Impact of integrating genomic data into the electronic health record on genetics care delivery.

PURPOSE: Integrating genomic data into the electronic health record (EHR) is key for optimally delivering genomic medicine. METHODS: The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative that has successfully linked orders and results from genetic testing laboratories with discrete genetic data in the EHR. We quantified the use of the genomic data within the EHR, performed a time study with genetic counselors, and conducted key informant interviews with PGI members to evaluate the effect of the PGI's efforts on genetics care delivery. RESULTS: The PGI has interfaced with 4 genetic testing laboratories, resulting in the creation of 420 unique computerized genetic testing orders that have been used 4073 times to date. In a time study of 96 genetic testing activities, EHR use was associated with significant reductions in time spent ordering (2 vs 8 minutes, P < .001) and managing (1 vs 5 minutes, P < .001) genetic results compared with the use of online laboratory-specific portals. In key informant interviews, multidisciplinary collaboration and institutional buy-in were identified as key ingredients for the PGI's success. CONCLUSION: The PGI's efforts to integrate genomic medicine into the EHR have substantially streamlined the delivery of genomic medicine.

Effect of Nudges to Clinicians, Patients, or Both to Increase Statin Prescribing: A Cluster Randomized Clinical Trial.

Importance: Statins reduce the risk of major adverse cardiovascular events, but less than one-half of individuals in America who meet guideline criteria for a statin are actively prescribed this medication. Objective: To evaluate whether nudges to clinicians, patients, or both increase initiation of statin prescribing during primary care visits. Design, Setting, and Participants: This cluster randomized clinical trial evaluated statin prescribing of 158 clinicians from 28 primary care practices including 4131 patients. The design included a 12-month preintervention period and a 6-month intervention period between October 19, 2019, and April 18, 2021. Interventions: The usual care group received no interventions. The clinician nudge combined an active choice prompt in the electronic health record during the patient visit and monthly feedback on prescribing patterns compared with peers. The patient nudge was an interactive text message delivered 4 days before the visit. The combined nudge included the clinician and patient nudges. Main Outcomes and Measures: The primary outcome was initiation of a statin prescription during the visit. Results: The sample comprised 4131 patients with a mean (SD) age of 65.5 (10.5) years; 2120 (51.3%) were male; 1210 (29.3%) were Black, 106 (2.6%) were Hispanic, 2732 (66.1%) were White, and 83 (2.0%) were of other race or ethnicity, and 933 (22.6%) had atherosclerotic cardiovascular disease. In unadjusted analyses during the preintervention period, statins were prescribed to 5.6% of patients (105 of 1876) in the usual care group, 4.8% (97 of 2022) in the patient nudge group, 6.0% (104 of 1723) in the clinician nudge group, and 4.7% (82 of 1752) in the combined group. During the intervention, statins were prescribed to 7.3% of patients (75 of 1032) in the usual care group, 8.5% (100 of 1181) in the patient nudge group, 13.0% (128 of 981) in the clinician nudge arm, and 15.5% (145 of 937) in the combined group. In the main adjusted analyses relative to usual care, the clinician nudge significantly increased statin prescribing alone (5.5 percentage points; 95% CI, 3.4 to 7.8 percentage points; P = .01) and when combined with the patient nudge (7.2 percentage points; 95% CI, 5.1 to 9.1 percentage points; P = .001). The patient nudge alone did not change statin prescribing relative to usual care (0.9 percentage points; 95% CI, -0.8 to 2.5 percentage points; P = .32). Conclusions and Relevance: Nudges to clinicians with and without a patient nudge significantly increased initiation of a statin prescription during primary care visits. The patient nudge alone was not effective. Trial Registration: ClinicalTrials.gov Identifier: NCT04307472.

Effect of Passive Choice and Active Choice Interventions in the Electronic Health Record to Cardiologists on Statin Prescribing: A Cluster Randomized Clinical Trial.

Importance: Statin 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. Interventions: In 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. Results: The 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 Relevance: The 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. Trial Registration: ClinicalTrials.gov Identifier: NCT03271931.

Development, implementation, and impact of an automated early warning and response system for sepsis.

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 preimplementation/postimplementation study with multivariable adjustment measured impact. SETTING: Urban academic healthcare system. PATIENTS: Adult non-ICU patients admitted to acute inpatient units from October 1, 2011 to October 31, 2011 for tool derivation, June 6, 2012 to July 5, 2012 for tool validation, and June 6, 2012 to September 4, 2012 and June 6, 2013 to September 4, 2013 for the preimplementation/postimplementation 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 single 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 2 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.

Clinician Perception of the Effectiveness of an Automated Early Warning and Response System for Sepsis in an Academic Medical Center.

RATIONALE: We implemented an electronic early warning and response system (EWRS) to improve detection of and response to severe sepsis. Sustainability of such a system requires stakeholder acceptance. We hypothesized that clinicians receiving such alerts perceive them to be useful and effective. OBJECTIVES: To survey clinicians after EWRS notification about perceptions of the system. METHODS: For a 6-week study period 1 month after EWRS implementation in a large tertiary referral medical center, bedside clinicians, including providers (physicians, advanced practice providers) and registered nurses (RNs), were surveyed confidentially within 2 hours of an alert. MEASUREMENTS AND MAIN RESULTS: For the 247 alerts that triggered, 127 providers (51%) and 105 RNs (43%) completed the survey. Clinicians perceived most patients as stable before and after the alert. Approximately half (39% providers, 48% RNs) felt the alert provided new information, and about half (44% providers, 56% RNs) reported changes in management as a result of the alert, including closer monitoring and additional interventions. Over half (54% providers, 65% RNs) felt the alert was appropriately timed. Approximately one-third found the alert helpful (33% providers, 40% RNs) and fewer felt it improved patient care (24% providers, 35% RNs). CONCLUSIONS: 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.

The readmission risk flag: using the electronic health record to automatically identify patients at risk for 30-day readmission.

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 3 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.