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.
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.
Authors: Saul Blecker; Stuart D Katz; Leora I Horwitz; Gilad Kuperman; Hannah Park; Alex Gold; David Sontag Journal: JAMA Cardiol Date: 2016-12-01 Impact factor: 14.676
Authors: Rebecca T Levinson; Nataraja Sarma Vaitinidin; Quinn S Wells; Jonathan D Mosley; Eric Farber-Eger; Dan M Roden; Thomas A Lasko Journal: Sci Rep Date: 2021-09-20 Impact factor: 4.379