An Electronic Medical Record Report Improves Identification of Hospitalized Patients With Heart Failure.

BACKGROUND: Early identification of inpatients with heart failure (HF) may help to reduce readmissions. We found that many patients identified by our coding team as having a primary diagnosis of HF were not identified by our clinical team. We hypothesized that an electronic medical record (EMR)-based report would improve identification of hospitalized patients eventually diagnosed with HF. METHODS AND RESULTS: We constructed an automated EMR-based tool to allow our team to identify patients with HF more quickly and accurately. We selected criteria that could potentially identify the cohort as patients with an exacerbation of HF. We performed monthly reconciliations, comparing the list of patients identified by our coding team as having a primary diagnosis of HF versus the patients identified by our team as having HF. We reduced a baseline 17% discrepancy of patients coded as HF but not identified by our team to 9.5% in the year after implementation of our screening tool (P = .006), and to 5.4% in the next year (P = .03); 56% of patients that were identified as having HF by our CNS team were coded as having HF, versus 49% in the 2 years after implementation (P = .15). Thirty-day readmission rates to our hospital decreased from 16% to 11% (P = .029). CONCLUSIONS: An EMR-based approach significantly improved identification of patients discharged with a primary diagnosis of HF. Future investigations should determine whether early identification of inpatients with HF can independently lower readmissions, and whether this strategy can successfully identify outpatients with HF.

An informatics-based approach to reducing heart failure all-cause readmissions: the Stanford heart failure dashboard.

BACKGROUND: Reduction of 30-day all-cause readmissions for heart failure (HF) has become an important quality-of-care metric for health care systems. Many hospitals have implemented quality improvement programs designed to reduce 30-day all-cause readmissions for HF. Electronic medical record (EMR)-based measures have been employed to aid in these efforts, but their use has been largely adjunctive to, rather than integrated with, the overall effort. OBJECTIVES: We hypothesized that a comprehensive EMR-based approach utilizing an HF dashboard in addition to an established HF readmission reduction program would further reduce 30-day all-cause index hospital readmission rates for HF. METHODS: After establishing a quality improvement program to reduce 30-day HF readmission rates, we instituted EMR-based measures designed to improve cohort identification, intervention tracking, and readmission analysis, the latter 2 supported by an electronic HF dashboard. Our primary outcome measure was the 30-day index hospital readmission rate for HF, with secondary measures including the accuracy of identification of patients with HF and the percentage of patients receiving interventions designed to reduce all-cause readmissions for HF. RESULTS: The HF dashboard facilitated improved penetration of our interventions and reduced readmission rates by allowing the clinical team to easily identify cohorts with high readmission rates and/or low intervention rates. We significantly reduced 30-day index hospital all-cause HF readmission rates from 18.2% at baseline to 14% after implementation of our quality improvement program ( P = .045). Implementation of our EMR-based approach further significantly reduced 30-day index hospital readmission rates for HF to 10.1% ( P for trend = .0001). Daily time to screen patients decreased from 1 hour to 15 minutes, accuracy of cohort identification improved from 83% to 94.6% ( P = .0001), and the percentage of patients receiving our interventions, such as patient education, also improved significantly from 22% to 100% over time ( P < .0001). CONCLUSIONS: In an institution with a quality improvement program already in place to reduce 30-day readmission rates for HF, an EMR-based approach further significantly reduced 30-day index hospital readmission rates.

Modeling the impact of increased adherence to asthma therapy.

BACKGROUND: Nonadherence to medications occurs in up to 70% of patients with asthma. The effect of improving adherence is not well quantified. We developed a mathematical model with which to assess the population-level effects of improving medication prescribing and adherence for asthma. METHODS: A mathematical model, calibrated to clinical trial data from the U.S. NHLBI-funded SOCS trial and validated using data from the NHLBI SLIC trial, was used to model the effects of increased prescribing and adherence to asthma controllers. The simulated population consisted of 4,930 individuals with asthma, derived from a sample the National Asthma Survey. Main outcomes were controller use, reliever use, unscheduled doctor visits, emergency department (ED) visits, and hospitalizations. RESULTS: For the calibration, simulated outcomes agreed closely with SOCS trial outcomes, with treatment failure hazard ratios [95% confidence interval] of 0.92 [0.58-1.26], 0.97 [0.49-1.45], and 1.01 [0-1.87] for simulation vs. trial in the in placebo, salmeterol, and triamcinolone arms, respectively. For validation, simulated outcomes predicted mid- and end-point treatment failure rates, hazard ratios 1.21 [0.08-2.34] and 0.83 [0.60-1.07], respectively, for patients treated with salmeterol/triamcinolone during the first half of the SLIC study and salmeterol monotherapy during the second half. The model performed less well for patients treated with salmeterol/triamcinolone during the entire study duration, with mid- and end-point hazard ratios 0.83 [0.00-2.12] and 0.37 [0.10-0.65], respectively. Simulation of optimal adherence and prescribing indicated that closing adherence and prescription gaps could prevent as many as nine million unscheduled doctor visits, four million emergency department visits, and one million asthma-related hospitalizations each year in the U.S. CONCLUSIONS: Improvements in medication adherence and prescribing could have a substantial impact on asthma morbidity and healthcare utilization.