Top 5 Reasons to Analyze Real-World EHR Data Vs. Traditional Claims Data

By the year 2050, the number of seniors using long-term care and skilled nursing facilities is expected to increase more than 107%, making it more important than ever to learn how to treat and help this population to stay healthy, to assess new therapies, and to achieve the best outcomes for patients. Diseases like diabetes and dementia, heart conditions, and mobility issues all affect quality of life and long-term health for the 65+ population. To develop a roadmap for effective preventive healthcare and treatment plans, the highest quality data is needed to empower better decisions and insights.

While claims data can provide general information on patients’ disease types and ages, it has limited useful data for analyzing outcomes. With electronic health record (EHR) data, you can examine granular details such as patients’ daily weights, vitals, and dietary information. In addition, the data capturing neurologic and mental health conditions offers deeper insights into the impact of comorbidities.

These are the top four reasons why real-world EHR data is superior to traditional claims data for developing therapies and raising the standard of care.

1. Consider the Source

Data from health insurance claims include the healthcare services provided to a patient, as well as which medications and therapies insurance companies and payers paid. This snapshot in time lacks detail and more importantly, misses the outcome from the prescribed medication or therapy. On the other hand, EHR data reflects the actual medical records from healthcare providers and captures measurable, actionable data, including treatment patterns, disease progression, comorbidities, and laboratory findings. This panoramic view of a patients’ data empowers anyone who needs a robust dataset when developing novel drug treatments and therapies. It helps connect the data points between cost of treatment and outcome.

2. Timeliness Counts

EHR data reveals real-time data records where traditional claims data has a lag. A delay is especially detrimental when assessing adverse drug reactions or side-effects, cost of care, and preventative health planning. Analysis requires real-time data to evaluate the utility of medications for treating approved conditions, as well as application for a newly indicated condition. Real-time data is also critical for patients participating in clinical trials and for drug manufacturers in various stages of the approval process.

3. More IS Better

One of the primary functions of traditional claims data is describing the patients’ medications, but EHR data delineates medication, blood pressure, other vitals like BMI and daily activity among other measures. While claims data can offer details about medications prescribed, diagnosis codes, or inpatient procedure, EHR data provides detailed clinical information that would be more valuable for clinical studies, treatment development, and research on efficacy of medications and therapies. EHR data records contain information including:

• Administrative and billing data
• Patient demographics
• Progress notes
• Vital signs
• Medical histories
• Diagnoses
• Medications
• Immunization dates
• Allergies
• Radiology images
• Lab and test results

Detailed data over extended periods of time provides the ability to analyze with accurate average values used in statistical analysis.

4. Capture all Medication Administration

Medication administration information is helpful when measuring adherence for any drug, but it’s also critical for “pro re nata” (PRN) medications that are taken as needed. Whenever a drug is administered to a long-term care resident, details such as time, dose and reason are captured in the EHR.

5. Zero in on your Target

PointClickCare was purpose-built for the 65+ population and represents nearly 70% of the long-term care and skilled nursing facilities in the U.S. As a result, the demographic focus provides a level of reliable detail which drives decisions about future drug and therapy development.

Insights provided from high-risk populations and the robust clinical datasets from real-time EHR anonymized data provides the information needed to develop treatments, medications, and population health plans.