Background Early recognition and timely intervention significantly reduce sepsis-related mortality. Objective Describe the development, implementation and impact of an Early Warning and Response System (EWRS) for Sepsis. Design After tool derivation and validation, a pre/post study with multivariable adjustment measured impact. Setting Urban academic healthcare system Patients Adult non-ICU patients admitted to acute inpatient units from: 10/01–10/31/2011 for tool derivation, 06/06–07/05/2012 for tool validation, and 06/06–09/04/2012 and 06/06–09/04/2013 for the pre/post analysis. Intervention An EWRS in our electronic health record monitored laboratory values and vital signs in real time. If a patient had >= 4 predefined abnormalities at any one time, the provider, nurse, and rapid response coordinator were notified and performed an immediate bedside patient evaluation. Measurements Screen positive rates, test characteristics, predictive values and likelihood ratios; system utilization; and resulting changes in processes and outcomes. Results The tool’s screen positive, sensitivity, specificity, and positive and negative predictive values and likelihood ratios for our composite of intensive care unit (ICU) transfer, rapid response team call or death in the derivation cohort was 6%, 16%, 97%, 26%, 94%, 5.3 and 0.9, respectively. Validation values were similar. The EWRS resulted in a statistically significant increase in early sepsis care, ICU transfer, and sepsis documentation, and decreased sepsis mortality and increased discharge to home, although neither of these latter two findings reached statistical significance. Conclusions An automated prediction tool identified at risk patients and prompted a bedside evaluation resulting in more timely sepsis care, improved documentation, and a suggestion of reduced mortality.

Background Identification of patients at high risk for readmission is a crucial step toward improving care and reducing readmissions. The adoption of electronic health records (EHR) may prove important to strategies designed to risk stratify patients and introduce targeted interventions. Objective To develop and implement an automated prediction model integrated into our health system’s EHR that identifies on admission patients at high risk for readmission within 30 days of discharge. Design Retrospective and prospective cohort. Setting Healthcare system consisting of three hospitals. Patients All adult patients admitted from August 2009 to September 2012. Interventions An automated readmission risk flag integrated into the EHR. Measures Thirty-day all-cause and 7-day unplanned healthcare system readmissions. Results Using retrospective data, a single risk factor, ≥2 inpatient admissions in the past 12 months, was found to have the best balance of sensitivity (40%), positive predictive value (31%), and proportion of patients flagged (18%), with a c-statistic of 0.62. Sensitivity (39%), positive predictive value (30%), proportion of patients flagged (18%) and c-statistic (0.61) during the 12-month period after implementation of the risk flag were similar. There was no evidence for an effect of the intervention on 30-day all-cause and 7-day unplanned readmission rates in the 12-month period after implementation. Conclusions An automated prediction model was effectively integrated into an existing EHR and identified patients on admission who were at risk for readmission within 30 days of discharge.

IMPORTANCEStatin therapy is underused for many patients who could benefit. OBJECTIVE To evaluate the effect of passive choice and active choice interventions in the electronic health record (EHR) to promote guideline-directed statin therapy.DESIGN, SETTING, AND PARTICIPANTS Three-arm randomized clinical trial with a 6-month preintervention period and 6-month intervention. Randomization conducted at the cardiologist level at 16 cardiology practices in Pennsylvania and New Jersey. The study included 82 cardiologists and 11 693 patients. Data were analyzed between May 8, 2019, and January 9, 2020. INTERVENTIONSIn passive choice, cardiologists had to manually access an alert embedded in the EHR to select options to initiate or increase statin therapy. In active choice, an interruptive EHR alert prompted the cardiologist to accept or decline guideline-directed statin therapy. Cardiologists in the control group were informed of the trial but received no other interventions.MAIN OUTCOMES AND MEASURES Primary outcome was statin therapy at optimal dose based on clinical guidelines. Secondary outcome was statin therapy at any dose. RESULTSThe sample comprised 11 693 patients with a mean (SD) age of 63.8 (9.1) years; 58% were male (n = 6749 of 11 693), 66% were White (n = 7683 of 11 693), and 24% were Black (n = 2824 of 11 693). The mean (SD) 10-year atherosclerotic cardiovascular disease (ASCVD) risk score was 15.4 (10.0); 68% had an ASVCD clinical diagnosis. Baseline statin prescribing rates at the optimal dose were 40.3% in the control arm, 39.1% in the passive choice arm, and 41.2% in the active choice arm. In adjusted analyses, the change in statin prescribing rates at optimal dose over time was not significantly different from control for passive choice (adjusted difference in percentage points, 0.2; 95% CI, −2.9 to 2.8; P = .86) or active choice (adjusted difference in percentage points, 2.4; 95% CI, −0.6 to 5.0; P = .08). In adjusted analyses of the subset of patients with clinical ASCVD, the active choice intervention resulted in a significant increase in statin prescribing at optimal dose relative to control (adjusted difference in percentage points, 3.8; 95% CI, 1.0-6.4; P = .008). No other subset analyses were significant. There were no significant changes in statin prescribing at any dose for either intervention. CONCLUSIONS AND RELEVANCEThe passive choice and active choice interventions did not change statin prescribing. In the subgroup of patients with clinical ASCVD, the active choice intervention led to a small increase in statin prescribing at the optimal dose, which could inform the design or targeting of future interventions.

A minority of responders perceived the EWRS to be useful, likely related to the perception that most patients identified were stable. However, management was altered half the time after an alert. These results suggest further improvements to the system are needed to enhance clinician perception of the system's utility.