Estimating state-specific population-based hospitalization rates from in-state hospital discharge data.

OBJECTIVE: To develop weights to estimate state population-based hospitalization rates for all residents of a state using only data from in-state hospitals which exclude residents treated in other states. DATA SOURCES AND STUDY SETTING: Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project (HCUP), State Inpatient Databases (SID), 2018-2019, 47 states+DC. STUDY DESIGN: We identified characteristics for patients hospitalized in each state differentiating movers (discharges for patients hospitalized outside state of residence) from stayers (discharges for patients hospitalized in state of residence) and created weights based on 2018 data informed by these characteristics. We calculated standard errors using a sampling framework and compared weight-based estimates against complete observed values for 2019. DATA COLLECTION/EXTRACTION METHODS: SID are based on administrative billing records collected by hospitals, shared with statewide data organizations, and provided to HCUP. PRINCIPAL FINDINGS: Of 34,186,766 discharged patients in 2018, 4.2% were movers. A higher share of movers (vs. stayers) lived in state border and rural counties; a lower share had discharges billed to Medicaid or were hospitalized for maternal/neonatal services. The difference between 2019 observed and estimated total discharges for all included states and DC was 9402 (mean absolute percentage error = 0.2%). We overestimated discharges with an expected payer of Medicaid, from the lowest income communities, and for maternal/neonatal care. We underestimated discharges with an expected payer of private insurance, from the highest income communities, and with injury diagnoses and surgical services. Estimates for most subsets were not within a 95% confidence interval, likely due to factors impossible to account for (e.g., hospital closures/openings, shifting consumer preferences). CONCLUSIONS: The weights offer a practical solution for researchers with access to only a single state's data to account for movers when calculating population-based hospitalization rates.

Methods for estimating the cost of treat-and-release emergency department visits.

OBJECTIVE: To evaluate and compare approaches to estimating the service delivery cost of emergency department (ED) visits from total charge data only. DATA SOURCES: The 2013-2017 Healthcare Cost and Utilization Project's (HCUP) State Emergency Department Databases (SEDD) and the Centers for Medicare and Medicaid Services Healthcare Cost Report Information System (HCRIS) public use files. STUDY DESIGN: Compare a baseline approach (requiring cost-center-level charge detail) and four alternative methods (relying on total charges only) for estimating ED visit costs. Estimation errors are calculated after applying each method to a sample of ED visits, treating estimates from the baseline approach as the "true" cost. Performance metrics are calculated at the visit and hospital levels. DATA COLLECTION/EXTRACTION METHODS: The charges, revenue center codes, and patient/hospital characteristics were extracted from the SEDD. Detailed costs and charges were extracted from HCRIS public use files. PRINCIPAL FINDINGS: Baseline ("true") ED visit costs increased from $383 to $420 per visit between 2013 and 2017. Three methods performed comparatively well estimating mean cost per visit. The method using an overall cost-to-charge ratio (CCR) for all ancillary cost centers without regression adjustment (ANC-CCR) performed the worst, overestimating "true" costs by $63-$113 per visit. The other three methods, which used CCRs computed from selected cost centers, exhibited much smaller bias, with two of the methods yielding estimates within $2 of the "true" cost in 2017. Compared with ANC-CCR, the other three methods had more compact estimation error distributions. The estimated mean visit costs from all four methods have relatively small statistical variance, with 95% confidence intervals for mean cost in a hospital with 25,000 ED visits ranging between $4 and $7. CONCLUSIONS: When cost-center-level charge detail for ED visits is unavailable, alternative methods relying on total ED charges can estimate ED service costs for patient and hospital segments.

The Workforce wellness index: a method for valuing US workers' health.

OBJECTIVE: To devise a methodology to create a single health risk-cost score that can be applied to health risk assessment survey data and account for the medical costs associated with modifiable risks. METHODS: We linked person-level health risk assessment data with medical benefit eligibility and claims data for 341,650 workers for the period 2005 to 2010 and performed multivariate analyses to estimate costs associated with high risks. We used the estimated costs and risk prevalence rates to create a composite Workforce Wellness Index (WWI) score. RESULTS: Increasing obesity rates among employees was found to be the most important contributor to increased health care spending and the main reason the WWI score worsened over time. CONCLUSIONS: Employers that address employees' health risk factors may be able to reduce their medical spending and achieve an improvement in their WWI scores.

Association of a clinical knowledge support system with improved patient safety, reduced complications and shorter length of stay among Medicare beneficiaries in acute care hospitals in the United States.

BACKGROUND: Electronic clinical knowledge support systems have decreased barriers to answering clinical questions but there is little evidence as to whether they have an impact on health outcomes. METHODS: We compared hospitals with online access to UpToDate with other acute care hospitals included in the Thomson 100 Top Hospitals Database (Thomson database). Metrics used in the Thomson database differentiate hospitals on a variety of performance dimensions such as quality and efficiency. Prespecified outcomes were risk-adjusted mortality, complications, the Agency of Healthcare Research and Quality Patient Safety Indicators, and hospital length of stay among Medicare beneficiaries. Linear regression models were developed that included adjustment for hospital region, teaching status, and discharge volume. RESULTS: Hospitals with access to UpToDate (n=424) were associated with significantly better performance than other hospitals in the Thomson database (n=3091) on risk-adjusted measures of patient safety (P=0.0163) and complications (P=0.0012) and had significantly shorter length of stay (by on average 0.167 days per discharge, 95% confidence interval 0.081-0.252 days, P<0.0001). All of these associations correlated significantly with how much UpToDate was used at each hospital. Mortality was not significantly different between UpToDate and non-UpToDate hospitals. LIMITATIONS: The study was retrospective and observational and could not fully account for additional features at the included hospitals that may also have been associated with better health outcomes. CONCLUSIONS: An electronic clinical knowledge support system (UpToDate was associated with improved health outcomes and shorter length of stay among Medicare beneficiaries in acute care hospitals in the United States. Additional studies are needed to clarify whether use of UpToDate is a marker for the better performance, an independent cause of it, or a synergistic part of other quality improvement characteristics at better-performing hospitals.