Article

How often are potential patient safety events present on admission?

Thomson Healthcare, Santa Barbara, California, USA.
Joint Commission journal on quality and patient safety / Joint Commission Resources 04/2008; 34(3):154-63.
Source: PubMed

ABSTRACT 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.
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.
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.
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.

2 Followers
 · 
95 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: To examine the risk of cardiovascular outcomes and diabetes mellitus in patients prescribed second-generation antipsychotics. From the MarketScan claims database, nondiabetic adults prescribed aripiprazole between July 2003 and March 2010 were propensity score-matched with patients prescribed olanzapine, quetiapine, risperidone, and ziprasidone. Patients were followed through the claims for International Classification of Diseases, Ninth Revision codes indicating myocardial infarction, stroke, heart failure, coronary bypass/angioplasty procedures, and incident diabetes. Incidence rates of each outcome were calculated and compared between aripiprazole and the other second-generation antipsychotics using Cox models. Aripiprazole initiators were matched 1:1 to 9,917 olanzapine, 14,935 quetiapine, 10,192 risperidone, and 5,696 ziprasidone initiators. Increased risk was found with olanzapine for stroke (hazard ratio = 1.43; 95% confidence interval, 1.05-1.95) and any cardiovascular event (1.28; 1.05-1.55); with quetiapine for stroke (1.58; 1.19-2.09), heart failure (1.55; 1.15-2.11), and any cardiovascular event (1.50; 1.25-1.79); and with risperidone for stroke (1.54; 1.12-2.12), heart failure (1.43; 1.02-1.99), and any cardiovascular event (1.49; 1.21-1.83). Ziprasidone showed no significant difference in risk from aripiprazole for any outcome. Incidence of diabetes ranged from 18 to 21 events per 1,000 person-years in each cohort and did not differ significantly between second-generation drugs. This analysis of real-world data found lower risk of some cardiovascular events with aripiprazole than with olanzapine, quetiapine, or risperidone, but no differences were found with ziprasidone. There were no significant differences in risk of diabetes. Limitations include use of claims data and inability to adequately control for differential prescribing of second-generation antipsychotics to patients at higher risk of diabetes.
    The Journal of Clinical Psychiatry 12/2013; 74(12):1199-206. DOI:10.4088/JCP.13m08642 · 5.14 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Certificate-of-need (CON) regulations can promote hospital efficiency by reducing duplication of services; however, there are practical and theoretical reasons why they might be ineffective, and the empirical evidence generated has been mixed. This study compares the cost-inefficiency of urban, acute care hospitals in states with CON regulations against those in states without CON requirements. Stochastic frontier analysis was performed on pooled time-series, cross-sectional data from 1,552 hospitals in 37 states for the period 2005 to 2009 with controls for variations in hospital product mix, quality, and patient burden of illness. Average estimated cost-inefficiency was less in CON states (8.10%) than in non-CON states (12.46%). Results suggest that CON regulation may be an effective policy instrument in an era of a new medical arms race. However, broader analysis of the effects of CON regulation on efficiency, quality, access, prices, and innovation is needed before a policy recommendation can be made.
    Medical Care Research and Review 01/2014; 71(3). DOI:10.1177/1077558713519167 · 2.57 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls.Methods/designThis validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart.DiscussionThere is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.
    Implementation Science 01/2015; 10(1):5. DOI:10.1186/s13012-014-0197-6 · 3.47 Impact Factor

Full-text

Download
67 Downloads
Available from
Jun 2, 2014