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IMPORTANCE: Chronic kidney disease (CKD) affects 37 million adults in the United States, and for patients with CKD, hypertension is a key risk factor for adverse outcomes, such as kidney failure, cardiovascular events, and death. OBJECTIVE: To evaluate a computerized clinical decision support (CDS) system for the management of uncontrolled hypertension in patients with CKD. DESIGN, SETTING, AND PARTICIPANTS: This multiclinic, randomized clinical trial randomized primary care practitioners (PCPs) at a primary care network, including 15 hospital-based, ambulatory, and community health center–based clinics, through a stratified, matched-pair randomization approach February 2021 to February 2022. All adult patients with a visit to a PCP in the last 2 years were eligible and those with evidence of CKD and hypertension were included. INTERVENTION: The intervention consisted of a CDS system based on behavioral economic principles and human-centered design methods that delivered tailored, evidence-based recommendations, including initiation or titration of renin-angiotensin-aldosterone system inhibitors. The patients in the control group received usual care from PCPs with the CDS system operating in silent mode. MAIN OUTCOMES AND MEASURES: The primary outcome was the change in mean systolic blood pressure (SBP) between baseline and 180 days compared between groups. The primary analysis was a repeated measures linear mixed model, using SBP at baseline, 90 days, and 180 days in an intention-to-treat repeated measures model to account for missing data. Secondary outcomes included blood pressure (BP) control and outcomes such as percentage of patients who received an action that aligned with the CDS recommendations. RESULTS: The study included 174 PCPs and 2026 patients (mean [SD] age, 75.3 [0.3] years; 1223 [60.4%] female; mean [SD] SBP at baseline, 154.0 [14.3] mm Hg), with 87 PCPs and 1029 patients randomized to the intervention and 87 PCPs and 997 patients randomized to usual care. Overall, 1714 patients (84.6%) were treated for hypertension at baseline. There were 1623 patients (80.1%) with an SBP measurement at 180 days. From the linear mixed model, there was a statistically significant difference in mean SBP change in the intervention group compared with the usual care group (change, −14.6 [95% CI, −13.1 to −16.0] mm Hg vs −11.7 [−10.2 to −13.1] mm Hg; P = .005). There was no difference in the percentage of patients who achieved BP control in the intervention group compared with the control group (50.4% [95% CI, 46.5% to 54.3%] vs 47.1% [95% CI, 43.3% to 51.0%]). More patients received an action aligned with the CDS recommendations in the intervention group than in the usual care group (49.9% [95% CI, 45.1% to 54.8%] vs 34.6% [95% CI, 29.8% to 39.4%]; P < .001). CONCLUSIONS AND RELEVANCE: These findings suggest that implementing this computerized CDS system could lead to improved management of uncontrolled hypertension and potentially improved clinical outcomes at the population level for patients with CKD. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03679247

Purpose: The purpose of the study was to examine differences among adult patients with diabetes who receive care through a telementoring model versus care at an academic specialty clinic on guideline-recommended diabetes care and self-management behaviors.Methods: Endocrinology-focused Extension for Community Healthcare Outcomes (ECHO Endo) patients completed surveys assessing demographics, access to care, health care quality, and self-management behaviors at enrollment and 1 year after program enrollment. Diabetes Comprehensive Care Center (DCCC) patients completed surveys at comparable time points.Results: At baseline, ECHO patients were less likely than DCCC patients to identify English as their primary language, have postsecondary education, and private insurance. One year postenrollment, ECHO patients visited their usual source of diabetic care more frequently. There were no differences in A1C testing or feet checking by health care professionals, but ECHO patients were less likely to report eye exams and smoking status assessment. ECHO and DCCC patients did not differ in consumption of high-fat foods and soda, physical activity, or home feet checks. ECHO patients were less likely to space carbohydrates evenly and test glucose levels and more likely to have smoked cigarettes.Conclusions: Endo ECHO is a suitable alternative to specialty care for patients in underserved communities with restricted access to specialty care. Results support the value of the Project ECHO telementoring model in addressing barriers to high-quality care for underserved communities.

Supplemental Digital Content is Available in the Text.Structures (context of care delivery) and processes (actions aimed at delivery care) are posited to drive patient outcomes. Despite decades of primary care research, there remains a lack of evidence connecting specific structures/processes to patient outcomes to determine which of the numerous recommended structures/processes to prioritize for implementation. The objective of this study was to identify structures/processes most commonly present in high-performing primary care practices for chronic care management and prevention. We conducted key informant interviews with a national sample of 22 high-performing primary care practices. We identified the 10 most commonly present structures/processes in these practices, which largely enable 2 core functions: mobilizing staff to conduct patient outreach and helping practices avoid gaps in care. Given the costs of implementing and maintaining numerous structures/processes, our study provides a starting list for providers to prioritize and for researchers to investigate further for specific effects on patient outcomes.

Large registries, administrative data, and the electronic health record (EHR) offer opportunities to identify patients with heart failure, which can be used for research purposes, process improvement, and optimal care delivery. Identification of cases is challenging because of the heterogeneous nature of the disease, which encompasses various phenotypes that may respond differently to treatment. The increasing availability of both structured and unstructured data in the EHR has expanded opportunities for cohort construction. This article reviews the current literature on approaches to identification of heart failure, and looks toward the future of machine learning, big data, and phenomapping.

Electronic health record (EHR) tools can improve prescribing of guideline-recommended therapies for heart failure with reduced ejection fraction (HFrEF), but their effectiveness may vary by physician workload.

IMPORTANCE: Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation. OBJECTIVE: To develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital. DESIGN, SETTING, AND PARTICIPANTS: We performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data. MAIN OUTCOMES AND MEASURES: Heart failure diagnosis based on discharge diagnosis and physician review of sampled medical records. RESULTS: A total of 47 119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively. CONCLUSIONS AND RELEVANCE: The problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.

This study sought to determine whether racial and ethnic differences exist among patients with similar access to care. We examined outcomes after heart failure hospitalization within a large municipal health system.

Introduction:Heart failure with reduced ejection fraction (HFrEF) is a leading cause of morbidity and mortality. However, shortfalls in prescribing of proven therapies, particularly mineralocorticoid receptor antagonist (MRA) therapy, account for several thousand preventable deaths per year nationwide. Electronic clinical decision support (CDS) is a potential low-cost and scalable solution to improve prescribing of therapies. However, the optimal timing and format of CDS tools is unknown. We developed two CDS tools to inform cardiologists of gaps in MRA therapy for patients with HFrEF and without contraindication to MRA therapy: 1) a best practice alert (BPA) that notifies cardiologists at the time of patient visit, and 2) an automated in-basket message that allows for review between visits (Figure).Objective:The BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) trial aims to compare the effectiveness of a BPA vs. in-basket message vs. usual care on the primary outcome of MRA prescribing.Methods:This is a pragmatic, cluster-randomized, three-arm intervention trial. Patients are randomized as a cluster at the level of their cardiologist in a 1:1:1 fashion (180 cardiologists included). The primary outcome is MRA prescription at the end of the study period, and secondary outcomes include prescribing of other GDMT, time to prescription, and provider-level changes in prescribing rates. We will require a total of 1,503 patients (501 per arm) to detect 10% improvement in prescribing with 80% power, and estimate a trial duration of 6 months.Implications:To our knowledge, no study has directly compared the efficacy of a BPA vs. in-basket message. If effective, our findings can be rapidly disseminated to improve morbidity and mortality for patients with HFrEF, and can also inform the development of future CDS interventions for other disease states.Trial registration:NCT05275920

Introduction:Shortfalls in prescribing of proven therapies for patients with heart failure and reduced ejection fraction (HFrEF), particularly for mineralocorticoid receptor antagonists (MRA), account for several thousand preventable deaths per year nationwide. Electronic clinical decision support (CDS) is a potential low-cost and scalable solution to improve prescribing, but optimal timing and format of CDS tools are unknown.Objective:We developed two CDS interventions to inform cardiologists of gaps in MRA therapy for patients with HFrEF and without contraindication to MRA therapy: 1) a best practice alert (BPA) that notifies cardiologists at the time of patient visit, and 2) an automated in-basket message that allows for review between visits.Methods:We designed CDS tools using an established CDS framework and findings from semi-structured interviews with cardiologists. We then pilot-tested both CDS tools at two cardiology practices for 12 weeks in a cross-over design, and further enhanced them based on additional semi-structured interviews.Results:Based on pilot testing (n=596 patients) and semi-structured interviews (n=11 cardiologists), in-basket messages were modified to reduce the number of patients listed, include future visits, and list date of next visit. The BPA was modified to improve noticeability, reduce extraneous information on guidelines, and include key information on contraindications. Final CDS interventions after incorporation of feedback are depicted in the Figure.Conclusions:Pilot testing and semi-structured interviews were instrumental in the development of CDS interventions and should be employed for other similar studies. We will compare the effectiveness of the BPA vs the in-basket message vs usual care on the primary outcome of MRA prescribing in The BETTER CARE-HF trial (Building Electronic Tools to Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) (Clinicaltrials.gov NCT05275920)

IMPORTANCE: Reduced rates of cardiac catheterization, percutaneous coronary intervention (PCI), and coronary artery bypass graft (CABG) are an unintended consequence of public reporting of cardiogenic shock outcomes in New York. OBJECTIVES: To evaluate whether the referral rates for cardiac catheterization, PCI, or CABG have improved in New York since cardiogenic shock was excluded from public reporting in 2008 and compare them with corresponding rates in Michigan, New Jersey, and California. DESIGN, SETTING, AND PARTICIPANTS: Patients with cardiogenic shock complicating acute myocardial infarction from 2002 to 2011 were identified using the National Inpatient Sample. Propensity score matching was used to assemble a cohort of patients with cardiogenic shock with similar baseline characteristics in New York and Michigan. MAIN OUTCOMES AND MEASURES: Percutaneous coronary intervention (primary outcome), invasive management (cardiac catheterization, PCI, or CABG), revascularization (PCI or CABG), and CABG were evaluated with reference to 3 calendar year periods: 2002-2005 (time 1: cardiogenic shock included in publicly reported outcomes), 2006-2007 (time 2: cardiogenic shock excluded on a trial basis), and 2008 and thereafter (time 3: cardiogenic shock excluded permanently) in New York and compared with Michigan. RESULTS: Among 2126 propensity score–matched patients representing 10 795 (weighted) patients with myocardial infarction complicated by cardiogenic shock in New York and Michigan, 905 (42.6%) were women and mean (SE) age was 69.5 (0.3) years. A significantly higher proportion of the patients underwent PCI (time 1 vs 2 vs 3: 31.1% vs 39.8% vs 40.7% [OR, 1.50; 95% CI, 1.12-2.01; P = .005 for time 3 vs 1]), invasive management (time 1 vs 2 vs 3: 59.7% vs 70.9% vs 73.8% [OR, 1.84; 95% CI, 1.37-2.47; P < .001 for time 3 vs 1]), or revascularization (43.1% vs 55.9% vs 56.3% [OR, 1.66; 95% CI, 1.26-2.20; P < .001 for time 3 vs 1]) after the exclusion of cardiogenic shock from public reporting in New York. However, during the same periods, a greater proportion of patients underwent PCI (time 1 vs 2 vs 3: 41.2% vs 52.6% vs 57.8% [OR, 1.93; 95% CI, 1.45-2.56; P < .001 for time 3 vs 1]), invasive management (time 1 vs 2 vs 3: 64.4% vs 80.5% vs 78.6% [OR, 2.01; 95% CI, 1.47-2.74; P < .001 for time 3 vs 1]), or revascularization (51.2% vs 65.8% vs 68.0% [OR, 2.00; 95% CI, 1.50-2.66; P < .001 for times 3 vs 1]) in Michigan. Results were largely similar in several sensitivity analyses comparing New York with New Jersey or California. CONCLUSIONS AND RELEVANCE: Although the rates of PCI, invasive management, and revascularization have increased substantially after the exclusion of cardiogenic shock from public reporting in New York, these rates remain consistently lower than those observed in other states without public reporting.

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Although hospice use may be increasing among heart failure patients, its association with both cost and intensity of care in this population has not been well examined.

BACKGROUND: Urgent, unplanned hospital readmissions are increasingly being used to gauge the quality of care. We reviewed urgent readmissions to determine which were potentially avoidable and compared rates of all-cause and avoidable readmissions. METHODS: In a multicentre, prospective cohort study, we reviewed all urgent readmissions that occurred within six months among patients discharged to the community from 11 teaching and community hospitals between October 2002 and July 2006. Summaries of the readmissions were reviewed by at least four practising physicians using standardized methods to judge whether the readmission was an adverse event (poor clinical outcome due to medical care) and whether the adverse event could have been avoided. We used a latent class model to determine whether the probability that each readmission was truly avoidable exceeded 50%. RESULTS: Of the 4812 patients included in the study, 649 (13.5%, 95% confidence interval [CI] 12.5%–14.5%) had an urgent readmission within six months after discharge. We considered 104 of them (16.0% of those readmitted, 95% CI 13.3%–19.1%; 2.2% of those discharged, 95% CI 1.8%–2.6%) to have had a potentially avoidable readmission. The proportion of patients who had an urgent readmission varied significantly by hospital (range 7.5%–22.5%; χ2= 92.9, p < 0.001); the proportion of readmissions deemed avoidable did not show significant variation by hospital (range 1.2%–3.7%; χ2= 12.5, p < 0.25). We found no association between the proportion of patients who had an urgent readmission and the proportion of patients who had an avoidable readmission (Pearson correlation 0.294; p = 0.38). In addition, we found no association between hospital rankings by proportion of patients readmitted and rankings by proportion of patients with an avoidable readmission (Spearman correlation coefficient 0.28, p = 0.41). INTERPRETATION: Urgent readmissions deemed potentially avoidable were relatively uncommon, comprising less than 20% of all urgent readmissions following hospital discharge. Hospital-specific proportions of patients who were readmitted were not related to proportions with a potentially avoidable readmission.

Albuminuria has been associated with cardiovascular risk, but the relationship of high-normal albuminuria to subsequent heart failure has not been well established.

Introduction:Advanced or “Stage D” heart failure (Adv HF) patients are at high risk of poor outcomes. However, recognition of Adv HF is limited as diagnostic criteria are complex. Administrative algorithms to identify Adv HF could enable better understanding of who has advanced HF and lead to enhanced therapeutics development, dissemination, and outcomes.Methods:In a population-based cohort of all Olmsted County, Minnesota residents with ≥1 HF billing code 2007-2017 (n=9881), we identified all patients with Adv HF (n=753) by applying the gold standard European Society of Cardiology Adv HF criteria via manual medical review by a HF cardiologist. The Adv HF index date was the date the patient first met all ESC criteria. We then developed candidate algorithms to identify Adv HF using only administrative data (billing codes and HF prescriptions or comorbidities known to affect HF outcomes), applied them to the entire HF cohort and assessed their ability to identify patients with Adv HF on or after their Adv HF index date.Results:A single hospitalization (Hsp) for HF or ventricular arrhythmias (VA, 2007-2017) identified all patients with Adv HF (sensitivity 100%), but the PPV was low (36.4%). Requiring more Hsp, other signs of Adv HF such as hyponatremia, acute kidney injury, hypotension or high-dose diuretic use decreased the sensitivity but improved specificity and PPV. As Adv HF was relatively uncommon, negative predictive values (NPV) were high regardless of algorithm characteristics.Conclusions:Algorithms using administrative data can identify patients with Adv HF with clinically useful accuracy.

Introduction:Some patients with heart failure (HF) will go on to develop advanced HF, characterized by severe HF symptoms despite attempts to optimize medical therapy. Our understanding of advanced HF at a population level remains limited. The goals of this study were to determine the risk of developing advanced HF in patients with newly diagnosed HF, identify risk factors for developing advanced HF, and evaluate the impact of advanced HF on outcomes.Methods:This was a population-based, retrospective cohort study of Olmsted County, Minnesota residents with a new clinical diagnosis of HF between 2007-2017. The primary outcome was development of advanced HF, defined using the 2018 European Society of Cardiology Criteria. Risk factors for development of advanced HF from the time of incident HF diagnosis were examined using cause-specific Cox proportional hazard regression models. The associations of development of advanced HF with risks of hospitalization and mortality were examined using Andersen-Gill and Cox models, respectively, with advanced HF treated as a time dependent covariate.Results:There were 4597 residents with incident HF from 2007 to 2017. The cumulative incidence of advanced HF was 6.0%, 9.1%, and 11.5% at 2, 4, and 6 years after incident HF diagnosis. Characteristics independently associated with development of advanced HF included older age, male sex, diabetes, chronic obstructive pulmonary disease, peripheral vascular disease, lower serum sodium, worse right ventricular function, left atrial enlargement, higher estimated filling pressures, and higher right ventricular systolic pressure (p<0.05 for each). Development of advanced HF was associated with increased risks of all-cause hospitalization (adjusted HR 3.00; 95% CI 2.65-3.41, p<0.001), HF hospitalization (HR 10.24, 95% CI 8.67-12.10), all-cause mortality (HR 5.00, 95% CI 4.46-5.59, p<0.001), and cardiovascular mortality (HR 7.84, 95% CI 6.74-9.12).Conclusions:In this population-based study, clinical, laboratory and echocardiographic characteristics at the time of incident HF diagnosis were associated with future risk of advanced HF. Development of advanced HF was associated with markedly increased morbidity and mortality.

Introduction:Guideline-directed medical therapy (GDMT) dramatically improves outcomes in heart failure with reduced ejection fraction (HFrEF). Our goal was to examine GDMT use in community patients with newly diagnosed HFrEF.Methods:We performed a population-based, retrospective cohort study of all Olmsted County, Minnesota residents with newly diagnosed HFrEF (EF≤40%) 2007-2017. We excluded patients with contraindications to medication initiation (allergy, intolerance, heart rate<50 for beta blockers, SBP <80mm Hg for beta blockers, ACEi/ARB/ARNI; high creatinine (>3 mg/dL ACEi/ARB/ARNI, >2.5 men or >2.0 mg/dL women for mineralocorticoid receptor antagonists, MRAs) or hyperkalemia (potassium >5 meQ/L for ACEi/ARB/ARNI, MRA). We examined use and peak dose achieved for beta blockers, HF beta blockers (metoprolol succinate, carvedilol, bisoprolol), ACEi/ARB/ARNI, and MRA in the first year after HFrEF diagnosis. We used logistic regression to evaluate predictors of GDMT use.Results:From 2007-2017, 1160 patients were diagnosed with HFrEF (mean age 69.7 years, 65.6% men). Most eligible patients received beta blockers (92.1%) and ACEi/ARB/ARNI (86.5%) in the first year after HFrEF. However, only 63.6% of patients were treated with a HF beta blocker, and most did not receive MRAs (82.6%). The percentage of treated patients reaching medication target doses was 20.5% for HF beta blockers, 25.3% for ACEi/ARB/ARNI, and 11.2% for MRA. Compared to patients not seen in an HF clinic, patients seen in an HF clinic (n=380, 32.8%) were at greater odds of receiving beta blockers (OR 3.85, 95% CI 1.79-8.33); HF beta blockers (OR 3.85, 95% CI 2.63-5.26); ACEi/ARB/ARNIs (OR 3.85, 95% CI 2.17-6.67); and MRAs (OR 3.03, 95% CI 2.08-4.35). Other independent predictors of GDMT use included younger age (beta blockers, ACEi/ARB/ARNI), male gender (MRAs), higher SBP (beta blockers, ACEi/ARB/ARNI), lower EF (HF beta blockers), higher BMI (MRAs), and diabetes (ACEi/ARB/ARNI, p<0.05 for each).Conclusions:In this population-based study, most patients with newly diagnosed HFrEF received beta blockers and ACEi/ARB/ARNIs, but goal doses were usually not achieved. GDMT use was much higher in patients referred to an HF clinic.

Background:National registries reveal significant gaps in medical therapy for patients with heart failure and reduced ejection fraction (HFrEF), but may not completely capture the population eligible for therapy. We developed an electronic health record-based algorithm to identify eligible HFrEF patients, and used this to assess prescribing in a large, diverse, urban health system.Methods:In this cross-sectional study of NYU Langone Health outpatients (>350 sites) with EF≤40% on echocardiogram and clinic visit from 3/1/2019-2/29/2020, we assessed contraindication to and prescription of the following: beta-blocker (BB), angiotensin converting enzyme inhibitor (ACE-I)/angiotensin receptor blocker (ARB)/angiotensin receptor neprilysin inhibitor (ARNI), and mineralocorticoid receptor antagonist (MRA).Results:We electronically identified 2,732 patients for analysis. Among those eligible for each medication class, 84.8% and 79.7% were appropriately prescribed BB and ACE-I/ARB/ARNI, respectively, while only 23.9% and 22.7% were appropriately prescribed MRA and ARNI, respectively. In adjusted models (Table), younger age, cardiology visit, and lower EF were associated with increased prescribing. Medicaid insurance was associated with increased prescribing of ARNI and Black race with prescribing of MRA.Conclusions:We observed significant gaps in MRA and ARNI therapy for HFrEF patients. Medicaid insurance was associated with increased ARNI use, possibly due to lack of prior authorization requirements under New York State Medicaid policy. Our findings support system- and policy-level interventions to increase use of these life-saving therapies.

Some patients with heart failure (HF) progress to advanced HF, characterized by debilitating HF symptoms refractory to therapy. There are limited data on the epidemiology and outcomes of patients with advanced HF.

Background:Evidence-based therapy for heart failure remains underutilized at hospital discharge, particularly for patients with a secondary diagnosis of heart failure. We developed a clinical decision support (CDS) tool to recommend an ACE inhibitor during hospitalization. The CDS was designed to be implemented in both interruptive and non-interruptive versions. The purpose of this study was to compare the effectiveness of the two CDS implementation approaches.Methods:We implemented CDS recommending prescribing an ACE inhibitor for patients with an ejection fraction?40% and no contraindications to therapy. Hospitalizations were pseudo-randomized to the interruptive (pop-up) and non-interruptive (passive checklist) version of the CDS based on even or odd medical record number. We compared discharge prescription of an ACE inhibitor or ARB for these two implementation approaches. We also assessed adoption and implementation fidelity of the CDS.Results:Hospitalizations eligible for the interruptive alert (n=465) had higher rates of ACE inhibitor or ARB use than hospitalizations eligible for the non-interruptive alert (n=493), with rates of 79.6% vs. 74.2%, respectively (p=0.05). ACE inhibitor or ARB prescriptions were higher for patients eligible for the interruptive versus non-interruptive alert among hospitalizations with a secondary diagnosis of heart failure (79.8% vs. 73.4%; p=0.05) but not among those with a principal heart failure diagnosis (p=0.49). As compared to hospitalizations receiving the non-interruptive CDS, those receiving the interruptive alert were more likely to have had: at least one alert that was not dismissed (40.6% vs. 13.1%, p<0.001), contraindications reported (33.1% vs 11.3%, p<0.001), and an inpatient ACE inhibitor ordered within 12 hours of the alert (17.6% vs 10.3%, p<0.01). The response rate for the interruptive alert was 1.7%, and a median (25th, 75thpercentile) of 14 (5,32) alerts were triggered per hospitalization.Conclusions:A CDS implemented as an interruptive alert was associated with improved quality of care for heart failure as compared to the CDS implemented as a passive alert. Whether the comparative benefit was worth the high burden of interruptive alerts deserves further consideration.

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Background:Although the majority of hospitalizations for heart failure (HF) originate in the emergency department (ED), many of these patients might be adequately treated and released in the ED or managed for a short period in an observation unit. Both ED and observation management have been shown to reduce costs and avoid the penalties related to rehospitalization. The purpose of this study was to examine trends in ED visits for HF and disposition following these visits. Because of increasing policy pressure to reduce rehospitalization for HF, we hypothesized that the number of HF patients hospitalized by ED providers decreased over time with a concurrent increase in admissions to the observation unit. We further hypothesized that the overall number of ED visits for HF decreased as a result of improved therapy for HF the last two decades.

Background:Hospital quality performance measures for heart failure (HF) target patients with a principal discharge diagnosis of HF. However, patients with chronic HF are commonly admitted for other causes and are recommended treatments such as angiotensin converting enzyme (ACE) inhibitors for left ventricular (LV) systolic dysfunction. The purpose of this study was to assess the quality of care for HF patients who are hospitalized for all causes.