Physician Data Analytics – Use in a Hypertension and Diabetes Case Study

Blog by Richard Howe, PhD, Executive Director, North Texas Regional Extension Center

Hypertension

Over the last two months, I discussed some basic “rules of the road” for improving use of your electronic health record (EHR). This month I would like to describe how an EHR, along with a data analytics tool, could be used to support a hypertension and diabetes case study.

Background
The North Texas Regional Extension Center (NTREC), under the DFW Hospital Council Foundation (DFWHC Foundation), recently received a new grant from the Texas Department of State Health Services (DSHS) to increase the use of technology in the treatment and prevention of hypertension, pre-diabetes and diabetes.

This grant is focused on assisting physician practice groups on these three clinical conditions and helping practices move towards a “performance based” or “value-based” model. This project involves evaluation and use of a practice group’s EHR to improve the treatment of hypertension, pre-diabetes and diabetes.

We have currently signed up nine practice groups and are in the process of signing up additional groups for the study. Each practice group is compensated $2,000 to cover the staff’s time to complete the quarterly surveys. If interested in participating, please contact us ([email protected]).

What activities and measures are being assessed?
• Performance measure: Proportion of physician practices with an EHR appropriate for treating patients with hypertension, pre-diabetes, and diabetes;

• Performance measure: Proportion of patients that are in a physician practice with an EHR appropriate for treating patients with hypertension, pre-diabetes, and diabetes;

• Performance measure: Proportion of physician practices reporting on NQF 18;

• Performance measure: Proportion of physician practices reporting on NQF 59.


A key component of collecting and measuring this data is to use the EHR’s internal reporting capabilities related to these clinical conditions and NQF measures. The output data from the EHR will be imported into a data collection and analytics tool to track the overall progress over time of these clinical conditions across all patients within a physician practice, as well as across different practice groups.

What are the benefits?
This is a simple example of how your EHR, along with a data analytics tool, can be used to support tracking of patients with chronic clinical conditions to improve outcomes. Obviously, the same tools can be used to not only support a clinical research project, but can also be used to support a “value-based” model of care. Next month, I will describe in more detail changes at the Federal level that are going to move healthcare from a fee-for-service model to a value-based model and why data analytics will be critical in this process.