Clinic outcomes of patients with COVID-19 infection in the ICU, hospital, and community compare favorably with those reported nationally. This likely reflects the impact of interprofessional multidisciplinary team evaluation, effective leveraging of clinical trials and available treatments, deployment of remote monitoring tools, and maintenance of adequate operating capacity to not require surge adjustments. These best practices can help guide other health care systems with the continuing response to the COVID-19 pandemic.

Measuring Sensitivity and Precision of Real-Time Location Systems (RTLS): Definition, Protocol and Demonstration for Clinical Relevance.

The ability of a Real Time Location System (RTLS) to provide correct information in a clinical environment is an important consideration in evaluating the effectiveness of the technology. While past efforts describe how well the technology performed in a lab environment, the performance of such technology has not been specifically defined or evaluated in a practice setting involving workflow and movement. Clinical environments pose complexity owing to various layouts and various movements. Further, RTL systems are not equipped to provide true negative information (where an entity is not located). Hence, this study defined sensitivity and precision in this context, and developed a simulation protocol to serve as a systematic testing framework using actors in a clinical environment. The protocol was used to measure the sensitivity and precision of an RTL system in the emergency department space of a quaternary care medical center. The overall sensitivity and precision were determined to be 84 and 93% respectively. These varied for patient rooms, staff area, hallway and other rooms.

Extended Patient Alone Time in Emergency Department Leads to Increased Risk of 30-Day Hospitalization.

OBJECTIVES: This study was conducted to describe patients at risk for prolonged time alone in the emergency department (ED) and to determine the relationship between clinical outcomes, specifically 30-day hospitalization, and patient alone time (PAT) in the ED. METHODS: An observational cohort design was used to evaluate PAT and patient characteristics in the ED. The study was conducted in a tertiary academic ED that has both adult and pediatric ED facilities and of patients placed in an acute care room for treatment between May 1 and July 31, 2016, excluding behavioral health patients. Simple linear regression and t tests were used to evaluate the relationship between patient characteristics and PAT. Logistic regression was used to evaluate the relationship between 30-day hospitalization and PAT. RESULTS: Pediatric patients had the shortest total PAT compared with all older age groups (86.4 minutes versus 131 minutes, P < 0.001). Relationships were seen between PAT and patient characteristics, including age, geographic region, and the severity and complexity of the health condition. Controlling for Charlson comorbidity index and other potentially confounding variables, a logistic regression model showed that patients are more likely to be hospitalized within 30 days after their ED visit, with an odds ratio (95% confidence interval) of 1.056 (1.017-1.097) for each additional hour of PAT. CONCLUSIONS: Patient alone time is not equal among all patient groups. Study results indicate that PAT is significantly associated with 30-day hospitalization. This conclusion indicates that PAT may affect patient outcomes and warrants further investigation.

Best Practices in Managing Cardiac Arrest in the Emergency Department During the COVID-19 Pandemic.

Infection by severe acute respiratory syndrome coronavirus 2 has led to cardiac complications including an increasing incidence of cardiac arrest. The resuscitation of these patients requires a conscious effort to minimize the spread of the virus. We present a best-practice model based in four guiding principles: (1) reduce the risk of exposure to the entire health care team; (2) decrease the number of aerosol generating procedures; (3) use a small resuscitation team to limit potential exposure; and (4) consider early termination of resuscitative efforts.

Impact of the SARS-CoV-2 Pandemic on Emergency Department Presentations in an Integrated Health System.

OBJECTIVE: To quantify the impact of the severe acute respiratory syndrome coronavirus 2 pandemic on emergency department volumes and patient presentations and evaluate changes in community mortality for the purpose of characterizing new patterns of emergency care use. PATIENTS AND METHODS: This is an observational cross-sectional study using electronic health records for emergency department visits in an integrated multihospital system with academic and community practices across 4 states for visits between March 17 and April 21, 2019, and February 9 and April 21, 2020. We compared numbers and proportions of common and critical chief symptoms and diagnoses, triage assessments, throughput, disposition, and selected hospital lengths of stay and out-of-hospital deaths. RESULTS: In the period of interest, emergency department visits decreased by nearly 50% (35037 to 18646). Total numbers of patients with myocardial infarctions, stroke, appendicitis, and cholecystitis diagnosed decreased. The percentage of visits for mental health symptoms increased. There was an increase in deaths, driven by out-of-hospital mortality. CONCLUSION: Fewer patients presenting with acute and time-sensitive diagnoses suggests that patients are deferring care. This may be further supported by an increase in out-of-hospital mortality. Understanding which patients are deferring care and why will allow us to develop outreach strategies and ensure that those in need of rapid assessment and treatment will do so, preventing downstream morbidity and mortality.

Clinical evaluation and diagnostic yield following evaluation of abnormal pulse detected using Apple Watch.

OBJECTIVE: The study sought to characterize the evaluation of patients who present following detection of an abnormal pulse using Apple Watch. MATERIALS AND METHODS: We conducted a retrospective review of patients evaluated for abnormal pulse detected using Apple Watch over a 4-month period. RESULTS: Among 264 included patients, clinical documentation for 41 (15.5%) explicitly noted an abnormal pulse alert. Preexisting atrial fibrillation was noted in 58 (22.0%). Most commonly performed testing included 12-lead echocardiography (n = 158; 59.8%), Holter monitor (n = 77; 29.2%), and chest x-ray (n = 64; 24.2%). A clinically actionable cardiovascular diagnosis of interest was established in only 30 (11.4%) patients, including 6 of 41 (15%) patients who received an explicit alert. DISCUSSION: False positive screening results may lead to overutilization of healthcare resources. CONCLUSIONS: The Food and Drug Administration and Apple should consider the unintended consequences of widespread screening for asymptomatic ("silent") atrial fibrillation and use of the Apple Watch abnormal pulse detection functionality by populations in whom the device has not been adequately studied.

Unnecessary wait hours: a novel ED system-level metric.

BACKGROUND: Emergency department (ED) operations leaders are under increasing pressure to make care delivery more efficient. Publicly reported ED efficiency metrics are traditionally patient centred and do not show situational or facility-based improvement opportunities. We propose the consideration of a novel metric, the 'Number of Unnecessary Waits (NUW)' and the corresponding 'Unnecessary Wait Hours (UWH)', to measure space efficiency, and we describe how we used NUW to evaluate operational changes in our ED. METHODS: UWH summarises the relationship between the number of available rooms and the number of patients waiting by returning a value equal to the number of unnecessary patient waits. We used this metric to evaluate reassigning a clinical technician assistant (CTA) to the new role of flow CTA. RESULTS: We retrospectively analysed 3.5 months of data from before and after creation of the flow CTA. NUW metric analysis suggested that the flow CTA decreased the amount of unnecessary wait hours, while higher patient volumes had the opposite effect. CONCLUSIONS: Situational system-level metrics may provide a new dimension to evaluating ED operational efficiencies. Studies focussed on system-level metrics to evaluate an ED practice are needed to understand the role these metrics play in evaluation of a department's operations.

Attitudes and Behavior of Health Care Workers Before, During, and After Implementation of Real-Time Location System Technology.

OBJECTIVE: To assess how staff attitudes before, during, and after implementation of a real-time location system (RTLS) that uses radio-frequency identification tags on staff and patient identification badges and on equipment affected staff's intention to use and actual use of an RTLS. PARTICIPANTS AND METHODS: A series of 3 online surveys were sent to staff at an emergency department with plans to implement an RTLS between June 1, 2015, and November 29, 2016. Each survey corresponded with a different phase of implementation: preimplementation, midimplementation, and postimplementation. Multiple logistic regression with backward elimination was used to assess the relationship between demographic variables, attitudes about RTLSs, and intention to use or actual use of an RTLS. RESULTS: Demographic variables were not associated with intention to use or actual use of the RTLS. Before implementation, poor perceptions about the technology's usefulness and lack of trust in how employers would use tracking data were associated with weaker intentions to use the RTLS. During and after implementation, attitudes about the technology's use, not issues related to autonomy and privacy, were associated with less use of the technology. CONCLUSION: Real-time location systems have the potential to assess patterns of health care delivery that could be modified to reduce costs and improve the quality of care. Successful implementation, however, may hinge on how staff weighs attitudes and concerns about their autonomy and personal privacy with organizational goals. With the large investments required for new technology, serious consideration should be given to address staff attitudes about privacy and technology in order to assure successful implementation.