The difficulty with the clinical registry product that they used is its heavy reliance on manual data entry. To cover a broader segment of the target population at much lower cost, we use automatic data feeds from clinical laboratories. But, the idea is the same and its a good one - exchange clinical information with public agencies to improve their ability to manage the health of the state's population.
Exploring the feasibility of combining chronic disease patient registry data to monitor the status of diabetes care.
Prev Chronic Dis. 2008 Oct;5(4):A124. Epub 2008 Sep 15.
Chronic Disease Prevention Unit, Washington State Department of Health, PO Box 47855, Olympia, WA 98504-7855, USA. firstname.lastname@example.org
INTRODUCTION: To provide direction and to support improvements in diabetes care, states must be able to measure the effectiveness of interventions and gain feedback on progress. We wanted to know if data from multiple health clinics that are implementing quality improvement strategies could be combined to provide useful measurements of diabetes care processes and control of intermediate outcomes. METHODS: We combined and analyzed electronic patient health data from clinic sites across Washington State that used the Chronic Disease Electronic Management System (CDEMS) registry. The data were used to determine whether national and state objectives for diabetes care were met. We calculated the percentage of patients that met standards of care in 2004. RESULTS: The pooled dataset included 17,349 adult patients with diabetes from 90 clinics. More than half of patients were above recommended target levels for hemoglobin A1c testing, foot examination, hemoglobin A1c control, and low-density lipoprotein cholesterol control. Fewer patients met recommendations for nephropathy assessment, eye examinations, and blood pressure control. In terms of meeting these standards, rates of diabetes care varied across clinics. CDEMS rates of care were compared with those reported by other data sources, but no consistent pattern of similarities or differences emerged. CONCLUSION: With committed staff time, provider support, and resources, data from clinical information systems like CDEMS can be combined to address a deficiency in state-level diabetes surveillance and evaluation systems--specifically, the inability to capture clinical biometric values to measure intermediate health outcomes. These data can complement other surveillance and evaluation data sources to help provide a better picture of diabetes care in a state.