Ambulances Required to Relieve Overcapacity Hospitals: A Novel Measure of Hospital Strain During the COVID-19 Pandemic in the United States.

STUDY OBJECTIVE: One in 4 deaths from COVID-19 has been attributed to hospital crowding. We simulated how many ambulances would be required to rebalance hospital load through systematic interhospital transfers. We assessed the potential feasibility of such a strategy and explored whether transfer requirement was a helpful measure and visualization of regional hospital crowding during COVID-19 surges. METHODS: Using data from the United States hospitals reporting occupancy to the Department of Health and Human Services from July 2020 to March 2022 and road network driving times, we estimated the number of ambulances required weekly to relieve overcapacity hospitals. RESULTS: During the peak week, which ended on January 8, 2021, approximately 1,563 ambulances would be needed for 15,389 simulated patient transports, of which 6,530 (42%) transports involved a 1-way driving time of more than 3 hours. Transfer demands were dramatically lower during most other weeks, with the median week requiring only 134 ambulances (interquartile range, 84 to 295) and involving only 116 transports with 1-way driving times above 3 hours (interquartile range, 4 to 548). On average, receiving hospitals were larger and located in more rural areas than sending hospitals. CONCLUSION: This simulation demonstrated that for most weeks during the pandemic, ambulance availability and bed capacity were unlikely to have been the main impediments to rebalancing hospital loads. Our metric provided an immediately available and much more complete measure of hospital system strain than counts of hospital admissions alone.

A Methodological Appraisal of the HEART Score and Its Variants.

We performed a methodological appraisal of the history, electrocardiogram, age, risk factors, and troponin (HEART) score and its variants in the context of Annals of Emergency Medicine's methodological standards for clinical decision rules. We note that this chest pain risk stratification tool was not formally derived, omits sex and other known predictors, has weak interrater reliability, and its 0, 1, and 2 score weightings do not align with their known predictivities. Its summary performance (pooled sensitivities of 96% to 97% with lower confidence interval bounds of 93% to 94%) is below that which emergency physicians state a willingness to accept, below the 98% sensitivity exhibited by baseline practice without the score, and below the 1% to 2% acceptable miss threshold specified by the American College of Emergency Physicians chest pain policy. Two variants (HEART Pathway, HEART-2) have the same inherent structural limitations and demonstrate slightly better but still suboptimal sensitivity. Although a simple prediction tool for chest pain outcomes is appealing, we believe that the widespread use of the HEART score and its variants should be reconsidered.

Excess Out-of-Hospital Mortality and Declining Oxygen Saturation: The Sentinel Role of Emergency Medical Services Data in the COVID-19 Crisis in Tijuana, Mexico.

STUDY OBJECTIVE: Emergency medical services (EMS) may serve as a key source of real-time data about the evolving health of coronavirus disease 2019 (COVID-19)-affected populations, especially in low- and middle-income countries with less rapid and reliable vital statistics registration systems. Although official COVID-19 statistics in Mexico report almost exclusively inhospital mortality events, excess out-of-hospital mortality has been identified in other countries, including 1 EMS study in Italy that showed a 58% increase. Additionally, EMS and hospital reports from several countries have suggested that silent hypoxemia-low Spo2 in the absence of dyspnea-is associated with COVID-19. It is unclear, however, how these phenomena can be generalized to low- and middle-income countries. We assess how EMS data can be used in a sentinel capacity in Tijuana, a city on the Mexico-United States border with earlier exposure to COVID-19 than many low- and middle-income country settings. METHODS: In this observational study, we calculated numbers of weekly out-of-hospital deaths and respiratory cases handled by EMS in Tijuana, and estimated the difference between peak epidemic rates and expected trends based on data from 2014 to 2019. Results were compared with official COVID-19 statistics, stratified by neighborhood socioeconomic status, and examined for changing demographic or clinical features, including mean Spo2. RESULTS: An estimated 194.7 excess out-of-hospital deaths (95% confidence interval 135.5 to 253.9 deaths) occurred during the peak window (April 14 to May 11), representing an increase of 145% (95% CI 70% to 338%) compared with expected levels. During the same window, only 5 COVID-19-related out-of-hospital deaths were reported in official statistics. This corresponded with an increase in respiratory cases of 236.5% (95% CI 100.7% to 940.0%) and a decrease in mean Spo2 to 77.7% from 90.2% at baseline. The highest out-of-hospital death rates were observed in low-socioeconomic-status areas, although respiratory cases were more concentrated in high-socioeconomic-status areas. CONCLUSION: EMS systems may play an important sentinel role in monitoring excess out-of-hospital mortality and other trends during the COVID-19 crisis in low- and middle-income countries. Using EMS data, we observed increases in out-of-hospital deaths in Tijuana that were nearly 3-fold greater than increases reported in EMS data in Italy. Increased testing in out-of-hospital settings may be required to determine whether excess mortality is being driven by COVID-19 infection, health system saturation, or patient avoidance of health care. We also found evidence of worsening rates of hypoxemia among respiratory patients treated by EMS, suggesting a possible increase in silent hypoxemia, which should be met with increased detection and clinical management efforts. Finally, we observed social disparities in out-of-hospital death that warrant monitoring and amelioration.

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis.

BACKGROUND: Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks. OBJECTIVE: The aim of this study is to investigate whether excess visits where the word "cough" was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years. METHODS: A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons. RESULTS: The percentage of patients presenting with an EHR reason for visit containing the word "cough" to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure. CONCLUSIONS: A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.

Emergency Medical Services Use Among Patients Receiving Involuntary Psychiatric Holds and the Safety of an Out-of-Hospital Screening Protocol to "Medically Clear" Psychiatric Emergencies in the Field, 2011 to 2016.

STUDY OBJECTIVE: Patients with acute psychiatric emergencies who receive an involuntary hold often spend hours in the emergency department (ED) because of a deficit in inpatient psychiatric beds. One solution to address the lack of prompt psychiatric evaluation in the ED has been to establish regional stand-alone psychiatric emergency services. However, patients receiving involuntary holds still need to be screened and evaluated to ensure that their behavior is not caused by an underlying and life-threatening nonpsychiatric illness. Although traditional regional emergency medical services (EMS) systems depend on the medical ED for this function, a field-screening protocol can allow EMS to directly transport a substantial portion of patients to a stand-alone psychiatric emergency service. The purpose of this investigation is to describe overall EMS use for patients receiving involuntary holds, compare patients receiving involuntary holds with all EMS patients, and evaluate the safety of field medical clearance of an established field-screening protocol in Alameda County, CA. METHODS: We obtained data for all EMS encounters between November 1, 2011, and November 1, 2016, using Alameda County's standardized data set. After unique patient identification, we describe the data at the patient level and at the encounter level. At the patient level, we compare "involuntary hold patients" (≥1 involuntary hold during the study period) with those who were "never held." Additionally, we assess the safety of out-of-hospital medical clearance by calculating the rate of failed diversion, defined as retransport of a patient to a medical ED within 12 hours of transport to the psychiatric emergency services by EMS. RESULTS: Of the 541,731 total EMS encounters in Alameda County during the study period, 10% (N=53,887) were identified as involuntary hold encounters. Of these involuntary hold patient encounters, 41% (N=22,074) resulted in direct transport of the patient to the stand-alone psychiatric emergency service for evaluation; 0.3% (N=60) failed diversion and required retransport within 12 hours. At the patient level, Alameda County EMS encountered 257,625 unique patients, and 10% (N=26,283) had at least one encounter for an involuntary hold during the study period. These "involuntary hold patients" were substantially younger, more likely to be men, and less likely to be insured. Additionally, they had higher overall EMS use: "involuntary hold patients" accounted for 24% of all encounters (N=128,003); 53,887 of these encounters were for involuntary holds, whereas an additional 74,116 were for other reasons. Similarly, 4% of "involuntary hold patients" had 20 or more encounters, whereas only 0.4% of "never held" patients were in this category. Last, the 7% of "involuntary hold patients" (N=1,907) who received greater than or equal to 5 involuntary holds during the study period accounted for 39% of all involuntary holds and 9% of all EMS encounters. CONCLUSION: Ten percent of all EMS encounters were for involuntary psychiatric holds. With an EMS-directed screening protocol, 41% of all such patient encounters resulted in direct transport of the patient to the psychiatric emergency service, bypassing medical clearance in the ED. Overall, only 0.3% of these patients required retransport to a medical ED within 12 hours of arrival to psychiatric emergency services. We found that 24% of all EMS encounters in Alameda County were attributable to "involuntary hold patients," reinforcing the importance of the effects of mental illness on EMS use.

Graphic Portrayal of Studies With Paired Data: A Tutorial.

Many clinical research studies involve paired outcome data, which consist of 2 measurements of the same variable in the same patient at 2 times. This article examines ways that researchers can graphically report data from such studies, meeting the dual goals of showing the experience of each patient while comparing differences between treatment groups. I emphasize the importance of hybrid parallel line plots, examine several useful variations of them, and provide Stata code to make them.

The Supply of Prescription Opioids: Contributions of Episodic-Care Prescribers and High-Quantity Prescribers.

STUDY OBJECTIVE: We determine episodic and high-quantity prescribers' contribution to opioid prescriptions and total morphine milligram equivalents in California, especially among individuals prescribed large amounts of opioids. METHODS: This was a cross-sectional descriptive analysis of opioid prescribing patterns during an 8-year period using the de-identified Controlled Substance Utilization Review and Evaluation System (CURES) database, the California subsection of the prescription drug monitoring program. We took a 10% random sample of all patients and stratified them by the amount of prescription opioids obtained during their maximal 90-day period. We identified "episodic prescribers" as those whose prescribing pattern included short-acting opioids on greater than 95% of all prescriptions, fewer than or equal to 31 pills on 95% of all prescriptions, only 1 prescription in the database for greater than 90% of all patients to whom they gave opioids, fewer than 6 prescriptions in the database to greater than 99% of patients given opioids, and fewer than 540 prescriptions per year. We identified top 5% prescribers by their morphine milligram equivalents per day in the database. We examined the relationship between patient opioid prescriptions and provider type, with the primary analysis performed on the patient cohort who received only short-acting opioids in an attempt to avoid guideline-concordant palliative, oncologic, and addiction care, and a secondary analysis performed on all patients. RESULTS: Among patients with short-acting opioid only, episodic prescribers (14.6% of 173,000 prescribers) wrote at least one prescription to 25% of 2.7 million individuals but were responsible for less than 9% of the 10.5 million opioid prescriptions and less than 3% of the 3.9 billion morphine milligram equivalents in our sample. Among individuals with high morphine milligram equivalents use, episodic prescribers were responsible for 2.8% of prescriptions and 0.6% of total morphine milligram equivalents. Conversely, the top 5% of prescribers prescribed at least 29.8% of prescriptions and 48.8% of total morphine milligram equivalents, with a greater contribution in patients with high morphine milligram equivalents. CONCLUSION: Episodic prescribers contribute minimally to total opioid prescriptions, especially among individuals categorized as using high morphine milligram equivalents. Interventions focused on reducing opioid prescriptions in the episodic care setting are unlikely to yield important reductions in the prescription opioid supply; conversely, targeting high-quantity prescribers has the potential to create substantial reductions.

Emergency Physician Risk Estimates and Admission Decisions for Chest Pain: A Web-Based Scenario Study.

STUDY OBJECTIVE: We conducted this study to better understand how emergency physicians estimate risk and make admission decisions for patients with low-risk chest pain. METHODS: We created a Web-based survey consisting of 5 chest pain scenarios that included history, physical examination, ECG findings, and basic laboratory studies, including a negative initial troponin-level result. We administered the scenarios in random order to emergency medicine residents and faculty at 11 US emergency medicine residency programs. We randomized respondents to receive questions about 1 of 2 endpoints, acute coronary syndrome or serious complication (death, dysrhythmia, or congestive heart failure within 30 days). For each scenario, the respondent provided a quantitative estimate of the probability of the endpoint, a qualitative estimate of the risk of the endpoint (very low, low, moderate, high, or very high), and an admission decision. Respondents also provided demographic information and completed a 3-item Fear of Malpractice scale. RESULTS: Two hundred eight (65%) of 320 eligible physicians completed the survey, 73% of whom were residents. Ninety-five percent of respondents were wholly consistent (no admitted patient was assigned a lower probability than a discharged patient). For individual scenarios, probability estimates covered at least 4 orders of magnitude; admission rates for scenarios varied from 16% to 99%. The majority of respondents (>72%) had admission thresholds at or below a 1% probability of acute coronary syndrome. Respondents did not fully differentiate the probability of acute coronary syndrome and serious outcome; for each scenario, estimates for the two were quite similar despite a serious outcome being far less likely. Raters used the terms "very low risk" and "low risk" only when their probability estimates were less than 1%. CONCLUSION: The majority of respondents considered any probability greater than 1% for acute coronary syndrome or serious outcome to be at least moderate risk and warranting admission. Physicians used qualitative terms in ways fundamentally different from how they are used in ordinary conversation, which may lead to miscommunication during shared decisionmaking processes. These data suggest that probability or utility models are inadequate to describe physician decisionmaking for patients with chest pain.

Completeness and Spin of medRxiv Preprint and Associated Published Abstracts of COVID-19 Randomized Clinical Trials.

This study examines publication timelines, completeness, and spin in the abstracts of all randomized clinical trials related to COVID-19 posted to medRxiv during the first 2 years of the pandemic and compared the latter 2 with their published counterparts.

Structured Clinical Decision Aids Are Seldom Compared With Subjective Physician Judgment, and Are Seldom Superior.

STUDY OBJECTIVE: We determine how often studies that evaluate the performance of an aid for decisionmaking, be it a simple laboratory or imaging test or a complex multielement decision instrument, compare the aid's performance to independent, unaided physician judgment. METHODS: This was a cross-sectional survey of all Original Research and Brief Research Report articles in Annals of Emergency Medicine from 1998 to 2015. We included all articles that evaluated the performance of an aid for decisionmaking in assisting a physician with a decision about testing, treatment, diagnosis, or disposition. Two authors independently characterized the intent and purpose of each aid for decisionmaking, determined whether each study had a comparison to unaided physician judgment within the article or in a separate article, and recorded the result of that comparison. RESULTS: One hundred seventy-one (8.3%) of 2,060 research articles studied the performance characteristics of an aid for decisionmaking, 48 of which were formal clinical decision instruments. Forty of the 171 studies retrospectively analyzed existing databases and therefore could not assess physician judgment. Investigators compared the aid for decisionmaking to physician judgment in 11% (15/131) of the prospective studies, including 15% (6/41) of studies that evaluated a formal clinical decision instrument. For 9 articles that had no comparison to physician judgment, we found 6 unique external publications that compared that aid to physician clinical judgment. The decision aid was superior to clinical judgment in 2 of the 21 studies that contained a comparison. CONCLUSION: Physician judgment is infrequently assessed when the performance of an aid for decisionmaking is evaluated, and, when reported, the decision aid seldom outperformed physician judgment.

The Effect of Peer Review on the Quality of Data Graphs in Annals of Emergency Medicine.

STUDY OBJECTIVE: We determine how peer review affects the quality of published data graphs and how the appointment of a graphics editor affects the quality of graphs in an academic medical journal. METHODS: We conducted an observational time-series analysis to quantify the qualities of data graphs in original manuscripts and published research articles in Annals of Emergency Medicine from 2006 to 2012. We retrospectively analyzed 3 distinct periods: before the use of a graphics editor, graph review after a manuscript's acceptance, and graph review just before the first request for revision. Raters blinded to study year scored the quality of original and published graphs using an 85-item instrument. Editorial comments about graphs were classified into 4 major and 16 minor categories. RESULTS: We studied 60 published articles and their corresponding original submissions during each period (2006, 2009, and 2012). The number of graphs increased 31%, their median data density increased 50%, and quality (completeness [+42%], visual clarity [+64%], and special features [+66%]) increased from submission to publication in all 3 periods. Although geometric mean (0.69, 0.86, and 1.2 pieces of information/cm2) and median data density (0.44, 0.70, and 1.2 pieces of information/cm2) were higher in the graphics editor phases, mean data density, completeness, visual clarity, and other markers of quality did not improve or decreased with dedicated graphics editing. The majority of published graphs were bar or pie graphs (49%, 53%, and 60% in 2006, 2009, and 2012, respectively) with low data density in all 3 years. CONCLUSION: Peer review unquestionably improved graph quality. However, data densities of most graphs barely exceeded that of printed text, and many graphs failed to present the majority of available data and did not convey those data clearly; there remains much room for improvement. The timing of graphics editor involvement appears to affect the effect of the graph review process.

Graph Quality in Top Medical Journals.

STUDY OBJECTIVE: Well-designed graphs can portray complex data and relationships in ways that are easier to interpret and understand than text and tables. Previous investigations of reports of clinical research showed that graphs are underused and, when used, often depict summary statistics instead of the data distribution. This descriptive study aims to evaluate the quantity and quality of graphs in the current medical literature across a broad range of better journals. METHODS: We performed a cross-sectional survey of 10 randomly selected original research articles per journal from the 2012 issues of 20 highly cited journals. We identified which figures were data graphs and limited analysis to a maximum of 5 randomly selected data graphs per article. We then described the graph type, data density, completeness, visual clarity, special features, and dimensionality of each graph in the sample. RESULTS: We analyzed 342 data graphs published in 20 journals. Our sample had a geometric mean data density index across all graphs of 1.18 data elements/cm2. More than half (54%) of the data graphs were simple univariate displays such as line or bar graphs. When analyzed by journal, excellence in one domain (completeness, visual clarity, or special features) was not strongly predictive of excellence in the other domains. CONCLUSION: Despite that graphs can efficiently and effectively convey complex study findings, we found their infrequent use and low data density to be the norm. The majority of graphs were univariate ones that failed to display the overall distribution of data.