Physician Data Analytics — Some Clinical Examples

Last month, I commented on physician analytics related to primary care’s changing role and work-life balance. This month I will provide examples of how collection of basic clinical data elements and use of a data analytics system, can be a very powerful approach to better and more efficient patient care.

Tracking Medications

One of the most difficult issues in patient care is tracking of ALL medications that a patient is using – both prescription and over-the-counter meds.  Tracking of medications is absolutely critical for patient safety.

When basic medication information is kept accurate and within an electronic database, there are some huge advantages available to the physician.  One is the ability of the system to do drug-drug interaction checks – especially between prescription and over-the-counter medications.   My mother, who suffered from severe chronic arthritis, was complaining that her prescription medications seemed to be providing little help anymore.  I asked her if she was taking anything else to relieve some of the pain, and she said “yes” that she started taking an over-the-counter pain medication a few months back.   In doing a drug-drug interaction test, I found that the over-the-counter med had a “severe” interaction with the prescription med, and told her to contact her physician immediately.

Recalling or Discontinuation of a Medication

When a medication is taken off the market or has a warning issued, it is important to find all of your patients that are on that medication.   As an example, in 2006 there was a warning issued for patients with diabetic retinopathy that they may have some additional complications if taking the drugs Avandia or Avandamet.  With a paper record, finding all of your patients with this diagnosis and these medications is almost impossible or at least very time consuming.  However, with an EMR and data analytics system, the “electronic” search of your database can be done in just a few minutes and your office can then contact each patient to provide them with an appropriate course of action.

Monitoring of Chronic Conditions

Chronically ill patients are sometimes the most difficult to treat and generally have the highest related healthcare costs.  But, what if you could use data analytics to “view” into all of your patients and extract even those patients that may be “trending” toward some chronic medical condition.  You could then use this information in a pro-active way to contact these patients and help them address the pre-chronic condition.  This is what we call using data for “predictive analytics.”

For example, you could look for elevated levels of HgA1C, LDL-C values, blood glucose, blood pressure, and body mass index (BMI) across all your patients and then have the data analytics system look for any co-morbidity indicators across these patients.   This would help you quickly identify those patients of highest risk that may be trending toward a chronic condition, such as diabetes.

Next month I would like to discuss how physician data analytics can be applied to population health.

The North Texas Regional Extension Center (NTREC) has grant-subsidized services to help you implement an EHR. For a preview of our services, check out this link:  http://youtu.be/k2omtt6_bsk. or view this video: