Patient and Visit Characteristics Associated With Physical Restraint Use in the Emergency Department.

Objective Physical restraints are used in emergency departments (EDs) to address behavioral emergencies in situations in which less restrictive methods have failed. The objective of this study was to evaluate for associations between patient/visit characteristics and physical restraint use. Study Design This study was designed as a cross-sectional, retrospective study of all encounters at Kaiser Permanente Northern California EDs from January 1, 2016, to December 31, 2019, to evaluate differences in patient and visit characteristics between visits involving physical restraint use and those without. Methods Using electronic health record data, this study identified physical restraint use among ED encounters and extracted demographic, clinical, and facility characteristics. The authors calculated odds ratios for physical restraint placement, adjusting for patient and visit characteristics and accounting for within-patient clustering. Results Among 4,410,816 encounters (representing 1,791,673 patients), 6369 encounters (0.1%) involved physical restraint use among 5,554 patients (0.3%). Variables associated with the lowest odds of physical restraint included female sex, presentation to the ED in more recent years, and presence of intentional self-harm/suicidal ideation. Variables associated with the highest odds of physical restraint included higher visit acuity and weekend presentations to the ED. Discussion This study, which leveraged a large, diverse patient sample generalizable to the Northern California population, found several patient and visit characteristics associated with physical restraint use in the ED. Conclusion Results of this study may help identify patient groups and situational factors that are most likely to lead to physical restraint use and structural factors contributing to disparities in care, thereby informing interventions to reduce physical restraint use when possible.

Quantifying the breadth of antibiotic exposure in sepsis and suspected infection using spectrum scores.

A retrospective cohort study. Studies to quantify the breadth of antibiotic exposure across populations remain limited. Therefore, we applied a validated method to describe the breadth of antimicrobial coverage in a multicenter cohort of patients with suspected infection and sepsis. We conducted a retrospective cohort study across 21 hospitals within an integrated healthcare delivery system of patients admitted to the hospital through the ED with suspected infection or sepsis and receiving antibiotics during hospitalization from January 1, 2012, to December 31, 2017. We quantified the breadth of antimicrobial coverage using the Spectrum Score, a numerical score from 0 to 64, in patients with suspected infection and sepsis using electronic health record data. Of 364,506 hospital admissions through the emergency department, we identified 159,004 (43.6%) with suspected infection and 205,502 (56.4%) with sepsis. Inpatient mortality was higher among those with sepsis compared to those with suspected infection (8.4% vs 1.2%; P < .001). Patients with sepsis had higher median global Spectrum Scores (43.8 [interquartile range IQR 32.0-49.5] vs 43.5 [IQR 26.8-47.2]; P < .001) and additive Spectrum Scores (114.0 [IQR 57.0-204.5] vs 87.5 [IQR 45.0-144.8]; P < .001) compared to those with suspected infection. Increased Spectrum Scores were associated with inpatient mortality, even after covariate adjustments (adjusted odds ratio per 10-point increase in Spectrum Score 1.31; 95%CI 1.29-1.33). Spectrum Scores quantify the variability in antibiotic breadth among individual patients, between suspected infection and sepsis populations, over the course of hospitalization, and across infection sources. They may play a key role in quantifying the variation in antibiotic prescribing in patients with suspected infection and sepsis.

Evaluation of Incident 7-Day Infection and Sepsis Hospitalizations in an Integrated Health System.

Rationale: Prehospital opportunities to predict infection and sepsis hospitalization may exist, but little is known about their incidence following common healthcare encounters. Objectives: To evaluate the incidence and timing of infection and sepsis hospitalization within 7 days of living hospital discharge, emergency department discharge, and ambulatory visit settings. Methods: In each setting, we identified patients in clinical strata based on the presence of infection and severity of illness. We estimated number needed to evaluate values with hypothetical predictive model operating characteristics. Results: We identified 97,614,228 encounters, including 1,117,702 (1.1%) hospital discharges, 4,635,517 (4.7%) emergency department discharges, and 91,861,009 (94.1%) ambulatory visits between 2012 and 2017. The incidence of 7-day infection hospitalization varied from 37,140 (3.3%) following inpatient discharge to 50,315 (1.1%) following emergency department discharge and 277,034 (0.3%) following ambulatory visits. The incidence of 7-day infection hospitalization was increased for inpatient discharges with high readmission risk (10.0%), emergency department discharges with increased acute or chronic severity of illness (3.5% and 4.7%, respectively), and ambulatory visits with acute infection (0.7%). The timing of 7-day infection and sepsis hospitalizations differed across settings with an early rise following ambulatory visits, a later peak following emergency department discharges, and a delayed peak following inpatient discharge. Theoretical number needed to evaluate values varied by strata, but following hospital and emergency department discharge, were as low as 15-25. Conclusions: Incident 7-day infection and sepsis hospitalizations following encounters in routine healthcare settings were surprisingly common and may be amenable to clinical predictive models.

The Presentation, Pace, and Profile of Infection and Sepsis Patients Hospitalized Through the Emergency Department: An Exploratory Analysis.

To characterize the signs and symptoms of sepsis, compare them with those from simple infection and other emergent conditions and evaluate their association with hospital outcomes. DESIGN SETTING PARTICIPANTS AND INTERVENTION: A multicenter, retrospective cohort study of 408,377 patients hospitalized through the emergency department from 2012 to 2017 with sepsis, suspected infection, heart failure, or stroke. Infected patients were identified based on Sepsis-3 criteria, whereas noninfected patients were identified through diagnosis codes. MEASUREMENTS AND MAIN RESULTS: Signs and symptoms were identified within physician clinical documentation in the first 24 hours of hospitalization using natural language processing. The time of sign and symptom onset prior to presentation was quantified, and sign and symptom prevalence was assessed. Using multivariable logistic regression, the association of each sign and symptom with four outcomes was evaluated: sepsis versus suspected infection diagnosis, hospital mortality, ICU admission, and time of first antibiotics (> 3 vs ≤ 3 hr from presentation). A total of 10,825 signs and symptoms were identified in 6,148,348 clinical documentation fragments. The most common symptoms overall were as follows: dyspnea (35.2%), weakness (27.2%), altered mental status (24.3%), pain (23.9%), cough (19.7%), edema (17.8%), nausea (16.9%), hypertension (15.6%), fever (13.9%), and chest pain (12.1%). Compared with predominant signs and symptoms in heart failure and stroke, those present in infection were heterogeneous. Signs and symptoms indicative of neurologic dysfunction, significant respiratory conditions, and hypotension were strongly associated with sepsis diagnosis, hospital mortality, and intensive care. Fever, present in only a minority of patients, was associated with improved mortality (odds ratio, 0.67, 95% CI, 0.64-0.70; p < 0.001). For common symptoms, the peak time of symptom onset before sepsis was 2 days, except for altered mental status, which peaked at 1 day prior to presentation. CONCLUSIONS: The clinical presentation of sepsis was heterogeneous and occurred with rapid onset prior to hospital presentation. These findings have important implications for improving public education, clinical treatment, and quality measures of sepsis care.

Assessment of Electronic Health Record Search Patterns and Practices by Practitioners in a Large Integrated Health Care System.

Importance: The electronic health record (EHR) is a source of practitioner dissatisfaction in part because of challenges with information retrieval. To improve data accessibility, a better understanding of practitioners' information needs within individual patient records is needed. Objective: To assess EHR users' searches using data from a large integrated health care system. Design, Setting, and Participants: This retrospective cross-sectional analysis used EHR search data from Kaiser Permanente Northern California, an integrated health care delivery system with more than 4.4 million members. Users' EHR search activity data were obtained from April 1, 2018, to May 15, 2019. Main Outcomes and Measures: Search term frequency was grouped by user and practitioner types. Network analyses were performed of co-occurring search terms within a single search episode, and centrality measures for search terms (degree and betweenness centrality) were calculated. Results: A total of 12 313 047 search activities (including 4 328 330 searches and 7 984 717 result views) conducted by 34 735 unique users within 977 160 unique patient EHRs were identified. In aggregate, users searched for 208 374 unique search terms and conducted a median of 4 searches (interquartile range, 1-28 searches). Of all 97 367 active EHR users, 34 735 (35.7%) conducted at least 1 search. However, of all 12 968 active EHR physician users, 9801 (75.6%) conducted at least 1 search, and of all 1908 active pharmacist users, 1402 (73.5%) conducted at least 1 search. The top 3 most commonly searched terms were statin (75 017 searches [1.7%]), colonoscopy (73 545 [1.7%]), and pft (54 990 [1.3%]). However, wide variation in top searches were noted across practitioner groups. Terms searched most often with another term in a single linked search episode included statin, lisinopril, colonoscopy, gabapentin, and aspirin. Conclusions and Relevance: Although physicians and pharmacists were the most active users of EHR searches, search volume and frequently searched terms varied considerably by and within user role. Further customization of the EHR interface may help leverage users' search content and patterns to improve targeted information retrieval.

Excess Readmission-Based Penalty: Is Arthroplasty Different From the Other Outcomes?

Whether factors not under a hospital's control affect readmissions remains intensely debated in the context of the Centers for Medicare & Medicaid Services' Hospital Readmission Reduction Program. This study aimed to evaluate the potential effects of poverty, race, and hospital volume on excess readmissions, with >3000 hospitals participating in "Hospital Compare." Correlations between excess readmission ratios for five eligible outcomes (including hip and knee arthroplasty) were assessed with the three area and hospital-level factors: poverty, race (percent of black population), and hospital volume (number of discharges). Correlation coefficients of the ratios with race were approximately r = 0.2, consistently larger than those with poverty (r = 0-0.1), and those with volume were r = 0 to -0.5. Hip and knee arthroplasty had unique findings: null correlation with poverty (r ≈ 0), largest variability, and strong monotonicity with volume (r ≈ -0.5). The percent of Hispanic population showed negligible correlations in secondary analysis. Penalty assessment and hospital profiling should consider areas with high percentages of black population and a small volume of hospitals and providers of hip and knee surgery. (Journal of Surgical Orthopaedic Advances 27(4):286-294, 2018).

Area-level poverty, race/ethnicity & dialysis star ratings.

The Centers for Medicare and Medicaid Services recently released a five star rating system as part of 'Dialysis Facility Compare' to help patients identify and choose high performing clinics in the US. Eight dialysis-related measures determine ratings. Little is known about the association between surrounding community sociodemographic characteristics and star ratings. Using data from the U.S. Census and over 6000 dialysis clinics across the country, we examined the association between dialysis clinic star ratings and characteristics of the local population: 1) proportion of population below the federal poverty level (FPL); 2) proportion of black individuals; and 3) proportion of Hispanic individuals, by correlation and regression analyses. Secondary analyses with Quality Incentive Program (QIP) scores and population characteristics were also performed. We observed a negligible correlation between star ratings and the proportion of local individuals below FPL; Spearman coefficient, R = -0.09 (p<0.0001), and a stronger correlation between star ratings and the proportion of black individuals; R = -0.21 (p<0.0001). Ordered logistic regression analyses yielded adjusted odds ratio of 0.91 (95% confidence interval [0.80-1.30], p = 0.12) and 0.55 ([0.48-0.63], p<0.0001) for high vs. low level of proportion below FPL and proportion of black individuals, respectively. In contrast, a near-zero correlation was observed between star ratings and the proportion of Hispanic individuals. Correlations varied substantially by country region, clinic profit status and clinic size. Analyses using clinic QIP scores provided similar results. Sociodemographic characteristics of the surrounding community, factors typically outside of providers' direct control, have varying levels of association with clinic dialysis star ratings.

Rethinking Thirty-Day Hospital Readmissions: Shorter Intervals Might Be Better Indicators Of Quality Of Care.

Public reporting and payment programs in the United States have embraced thirty-day readmissions as an indicator of between-hospital variation in the quality of care, despite limited evidence supporting this interval. We examined risk-standardized thirty-day risk of unplanned inpatient readmission at the hospital level for Medicare patients ages sixty-five and older in four states and for three conditions: acute myocardial infarction, heart failure, and pneumonia. The hospital-level quality signal captured in readmission risk was highest on the first day after discharge and declined rapidly until it reached a nadir at seven days, as indicated by a decreasing intracluster correlation coefficient. Similar patterns were seen across states and diagnoses. The rapid decay in the quality signal suggests that most readmissions after the seventh day postdischarge were explained by community- and household-level factors beyond hospitals' control. Shorter intervals of seven or fewer days might improve the accuracy and equity of readmissions as a measure of hospital quality for public accountability.