A Framework for Conversion to Electronic Data Capture

Problem: The Quality Measurement & Analytics department provides data and information to hospital leadership and clinical services to drive efforts to improve outcomes. A key function is to capture data from medical records. Much of these data are stored in unstructured formats that cannot be electronically extracted, requiring resource-intensive manual abstraction.

Measurement: Each registry-specific data element is tracked on an Excel spreadsheet and contains information regarding the definition, queriable status, location within the EHR, and quantifiable information regarding how many fields are electronically discoverable within the EHR. This information populates a greater dashboard that contains color coded automation information for each registry.

Analysis: Our analysis is comprised of simple counts and percentages. Color coding tracks our progress and creates a compelling visual regarding how far along we are with meeting our goals for automation.

Implementation: Conversion to electronic extraction consists of four phases: 1. Gap analysis of captured fields, 2. Creation of structured fields within EHR, 3. Data elements extracted from EHR, imported into reporting database, and 4. Utilization reports developed

Example success story: Development of a new note for use by providers that includes 126 required fields results/discussion ability to capture data points electronically has increased. Reducing the manual abstraction burden allows us to allocate efforts to other areas of performance improvement. Abstraction becomes more efficient; care and outcomes improve. For example, we are now capturing nearly one-third of all data elements electronically for STS Cardiac Surgery.

Speakers
Electronic Quality Reporting Data Manager
Electronic Quality Reporting Data Manager – Stony Brook Medicine

Speaker Type: Poster Presentations On-Demand

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