Feedback and training tool to improve provision of preventive care by physicians using EMRs: a randomised control trial

Background Electronic medical records (EMRs) have the potential to improve the provision of preventive care by allowing general practitioners (GPs) to track and recall eligible patients and record testing for feedback on their service provision. Objective This study evaluates the effect of an educational intervention and feedback tool designed to teach GPs how to use their EMRs to improve their provision of preventive care. Methods A randomised controlled trial comparing rates of mammography, Papanicolaou tests, faecal occult blood tests and albumin creatinine ratios one-year pre- and post-intervention was conducted. Nine primary care practices (PCPs) representing over 30 000 patients were paired by practice size and experience of GPs, and randomly allocated to intervention or control groups. Physicians at the four intervention practices received a two-hour feedback session on their current level of preventive care and training to generate eligible patient lists for preventive services from their EMR database. Results One-year post-intervention results provided no evidence of a difference. The intervention was not a significant predictor of the one-year post-intervention test rates for any of the four tests. On average, the intervention practices increased postintervention test rates on all tests by 16.8%, and control practices increased by 22.3%. Conclusion The non-significant results may be due to a variety of reasons, including the level of intensity of the educational intervention, the cointervention of a government programme which provided incentives to GPs meeting specific targets for preventive care testing or the level of recording of tests performed in the EMR

Familial Papillary Thyroid Carcinoma: A Retrospective Analysis

Background. Whether or not the familial form of papillary thyroid carcinoma is more aggressive than the sporadic form of the disease remains controversial. Methods. To explore this question and whether or not increased aggressiveness is more apparent in families with multiple affected members, we performed a chi square by trend analysis on our patients clinical and pathologic data comparing: first degree families with three or more affected members versus first degree families with two affected members versus sporadic cases of papillary thyroid carcinoma. Results. No statistically significant trends were seen for any presenting surgical pathology parameter, age at presentation, length of follow-up or gender distribution. The familial groups exhibited significant trends for higher rates of reoperation (P = 0.05) and/or requiring additional radioactive iodine therapy (P = 0.03), distant metastases (P = 0.003) and deaths (P = 0.01). These aggressive features were most apparent in certain families with three or more affected members. Conclusions. Using the chi square by trend analysis, a significant trend was seen for the familial form of papillary thyroid cancer to possess more aggressive features than the sporadic disease. Prompt recognition of the familial nature of the disease may provide earlier diagnosis and treatment in similarly affected family members

Investigating concordance in diabetes diagnosis between primary care charts (electronic medical records) and health administrative data: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>Electronic medical records contain valuable clinical information not readily available elsewhere. Accordingly, they hold important potential for contributing to and enhancing chronic disease registries with the goal of improving chronic disease management; however a standard for diagnoses of conditions such as diabetes remains to be developed. The purpose of this study was to establish a validated electronic medical record definition for diabetes.</p> <p>Methods</p> <p>We constructed a retrospective cohort using health administrative data from the Institute for Clinical Evaluative Sciences Ontario Diabetes Database linked with electronic medical records from the Deliver Primary Healthcare Information Project using data from 1 April 2006 - 31 March 2008 (N = 19,443). We systematically examined eight definitions for diabetes diagnosis, both established and proposed.</p> <p>Results</p> <p>The definition that identified the highest number of patients with diabetes (N = 2,180) while limiting to those with the highest probability of having diabetes was: individuals with ≥2 abnormal plasma glucose tests, or diabetes on the problem list, or insulin prescription, or ≥2 oral anti-diabetic agents, or HbA1c ≥6.5%. Compared to the Ontario Diabetes Database, this definition identified 13% more patients while maintaining good sensitivity (75%) and specificity (98%).</p> <p>Conclusions</p> <p>This study establishes the feasibility of developing an electronic medical record standard definition of diabetes and validates an algorithm for use in this context. While the algorithm may need to be tailored to fit available data in different electronic medical records, it contributes to the establishment of validated disease registries with the goal of enhancing research, and enabling quality improvement in clinical care and patient self-management.</p

A basic model for assessing primary health care electronic medical record data quality

Abstract Background The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs. Methods Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products. Results A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight). Conclusions This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding