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.

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.