Incorporating harms into the weighting of the revised Agency for Healthcare Research and Quality Patient Safety for Selected Indicators Composite (Patient Safety Indicator 90).

OBJECTIVE: To reweight the Agency for Healthcare Research and Quality Patient Safety for Selected Indicators Composite (Patient Safety Indicator [PSI] 90) from weights based solely on the frequency of component PSIs to those that incorporate excess harm reflecting patients' preferences for outcome-related health states. DATA SOURCES: National administrative and claims data involving hospitalizations in nonfederal, nonrehabilitation, acute care hospitals. STUDY DESIGN: We estimated the average excess aggregate harm associated with the occurrence of each component PSI using a cohort sample for each indicator based on denominator-eligible records. We used propensity scores to account for potential confounding in the risk models for each PSI and weighted observations to estimate the "average treatment effect in the treated" for those with the PSI event. We fit separate regression models for each harm outcome. Final PSI weights reflected both the disutilities and the frequencies of the harms. DATA COLLECTION/EXTRACTION METHODS: We estimated PSI frequencies from the 2012 Healthcare Cost and Utilization Project State Inpatient Databases with present on admission data and excess harms using 2012-2013 Centers for Medicare & Medicaid Services Medicare Fee-for-Service data. PRINCIPAL FINDINGS: Including harms in the weighting scheme changed individual component weights from the original frequency-based weighting. In the reweighted composite, PSIs 11 ("Postoperative Respiratory Failure"), 13 ("Postoperative Sepsis"), and 12 ("Perioperative Pulmonary Embolism or Deep Vein Thrombosis") contributed the greatest harm, with weights of 29.7%, 21.1%, and 20.4%, respectively. Regarding reliability, the overall average hospital signal-to-noise ratio for the reweighted PSI 90 was 0.7015. Regarding discrimination, among hospitals with greater than median volume, 34% had significantly better PSI 90 performance, and 41% had significantly worse performance than benchmark rates (based on percentiles). CONCLUSIONS: Reformulation of PSI 90 with harm-based weights is feasible and results in satisfactory reliability and discrimination, with a more clinically meaningful distribution of component weights.

Emergency department transfers and transfer relationships in United States hospitals.

OBJECTIVES: The objective was to describe transfers out of hospital-based emergency departments (EDs) in the United States and to identify different characteristics of sending and receiving hospitals, travel distance during transfer, disposition on arrival to the second hospital, and median number of transfer partners among sending hospitals. METHODS: Emergency department records were linked at transferring hospitals to ED and inpatient records at receiving hospitals in nine U.S. states using the 2010 Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases and State Inpatient Databases, the American Hospital Association Annual Survey, and the Trauma Information Exchange Program. Using the Clinical Classification Software (CCS) to categorize conditions, the 50 disease categories with the highest transfer rates were studied, and these were then placed into nine clinical groups. Records were included where both sending and receiving records were available; these data were tabulated to describe ED transfer patterns, hospital-to-hospital distances, final patient disposition, and number of transfer partners. RESULTS: A total of 97,021 ED transfer encounters were included in the analysis from the 50 highest transfer rate disease categories. Among these, transfer rates ranged from 1% to 13%. Circulatory conditions made up about half of all transfers. Receiving hospitals were more likely to be nonprofit, teaching, trauma, and urban and have more beds with greater specialty coverage and more advanced diagnostic and therapeutic resources. The median transfer distance was 23 miles, with 25% traveling more than 40 to 50 miles. About 8% of transferred encounters were discharged from the second ED, but that varied from 0.6% to 53% across the 50 conditions. Sending hospitals had a median of seven transfer partners across all conditions and between one and four per clinical group. CONCLUSIONS: Among high-transfer conditions in U.S. EDs, patients are often transferred great distances, more commonly to large teaching hospitals with greater resources. The large number of transfer partners indicates a possible lack of stable transfer relationships between U.S. hospitals.

The transfer instability index: a novel metric of emergency department transfer relationships.

OBJECTIVES: In this study, the objective was to characterize emergency department (ED) transfer relationships and study the factors that predict the stability of those relationships. A metric is derived for ED transfer relationships that may be useful in assessing emergency care regionalization and as a resource for future emergency medicine research. METHODS: Emergency department records at transferring hospitals were linked to ED and inpatient records at receiving hospitals in nine U.S. states using the 2010 Healthcare Cost and Utilization Project State Emergency Department Databases and State Inpatient Databases, the American Hospital Association Annual Survey, and the Trauma Information Exchange Program. Using the Clinical Classification Software to categorize conditions, high transfer rate conditions were placed into nine clinical groups. The authors created a new measure, the "transfer instability index," which estimates the effective number of "transfer partners" for each sending ED: this is designed to measure the stability of outgoing transfer relationships, where higher values of the index indicate less stable relationships. The index provides a measure of how many hospitals a transferring hospital sends its patients to (weighted by how often each transfer partner is used). Regression was used to analyze factors associated with higher values of the index. RESULTS: Sending hospitals had a median of 3.5 effective transfer partners across all conditions. The calculated transfer instability indices varied from 1 to 2.4 across disease categories. In general, higher index values were associated with treating a higher proportion of publicly insured patients: 10 and 12% increases in the Medicare and Medicaid share of ED encounters, respectively, were associated with 10 and 14% increases in the effective number of transfer partners. This public insurance effect held while studying all conditions together as well as within individual disease categories, such as cardiac, neurologic, and traumatic conditions. CONCLUSIONS: United States EDs that transfer patients to other hospitals often have multiple transfer partners. The stability of the transfer relationship, assessed by the transfer instability index, differs by condition. Less stable transfer relationships (i.e., hospitals with greater numbers of transfer partners) were more common in EDs with higher proportions of publicly insured patients.

How often are potential patient safety events present on admission?

BACKGROUND: Data fields that capture whether diagnoses are present on admission (POA)--distinguishing comorbidities from potential in-hospital complications--became part of the Uniform Bill for hospital claims in 2007. The AHRQ Patient Safety Indicators (PSIs) were initially developed as measures of potential patient safety problems that use routine administrative data without POA information. The impact of adding POA information to PSIs was examined. METHODS: Data were used from California (CA) and New York (NY) Healthcare Cost and Utilization Project (HCUP) state inpatient databases for 2003, which include POA codes. Analysis was limited to 13 of 20 PSIs for which POA information was relevant, such as complications of anesthesia, accidental puncture, and sepsis. RESULTS: In New York, 17% of cases revealed suspect POA coding, compared with 1%-2% in California. After suspect records were excluded, 92%-93% of secondary diagnoses in both CA and NY were POA. After incorporating POA information, most cases of decubitus ulcer (86%-89%), postoperative hip fracture (74%-79%), and postoperative pulmonary embolism/deep vein thrombosis (54%-58%) were no longer considered in-hospital patient safety events. DISCUSSION: Three of 13 PSIs appear not to be valid measures of in-hospital patient safety events, but the remaining 10 appear to be potentially useful measures even in the absence of POA codes.