How can you successfully collect and analyze data?
The development of a plan is essential for successful data collection and analysis. The first step in the planning process is determining why the data is being collected in the first place (e.g., internal quality improvement, funding requirement). After this is identified, the next steps involve ascertaining: who will collect the data, what data will be collected, when the data will be collected, where the data will be collected, and how the data will be collected.
The plan should also consider how the data will be analyzed and presented. For appropriate interpretation, the analysis of collected data should include a determination of the validity of EHR-extracted data. For clinical measures, variations in coding can affect the correct identification of the patient population. Thus, the data should be checked for collection and recording inconsistencies.
Once the planning phase is complete, the collection method should be tested and modified based on user feedback. For successful data collection and analysis, the process needs to be easy for the user. Data that needs to be collected for quality measures should be built as seamlessly as possible into user workflows. Users should also be aware of the data collection process and taught how to enter data correctly.
Rural providers will need to develop the infrastructure necessary to collect and analyze data. Support in terms of knowledge and funding can be available from multiple sources, including quality improvement organizations (QIOs) and state rural health offices.
Data collection and analysis for clinical performance can be enhanced by the use of computerized disease registries. Numerous EHR products are linked to registries, particularly for chronic disease management. Disease management registries have the capability of processing large amounts of data and generating reports at the point of care that can help providers manage their patients more effectively.
Resources for collecting and analyzing data:
Resources on computerized disease registries:
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