The accuracy of present-on-admission reporting in administrative data.
Health Ser…

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Effect of evidence-based acute pain management practices on inpatient costs.

Health Serv Res. 2009 Feb;44(1):245-63

Authors: Brooks JM, Titler MG, Ardery G, Herr K

OBJECTIVES: To estimate hospital cost changes associated with a behavioral intervention designed to increase the use of evidence-based acute pain management practices in an inpatient setting and to estimate the direct effect that changes in evidence-based acute pain management practices have on inpatient cost. DATA SOURCES/STUDY SETTING: Data from a randomized “translating research into practice” (TRIP) behavioral intervention designed to increase the use of evidence-based acute pain management practices for patients hospitalized with hip fractures. STUDY DESIGN: Experimental design and observational “as-treated” and instrumental variable (IV) methods. DATA COLLECTION/EXTRACTION METHODS: Abstraction from medical records and Uniform Billing 1992 (UB92) discharge abstracts. PRINCIPAL FINDINGS: The TRIP intervention cost on average $17,714 to implement within a hospital but led to cost savings per inpatient stay of more than $1,500. The intervention increased the cost of nursing services, special operating rooms, and therapy services per inpatient stay, but these costs were more than offset by cost reductions within other cost categories. “As-treated” estimates of the effect of changes in evidence-based acute pain management practices on inpatient cost appear significantly underestimated, whereas IV estimates are statistically significant and are distinct from, but consistent with, estimates associated with the intervention. CONCLUSIONS: A hospital treating more that 12 patients with acute hip fractures can expect to lower overall cost by implementing the TRIP intervention. We also demonstrated the advantages of using IV methods over “as-treated” methods to assess the direct effect of practice changes on cost.

PMID: 19146567 [PubMed - indexed for MEDLINE]

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The impact of medical errors on ninety-day costs and outcomes: an examination of surgical patients.

Health Serv Res. 2008 Dec;43(6):2067-85

Authors: Encinosa WE, Hellinger FJ

OBJECTIVE: To estimate the effect of medical errors on medical expenditures, death, readmissions, and outpatient care within 90 days after surgery. DATA SOURCES: 2001-2002 MarketScan insurance claims for 5.6 million enrollees. STUDY DESIGN: The Agency for Healthcare Research and Quality Patient Safety Indicators (PSIs) were used to identify 14 PSIs among 161,004 surgeries. We used propensity score matching and multivariate regression analyses to predict expenditures and outcomes attributable to the 14 PSIs. PRINCIPAL FINDINGS: Excess 90-day expenditures likely attributable to PSIs ranged from $646 for technical problems (accidental laceration, pneumothorax, etc.) to $28,218 for acute respiratory failure, with up to 20 percent of these costs incurred postdischarge. With a third of all 90-day deaths occurring postdischarge, the excess death rate associated with PSIs ranged from 0 to 7 percent. The excess 90-day readmission rate associated with PSIs ranged from 0 to 8 percent. Overall, 11 percent of all deaths, 2 percent of readmissions, and 2 percent of expenditures were likely due to these 14 PSIs. CONCLUSIONS: The effects of medical errors continue long after the patient leaves the hospital. Medical error studies that focus only on the inpatient stay can underestimate the impact of patient safety events by up to 20-30 percent.

PMID: 18662169 [PubMed - indexed for MEDLINE]

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Specialty and full-service hospitals: a comparative cost analysis.

Health Serv Res. 2008 Oct;43(5 Pt 2):1869-87

Authors: Carey K, Burgess JF, Young GJ

OBJECTIVE: To compare the costs of physician-owned cardiac, orthopedic, and surgical single specialty hospitals with those of full-service hospital competitors. DATA SOURCES: The primary data sources are the Medicare Cost Reports for 1998-2004 and hospital inpatient discharge data for three of the states where single specialty hospitals are most prevalent, Texas, California, and Arizona. The latter were obtained from the Texas Department of State Health Services, the California Office of Statewide Health Planning and Development, and the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project. Additional data comes from the American Hospital Association Annual Survey Database. STUDY DESIGN: We identified all physician-owned cardiac, orthopedic, and surgical specialty hospitals in these three states as well as all full-service acute care hospitals serving the same market areas, defined using Dartmouth Hospital Referral Regions. We estimated a hospital cost function using stochastic frontier regression analysis, and generated hospital specific inefficiency measures. Application of t-tests of significance compared the inefficiency measures of specialty hospitals with those of full-service hospitals to make general comparisons between these classes of hospitals. PRINCIPAL FINDINGS: Results do not provide evidence that specialty hospitals are more efficient than the full-service hospitals with whom they compete. In particular, orthopedic and surgical specialty hospitals appear to have significantly higher levels of cost inefficiency. Cardiac hospitals, however, do not appear to be different from competitors in this respect. CONCLUSIONS: Policymakers should not embrace the assumption that physician-owned specialty hospitals produce patient care more efficiently than their full-service hospital competitors.

PMID: 18662170 [PubMed - indexed for MEDLINE]

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Hospital quality: a PRIDIT approach.

Health Serv Res. 2008 Jun;43(3):988-1005

Authors: Lieberthal RD

BACKGROUND: Access to high quality medical care is an important determinant of health outcomes, but the quality of care is difficult to determine. OBJECTIVE: To apply the PRIDIT methodology to determine an aggregate relative measure of hospital quality using individual process measures. DESIGN: Retrospective analysis of Medicare hospital data using the PRIDIT methodology. SUBJECTS: Four-thousand-two-hundred-seventeen acute care and critical access hospitals that report data to CMS’ Hospital Compare database. MEASURES: Twenty quality measures reported in four categories: heart attack care, heart failure care, pneumonia care, and surgical infection prevention and five structural measures of hospital type. RESULTS: Relative hospital quality is tightly distributed, with outliers of both very high and very low quality. The best indicators of hospital quality are patients given assessment of left ventricular function for heart failure and patients given beta-blocker at arrival and patients given beta-blocker at discharge for heart attack. Additionally, teaching status is an important indicator of higher quality of care. CONCLUSIONS: PRIDIT allows us to rank hospitals with respect to quality of care using process measures and demographic attributes of the hospitals. This method is an alternative to the use of clinical outcome measures in measuring hospital quality. Hospital quality measures should take into account the differential value of different quality indicators, including hospital “demographic” variables.

PMID: 18454777 [PubMed - indexed for MEDLINE]

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Just what the doctor ordered. Review of the evidence of the impact of computerized physician order entry system on medication errors.

Health Serv Res. 2008 Feb;43(1 Pt 1):32-53

Authors: Shamliyan TA, Duval S, Du J, Kane RL

OBJECTIVE: To examine the association between computerization of physician orders and prescribing medication errors. Data Sources. Studies published in English language were identified through MEDLINE (1990 through December 2005), Cochrane Central Register of Controlled Trials, and bibliographies of retrieved articles. Of 252 identified in the search, 12 (4.8 percent) original investigations that compared rates of prescribing medication errors with handwritten and computerized physician orders were included. DATA COLLECTION: Information on study design, participant characteristics, clinical settings, and outcomes rates were abstracted independently by two investigators using a standardized protocol. PRINCIPAL FINDINGS: Compared with handwritten orders, 80 percent of studies (8/10 studies) reported a significant reduction in total prescribing errors, 43 percent in dosing errors (3/7 studies), and 37.5 percent in adverse drug events (3/8 studies). The use of computerized orders was associated with a 66 percent reduction in total prescribing errors in adults (odds ratio [OR]=0.34; 95 percent confidence interval [CI] 0.22-0.52) and a positive tendency in children (p for interaction=.028). The benefit of computerized orders was larger when the rate of errors was more than 12 percent with handwritten orders (p for interaction=.022). Significant heterogeneity in the results compromised pooled relative risks. One randomized controlled intervention demonstrated the greatest benefits of computerized orders on total prescribing errors (OR=0.02, 95 percent CI 0.01-0.02) and dosing errors (OR=0.28; 95 percent CI 0.15-0.52) with 775 avoided prescribing errors (95 percent CI 752-811) per 1,000 orders in a pediatric hospital. CONCLUSIONS: Computerization of physicians’ orders shows great promise. It will be more effective when linked to other computerized systems to detect and prevent prescribing errors.

PMID: 18211517 [PubMed - indexed for MEDLINE]

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