Machine Learning Methods for Predicting Patient-Level Emergency Department Workload.

BACKGROUND: Work Relative Value Units (wRVUs) are a component of many compensation models, and a proxy for the effort required to care for a patient. Accurate prediction of wRVUs generated per patient at triage could facilitate real-time load balancing between physicians and provide many practical operational and clinical benefits. OBJECTIVE: We examined whether deep-learning approaches could predict the wRVUs generated by a patient's visit using data commonly available at triage. METHODS: Adult patients presenting to an urban, academic emergency department from July 1, 2016-March 1, 2020 were included. Deidentified triage information included structured data (age, sex, vital signs, Emergency Severity Index score, language, race, standardized chief complaint) and unstructured data (free-text chief complaint) with wRVUs as outcome. Five models were examined: average wRVUs per chief complaint, linear regression, neural network and gradient-boosted tree on structured data, and neural network on unstructured textual data. Models were evaluated using mean absolute error. RESULTS: We analyzed 204,064 visits between July 1, 2016 and March 1, 2020. The median wRVUs were 3.80 (interquartile range 2.56-4.21), with significant effects of age, gender, and race. Models demonstrated lower error as complexity increased. Predictions using averages from chief complaints alone demonstrated a mean error of 2.17 predicted wRVUs per visit (95% confidence interval [CI] 2.07-2.27), the linear regression model: 1.00 wRVUs (95% CI 0.97-1.04), gradient-boosted tree: 0.85 wRVUs (95% CI 0.84-0.86), neural network with structured data: 0.86 wRVUs (95% CI 0.85-0.87), and neural network with unstructured data: 0.78 wRVUs (95% CI 0.76-0.80). CONCLUSIONS: Chief complaints are a poor predictor of the effort needed to evaluate a patient; however, deep-learning techniques show promise. These algorithms have the potential to provide many practical applications, including balancing workloads and compensation between emergency physicians, quantify crowding and mobilizing resources, and reducing bias in the triage process.

Comparing Clinician Consensus Recommendations to Patient-reported Opioid Use Across Multiple Hospital Systems.

OBJECTIVE: We compare consensus recommendations for 5 surgical procedures to prospectively collected patient consumption data. To address local variation, we combined data from multiple hospitals across the country. SUMMARY OF BACKGROUND DATA: One approach to address the opioid epidemic has been to create prescribing consensus reports for common surgical procedures. However, it is unclear how these guidelines compare to patient-reported data from multiple hospital systems. METHODS: Prospective observational studies of surgery patients were completed between 3/2017 and 12/2018. Data were collected utilizing post-discharge surveys and chart reviews from 5 hospitals (representing 3 hospital systems) in 5 states across the USA. Prescribing recommendations for 5 common surgical procedures identified in 2 recent consensus reports were compared to the prospectively collected aggregated data. Surgeries included: laparoscopic cholecystectomy, open inguinal hernia repair, laparoscopic inguinal hernia repair, partial mastectomy without sentinel lymph node biopsy, and partial mastectomy with sentinel lymph node biopsy. RESULTS: Eight hundred forty-seven opioid-naïve patients who underwent 1 of the 5 studied procedures reported counts of unused opioid pills after discharge. Forty-one percent did not take any opioid medications, and across all surgeries, the median consumption was 3 5 mg oxycodone pills or less. Generally, consensus reports recommended opioid quantities that were greater than the 75th percentile of consumption, and for 2 procedures, recommendations exceeded the 90th percentile of consumption. CONCLUSIONS: Although consensus recommendations were an important first step to address opioid prescribing, our data suggests that following these recommendations would result in 47%-56% of pills prescribed remaining unused. Future multi-institutional efforts should be directed toward refining and personalizing prescribing recommendations.

Evaluation of U.S. state opioid prescribing restrictions using patient opioid consumption patterns from a single, urban, academic institution.

Background: Since 2017, states, insurers, and pharmacies have placed blanket limits on the duration and quantity of opioid prescriptions. In many states, overlapping duration and daily dose limits yield maximum prescription limits of 150-350 morphine milligram equivalents (MMEs). There is limited knowledge of how these restrictions compare with actual patient opioid consumption; while changes in prescription patterns and opioid misuse rates have been studied, these are, at best, weak proxies for actual pain control consumption. We sought to determine how patients undergoing surgery would be affected by opioid prescribing restrictions using actual patient opioid consumption data. Methods: We constructed a prospective database of post-discharge opioid consumption: patients undergoing surgery at one institution were called after discharge to collect opioid consumption data. Patients whose opioid consumption exceeded 150 and 350 MME were identified. Results: Two thousand nine hundred and seventy-one patients undergoing 54 common surgical procedures were included in our study. Twenty-one percent of patients consumed more than the 150 MME limit. Only 7% of patients consumed above the 350 MME limit. Typical (non-outlier) opioid consumption, defined as less than the 75th percentile of consumption for any given procedure, exceeded the 150 MME and 350 MME limits for 41 and 7% of procedures, respectively. Orthopedic, spinal/neurosurgical, and complex abdominal procedures most commonly exceeded these limits. Conclusions: While most patients undergoing surgery are unaffected by recent blanket prescribing limits, those undergoing a specific subset of procedures are likely to require more opioids than the restrictions permit; providers should be aware that these patients may require a refill to adequately control post-surgical pain. Real consumption data should be used to guide these restrictions and inform future interventions so the risk of worsened pain control (and its troublesome effects on opioid misuse) is minimized. Procedure-specific prescribing limits may be one approach to prevent misuse, while also optimizing post-operative pain control.

Emergency physicians' active patient queues over the course of a shift.

OBJECTIVES: When emergency physicians see new patients in an ad libitum system, they see fewer patients as the shift progresses. However, it is unclear if this reflects a decreasing workload, as patient assessments often span many hours. We sought to investigate whether the size of a physician's queue of active patients similarly declines over a shift. METHODS: Retrospective cohort study, conducted over two years in three community hospitals in the Northeastern United States, with 8 and 9-h shifts. Timestamps of all encounters were recorded electronically. Generalized estimating equations were constructed to predict the number of active patients a physician concurrently managed per hour. RESULTS: We evaluated 64 physicians over a two-year period, with 9822 physician-shifts. Across all sites, physicians managed an increasing queue of active patients in the first several hours. This queue plateaued in the middle of the shift, declining in the final hours, independently of other factors. Physicians' queues of active patients increased slightly with greater volume and acuity, but did not affect the overall pattern of work. Similarly, working alone or with colleagues had little effect on the number of active patients managed. CONCLUSIONS: Emergency physicians in an ad libitum system tend to see new patients until reaching a stable roster of active patients. This pattern may help explain why physicians see fewer new patients over the course of a shift, should be factored into models of throughput, and suggests new avenues for evaluating relationships between physician workload, patient safety, physicians' well-being, and the quality of care.

Point of Care Image Capture with a Custom Smartphone Application: Experience with an Encounter-Based Workflow.

Medical documentation is one of the primary methods by which physicians share clinical information and impressions over time with one another. As the adage says, "a picture is worth a thousand words," and physicians have started leveraging consumer mobile technology to share images with one another. However, image sharing often uses short message service texting and similar methods, which can be noncompliant with privacy regulations and can also limit the ability to communicate information longitudinally and across specialties. Sharing of such information is increasingly important, however, as smaller practices are joining to create large geographically spread out health care networks. To this end, we developed an application to acquire and store images via smartphone and seamlessly transfer into the patient's electronic medical record (EMR) to enable digital consults and longitudinal evaluation in a private and compliant method.

The Effect of Signed-Out Emergency Department Patients on Resident Productivity.

BACKGROUND: Transitions of care and patient hand-offs between physicians have important implications for patient care. However, what effect caring for signed-out patients has on providing care to new patients and education is unclear. OBJECTIVE: We sought to determine whether the number of patients a physician receives in sign-out affects productivity. METHODS: This was a retrospective cohort study, conducted at an emergency medicine residency program. A general estimation equation was constructed to model productivity, defined as new patients evaluated and relative value units (RVUs) generated per shift, relative to the number of sign-outs received, and training year. A secondary analysis evaluated the effect of signed-out patients in observation. RESULTS: We evaluated 19,389 shifts from July 1, 2010 to July 1, 2017. Postgraduate year (PGY)-1 residents without sign-out evaluated 10.3 patients (95% confidence interval [CI] 9.83 to 10.7), generating 31.6 RVUs (95% CI 30.5 to 32.7). Each signed-out patient was associated with -0.07 new patients (95% CI -0.12 to -0.01), but no statistically significant decrease in RVUs (95% CI -0.07 to 0.28). PGY-2 residents without sign-out evaluated 13.6 patients (95% CI 12.6 to 14.6), generating 47.7 RVUs (95% CI 45.1 to 50.3). Each signed-out patient was associated with -0.25 (95% CI -0.40 to -0.10) new patients, and -0.89 (95% CI -1.22 to -0.55) RVUs. For all residents, observation patients were associated with more substantial decreases in new patients (-0.40; 95% CI -0.47 to -0.33) and RVUs (-1.11; 95% CI -1.40 to -0.82). CONCLUSIONS: Overall, sign-out burden is associated with a small decrease in resident productivity, except for observation patients. Program faculty should critically examine how signed-out patients are distributed to address residents' educational needs, throughput, and patient safety.

Modelling attending physician productivity in the emergency department: a multicentre study.

OBJECTIVES: Emergency physician productivity, often defined as new patients evaluated per hour, is essential to planning clinical operations. Prior research in this area considered this a static quantity; however, our group's study of resident physicians demonstrated significant decreases in hourly productivity throughout shifts. We now examine attending physicians' productivity to determine if it is also dynamic. METHODS: This is a retrospective cohort study, conducted from 2014 to 2016 across three community hospitals in the north-eastern USA, with different schedules and coverage. Timestamps of all patient encounters were automatically logged by the sites' electronic health record. Generalised estimating equations were constructed to predict productivity in terms of new patients per shift hour. RESULTS: 207 169 patients were seen by 64 physicians over 2 years, comprising 9822 physician shifts. Physicians saw an average of 15.0 (SD 4.7), 20.9 (SD 6.4) and 13.2 (SD 3.8) patients per shift at the three sites, with 2.97 (SD 0.22), 2.95 (SD 0.24) and 2.17 (SD 0.09) in the first hour. Across all sites, physicians saw significantly fewer new patients after the first hour, with more gradual decreases subsequently. Additional patient arrivals were associated with greater productivity; however, this attenuates substantially late in the shift. The presence of other physicians was also associated with slightly decreased productivity. CONCLUSIONS: Physician productivity over a single shift follows a predictable pattern that decreases significantly on an hourly basis, even if there are new patients to be seen. Estimating productivity as a simple average substantially underestimates physicians' capacity early in a shift and overestimates it later. This pattern of productivity should be factored into hospitals' staffing plans, with shifts aligned to start with the greatest volumes of patient arrivals.

Modeling Hourly Resident Productivity in the Emergency Department.

STUDY OBJECTIVE: Resident productivity, defined as new patients per hour, carries important implications for emergency department operations. In high-volume academic centers, essential staffing decisions can be made on the assumption that residents see patients at a static rate. However, it is unclear whether this model mirrors reality; previous studies have not rigorously examined whether productivity changes over time. We examine residents' productivity across shifts to determine whether it remained consistent. METHODS: This was a retrospective cohort study conducted in an urban academic hospital with a 3-year emergency medicine training program in which residents acquire patients ad libitum throughout their shift. Time stamps of all patient encounters were automatically logged. A linear mixed model was constructed to predict productivity per shift hour. RESULTS: A total of 14,364 8- and 9-hour shifts were worked by 75 residents between July 1, 2010, and June 20, 2015. This comprised 6,127 (42.7%) postgraduate year (PGY) 1 shifts, 7,236 (50.4%) PGY-2 shifts, and 998 (6.9%) PGY-3 nonsupervisory shifts (Table 1). Overall, residents treated a mean of 10.1 patients per shift (SD 3.2), with most patients at Emergency Severity Index level 3 or more acute (93.8%). In the initial hour, residents treated a mean of 2.14 patients (SD 1.2), and every subsequent hour was associated with a significant decrease, with the largest in the second, third, and final hours. CONCLUSION: Emergency medicine resident productivity during a single shift follows a reliable pattern that decreases significantly hourly, a pattern preserved across PGY years and types of shifts. This suggests that resident productivity is a dynamic process, which should be considered in staffing decisions and studied further.

Do Slow and Steady Residents Win the Race? Modeling the Effects of Peak and Overall Resident Productivity in the Emergency Department.

BACKGROUND: Emergency medicine residents need to be staffed in a way that balances operational needs with their educational experience. Key to developing an optimal schedule is knowing a resident's expected productivity, a poorly understood metric. OBJECTIVE: We sought to measure how a resident's busiest (peak) workload affects their overall productivity for the shift. METHODS: We conducted a retrospective, observational study of resident productivity at an urban, tertiary care center with a 3-year Accreditation Council for Graduate Medical Education-approved emergency medicine training program, with 55,000 visits annually. We abstracted resident productivity data from a database of patient assignments from July 1, 2010 to June 20, 2015, utilizing a generalized estimation equation method to evaluate physician shifts. Our primary outcome measure was the total number of patients seen by a resident over a shift. The secondary outcome was the number of patients seen excluding those in the peak hour. RESULTS: A total of 14,361 shifts were evaluated. Multivariate analysis showed that the total number of patients seen was significantly associated with the number of patients seen during the peak hour, level of training, the timing of the shift, but most prominently, lower variance in patients seen per hour (coefficient of variation < 0.10). CONCLUSIONS: A resident's peak productivity can be a strong predictor of their overall productivity, but the substantial negative effect of variability favors a steadier pace. This suggests that resident staffing and patient assignments should generally be oriented toward a more consistent workload, an effect that should be further investigated with attending physicians.

The Integration of Electronic Medical Student Evaluations Into an Emergency Department Tracking System is Associated With Increased Quality and Quantity of Evaluations.

BACKGROUND: Medical student evaluations are essential for determining clerkship grades. Electronic evaluations have various advantages compared to paper evaluations, such as increased ease of collection, asynchronous reporting, and decreased likelihood of becoming lost. OBJECTIVES: To determine whether electronic medical student evaluations (EMSEs) provide more evaluations and content when compared to paper shift card evaluations. METHODS: This before and after cohort study was conducted over a 2.5-year period at an academic hospital affiliated with a medical school and emergency medicine residency program. EMSEs replaced the paper shift evaluations that had previously been used halfway through the study period. A random sample of the free text comments on both paper and EMSEs were blindly judged by medical student clerkship directors for their helpfulness and usefulness. Logistic regression was used to test for any relationship between quality and quantity of words. RESULTS: A total of 135 paper evaluations for 30 students and then 570 EMSEs for 62 students were collected. An average of 4.8 (standard deviation [SD] 3.2) evaluations were completed per student using the paper version compared to 9.0 (SD 3.8) evaluations completed per student electronically (p < 0.001). There was an average of 8.8 (SD 8.5) words of free text evaluation on paper evaluations when compared to 22.5 (SD 28.4) words for EMSEs (p < 0.001). A statistically significant (p < 0.02) association between quality of an evaluation and the word count existed. CONCLUSIONS: EMSEs that were integrated into the emergency department tracking system significantly increased the number of evaluations completed compared to paper evaluations. In addition, the EMSEs captured more "helpful/useful" information about the individual students as evidenced by the longer free text entries per evaluation.

Assessment of respiratory rate monitoring in the emergency department.

Objectives: This study aimed to compare the different respiratory rate (RR) monitoring methods used in the emergency department (ED): manual documentation, telemetry, and capnography. Methods: This is a retrospective study using recorded patient monitoring data. The study population includes patients who presented to a tertiary care ED between January 2020 and December 2022. Inclusion and exclusion criteria were patients with simultaneous recorded RR data from all three methods and less than 10 min of recording, respectively. Linear regression and Bland-Altman analysis were performed between different methods. Results: A total of 351 patient encounters met study criteria. Linear regression yielded an R-value of 0.06 (95% confidence interval [CI] 0.00-0.12) between manual documentation and telemetry, 0.07 (95% CI 0.01-0.13) between manual documentation and capnography, and 0.82 (95% CI 0.79-0.85) between telemetry and capnography. The Bland-Altman analysis yielded a bias of -0.8 (95% limits of agreement [LOA] -12.2 to 10.6) between manual documentation and telemetry, bias of -0.6 (95% LOA -13.5 to 12.3) between manual documentation and capnography, and bias of 0.2 (95% LOA -6.2 to 6.6) between telemetry and capnography. Conclusion: There is a poor correlation between manual documentation and both automated methods, while there is relatively good agreement between the automated methods. This finding highlights the need to further investigate the methodology used by the ED staff in monitoring and documenting RR and ways to improve its reliability given that many important clinical decisions are made based on these assessments.

Postoperative Opioid Prescribing via Rule-Based Guidelines Derived from In-Hospital Consumption: An Assessment of Efficacy Based on Postdischarge Opioid Use.

BACKGROUND: Many institutions have developed operation-specific guidelines for opioid prescribing. These guidelines rarely incorporate in-hospital opioid consumption, which is highly correlated with consumption. We compare outcomes of several patient-centered approaches to prescribing that are derived from in-hospital consumption, including several experimental, rule-based prescribing guidelines and our current institutional guideline. STUDY DESIGN: We performed a retrospective, cohort study of all adults undergoing surgery at a single-academic medical center. Several rule-based guidelines, derived from in-hospital consumption (quantity of opioids consumed within 24 hours of discharge), were used to specify the theoretical quantity of opioid prescribed on discharge. The efficacy of the experimental guidelines was compared with 3 references: an approximation of our institution's tailored prescribing guideline; prescribing all patients the typical quantity of opioids consumed for patients undergoing the same operation; and a representative rule-based, tiered framework. For each scenario, we calculated the penalized residual sum of squares (reflecting the composite deviation from actual patient consumption, with 15% penalty for overprescribing) and the proportion of opioids consumed relative to prescribed. RESULTS: A total of 1,048 patients met inclusion criteria. Mean (SD) and median (interquartile range [IQR]) quantity of opioids consumed within 24 hours of discharge were 11.2 (26.9) morphine milligram equivalents and 0 (0 to 15) morphine milligram equivalents. Median (IQR) postdischarge consumption was 16 (0 to 150) morphine milligram equivalents. Our institutional guideline and the previously validated rule-based guideline outperform alternate approaches, with median (IQR) differences in prescribed vs consumed opioids of 0 (-60 to 27.25) and 37.5 (-37.5 to 37.5), respectively, corresponding to penalized residual sum of squares of 39,817,602 and 38,336,895, respectively. CONCLUSIONS: Rather than relying on fixed quantities for defined operations, rule-based guidelines offer a simple yet effective method for tailoring opioid prescribing to in-hospital consumption.

Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients.

Importance: Emergency department (ED) visits by older adults with life-limiting illnesses are a critical opportunity to establish patient care end-of-life preferences, but little is known about the optimal screening criteria for resource-constrained EDs. Objectives: To externally validate the Geriatric End-of-Life Screening Tool (GEST) in an independent population and compare it with commonly used serious illness diagnostic criteria. Design, Setting, and Participants: This prognostic study assessed a cohort of patients aged 65 years and older who were treated in a tertiary care ED in Boston, Massachusetts, from 2017 to 2021. Patients arriving in cardiac arrest or who died within 1 day of ED arrival were excluded. Data analysis was performed from August 1, 2023, to March 27, 2024. Exposure: GEST, a logistic regression algorithm that uses commonly available electronic health record (EHR) datapoints and was developed and validated across 9 EDs, was compared with serious illness diagnoses as documented in the EHR. Serious illnesses included stroke/transient ischemic attack, liver disease, cancer, lung disease, and age greater than 80 years, among others. Main Outcomes and Measures: The primary outcome was 6-month mortality following an ED encounter. Statistical analyses included area under the receiver operating characteristic curve, calibration analyses, Kaplan-Meier survival curves, and decision curves. Results: This external validation included 82 371 ED encounters by 40 505 unique individuals (mean [SD] age, 76.8 [8.4] years; 54.3% women, 13.8% 6-month mortality rate). GEST had an external validation area under the receiver operating characteristic curve of 0.79 (95% CI, 0.78-0.79) that was stable across years and demographic subgroups. Of included encounters, 53.4% had a serious illness, with a sensitivity of 77.4% (95% CI, 76.6%-78.2%) and specificity of 50.5% (95% CI, 50.1%-50.8%). Varying GEST cutoffs from 5% to 30% increased specificity (5%: 49.1% [95% CI, 48.7%-49.5%]; 30%: 92.2% [95% CI, 92.0%-92.4%]) at the cost of sensitivity (5%: 89.3% [95% CI, 88.8-89.9]; 30%: 36.2% [95% CI, 35.3-37.1]). In a decision curve analysis, GEST outperformed serious illness criteria across all tested thresholds. When comparing patients referred to intervention by GEST with serious illness criteria, GEST reclassified 45.1% of patients with serious illness as having low risk of mortality with an observed mortality rate 8.1% and 2.6% of patients without serious illness as having high mortality risk with an observed mortality rate of 34.3% for a total reclassification rate of 25.3%. Conclusions and Relevance: The findings of this study suggest that both serious illness criteria and GEST identified older ED patients at risk for 6-month mortality, but GEST offered more useful screening characteristics. Future trials of serious illness interventions for high mortality risk in older adults may consider transitioning from diagnosis code criteria to GEST, an automatable EHR-based algorithm.

Prospective evaluation of daily performance metrics to reduce emergency department length of stay for surgical consults.

BACKGROUND: As part of a quality improvement initiative to reduce Emergency Department (ED) length of stay (LOS) for surgical consult patients, we e-mailed performance metrics to key stakeholders on a daily basis. ED and Surgery leadership used these daily metrics to identify and remedy contributing factors for increased ED LOS in patients who received surgical consults. OBJECTIVE: To evaluate whether a quality improvement process driven by a daily performance metric e-mail would be associated with a change in ED LOS for surgical consult patients. METHODS: Prospective before-after study looking at ED LOS for surgical consult patients after an e-mail intervention at a tertiary academic teaching hospital. All consecutive adult ED patients between July 1, 2010 and October 1, 2010 who received a general surgical consult were enrolled. The primary outcome measure was ED LOS, and secondary outcome measure was time to consultation. RESULTS: There were 916 patients who had surgical consults placed during the study period; 459 patients presented before the intervention and 457 patients presented after the intervention. The median LOS decreased 54 min, from 463 min (interquartile range [IQR] 326-617) before the intervention to 409 min (IQR 294.5-528.5) after the intervention (p < 0.001). Time to consultation decreased 25 min, from a median of 160 min (IQR 87-265) to 135 min (IQR 70-239.5) (p = 0.002). There was no difference in age, severity, number of consults, or disposition. There was also no difference in median LOS for other consultation services or in previous years during the same time period. CONCLUSIONS: ED LOS and time to consultation were decreased for surgical consult patients after initiation of daily performance metric e-mails.

Comparing Rationale for Opioid Prescribing Decisions after Surgery with Subsequent Patient Consumption: A Survey of the Highest Quartile of Prescribers.

BACKGROUND: Opioid prescribing patterns, including those after surgery, have been implicated as a significant contributor to the US opioid crisis. A plethora of interventions-from nudges to reminders-have been deployed to improve prescribing behavior, but reasons for persistent outlier behavior are often unknown. STUDY DESIGN: Our institution employs multiple prescribing resources and a near real-time, feedback-based intervention to promote appropriate opioid prescribing. Since 2019, an automated system has emailed providers when a prescription exceeds the 75th percentile of typical opioid consumption for a given procedure-as defined by institutional data collection. Emails include population consumption metrics and an optional survey on rationale for prescribing. Responses were analyzed to understand why providers choose to prescribe atypically large discharge opioid prescriptions. We then compared provider prescriptions against patient consumption. RESULTS: During the study period, 10,672 eligible postsurgical patients were discharged; 2,013 prescriptions (29.4% of opioid prescriptions) exceeded our institutional guideline. Surveys were completed by outlier prescribers for 414 (20.6%) encounters. Among patients where both consumption data and prescribing rationale surveys were available, 35.2% did not consume any opioids after discharge and 21.5% consumed <50% of their prescription. Only 93 (39.9%) patients receiving outlier prescriptions were outlier consumers. Most common reasons for prescribing outlier amounts were attending preference (34%) and prescriber analysis of patient characteristics (34%). CONCLUSIONS: The top quartile of opioid prescriptions did not align with, and often far exceeded, patient postdischarge opioid consumption. Providers cite assessment of patient characteristics as a common driver of decision-making, but this did not align with patient usage for approximately 50% of patients.

Use of an Automated Discharge Instruction Module to Improve Outpatient Follow-Up for Emergency Department Patients with Elevated Blood Pressure.

INTRODUCTION: Hypertension is often incidentally discovered in the emergency department (ED); these patients may benefit from close follow-up. We developed a module to automatically include discharge instructions for patients with elevated blood pressure (BP) in the ED, aiming to improve 30-day follow-up. AIM: This study sought to determine if automated discharge instructions for patients with elevated blood pressure in the ED improved 30-day follow-up with a patient's primary care physician (PCP). METHODS: We developed an automated module with standardized instructions for patients with elevated BP. These were read upon discharge, and e-mailed to the PCP. We analyzed 193 patients during a 1-month interval after implementation, and 207 during 1-month the year prior. The groups were compared using Fisher's exact test. RESULTS: Thirty-day follow-up was 52.2% pre-implementation and 48.4% post-implementation, with no significant difference noted. For patients without known hypertension, follow-up slightly improved, but not significantly. For hypertensive patients, follow-up rates significantly decreased post-implementation. CONCLUSIONS: Despite implementation of automated discharge instructions, we found no improvement in 30-day follow-up. Patients without hypertension trended towards improved follow-up, possibly being more attentive to new abnormal BP readings. However, known hypertensive patients followed-up at a lower rate, which was unexpected and requires further investigation.

Machine learning nonresponse adjustment of patient-reported opioid consumption data to enable consumption-informed postoperative opioid prescribing guidelines.

Background: Post-discharge opioid consumption is a crucial patient-reported outcome informing opioid prescribing guidelines, but its collection is resource-intensive and vulnerable to inaccuracy due to nonresponse bias. Methods: We developed a post-discharge text message-to-web survey system for efficient collection of patient-reported pain outcomes. We prospectively recruited surgical patients at Beth Israel Deaconess Medical Center in Boston, Massachusetts from March 2019 through October 2020, sending an SMS link to a secure web survey to quantify opioids consumed after discharge from hospitalization. Patient factors extracted from the electronic health record were tested for nonresponse bias and observable confounding. Following targeted learning-based nonresponse adjustment, procedure-specific opioid consumption quantiles (medians and 75th percentiles) were estimated and compared to a previous telephone-based reference survey. Results: 6553 patients were included. Opioid consumption was measured in 44% of patients (2868), including 21% (1342) through survey response. Characteristics associated with inability to measure opioid consumption included age, tobacco use, and prescribed opioid dose. Among the 10 most common procedures, median consumption was only 36% of the median prescription size; 64% of prescribed opioids were not consumed. Among those procedures, nonresponse adjustment corrected the median opioid consumption by an average of 37% (IQR: 7, 65%) compared to unadjusted estimates, and corrected the 75th percentile by an average of 5% (IQR: 0, 12%). This brought median estimates for 5/10 procedures closer to telephone survey-based consumption estimates, and 75th percentile estimates for 2/10 procedures closer to telephone survey-based estimates. Conclusions: SMS-recruited online surveying can generate reliable opioid consumption estimates after nonresponse adjustment using patient factors recorded in the electronic health record, protecting patients from the risk of inaccurate prescription guidelines.

An Automated Tobacco Cessation Intervention for Emergency Department Discharged Patients.

INTRODUCTION: Nearly 14% of US adults currently smoke cigarettes. Cigarette smoking causes more than 480,000 deaths each year in the United States. Emergency department (ED) patients are frequently asked for their use of tobacco. Manual selection of pre-formed discharge instructions is the norm for most ED. Providing tobacco cessation discharge instructions to ED patients presents another avenue to combat the tobacco use epidemic we face. The objective of the study is to evaluate the effectiveness of an automated discharge instruction system in increasing the frequency of discharging current tobacco users with instructions for tobacco cessation. METHODS: The study was done at an urban academic tertiary care center. A before and after study was used to test the hypothesis that use of an automated discharged instruction system would increase the frequency that patients who use tobacco were discharged with tobacco cessation instructions. Patients that were admitted, left against medical advice, eloped or left without being seen were excluded. The before phase was from 09/21/14-10/21/14 and the after phase was from the same dates one year later, 09/21/15-10/21/15. This was done to account for confounding by time of year, ED volume and other factors. A Fisher's Exact Test was calculated to compare these two groups. RESULTS: Tobacco cessation DC instructions were received 2/486 (0.4%) of tobacco users in the pre-implementation period compared to 357/371 (96%) in the post-implementation period (p < 0.05). CONCLUSIONS: The automated discharge instructions system increases the proportion of tobacco users who receive cessation instructions. Given the public health ramifications of tobacco use, this could prove to be a significant piece in decreasing tobacco use in patients who go to the emergency department.

Who's at Risk in a Changing Climate? Mapping Electricity-Dependent Patient Populations in a Coastal City.

BACKGROUND: Climate change is causing increasingly frequent extreme weather events. This pilot study demonstrates a GIS-based approach for assessing risk to electricity-dependent patients of a coastal academic medical center during future hurricanes.  Methods: A single-center retrospective chart review was conducted and the spatial distribution of patients with prescriptions for nebulized medications was mapped. Census blocks at risk of flooding in future hurricanes were identified; summary statistics describing proportion of patients at risk are reported.  Results: Out of a local population of 2,101 patients with prescriptions for nebulized medications in the preceding year, 521 (24.8%) were found to live in a hurricane flood zone.  Conclusions: Healthcare systems can assess risk to climate-vulnerable patient populations using publicly available data in combination with hospital medical records. The approach described here could be applied to a variety of environmental hazards and can inform institutional and individual disaster preparedness efforts.

A visual representation of microbiological culture data improves comprehension: a randomized controlled trial.

OBJECTIVE: While the judicious use of antibiotics takes past microbiological culture results into consideration, this data's typical format in the electronic health record (EHR) may be unwieldy when incorporated into clinical decision-making. We hypothesize that a visual representation of sensitivities may aid in their comprehension. MATERIALS AND METHODS: A prospective parallel unblinded randomized controlled trial was undertaken at an academic urban tertiary care center. Providers managing emergency department (ED) patients receiving antibiotics and having previous culture sensitivity testing were included. Providers were randomly selected to use standard EHR functionality or a visual representation of patients' past culture data as they answered questions about previous sensitivities. Concordance between provider responses and past cultures was assessed using the kappa statistic. Providers were surveyed about their decision-making and the usability of the tool using Likert scales. RESULTS: 518 ED encounters were screened from 3/5/2018 to 9/30/18, with providers from 144 visits enrolled and analyzed in the intervention arm and 129 in the control arm. Providers using the visualization tool had a kappa of 0.69 (95% CI: 0.65-0.73) when asked about past culture results while the control group had a kappa of 0.16 (95% CI: 0.12-0.20). Providers using the tool expressed improved understanding of previous cultures and found the tool easy to use (P < .001). Secondary outcomes showed no differences in prescribing practices. CONCLUSION: A visual representation of culture sensitivities improves comprehension when compared to standard text-based representations.