Browsing School, Graduate by Subject "Data Accuracy"
Now showing items 1-2 of 2
A comparison of paper-based data submission to remote data capture for minimizing data entry errors in cancer clinical researchBackground. Patient data are essential for judging the safety and efficacy of cancer clinical trials. The current process of paper-based data entry provides opportunities for incurring data discrepancies. Automated systems have shown potential to reduce the number of data entry errors and preserve the quality of clinical trial data. To test this potential, this study examined case report forms (CRFs) to test for differences in the proportion of discrepancies and the time to resolve these discrepancies between a paper-based data entry and OracleRTM Clinical (OC) Remote Data Capture (RDC). Objective. The purpose of this study was to examine differences in the proportion of errors and the time to resolve specific errors between a paper-based CRF and an electronic RDC format. Reason's conceptual framework for error detection and recovery feedback loop was used to guide this research where the warning environmental cueing function provided feedback to the end-user. Results. The sample consisted of 445 RDC and 445 paper-based CRFs submitted to the Cancer Trial Support Unit (CTSU) from March 12, 2004 through March 28, 2005. There was a significant reduction in the proportion of overall data discrepancies for RDC as compared to paper-based CRFs (46.5% vs. 31.7%, p<.001). Similar results were found for univariate (58.6% vs. 41.2%, p<.001) and multivariate (64% vs. 36%, p<.001) discrepancies. Of the total sample of 890 CRFs analyzed for this study, 509 (57.2%) had no discrepancies. For the 381 (42.5%) forms with discrepancies there was no difference in the mean number of days to resolve discrepancies between RDC and paper-based (43 vs. 35). However, RDC had a greater proportion of resolved discrepancies (52% vs. 48%, p<.001).;Conclusion. The results from this study supported Reason's concept of error detection and recovery. RDC data entry format decreased overall, univariate and multivariate data discrepancies for patient information collected on a colon cancer study; however, there was no difference in the timeline for discrepancy resolution between the two formats. Further studies are recommended to test alternate definitions of discrepancy resolution time points. Results from this study can only be generalized to automated systems that use Oracle RTM Clinical and the instance configuration specific for the programmed edit checks used for the colon cancer study.
The influence of nursing home characteristics on the accuracy of the long term care minimum data setIn June 1998 nursing home providers began transmitting MDS data to the Health Care Financing Administration's national repository. By the end of 2000 the national MDS database contained approximately 36 million records. MDS data are used for casemix payment, to focus the survey process and to provide quality of care feedback information to nursing homes. In the near future, MDS data will also be used as part of the medical review (MR) system to ensure that Medicare beneficiaries are furnished appropriate services in skilled nursing facilities. The potential magnitude and implications of the decisions for beneficiaries that are and will be based upon MDS data underscores the need for the data in the system to be accurate. While this requirement may seem obvious there are, unfortunately, many indications that the data may indeed not be accurate (Harrington et al., 1996). The research suggests there are characteristics of the nursing home's organizational environment that may make it difficult to obtain accurate data (Mumford, Whetzel, & Reiter-Palmon, 1997; Phillips, 1995; Harrington, 1996; Abt, 2001). The purpose of this study was to determine the influence of nursing home characteristics on the accuracy of MDS data. This study was conducted in two phases. During phase one a model of nursing home characteristics that influence the accuracy of MDS data was developed. Ten MDS experts were interviewed and asked to identify nursing home characteristics that influence the accuracy of MDS. Nine characteristics of nursing homes were identified. The nine characteristics were used to construct a model for this study, based on Donabedian's (1980, 1982) components of structure, process and outcome. The structure component included the following facility characteristics: the number of beds & units, occupancy rate, ownership, staffing levels, used of per diem staff, a designated MDS coordinator, MDS coordinator characteristics (years of experience, training, employment status), and resident characteristics (cognitive impairment and medicare status). The process component of the model included the tenure of the MDS coordinator, the process used to complete the MDS and the turnover of the DON. The outcome component (dependent variable) was the accuracy (quality) of the MDS data.;The sample for this study was limited to fifty-one nursing homes in the state of Maine. Three nursing home characteristics were found to have an association with the accuracy of MDS data. MDS data were more accurate in facilities that had a designated MDS coordinator. A positive relationship was found between the level of experience of the MDS coordinator and MDS data accuracy. Data appeared to be more accurate in facilities where the MDS coordinator had more experience. The accuracy of the MDS data was lower in facilities that use per diem staff. The findings from this study suggest more research that examines the impact of the MDS coordinator and the necessary qualifications for this role needs to be conducted. Further, the alternative methods to assess MDS data accuracy are needed. Lastly, consideration must be given to refining and decreasing the complexity of the MDS.