Faculty Bibliography

BACKGROUND:Surgical site infections (SSIs) portend high patient morbidity and mortality. Although evidence-based clinical interventions can reduce SSIs, they are not reliably delivered in practice, and data are limited on the best approach to improve adherence. OBJECTIVE:To summarize implementation strategies aimed at improving adherence to evidence-based interventions that reduce SSIs. DESIGN/METHODS:Systematic reviewMethods:We searched PubMed, Embase, CINAHL, the Cochrane Library, the WHO Regional databases, AFROLIB, and Africa-Wide for studies published between January 1990 and December 2015. The Effective Practice and Organization Care (EPOC) criteria were used to identify an acceptable-quality study design. We used structured forms to extract data on implementation strategies and grouped them into an implementation model called the "Four Es" framework (ie, engage, educate, execute, and evaluate). RESULTS:In total, 125 studies met our inclusion criteria, but only 8 studies met the EPOC criteria, which limited our ability to identify best practices. Most studies used multifaceted strategies to improve adherence with evidence-based interventions. Engagement strategies included multidisciplinary work and strong leadership involvement. Education strategies included various approaches to introduce evidence-based practices to clinicians and patients. Execution strategies standardized the interventions into simple tasks to facilitate uptake. Evaluation strategies assessed adherence with evidence-based interventions and patient outcomes, providing feedback of performance to providers. CONCLUSIONS:Multifaceted implementation strategies represent the most common approach to facilitating the adoption of evidence-based practices. We believe that this summary of implementation strategies complements existing clinical guidelines and may accelerate efforts to reduce SSIs.

Researchers need to evaluate the strengths and weaknesses of data sets to choose a secondary data set to use for a health care study. This research method review informs the reader of the major issues necessary for investigators to consider while incorporating secondary data into their repertoire of potential research designs and shows the range of approaches the investigators may take to answer nursing research questions in a variety of context areas. The researcher requires expertise in locating and judging data sets and in the development of complex data management skills for managing large numbers of records. There are important considerations such as firm knowledge of the research question supported by the conceptual framework and the selection of appropriate databases, which guide the researcher in delineating the unit of analysis. Other more complex issues for researchers to consider when conducting secondary data research methods include data access, management and security, and complex variable construction.

This research addresses an important methodological issue on patient safety and obesity for the purposes of examining clinical and administrative data for the reliability of using International Classification of Diseases (ICD) diagnoses codes alone to reliably identify obesity as a comorbidity and risk factor in care and management. The findings of this research confirm ICD codes for the obese surgical populations were underutilized. Despite more than 70% of patients classified as overweight or obese, ICD-9 codes for obesity were assigned in less than 10% of the overall sample. Patients in the extreme category of obesity (body mass index [BMI] >40 kg/m) were more likely to have a corresponding ICD-9 code compared with patients in the BMI range of 25 to 40 kg/m. International Classification of Diseases, Ninth Revision coding for obesity was underutilized in patients with a BMI of greater than 25 kg/m. The associated health risks, costs, and potential adverse events associated with obesity make it imperative to continue to study the barriers to coding.

PURPOSE/OBJECTIVE:As the US health care system transitions toward value-based reimbursement, there is an increasing need for metrics to quantify health care quality. Within radiology, many quality metrics are in use, and still more have been proposed, but there have been limited attempts to systematically inventory these measures and classify them using a standard framework. The purpose of this study was to develop an exhaustive inventory of public and private sector imaging quality metrics classified according to the classic Donabedian framework (structure, process, and outcome). METHODS:A systematic review was performed in which eligibility criteria included published articles (from 2000 onward) from multiple databases. Studies were double-read, with discrepancies resolved by consensus. For the radiology benefit management group (RBM) survey, the six known companies nationally were surveyed. Outcome measures were organized on the basis of standard categories (structure, process, and outcome) and reported using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS:The search strategy yielded 1,816 citations; review yielded 110 reports (29 included for final analysis). Three of six RBMs (50%) responded to the survey; the websites of the other RBMs were searched for additional metrics. Seventy-five unique metrics were reported: 35 structure (46%), 20 outcome (27%), and 20 process (27%) metrics. For RBMs, 35 metrics were reported: 27 structure (77%), 4 process (11%), and 4 outcome (11%) metrics. The most commonly cited structure, process, and outcome metrics included ACR accreditation (37%), ACR Appropriateness Criteria (85%), and peer review (95%), respectively. CONCLUSIONS:Imaging quality metrics are more likely to be structural (46%) than process (27%) or outcome (27%) based (P < .05). As national value-based reimbursement programs increasingly emphasize outcome-based metrics, radiologists must keep pace by developing the data infrastructure required to collect outcome-based quality metrics.