Association of a Callback Program With Emergency Department Revisit Rates Among Patients Seeking Emergency Care.

Importance: The emergency department (ED) discharge process often involves haste and poor communication. Objectives: To assess the association of an automated telephone call 2 days after ED discharge with the likelihood of an unplanned ED revisit at both 72 hours and 7 days after the index visit and with perceived care metrics measured at 14 days. Design, Setting, and Participants: This prospective nonrandomized clinical trial was conducted at a single academically affiliated county emergency department among 8110 patients presenting to and discharged from the ED during a 10-week period from June 25 to August 30, 2018. Initial statistical analysis was performed from February 1 to November 30, 2020, with additional analyses performed from March 1 to 16, 2022. Interventions: Participants were allocated in a nonrandom fashion to 1 of 2 groups: patients who received an automated telephone call 2 days after discharge were compared with patients who received no call 2 days after discharge. All patients received a telephone questionnaire at 14 days to assess secondary outcome measures. Main Outcomes and Measures: The primary outcome was a return visit to the ED at 7 days. Secondary outcomes included a return visit to the ED within 72 hours, a return visit to the ED within 7 days resulting in hospital admission, and patient-reported perceptions of their care measured by 4 questions related to quality metrics assessed at 14 days. A secondary analysis compared patients who actually responded to the initial call at 2 days with all nonresponders, regardless of whether they received a call. Analyses were made on an intention-to-treat basis. Results: More than 15 000 patients were seen in the ED during the 10-week study period, and 10 948 were discharged. A total of 8110 patient encounters (4460 male patients [55.0%]; 3313 Hispanic patients [40.9%]; mean [SD] age, 40.5 [19.4] years) were enrolled. A total of 2958 patients (36.5%) received an automated telephone call at 2 days after discharge, while 5152 (63.5%) received no call. Rates of ED return within 7 days of the initial index visit were significantly lower among those who received a call at 2 days than those who did not receive a call (224 of 2958 [7.6%] vs 533 of 5152 [10.3%]; P < .001). Patients who received a 2-day call were more likely than those who did not receive a call to have followed up with a health care clinician (67.9% [374 of 551] vs 66.3% [604 of 911]), understood their health issues (77.5% [490 of 632] vs 74.9% [780 of 1042]), and have received their discharge medications (87.0% [507 of 583] vs 83.6% [793 of 949]), although none of these differences between the 2 groups were statistically significant. Conclusions and Relevance: A telephone call to patients 2 days after discharge from the ED was associated with decreased ED use at 7 days after the index visit and may have been associated with marginal improvements in measured quality of care metrics.

Odds of return: a prospective study using provider assessment to predict short-term patient return visits to the emergency department.

OBJECTIVE: Previous studies have assessed patient-level characteristics associated with emergency department (ED) return visits, but none have used provider assessment. We prospectively investigate whether clinical providers could accurately predict ED return visits. METHODS: Prospective cohort study. SETTING: Single academically affiliated urban county hospital. PARTICIPANTS: Discharged ED patients over a 14-month period with a provider assessment of the likelihood of patient return within 7 days of ED discharge. MAIN OUTCOME MEASURES: The primary outcome of interest was a return visit to the ED within 7 days. Additional outcome measures included a return visit within 72 hours and a return visit resulting in admission. We also measured the accuracy of provider gestalt, and provide measures of sensitivity, specificity, predictive values, and likelihood ratios. RESULTS: Of the 11 922 ED discharges included in this study, providers expected 2116 (17.7%) to result in a return visit within 7 days. Providers were much more likely to perceive a return visit if the patient left against medical advice (OR: 5.97, 95% CI: 4.67 to 7.62), or was homeless (OR: 5.69, 95% CI: 5.14 to 6.29). Patients who actually returned were also more likely to be homeless, English speaking and to have left the ED against medical advice on the initial encounter. The strongest predictor of a return visit at both 72 hours and 7 days in multivariable modelling was provider assessment (OR: 3.77, 95% CI: 3.25 to 4.37; OR: 3.72, 95% CI: 3.29 to 4.21, respectively). Overall sensitivity and specificity of provider gestalt as a measure of patient return within 7 days were 47% and 87%, respectively. The positive and negative likelihood ratios were 3.51 and 0.61, respectively. CONCLUSIONS: Clinician assessment was the strongest predictor of a return visit in this dataset. Clinician assessment may be used as a way to screen patients during the index visit and enrol them in efforts to decrease return visits.

By Default: The Effect of Prepopulated Prescription Quantities on Opioid Prescribing in the Emergency Department.

INTRODUCTION: Opioid prescribing patterns have come under increasing scrutiny with the recent rise in opioid prescriptions, opioid misuse and abuse, and opioid-related adverse events. To date, there have been limited studies on the effect of default tablet quantities as part of emergency department (ED) electronic order entry. Our goal was to evaluate opioid prescribing patterns before and after the removal of a default quantity of 20 tablets from ED electronic order entry. METHODS: We performed a retrospective observational study at a single academic, urban ED with 58,000 annual visits. We identified all adult patients (18 years or older) seen in the ED and discharged home with prescriptions for tablet forms of hydrocodone and oxycodone (including mixed formulations with acetaminophen). We compared the quantity of tablets prescribed per opioid prescription 12 months before and 10 months after the electronic order-entry prescription default quantity of 20 tablets was removed and replaced with no default quantity. No specific messaging was given to providers, to avoid influencing prescribing patterns. We used two-sample Wilcoxon rank-sum test, two-sample test of proportions, and Pearson's chi-squared tests where appropriate for statistical analysis. RESULTS: A total of 4,104 adult patients received discharge prescriptions for opioids in the pre-intervention period (151.6 prescriptions per 1,000 discharged adult patients), and 2,464 post-intervention (106.69 prescriptions per 1,000 discharged adult patients). The median quantity of opioid tablets prescribed decreased from 20 (interquartile ration [IQR] 10-20) to 15 (IQR 10-20) (p<0.0001) after removal of the default quantity. While the most frequent quantity of tablets received in both groups was 20 tablets, the proportion of patients who received prescriptions on discharge that contained 20 tablets decreased from 0.5 (95% confidence interval [CI] [0.48-0.52]) to 0.23 (95% CI [0.21-0.24]) (p<0.001) after default quantity removal. CONCLUSION: Although the median number of tablets differed significantly before and after the intervention, the clinical significance of this is unclear. An observed wider distribution of the quantity of tablets prescribed after removal of the default quantity of 20 may reflect more appropriate prescribing patterns (i.e., less severe indications receiving fewer tabs and more severe indications receiving more). A default value of 20 tablets for opioid prescriptions may be an example of the electronic medical record's ability to reduce practice variability in medication orders actually counteracting optimal patient care.

A whole-genome admixture scan finds a candidate locus for multiple sclerosis susceptibility.

Multiple sclerosis is a common disease with proven heritability, but, despite large-scale attempts, no underlying risk genes have been identified. Traditional linkage scans have so far identified only one risk haplotype for multiple sclerosis (at HLA on chromosome 6), which explains only a fraction of the increased risk to siblings. Association scans such as admixture mapping have much more power, in principle, to find the weak factors that must explain most of the disease risk. We describe here the first high-powered admixture scan, focusing on 605 African American cases and 1,043 African American controls, and report a locus on chromosome 1 that is significantly associated with multiple sclerosis.