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Objectives: During the coronavirus disease 2019 (COVID-19) pandemic surge, alternate care sites (ACS) such as the waiting room or hospital lobby were created amongst hospitals nationwide to help alleviate emergency department (ED) overflow. Despite the end of the pandemic surge, many of these ACS remain functional given the burden of prolonged ED wait times, with providers now utilizing the waiting room or ACS to initiate care. Therefore, the objective of this study is to evaluate if initiating patient care in ACS helps to decrease time to disposition. Methods: Retrospective data were collected on 61,869 patient encounters presenting to an academic medical center ED. Patients with an emergency severity index (ESI) of 1 were excluded. The "pre-ACS" or control data consisted of 38,625 patient encounters from September 30, 2018 to October 1, 2019, prior to the development of ACS, in which the patient was seen by a physician after they were brought to an assigned ED room. The "post-ACS" study cohort consisted of 23,244 patient encounters from September 30, 2022 to October 1, 2023, after the initiation of ACS, during which patients were initially seen by a provider in an ACS. ACS at this institution included the three following areas: waiting room, ambulance waiting area, and a newly constructed ACS that was built next to the ED entrance on the first floor of the hospital. The newly constructed ACS consisted of 16 care spaces each containing an upright exam chair with dividers between each care space. Door-to-disposition time (DTD) was calculated by identifying the time when the patient entered the ED and the time when disposition was decided (admission requested or patient discharged). Using regression analysis, we compared the two data sets to determine significant differences among DTD time. Results: The largest proportion of encounters were among ESI 3 patients, that is, 56.1%. There was a significant increase in median DTD for ESI 2 and 3 patients who were seen initially in an ACS compared to those who were not seen until they were in an assigned ER room. Specifically, there was a median increase of 40.9 min for ESI 2 patients and 18.8 min for ESI 3 patients who were seen initially in an ACS (p < 0.001). There was a 29-min decrease in median DTD for ESI 5 patients who were seen in ACS (p = 0.09). Conclusions: Initiating patient care earlier in ACS did not appear to decrease DTD time for patients in the ED. Overall, the benefits of early initiation of care likely lie elsewhere within patient care and the ED throughput process.
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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.
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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 82â¯371 ED encounters by 40â¯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.
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Serviço Hospitalar de Emergência , Assistência Terminal , Humanos , Idoso , Feminino , Masculino , Idoso de 80 Anos ou mais , Assistência Terminal/estatística & dados numéricos , Serviço Hospitalar de Emergência/estatística & dados numéricos , Avaliação Geriátrica/métodos , Avaliação Geriátrica/estatística & dados numéricos , Boston/epidemiologia , Prognóstico , MortalidadeRESUMO
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.
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Analgésicos Opioides , Dor Pós-Operatória , Alta do Paciente , Guias de Prática Clínica como Assunto , Padrões de Prática Médica , Humanos , Analgésicos Opioides/uso terapêutico , Dor Pós-Operatória/tratamento farmacológico , Estudos Retrospectivos , Feminino , Masculino , Pessoa de Meia-Idade , Padrões de Prática Médica/estatística & dados numéricos , Padrões de Prática Médica/normas , Adulto , Prescrições de Medicamentos/estatística & dados numéricos , Prescrições de Medicamentos/normas , IdosoRESUMO
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.
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Assistência ao Convalescente , Analgésicos Opioides , Humanos , Analgésicos Opioides/uso terapêutico , Padrões de Prática Médica , Alta do Paciente , BenchmarkingRESUMO
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.
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Serviço Hospitalar de Emergência , Carga de Trabalho , Adulto , Humanos , Triagem/métodos , Algoritmos , Aprendizado de MáquinaRESUMO
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.
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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.
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Hipertensão , Alta do Paciente , Pressão Sanguínea , Serviço Hospitalar de Emergência , Seguimentos , Humanos , Hipertensão/diagnóstico , Hipertensão/terapia , Pacientes AmbulatoriaisRESUMO
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.
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Analgésicos Opioides , Transtornos Relacionados ao Uso de Opioides , Assistência ao Convalescente , Analgésicos Opioides/uso terapêutico , Humanos , Transtornos Relacionados ao Uso de Opioides/tratamento farmacológico , Dor Pós-Operatória/tratamento farmacológico , Alta do Paciente , Padrões de Prática Médica , Estudos RetrospectivosRESUMO
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 5âmg 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.
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Analgésicos Opioides/uso terapêutico , Consenso , Prescrições de Medicamentos/estatística & dados numéricos , Uso de Medicamentos/estatística & dados numéricos , Dor Pós-Operatória/tratamento farmacológico , Guias de Prática Clínica como Assunto , Procedimentos Cirúrgicos Operatórios , Hospitais , Humanos , Estados UnidosRESUMO
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.
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Mudança Climática , Inundações , Eletricidade , Humanos , Projetos Piloto , Estudos RetrospectivosRESUMO
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.
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Compreensão , Registros Eletrônicos de Saúde , Serviço Hospitalar de Emergência , Humanos , Estudos Prospectivos , Inquéritos e QuestionáriosRESUMO
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.
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Alta do Paciente , Abandono do Uso de Tabaco , Adulto , Aconselhamento , Serviço Hospitalar de Emergência , Comportamentos Relacionados com a Saúde , Humanos , Estados UnidosRESUMO
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.
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Serviço Hospitalar de Emergência , Padrões de Prática Médica/estatística & dados numéricos , Tolerância ao Trabalho Programado , Fluxo de Trabalho , Carga de Trabalho , Competência Clínica , Feminino , Humanos , Masculino , Estudos Retrospectivos , Estados UnidosRESUMO
STUDY OBJECTIVE: Triage quickly identifies critically ill patients, facilitating timely interventions. Many emergency departments (EDs) use emergency severity index (ESI) or abnormal vital sign triggers to guide triage. However, both use fixed thresholds, and false activations are costly. Prior approaches using machinelearning have relied on information that is often unavailable during the triage process. We examined whether deep-learning approaches could identify critically ill patients only using data immediately available at triage. METHODS: We conducted a retrospective, cross-sectional study at an urban tertiary care center, from January 1, 2012-January 1, 2020. De-identified triage information included structured (age, sex, initial vital signs) and textual (chief complaint) data, with critical illness (mortality or ICU admission within 24 hours) as the outcome. Four progressively complex deep-learning models were trained and applied to triage information from all patients. We compared the accuracy of the models against ESI as the standard diagnostic test, using area under the receiver-operator curve (AUC). RESULTS: A total of 445,925 patients were included, with 60,901 (13.7%) critically ill. Vital sign thresholds identified critically ill patients with AUC 0.521 (95% confidence interval [CI] = 0.519-0.522), and ESI <3 demonstrated AUC 0.672 (95% CI = 0.671-0.674), logistic regression classified patients with AUC 0.803 (95% CI = 0.802-0.804), 2-layer neural network with structured data with AUC 0.811 (95% CI = 0.807-0.815), gradient tree boosting with AUC 0.820 (95% CI = 0.818-0.821), and the neural network model with textual data with AUC 0.851 (95% CI = 0.849-0.852). All successive increases in AUC were statistically significant. CONCLUSION: Deep-learning techniques represent a promising method of augmenting triage, even with limited information. Further research is needed to determine if improved predictions yield clinical and operational benefits.
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INTRODUCTION: Transfers of skilled nursing facility (SNF) residents to emergency departments (ED) are linked to morbidity, mortality and significant cost, especially when transfers result in hospital admissions. This study investigated an alternative approach for emergency care delivery comprised of SNF-based telemedicine services provided by emergency physicians (EP). We compared this on-site emergency care option to traditional ED-based care, evaluating hospital admission rates following care by an EP. METHODS: We conducted a retrospective, observational study of SNF residents who underwent emergency evaluation between January 1, 2017-January 1, 2018. The intervention group was comprised of residents at six urban SNFs in the Northeastern United States, who received an on-demand telemedicine service provided by an EP. The comparison group consisted of residents of SNFs that did not offer on-demand services and were transferred via ambulance to the ED. Using electronic health record data from both the telemedicine and ambulance transfers, our primary outcome was the odds ratio (OR) of a hospital admission. We also conducted a subanalysis examining the same OR for the three most common chronic disease-related presentations found among the telemedicine study population. RESULTS: A total of 4,606 patients were evaluated in both the SNF-based intervention and ED-based comparison groups (n=2,311 for SNF based group and 2,295 controls). Patients who received the SNF-based acute care were less likely to be admitted to the hospital compared to patients who were transferred to the ED in our primary and subgroup analyses. Overall, only 27% of the intervention group was transported to the ED for additional care and presumed admission, whereas 71% of the comparison group was admitted (OR for admission = 0.15 [9% confidence interval, 0.13-0.17]). CONCLUSION: The use of an EP-staffed telemedicine service provided to SNF residents was associated with a significantly lower rate of hospital admissions compared to the usual ED-based care for a similarly aged population of SNF residents. Providing SNF-based care by EPs could decrease costs associated with hospital-based care and risks associated with hospitalization, including cognitive and functional decline, nosocomial infections, and falls.
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Serviços Médicos de Emergência/métodos , Serviço Hospitalar de Emergência/estatística & dados numéricos , Hospitalização/tendências , Transferência de Pacientes/tendências , Instituições de Cuidados Especializados de Enfermagem/estatística & dados numéricos , Idoso , Feminino , Humanos , Masculino , New England , Estudos Retrospectivos , TelemedicinaRESUMO
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.
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Sistemas Automatizados de Assistência Junto ao Leito , Smartphone , Documentação , Registros Eletrônicos de Saúde , Humanos , Fluxo de TrabalhoRESUMO
OBJECTIVE: Linking emergency medical services (EMS) electronic patient care reports (ePCRs) to emergency department (ED) records can provide clinicians access to vital information that can alter management. It can also create rich databases for research and quality improvement. Unfortunately, previous attempts at ePCR and ED record linkage have had limited success. In this study, we use supervised machine learning to derive and validate an automated record linkage algorithm between EMS ePCRs and ED records. MATERIALS AND METHODS: All consecutive ePCRs from a single EMS provider between June 2013 and June 2015 were included. A primary reviewer matched ePCRs to a list of ED patients to create a gold standard. Age, gender, last name, first name, social security number, and date of birth were extracted. Data were randomly split into 80% training and 20% test datasets. We derived missing indicators, identical indicators, edit distances, and percent differences. A multivariate logistic regression model was trained using 5-fold cross-validation, using label k-fold, L2 regularization, and class reweighting. RESULTS: A total of 14 032 ePCRs were included in the study. Interrater reliability between the primary and secondary reviewer had a kappa of 0.9. The algorithm had a sensitivity of 99.4%, a positive predictive value of 99.9%, and an area under the receiver-operating characteristic curve of 0.99 in both the training and test datasets. Date-of-birth match had the highest odds ratio of 16.9, followed by last name match (10.6). Social security number match had an odds ratio of 3.8. CONCLUSIONS: We were able to successfully derive and validate a record linkage algorithm from a single EMS ePCR provider to our hospital EMR.
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Serviços Médicos de Emergência , Serviço Hospitalar de Emergência , Registro Médico Coordenado/métodos , Aprendizado de Máquina Supervisionado , Algoritmos , Feminino , Humanos , Modelos Logísticos , Masculino , Estudos RetrospectivosRESUMO
Importance: Electronic health records allow teams of clinicians to simultaneously care for patients, but an unintended consequence is the potential for duplicate orders of tests and medications. Objective: To determine whether a simple visual aid is associated with a reduction in duplicate ordering of tests and medications. Design, Setting, and Participants: This cohort study used an interrupted time series model to analyze 184â¯694 consecutive patients who visited the emergency department (ED) of an academic hospital with 55â¯000 ED visits annually. Patient visits occurred 1 year before and after each intervention, as follows: for laboratory orders, from August 13, 2012, to August 13, 2014; for medication orders, from February 3, 2013, to February 3, 2015; and for radiology orders, from December 12, 2013, to December 12, 2015. Data were analyzed from April to September 2019. Exposure: If an order had previously been placed during the ED visit, a red highlight appeared around the checkbox of that order in the computerized provider order entry system. Main Outcomes and Measures: Number of unintentional duplicate laboratory, medication, and radiology orders. Results: A total of 184â¯694 patients (mean [SD] age, 51.6 [20.8] years; age range, 0-113.0 years; 99â¯735 [54.0%] women) who visited the ED were analyzed over the 3 overlapping study periods. After deployment of a noninterruptive nudge in electronic health records, there was an associated 49% decrease in the rate of unintentional duplicate orders for laboratory tests (incidence rate ratio, 0.51; 95% CI, 0.45-0.59), from 4485 to 2731 orders, and an associated 40% decrease in unintentional duplicate orders of radiology tests (incidence rate ratio, 0.60; 95% CI, 0.44-0.82), from 956 to 782 orders. There was not a statistically significant change in unintentional duplicate orders of medications (incidence rate ratio, 1.17; 95% CI, 0.52-2.61), which increased from 225 to 287 orders. The nudge eliminated an estimated 17â¯936 clicks in our electronic health record. Conclusions and Relevance: In this interrupted time series cohort study, passive visual cues that provided just-in-time decision support were associated with reductions in unintentional duplicate orders for laboratory and radiology tests but not in unintentional duplicate medication orders.
Assuntos
Recursos Audiovisuais/estatística & dados numéricos , Serviço Hospitalar de Emergência/estatística & dados numéricos , Implementação de Plano de Saúde/estatística & dados numéricos , Mau Uso de Serviços de Saúde/prevenção & controle , Sistemas de Registro de Ordens Médicas/estatística & dados numéricos , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Registros Eletrônicos de Saúde , Feminino , Humanos , Lactente , Recém-Nascido , Análise de Séries Temporais Interrompida , Masculino , Pessoa de Meia-Idade , Adulto JovemRESUMO
OBJECTIVES: To determine the effect of a domain-specific ontology and machine learning-driven user interfaces on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED). METHODS: As part of a quality improvement project, we simultaneously implemented three interventions: a domain-specific ontology, contextual autocomplete, and top five suggestions. Contextual autocomplete is a user interface that ranks concepts by their predicted probability which helps nurses enter data about a patient's presenting problems. Nurses were also given a list of top five suggestions to choose from. These presenting problems were represented using a consensus ontology mapped to SNOMED CT. Predicted probabilities were calculated using a previously derived model based on triage vital signs and a brief free text note. We evaluated the percentage and quality of structured data captured using a mixed methods retrospective before-and-after study design. RESULTS: A total of 279,231 consecutive patient encounters were analyzed. Structured data capture improved from 26.2% to 97.2% (pâ¯<â¯0.0001). During the post-implementation period, presenting problems were more complete (3.35 vs 3.66; pâ¯=â¯0.0004) and higher in overall quality (3.38 vs. 3.72; pâ¯=â¯0.0002), but showed no difference in precision (3.59 vs. 3.74; pâ¯=â¯0.1). Our system reduced the mean number of keystrokes required to document a presenting problem from 11.6 to 0.6 (pâ¯<â¯0.0001), a 95% improvement. DISCUSSION: We demonstrated a technique that captures structured data on nearly all patients. We estimate that our system reduces the number of man-hours required annually to type presenting problems at our institution from 92.5â¯h to 4.8â¯h. CONCLUSION: Implementation of a domain-specific ontology and machine learning-driven user interfaces resulted in improved structured data capture, ontology usage compliance, and data quality.