Hospitals during recession and recovery: Vulnerable institutions and quality at risk

For generations, American hospitals have been considered recession-proof, but there is reason to believe the current economic crisis is an exception. Hospitals have shown declining financial margins and decreased admissions. The severe recession has adversely affected many hospitals' finances, creating a risk of closure and constraining plans for expansion. We believe there is also a risk of harming clinical quality, through decreased staffing that may limit the momentum of the hospital quality movement, especially in fiscally vulnerable institutions. We consider ways the federal government could aid hospitals by promoting hospital quality while providing employment. Journal of Hospital Medicine 2010;5:302–305. © 2010 Society of Hospital Medicine.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77425/1/654_ftp.pd

Conflicting measures of hospital quality: Ratings from “Hospital Compare” versus “Best Hospitals”

BACKGROUND In April 2005 the Centers for Medicare and Medicaid Services launched “Hospital Compare,” the first government-sponsored hospital quality scorecard. We compared the ranking of U.S. News and World Report 's “Best Hospitals” with Hospital Compare performance ratings. METHODS We examined Hospital Compare scores for core measures related to care for acute myocardial infarction (AMI), congestive heart failure (CHF), and community-acquired pneumonia (CAP). We calculated composite scores for the disease-specific sets of core measures and a composite combined score for the 14 core measures (across 3 diseases) and determined national score quartile cut points for each set. We then characterized the quartile distribution of Hospital Compare scores for the Best Hospitals for care of cardiac conditions and respiratory disorders in each year, as well as for the Best Hospital “Honor Roll” institutions. RESULTS AMI scores were available for 2165 hospitals, CHF scores for 3130, and CAP scores for 3462. In both 2004 and 2005, fewer than 50% of the Best Hospitals for cardiac care rated in the top quartile of Hospital Compare scores for AMI and CHF. Among the Best Hospitals for care of respiratory disorders, fewer than 15% scored in the top Hospital Compare quartile for CAP. Among Honor Roll institutions, only 5 (of 14 hospitals in 2004; of 16 in 2005) ranked in the top quartile for the combined core measure score. CONCLUSIONS Hospital Compare scores are frequently discordant with Best Hospital rankings, which is likely attributable to the markedly different methods each rating approach employs. Such discordance between major quality rating systems paints a conflicting picture of institutional performance for the public to interpret. Journal of Hospital Medicine 2007;2:128–134. © 2007 Society of Hospital Medicine.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56098/1/176_ftp.pd

Variation in Estimated Medicare Prescription Drug Plan Costs and Affordability for Beneficiaries Living in Different States

BACKGROUND: Medicare Part D prescription drug plans (PDPs) implemented in January 2006 are designed to improve beneficiaries’ access to pharmaceuticals and use market competition to yield affordable drug costs. Variations in estimated PDP costs for beneficiaries living in different states have not previously been characterized. OBJECTIVE: To describe variations in the estimated costs of PDPs (plan premium, copays, and coinsurance) within and across states. DESIGN: To estimate PDP costs based on 4 actual patient cases that exemplify common conditions and prescription drug combinations for Medicare beneficiaries, we used the online tool provided by the Centers for Medicare and Medicaid Services. MEASUREMENTS: Principal study outcomes included (a) variation across states in the estimated annual cost of the lowest-cost PDP for each case and (b) variation in the estimated affordability of the lowest-cost PDPs across states, based on cost-of-living-adjusted median income for zero-earner households. RESULTS: For all 4 patient cases, we found substantive within-state and between-state differences in the estimated costs of Medicare PDPs incurred by beneficiaries. The estimated annual costs to beneficiaries of the lowest-cost PDPs varied across states by as much as $320 for medications in the least expensive scenario, and by as much as$13,000 for the most expensive scenario. On average across states, a beneficiary with cost-of-living-adjusted median income would expect to spend 3%–28% of annual income to pay for medications in the lowest-cost PDPs in the 4 patient cases. The affordability of the lowest-cost plans varied across states, and for 2 of the 4 cases the lowest-cost PDP estimates were negatively correlated with cost-of-living-adjusted median income. CONCLUSIONS: Substantive differences in estimated PDP costs are evident across states for patients with common Medicare conditions. Importantly, the lowest-cost plans were not proportionally affordable with respect to state-specific cost-of-living-adjusted median income. Refinement of the Medicare drug program may be needed to improve national balance in PDP affordability for beneficiaries living in different states. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available for this article at http://dx.doi.org/10.1007/s11606-006-0018-y and is accessible for authorized users

2007 Focused update of the ACC/AHA 2004 guidelines for the management of patients with ST-elevation myocardial infarction: A report of the American College of Cardiology/American Heart Association task force on practice guidelines

Late-breaking clinical trials presented at the 2005 and 2006 annual scientific meetings of the ACC, AHA, and European Society of Cardiology, as well as selected other data, were reviewed by the standing guideline writing committee along with the parent Task Force and other experts to identify those trials and other key data that might impact guidelines recommendations. On the basis of the criteria/considerations noted above, recent trial data and other clinical information were considered important enough to prompt a focused update of the 2004 ACC/AHA Guidelines for the Management of Patients With ST-Elevation Myocardial Infarctio

Hospital medicine: Just the facts

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Hospitalist Handoffs: A Systematic Review and Task Force Recommendations

BACKGROUND: Handoffs are ubiquitous to Hospital Medicine and considered a vulnerable time for patient safety. PURPOSE: To develop recommendations for hospitalist handoffs during shift change and service change. DATA SOURCES: PubMed (through January 2007), AHRQ Patient Safety Network, white papers, and hand search of article bibliographies. STUDY SELECTION: Controlled studies evaluating interventions to improve in-hospital handoffs (n = 10). DATA EXTRACTION: Studies were abstracted for design, setting, target, outcomes (including patient, staff, or system level outcomes), and relevance to hospitalists. DATA SYNTHESIS: Although there were no studies of hospitalist handoffs, the existing literature from related disciplines and expert opinion support the use of a verbal handoff supplemented with written documentation in a structured format or technology solution. Technology solutions were associated with a reduction in preventable adverse events, improved satisfaction with handoff quality, and improved provider identification. Nursing studies demonstrate that supplementing verbal exchange with a written medium leads to improved retention of information. White papers characterized effective verbal exchange as focusing on ill patients and actions required, with time for questions and minimal interruptions. In addition, content should be updated daily to ensure communication of the latest clinical information. Using this literature, recommendations for hospitalist handoffs are presented with corresponding levels of evidence. Recommendations were reviewed by hospitalists at the Society of Hospital Medicine (SHM) Annual Meeting and by an interdisciplinary team of expert consultants and were endorsed by the SHM governing Board. CONCLUSIONS: The systematic review and resulting recommendations provide hospitalists a starting point from which to improve in-hospital handoffs

An operationally implementable model for predicting the effects of an infectious disease on a comprehensive regional healthcare system

An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.</p

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

Background: Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. Objective: We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system. Materials & methods: We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance. Results: Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85 Discussion: We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.</p