Background and Objectives Early presentation and acute treatment for patients presenting with ischemic stroke are associated with improved outcomes. The onset of the COVID-19 pandemic was associated with a large decrease in patients presenting with ischemic stroke, but it is unknown whether these changes persisted. Methods This study analyzed emergency department (ED) stroke presentations (n = 158,060) to all nonfederal hospitals in the 50 states and Washington, D.C., from 2019 through 2021 using administrative claims data of traditional fee-for-service Medicare enrollees aged 66 years or older. Patients presenting with stroke were identified using the ICD-10 CM (I63.X). We examined the number of beneficiaries presenting with ischemic stroke to the ED, both overall and by demographic categories (race, age, sex, region, Medicaid eligibility, comorbidity status), admission rates conditional on presentation, use of neurovascular interventions, thirty-day mortality, intensive care unit and mechanical ventilation use, length of stay, and discharge destination. Results With the onset of the pandemic in March 2020, there was a drop of 32.1% in ED stroke presentations compared with March 2019 levels, and by December 2021, the rate remained 17.7% lower than baseline levels in December 2019. Relative to the prepandemic period, there were decreases in the proportions of those dually eligible for Medicaid (-0.8%, p < 0.0001) or Black (-0.8%, p < 0.0001), as well as those with atrial fibrillation (-1.1%, p < 0.0001), hypertension (-0.7%, p < 0.0001), and chronic obstructive pulmonary disease (-1.8%, p < 0.0001). Admitted patients were more often discharged to home as opposed to postacute care settings (+3.5%, p < 0.0001). The percentage of patients receiving intravenous thrombolysis changed minimally while those receiving intracranial mechanical thrombectomy (+17.8%, p < 0.0001) and carotid interventions (+6.9%, p < 0.0001) increased from baseline throughout the pandemic. Adjusted thirty-day mortality or referral to hospice increased (+1.81%, p < 0.0001) with larger increases seen among Black beneficiaries and those dually eligible for Medicaid. Discussion After an initial sharp decline, stroke presentations remained substantially lower than at baseline through the end of 2021, especially among racial minority and those dually eligible for Medicaid. The observed increased mortality rates for those presenting with stroke may have resulted from later time of presentation after the onset of symptoms or preferential presentation of more vs less severe strokes.

STUDY OBJECTIVE:To characterize the association between emergency department (ED) clinician risk tolerance and the decision to admit, a common and high-cost decision. METHODS:In this observational cohort study, data on 100% of traditional Medicare beneficiaries for all ED visits in Massachusetts from October 2015 through September 2020 were linked to surveys of clinician risk tolerance. We estimated a generalized mixed-effect linear regression model to assess the association between the risk scales, divided into tertiles, and the decision to admit. The main outcome measure was ED disposition, defined as admitted or discharged to home. Risk tolerance was measured using 4 related scales: the Risk-Taking Scale, the Stress from Uncertainty Scale, the Fear of Malpractice Scale, and the Need for (Cognitive) Closure Scale. RESULTS:The total study sample included 421,301 ED visits seen by 889 emergency clinicians. Patients were predominantly women (57.4%), and the average age was 72.6 years. Mean clinician age was 46.5 years. In total, 77.1% were physicians, 59.3% were men, and 86.6% were White. We found a consistent relationship between lower risk tolerance and higher admission rates. This magnitude of the relationship was stronger for conditions with a higher rate of admissions. CONCLUSION:The risk scales were significantly associated with the tendency to admit. This suggests that clinician risk tolerance meaningfully contributes to variation in clinician behavior and points to the potential utility for interventions that interface with clinician behavior to affect admission rates.

Although emergency department (ED) and hospital overcrowding were reported during the later parts of the COVID-19 pandemic, the true extent and potential causes of this overcrowding remain unclear. Using data on the traditional fee-for-service Medicare population, we examined patterns in ED and hospital use during the period 2019-22. We evaluated trends in ED visits, rates of admission from the ED, and thirty-day mortality, as well as measures suggestive of hospital capacity, including hospital Medicare census, length-of-stay, and discharge destination. We found that ED visits remained below baseline throughout the study period, with the standardized number of visits at the end of the study period being approximately 25 percent lower than baseline. Longer length-of-stay persisted through 2022, whereas hospital census was considerably above baseline until stabilizing just above baseline in 2022. Rates of discharge to postacute facilities initially declined and then leveled off at 2 percent below baseline in 2022. These results suggest that widespread reports of overcrowding were not driven by a resurgence in ED visits. Nonetheless, length-of-stay remains higher, presumably related to increased acuity and reduced available bed capacity in the postacute care system.

Importance Much remains unknown about the extent of and factors that influence clinician-level variation in rates of admission from the emergency department (ED). In particular, emergency clinician risk tolerance is a potentially important attribute, but it is not well defined in terms of its association with the decision to admit. Objective To further characterize this variation in rates of admission from the ED and to determine whether clinician risk attitudes are associated with the propensity to admit. Design, Setting, and Participants In this observational cohort study, data were analyzed from the Massachusetts All Payer Claims Database to identify all ED visits from October 2015 through December 2017 with any form of commercial insurance or Medicaid. ED visits were then linked to treating clinicians and their risk tolerance scores obtained in a separate statewide survey to examine the association between risk tolerance and the decision to admit. Statistical analysis was performed from 2022 to 2023. Main Outcomes and Measures The ratio between observed and projected admission rates was computed, controlling for hospital, and then plotted against the projected admission rates to find the extent of variation. Pearson correlation coefficients were then used to examine the association between the mean projected rate of admission and the difference between actual and projected rates of admission. The consistency of clinician admission practices across a range of the most common conditions resulting in admission were then assessed to understand whether admission decisions were consistent across different conditions. Finally, an assessment was made as to whether the extent of deviation from the expected admission rates at an individual level was associated with clinician risk tolerance. Results The study sample included 392 676 ED visits seen by 691 emergency clinicians. Among patients seen for ED visits, 221 077 (56.3%) were female, and 236 783 (60.3%) were 45 years of age or older; 178 890 visits (46.5%) were for patients insured by Medicaid, 96 947 (25.2%) were for those with commercial insurance, 71 171 (18.5%) were Medicare Part B or Medicare Advantage, and the remaining 37 702 (9.8%) were other insurance category. Of the 691 clinicians, 429 (62.6%) were male; mean (SD) age was 46.5 (9.8) years; and 72 (10.4%) were Asian, 13 (1.9%) were Black, 577 (83.5%) were White, and 29 (4.2%) were other race. Admission rates across the clinicians included ranged from 36.3% at the 25th percentile to 48.0% at the 75th percentile (median, 42.1%). Overall, there was substantial variation in admission rates across clinicians; physicians were just as likely to overadmit or underadmit across the range of projected rates of admission (Pearson correlation coefficient, 0.046 [ P = .23]). There also was weak consistency in admission rates across the most common clinical conditions, with intraclass correlations ranging from 0.09 (95% CI, 0.02-0.17) for genitourinary/syncope to 0.48 (95% CI, 0.42-0.53) for cardiac/syncope. Greater clinician risk tolerance (as measured by the Risk Tolerance Scale) was associated with a statistically significant tendency to admit less than the projected admission rate (coefficient, −0.09 [ P = .04]). The other scales studied revealed no significant associations. Conclusions and Relevance In this cohort study of ED visits from Massachusetts, there was statistically significant variation between ED clinicians in admission rates and little consistency in admission tendencies across different conditions. Admission tendencies were minimally associated with clinician innate risk tolerance as assessed by this study’s measures; further research relying on a broad range of measures of risk tolerance is needed to better understand the role of clinician attitudes toward risk in explaining practice patterns and to identify additional factors that may be associated with variation at the clinician level.

This chapter examines the Orientia tsutsugamushi (scrub typhus) attack and its pathophysiology and treatment. Possible attack scenarios and preparedness and response actions are also discussed.

Background: Engaging with human emotions is an integral but poorly understood part of the work of emergency healthcare providers. Patient factors (e.g., irritable behavior; mental illness) can evoke strong emotions, and evidence suggests that these emotions can impact care quality and patient safety. Given that nurses play a critical role in providing high quality care, efforts to identify and remedy factors that may compromise care are needed. Yet to date, few experiments have been conducted.Objective: To examine the effects of emotionally evocative patient behavior as well as the presence of mental illness on emergency nurses' emotions, patient assessments, testing advocacy, and written handoffs.Design: Experimental vignette research.Setting: Online experiment distributed via email between October and December 2020.Participants: Convenience sample of 130 emergency nurses from seven hospitals in the Northeastern United States and one hospital in the mid-Atlantic region in the United States.Methods: Nurses completed four multimedia computer-simulated patient encounters in which patient behavior (irritable vs. calm) and mental illness (present vs. absent) were experimentally varied. Nurses reported their emotions and clinical assessments, recommended diagnostic tests, and provided written handoffs. Tests were coded for whether the test would result in a correct diagnosis, and handoffs were coded for negative and positive patient descriptions and the presence of speafic clinical information.Results: Nurses experienced more negative emotions (anger, unease) and reported less engagement when assessing patients exhibiting irritable (vs. calm) behavior. Nurses also judged patients with irritable (vs. calm) behavior as more likely to exaggerate their pain and as poorer historians, and as less likely to cooperate, return to work, and recover. Nurses' handoffs were more likely to communicate negative descriptions of patients with irritable (vs. calm) behavior and omit speafic clinical information (e.g., whether tests were ordered, per-sonal information). The presence of mental illness increased unease and sadness and resulted in nurses being less likely to recommend a necessary test for a correct diagnosis.Conclusions: Emergency nurses' assessments and handoffs were impacted by patient factors, particularly irritable patient behavior. As nurses are central to the clinical team and experience regular, close contact with patients, the effects of irritable patient behavior on nursing assessments and care practices have important implications. We discuss potential approaches to address these ill effects, including reflexive practice, teamwork, and standardiza-tion of handoffs.Tweetable abstract: Experimental evidence links irritable patient behaviors to lower quality emergency depart-ment nurse handoffs, which may compromise patient safety @(lindamisbell) @(Nathan_Huff_1).(c) 2023 Elsevier Ltd. All rights reserved.

Smulowitz, Peter B. MD, MPH; McCoy, Jeanne MD; Thurlo-Walsh, Bert MM, RN, CPHQ Author Information

Importance:The role of patient-level factors that are unrelated to the specific clinical condition leading to an emergency department (ED) visit, such as functional status, cognitive status, social supports, and geriatric syndromes, in admission decisions is not well understood, partly because these data are not available in administrative databases. Objective:To determine the extent to which patient-level factors are associated with rates of hospital admission from the ED. Design, Setting, and Participants:This cohort study analyzed survey data collected from participants (or their proxies, such as family members) enrolled in the Health and Retirement Study (HRS) from January 1, 2000, to December 31, 2018. These HRS data were linked to Medicare fee-for-service claims data from January 1, 1999, to December 31, 2018. Information on functional status, cognitive status, social supports, and geriatric syndromes was obtained from the HRS data, whereas ED visits, subsequent hospital admission or ED discharge, and other claims-derived comorbidities and sociodemographic characteristics were obtained from Medicare data. Data were analyzed from September 2021 to April 2023. Main Outcomes and Measures:The primary outcome measure was hospital admission after an ED visit. A baseline logistic regression model was estimated, with a binary indicator of admission as the dependent variable of interest. For each primary variable of interest derived from the HRS data, the model was reestimated, including the HRS variable of interest as an independent variable. For each of these models, the odds ratio (OR) and average marginal effect (AME) of changing the value of the variable of interest were calculated. Results:A total of 42 392 ED visits by 11 783 unique patients were included. At the time of the ED visit, patients had a mean (SD) age of 77.4 (9.6) years, and visits were predominantly for female (25 719 visits [60.7%]) and White (32 148 visits [75.8%]) individuals. The overall percentage of patients admitted was 42.5%. After controlling for ED diagnosis and demographic characteristics, functional status, cognition status, and social supports all were associated with the likelihood of admission. For instance, difficulty performing 5 activities of daily living was associated with an 8.5-percentage point (OR, 1.47; 95% CI, 1.29-1.66) AME increase in the likelihood of admission. Having dementia was associated with an AME increase in the likelihood of admission of 4.6 percentage points (OR, 1.23; 95% CI, 1.14-1.33). Living with a spouse was associated with an AME decrease in the likelihood of admission of 3.9 percentage points (OR, 0.84; 95% CI, 0.79-0.89), and having children living within 10 miles was associated with an AME decrease in the likelihood of admission of 5.0 percentage points (OR, 0.80; 95% CI, 0.71-0.89). Other common geriatric syndromes, including trouble falling asleep, waking early, trouble with vision, glaucoma or cataract, use of hearing aids or trouble with hearing, falls in past 2 years, incontinence, depression, and polypharmacy, were not meaningfully associated with the likelihood of admission. Conclusion and Relevance:Results of this cohort study suggest that the key patient-level characteristics, including social supports, cognitive status, and functional status, were associated with the decision to admit older patients to the hospital from the ED. These factors are critical to consider when devising strategies to reduce low-value admissions among older adult patients from the ED.

OBJECTIVE:To examine whether the correlation between a provider's effect on one population of patients and the same provider's effect on another population is underestimated if the effects for each population are estimated separately as opposed to being jointly modeled as random effects, and to characterize how the impact of the estimation procedure varies with sample size.DATA SOURCES:Medicare claims and enrollment data on emergency department (ED) visits, including patient characteristics, the patient's hospitalization status, and identification of the doctor responsible for the decision to hospitalize the patient.STUDY DESIGN:We used a three-pronged investigation consisting of analytical derivation, simulation experiments, and analysis of administrative data to demonstrate the fallibility of stratified estimation. Under each investigation method, results are compared between the joint modeling approach to those based on stratified analyses.DATA COLLECTION/EXTRACTION METHODS:We used data on ED visits from administrative claims from traditional (fee-for-service) Medicare from January 2012 through September 2015.PRINCIPAL FINDINGS:The simulation analysis demonstrates that the joint modeling approach is generally close to unbiased, whereas the stratified approach can be severely biased in small samples, a consequence of joint modeling benefitting from bivariate shrinkage and the stratified approach being compromised by measurement error. In the administrative data analyses, the estimated correlation of doctor admission tendencies between female and male patients was estimated to be 0.98 under the joint model but only 0.38 using stratified estimation. The analogous correlations for White and non-White patients are 0.99 and 0.28 and for Medicaid dual-eligible and non-dual-eligible patients are 0.99 and 0.31, respectively. These results are consistent with the analytical derivations.CONCLUSIONS:Joint modeling targets the parameter of primary interest. In the case of population correlations, it yields estimates that are substantially less biased and higher in magnitude than naive estimators that post-process the estimates obtained from stratified models.

This cross-sectional study analyzes responses to a survey about medical error outcomes completed by emergency department attending physicians and advanced practice clinicians.

Objectives Nurse practitioners and physician assistants (NPs/PAs) increasingly practice in emergency departments (EDs), yet limited research has compared their practice patterns with those of physicians. Design, setting and participants Using nationally representative data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), we analysed ED visits among NPs/PAs and physicians between 1 January 2009 and 31 December 2017. To compare NP/PA and physician utilisation, we estimated propensity score-weighted multivariable regressions adjusted for clinical/sociodemographic variables, including triage acuity score (1=sickest/5=healthiest). Because NPs/PAs may preferentially consult physicians for more complex patients, we performed sensitivity analyses restricting to EDs with >95% of visits including the NP/PA–physician combination. Exposures NPs/PAs. Main outcome measures Use of hospitalisations, diagnostic tests, medications, procedures and six low-value services, for example, CT/MRI for uncomplicated headache, based on Choosing Wisely and other practice guidelines. Results Before propensity weighting, we studied visits to 12 410 NPs/PAs-alone, 21 560 to the NP/PA–physician combination and 143 687 to physicians-alone who saw patients with increasing age (41, 45 and 47 years, p<0.001) and worsening triage acuity scores (3.03, 2.85 and 2.67, p<0.001), respectively. After weighting, NPs/PAs-alone used fewer medications (2.62 vs 2.80, p=0.002), diagnostic tests (3.77 vs 4.66, p<0.001), procedures (0.67 vs 0.77, p<0.001), hospitalisations (OR 0.35 (95% CI 0.26 to 0.46)) and low-value CT/MRI studies (OR 0.65 (95% CI 0.53 to 0.80)) than physicians. Contrastingly, the NP/PA–physician combination used more medications (3.08 vs 2.80, p<0.001), diagnostic tests (5.07 vs 4.66, p<0.001), procedures (0.86 vs 0.77, p<0.001), hospitalisations OR 1.33 (95% CI 1.17 to 1.51) and low-value CT/MRI studies (OR 1.23 (95% CI 1.07 to 1.43)) than physicians—results were similar among EDs with >95% of NP/PA visits including the NP/PA–physician combination. Conclusions and relevance While U.S. NPs/PAs-alone used less care and low-value advanced diagnostic imaging, the NP/PA–physician combination used more care and low-value advanced diagnostic imaging than physicians alone. Findings were reproduced among EDs where nearly all NP/PA visits were collaborative with physicians, suggesting that NPs/PAs seeing more complex patients used more services than physicians alone, but the converse might be true for more straightforward patients.

Concerns about avoidance or delays in seeking emergency care during the COVID-19 pandemic are widespread, but national data on emergency department (ED) visits and subsequent rates of hospitalization and outcomes are lacking. Using data on all traditional Medicare beneficiaries in the US from October 1, 2018, to September 30, 2020, we examined trends in ED visits and rates of hospitalization and thirty-day mortality conditional on an ED visit for non-COVID-19 conditions during several stages of the pandemic and for areas that were considered COVID-19 hot spots versus those that were not. We found reductions in ED visits that were largest by the first week of April 2020 (52 percent relative decrease), with volume recovering somewhat by mid-June (25 percent relative decrease). These reductions were of similar magnitude in counties that were and were not designated as COVID-19 hot spots. There was an early increase in hospitalizations and in the relative risk for thirty-day mortality, starting with the first surge of the pandemic, peaking at just over a 2-percentage-point increase. These results suggest that patients were presenting with more serious illness, perhaps related to delays in seeking care.

Study objective: Rates of admission from the emergency department (ED) vary widely across regions of the country, hospitals within regions, and physicians within hospitals. Our objective was to determine the extent to which variation in admission decisions was described by differences in admission rates at these 3 levels. This understanding will serve to better target interventions to modify rates of admission where appropriate. Methods: In this cross-sectional observational cohort study, we analyzed Medicare fee-for-service claims for ED visits from 2012 to 2015 in a 20% random sample of beneficiaries. We first estimated the total regional-, hospital-, and physician-level variations in rates of admission and their proportions of the total variation after adjusting for patient and each level's covariates. We then estimated the extent to which each level's characteristics accounted for variation at that respective level. Results: Our study sample included 5,778,218 visits with 45,491 physicians at 3,480 EDs across 306 hospital referral regions. The mean rate of admission was 38.9% and ranged from 21.4% to 53.0% for physicians at the 10th and 90th percentile of the distribution, respectively. The residual (unexplained) variations at the regional, hospital, and physician levels were 13.3% (95% confidence interval [CI], 11.2 to 15.5%), 60.1% (57.1 to 62.9%), and 26.7% (26.4 to 26.9%), respectively. Regional, hospital, and physician characteristics accounted for 9.1% (95% CI, -5.6 to 23.8%), 51.1% (48.8 to 53.5%), and 2.7% (1.3 to 4.1%), respectively, of the explained variation at their respective levels. Conclusion: Within-area variation, both across hospitals within a region and across physicians within a hospital, is a more substantial component of observed variation in admission rates from the ED than regional level variation. These findings suggest that variation in admission rates is at least in part related to institutional norms and cultures as well as heterogeneity of physician decisionmaking within hospitals, both of which could be targets of interventions to modify rates of admission.

IMPORTANCE Sociodemographic disparities in health care and variation in physician practice patterns have been well documented; however, the contribution of variation in individual physician care practices to health disparities is challenging to quantify. Emergency department (ED) physicians vary in their propensity to admit patients. The consistency of this variation across sociodemographic groups may help determine whether physician-specific factors are associated with care differences between patient groups. OBJECTIVE To estimate the consistency of ED physician admission propensities across categories of patient sex, race and ethnicity, and Medicaid enrollment. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study analyzed Medicare fee-for-service claims for ED visits from January 1, 2016, to December 31, 2019, in a 10% random sample of hospitals. The allocation of patients to ED physicians in the acute care setting was used to isolate physician-level variation in admission rates that reflects variation in physician decision-making. Multi-level models with physician random effects and hospital fixed effects were used to estimate the within-hospital physician variation in admission propensity for different patient sociodemographic subgroups and the covariation in these propensities between subgroups (consistency), adjusting for primary diagnosis and comorbidities. MAIN OUTCOMES AND MEASURES Admission from the ED. RESULTS The analysis included 4 567 760 ED visits involving 2 334 361 beneficiaries and 15 767 physicians in 396 EDs. The mean (SD) age of the beneficiaries was 78 (8.2) years, 2 700 661 visits (59.1%) were by women, and most patients (3 839 055 [84.1%]) were not eligible for Medicaid. Of 4 473 978 race and ethnicity reports on enrollment, 103 699 patients (2.3%) were Asian/Pacific Islander, 421 588 (9.4%) were Black, 257 422 (5.8%) were Hispanic, and 3 691 269 (82.5%) were non-Hispanic White. Within hospitals, adjusted rates of admission were higher for men (36.8%; 95% CI, 36.8%-36.9%) than for women (33.7%; 95% CI, 33.7%-33.8%); higher for non-Hispanic White (36.0%; 95% CI, 35.9%-36.0%) than for Asian/Pacific Islander (33.6%; 95% CI, 33.3%-33.9%), Black (30.2%; 95% CI, 30.0%-30.3%), or Hispanic (31.1%; 95% CI, 30.9%-31.2%) beneficiaries; and higher for beneficiaries dually enrolled in Medicaid (36.3%; 95% CI, 36.2%-36.5%) than for those who were not (34.7%; 95% CI, 34.7%-34.8%). Within hospitals, physicians varied in the percentage of patients admitted, ranging from 22.4% for physicians at the 10th percentile to 47.6% for physicians at the 90th percentile of the estimated distribution. Physician admission propensities were correlated between men and women (r = 0.99), Black and non-Hispanic White patients (r = 0.98), and patients who were dually enrolled and not dually enrolled in Medicaid (r = 0.98). CONCLUSIONS AND RELEVANCE This cross-sectional study indicated that, although overall rates of admission differ systematically by patient sociodemographic factors, an individual physician's propensity to admit relative to other physicians appears to be applied consistently across sociodemographic groups of patients.

Hospitalizations account for the largest share of health care spending. New payment models increasingly encourage health care providers to reduce hospital admissions. Although emergency department (ED) physicians play a major role in the decision to admit a patient, the extent to which admission rates vary among ED physicians even within the same hospital remains poorly understood. In this study we examined physician-level variation in ED admission rates for Medicare patients. We found meaningful variation in admission rates: The mean physician-level adjusted admission rate was 38.9 percent and ranged from 32.2 percent to 45.6 percent for physicians at the tenth and ninetieth percentiles, respectively, of the estimated distribution within the same hospital. In contrast, the predicted risk for admission based on patient characteristics varied little among these physicians, suggesting that the variation in admission rates was not due to differences in patients seen. Our results suggest that strategies targeting physician decision making could modify (by either increasing or decreasing when appropriate) rates of admissions.

Academic Emergency MedicineVolume 28, Issue 11 p. 1318-1320 RESEARCH LETTERFree Access Patient perceptions of diagnostic certainty at discharge and patient satisfaction in the emergency department Serena F. Hagerty, Corresponding Author Serena F. Hagerty [email protected] orcid.org/0000-0002-8248-1463 Marketing, Harvard Business School, Boston, Massachusetts, USA Correspondence Serena F. Hagerty, Marketing, Harvard Business School, Boston, MA 02163, USA. Email: [email protected]Search for more papers by this authorRyan C. Burke PhD, MPH, Ryan C. Burke PhD, MPH Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USASearch for more papers by this authorLinda M. Isbell PhD, Linda M. Isbell PhD Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USASearch for more papers by this authorKate Barasz DBA, Kate Barasz DBA Marketing, Universitat Ramon Llull, ESADE, Barcelona, SpainSearch for more papers by this authorPeter Smulowitz MD, Peter Smulowitz MD Emergency Medicine, Milford Regional Medical Center, Milford, Massachusetts, USA Emergency Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USASearch for more papers by this author Serena F. Hagerty, Corresponding Author Serena F. Hagerty [email protected] orcid.org/0000-0002-8248-1463 Marketing, Harvard Business School, Boston, Massachusetts, USA Correspondence Serena F. Hagerty, Marketing, Harvard Business School, Boston, MA 02163, USA. Email: [email protected]Search for more papers by this authorRyan C. Burke PhD, MPH, Ryan C. Burke PhD, MPH Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USASearch for more papers by this authorLinda M. Isbell PhD, Linda M. Isbell PhD Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USASearch for more papers by this authorKate Barasz DBA, Kate Barasz DBA Marketing, Universitat Ramon Llull, ESADE, Barcelona, SpainSearch for more papers by this authorPeter Smulowitz MD, Peter Smulowitz MD Emergency Medicine, Milford Regional Medical Center, Milford, Massachusetts, USA Emergency Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USASearch for more papers by this author First published: 08 April 2021 https://doi.org/10.1111/acem.14262 Supervising Editor: Jeffrey A. Kline, MD AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Patient satisfaction has evolved into a standard measure for quality and value in health care. Given the importance of patient satisfaction to overall hospital quality measures, a growing literature has investigated a number of variables that affect satisfaction in the emergency department (ED). For instance, studies have demonstrated that certain objective visit-related metrics, such as reduced wait time to see a provider, shorter length of stay, and a higher number of administered treatments, underlie higher patient satisfaction in the ED.1-3 However, recent research has also highlighted that subjective measures of a patient's experience may be greater determinants of satisfaction than objective measures of care.4 For example, patient perception of wait time is a stronger predictor of satisfaction than objectively measured wait time.5, 6 These results reveal the importance of understanding the subjective aspects of a patient's experience in an ED that may predict overall satisfaction. We investigated one critical factor that may influence patients' subjective experience (and, in turn, satisfaction): perceived diagnostic certainty—or the extent to which patients leave the ED feeling certain they know what caused their underlying condition. Psychology research has established individuals' aversion to uncertainty and ambiguity and the negative affect induced by decisions involving uncertainty.7 There is reason to believe that diagnostic certainty may also play a role in patient satisfaction within the ED. Given the nature of the ED practice environment (i.e., relatively limited information, little to no prior relationship with patients, time constraints), the main goal is often to rule out truly emergent causes of patients' presenting complaints—not necessarily to achieve a definitive diagnosis. Yet, many patients present with expectations of receiving a definitive diagnosis.8 Violating this expectation by discharging patients without a definitive diagnosis, thus leaving them in an otherwise uncomfortable state of uncertainty, may decrease positive affect and, in turn, decrease patient satisfaction. To develop a baseline understanding of this phenomenon, we sought to (1) determine whether perceptions of diagnostic certainty are associated with greater patient satisfaction and (2) assess the strength of the relationship between diagnostic certainty and patient satisfaction relative to other potentially relevant variables, including reported level of pain, length of stay, and number and types of follow-up recommendations provided. We administered a survey to a convenience sample of patients in a single academic tertiary care ED with an annual volume of 55,000 visits. Patients with a chief complaint of abdominal pain, back pain, chest pain, or headache who were listed for discharge from the ED were identified and approached by a research assistant between 8:00 a.m. and 11:00 p.m. These conditions were chosen by consensus because they were thought to reflect conditions where the focus of the emergency physician is often ruling out the worst-case scenario and where a definitive diagnosis might often not be reached even after a thorough ED evaluation. A survey was created using online software and given to each patient for completion on an electronic tablet. Each patient survey was paired with data obtained from an administrative database that provided details about the patient's stay, including length of stay, tests ordered, and medications administered. All survey items and data collection methods were approved by the institution's ethical review board. Our primary measures were patient affect, perceived diagnostic certainty, and satisfaction with care. To measure affect, participants rated (using a modified 7-point Likert scale ranging from 1 = not at all to 7 = very much) the extent to which they felt anxious (reverse-coded); relieved, happy, satisfied, angry (reverse-coded); frustrated (reverse-coded); nervous (reverse-coded); discouraged (reverse-coded), and confused (reverse-coded) based on their visit. We averaged all 10 affect items to create a composite of positive affect (α = 0.87). To measure perceived diagnostic certainty, participants indicated their level of agreement (1 = strongly disagree, 7 = strongly agree) with two statements: I am sure about exactly what is wrong and My doctors know exactly what is wrong. We averaged the two measures to create a composite of diagnostic certainty (α = 0.86). Finally, to measure satisfaction, patients indicated their level of agreement (1 = strongly disagree, 7 = strongly agree) with one statement: I am satisfied with the quality of care I received in the ER. Over the 14-month study period, 148 ED patients participated (note that data collection was paused for 3 months due to COVID-19). Mean patient age was 49.5 years, 58.8% (n = 87) of patients were female, and 39.9% (n = 59) self-identified their race/ethnicity as White non-Hispanic. Of the study sample, 25.0% (n = 37) presented with abdominal pain, 23.0% (n = 34) presented with back pain, 27.7% (n = 41) presented with chest pain, and 23.0% (n = 34) presented with a headache. Our results show that patient perception of diagnostic certainty was a significant predictor of positive affect. Multiple regression analysis (R2 = 0.18, p < 0.001) revealed that perception of diagnostic certainty (β = 0.39, 95% confidence interval [CI] = 0.24 to 0.56) was more strongly associated with positive affect than patient self-reported pain (β = –0.10, 95% CI = –0.26 to 0.07), length of stay (β = 0.02, 95% CI = –0.15 to 0.18), number of tests conducted during stay (β = 0.09, 95% CI = –0.08 to 0.26), number of follow-up actions prescribed (β = 0.07, 95% CI = –0.09 to 0.23), or type of complaint (β = –0.05, 95% CI = –0.21 to 0.11; see Table 1). TABLE 1. Results of multiple linear regression (N = 140) Variable Standardized coefficient 95% CI Unstandardized coefficient 95% CI p-value Patient positive affect Diagnostic certainty 0.39 0.24 to 0.56 0.22 0.13 to 0.31 <0.001 Self-reported pain –0.10 –0.26 to 0.07 –0.07 –0.18 to 0.05 0.25 Length of stay 0.02 –0.15 to 0.18 0.00 –0.02 to 0.03 0.82 Number of tests 0.09 –0.08 to 0.26 0.12 –0.10 to 0.34 0.28 Number of follow-up items 0.07 –0.09 to 0.23 0.07 –0.09 to 0.22 0.39 Chief complaint –0.05 –0.21 to 0.11 –0.06 –0.24 to 0.13 0.55 Patient satisfaction with care Diagnostic certainty 0.38 0.22 to 0.54 0.24 0.14 to 0.34 <0.001 Self-reported pain –0.13 –0.29 to 0.03 –0.10 –0.22 to 0.02 0.11 Length of stay –0.04 –0.20 to 0.13 –0.01 –0.03 to 0.02 0.66 Number of tests 0.19 0.03 to 0.36 0.29 0.05 to 0.53 0.02 Number of follow-up items 0.16 0.00 to 0.32 0.17 0.00 to 0.34 0.05 Chief complaint –0.11 –0.27 to 0.05 –0.14 –0.34 to 0.07 0.19 Patient perception of diagnostic certainty was also significantly associated with patient satisfaction. Multiple regression analysis (R2 = 0.21, p < 0.001) revealed that perception of diagnostic certainty (β = 0.38, 95% CI = 0.22 to 0.54) was more strongly associated with patient satisfaction than patient self-reported pain (β = –0.13, 95% CI = –0.29 to 0.03), length of stay (β = –0.04, 95% CI = –0.20 to 0.13), number of tests conducted during stay (β = 0.19, 95% CI = 0.03 to 0.36), number of follow-up actions prescribed (β = 0.16, 95% CI = 0.00 to 0.32), or type of complaint (β = –0.11, 95% CI = –0.27 to 0.05; see Table 1). Based on 5,000 bootstrapped samples, a mediation analysis indicated that greater diagnostic certainty was significantly correlated with greater positive affect (b = 0.21, 95% CI = 0.13 to 0.30, p < 0.001) and, in turn, greater positive affect was significantly associated with greater patient satisfaction (b = 0.64, 95% CI = 0.48 to 0.79, p < 0.001).9 In other words, perceptions of diagnostic certainty increased satisfaction by increasing patients' positive affective experience (indirect effect: b = 0.14, 95% CI = 0.07 to 0.23). These findings do not suggest that diagnostic certainty is the only, or the most, important determinant of patient affect or satisfaction; these models do not explain the majority of variance in patient affect or satisfaction. However, these findings do suggest that perceptions of diagnostic certainty play a significant role in a patient's experience and deserve further consideration. By highlighting the importance of a patient's perceived diagnostic certainty on overall satisfaction with care, this study adds to a growing literature investigating the subjective measures of patient experiences that contribute to patient satisfaction with health care. Specifically, when patients feel a greater sense of certainty regarding the diagnosis of their health condition, they also have greater positive affect and report higher satisfaction with care received. Notably, a patient's sense of diagnostic certainty is associated with their satisfaction of care—above and beyond several standard, objective measures, including length of stay, number of tests run, and number of follow-up actions prescribed. While our findings are limited by relatively small sample size and should be viewed as preliminary, we believe that our findings have important implications for ED physicians. ED physicians are trained to assess for and rule out "worst-case" diagnoses and generally not to evaluate conditions that are more appropriate for an outpatient setting. For example, ED physicians focus on "ruling out" a myocardial infarction for a patient presenting with chest pain, rather than "ruling in" any other sources of the pain. Although this approach is standard and often optimal for the ED setting, additional testing, either to rule out worst-case diagnoses or done mainly to reassure patients, may still not provide patients with sufficient diagnostic certainty.10 Rather than performing additional potentially low-value testing, ED physicians may consider simple interventions, such as setting better expectations about the level of diagnostic certainty they believe is possible or changing the ways in which they communicate any degree of diagnostic uncertainty at discharge. Importantly, we do not suggest that this should necessarily change how ED physicians approach the diagnostic evaluation of patients with these or other undifferentiated conditions; overtesting for the sake of certainty could, itself, have significant untoward consequences. However, our findings should factor into how ED physicians communicate the diagnostic approach in the ED and how they frame any remaining uncertainty. Finally, our study raises important questions with respect to the appropriateness or utility of current measures of patient satisfaction in the ED. If such a disconnect exists vis-à-vis the basic goal of an ED evaluation between patient and physician, then evaluating patient satisfaction after an ED visit may be capturing aspects beyond the control of the treatment team. Such a focus on patient satisfaction may then actually be harmful—for example, if physicians were to order additional testing simply to address the concern over patient discomfort with diagnostic uncertainty. These aspects are important when considering the utility of measures of patient satisfaction in the ED setting. REFERENCES 1Bastani A, Shaqiri B, Palomba K, Bananno D, Anderson W. An ED scribe program is able to improve throughput time and patient satisfaction. Am J Emerg Med. 2014; 32(5): 399- 402. 2Parker BT, Marco C. Emergency department length of stay: accuracy of patient estimates. West J Emerg Med. 2014; 15(2): 170. 3Sun BC, Adams J, Orav EJ, Rucker DW, Brennan TA, Burstin HR. Determinants of patient satisfaction and willingness to return with emergency care. Ann Emerg Med. 2000; 35(5): 426- 434. 4Boudreaux ED, O'Hea EL. Patient satisfaction in the emergency department: a review of the literature and implications for practice. J Emerg Med. 2004; 26(1): 13- 26. 5Hedges JR, Trout A, Magnusson AR. Satisfied Patients Exiting the Emergency Department (SPEED) study. Acad Emerg Med. 2002; 9(1): 15- 21. 6Thompson DA, Yarnold PR. Relating patient satisfaction to waiting time perceptions and expectations: the disconfirmation paradigm. Acad Emerg Med. 1995; 2: 1057- 1062. 7Anderson EC, Carleton RN, Diefenbach M, Han PK. The relationship between uncertainty and affect. Front Psychol. 2019; 10: 2504. 8Gerolamo AM, Jutel A, Kovalsky D, Gentsch A, Doty AM, Rising KL. Patient-identified needs related to seeking a diagnosis in the emergency department. Ann Emerg Med. 2018; 72(3): 282- 288. 9Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008; 40(3): 879- 891. 10Rolfe A, Burton C. Reassurance after diagnostic testing with a low pretest probability of serious disease: systematic review and meta-analysis. JAMA Intern Med. 2013; 173(6): 407- 416. Volume28, Issue11November 2021Pages 1318-1320 ReferencesRelatedInformation

Objective Risk aversion is a personality trait influential to decision making in medicine. Little is known about how emergency department (ED) clinicians differ in their attitudes toward risk taking. Methods We conducted a cross‐sectional survey of practicing ED clinicians (physicians and advanced practice clinicians [APCs]) in Massachusetts using the following 4 existing validated scales: the Risk‐Taking Scale (RTS), Stress from Uncertainty Scale (SUS), the Fear of Malpractice Scale (FMS), and the Need for (Cognitive) Closure Scale (NCC). We used Cronbach's α to assess the reliability of each scale and performed multivariable linear regressions to analyze the association between the score for each scale and clinician characteristics. Results Of 1458 ED clinicians recruited for participation, 1116 (76.5%) responded from 93% of acute care hospitals in Massachusetts. Each of the 4 scales demonstrated high internal consistency reliability with Cronbach's αs ranging from 0.76 to 0.92. The 4 scales also were moderately correlated with one another (0.08 to 0.54; all P < 0.05). The multivariable results demonstrated differences between physicians and APCs, with physicians showing a greater tolerance for risk or uncertainty (NCC difference, −3.58 [95% confidence interval, CI, −5.26 to −1.90]; SUS difference, −3.14 [95% CI: −4.99 to −1.29]) and a higher concern about malpractice (FMS difference, 1.14 [95% CI, 0.11–2.17]). Differences were also observed based on clinician age (a proxy for years of experience), with greater age associated with greater tolerance of risk or uncertainty (age older than 50 years compared with age 35 years and younger; NCC difference, −2.84 [95% CI, −4.69 to −1.00]; SUS difference, −4.71 [95% CI, −6,74 to −2.68]) and less concern about malpractice (FMS difference, −3.19 [95% CI, −4.31 to −2.06]). There were no appreciable differences based on sex, and there were no consistent associations between scale scores and the practice and payment characteristics assessed. Conclusion We found that risk attitudes of ED clinicians were associated with type of training (physician vs APC) and age (experience). These differences suggest one possible explanation for the observed differences in decision making.

Thirteen years ago, only a few months after completing my residency in emergency medicine, I walked into a night shift ready for anything. One of the first patients I encountered was a young man with right-sided thoracic back pain after having spent a day lifting moving boxes. Acute back pain is of course a common reason for people to visit an emergency department, and along with his age, the location and context of the pain seemed fairly typical for muscular strain. But as a junior attending I was appropriately more conservative than how I suspect I would act today. Responding to my nerves, an elicited history of cocaine use and leucocytosis, we ordered an MRI of the back to look for an epidural abscess and treated his pain. The MRI was performed and reported as a normal study. While we assessed whether he was comfortable for discharge, I proceeded to focus my attention on other patients who required immediate stabilisation and management. Sometime later during the shift I was suddenly startled by a low-pitched thud near my desk. I looked over and saw someone in a patient gown lying on the floor. Sprinting over to that spot, I soon realised it was this young man collapsed onto the floor in cardiac arrest. During the ongoing resuscitation, the proverbial light bulb went off in my head and I sent the resident physician back to speak with the radiologist again about the MRI, focusing specifically on the aorta. By the time we confirmed a type A aortic dissection ruptured into the right hemithorax and attempted to rush the patient to the operating room, it was too late. Despite our best efforts, he died. Nothing in my medical training up to that point prepared me for such failure. During medical school and residency …