Pitfalls to Avoid when implementing Health Information Exchange
In the healthcare payer field, having the ability to safely transfer and exchange patient data could be the difference between quality care and below standards Healthcare Effectiveness Data and Information Set (HEDIS) scores.
In recent years, Health Information Exchange (HIE) capabilities have helped mobilize patient data allowing clinicians and healthcare managers to provide near real time diagnosis closing the gap on member care. Additionally, HIEs will improve interoperability, allowing for better patient care. However, since healthcare payers have typically relied on administrative data, the inclusion of clinical data from an HIE presents a new challenge. To make matters worse, there will be considerable overlap in services between the clinical and administrative data.
The absence of such a critical framework leads to a lack of understanding in the data. This will adversely affect the integrity of the data and create distrust of the data amongst end users. We have worked with many healthcare payer clients specifically on this issue. Those experiences taught us a thing or two about how to successfully incorporate Health Information Exchange capabilities and what pitfalls to avoid.
To avoid a boil the ocean type scenario, don’t attempt to bring in all data at once. Instead, opt for a more gradual approach by focusing on a smaller subset such as Result or Vital. This will certainly ease the burden of duplicated data since the measure will be looking for the most recent reading. This will also limit auditor concern when incorporating new data.
Understand the Measures
Before parsing out the data, we recommend understanding the measures that will be impacted. For instance, immunizations will have deduplication logic included in the measure calculation. This will allow health plans to better understand and anticipate the impact. Also, a ROI component could be included such as targeting data that impacts higher weighted measures.
Lack of Benchmarking
Executing two measure calculations, with and without the new data can not only help explain to auditors why a measure rate changed but can help identify potential duplication of data. For instance, a strong rate increase in a measure calculating medication usage could indicate duplicate data. Especially if the new data was expected to have a limited impact.
Adjust for National Standards
A health plan may provide its own identifiers for pharmacies and providers, but when comparing across data source its best to use a nationally recognized standard. This will enable processes to understand that a medication was issued by the same pharmacy and/or provider.
At Data Ideology our bread and butter is assisting health insurance organizations with their data needs that improve HEDIS and STAR ratings. Our successful track record has put our clients in a position to increase scores that not only allow for higher state and federal reimbursements but also improve overall member care. To learn more about our healthcare services and how we can help, contact us to schedule a quick discovery session.
Written by Toby George
Co-Founder & Chief Executive Officer at Data Ideology
Toby George is the CEO and Co-Founder of Data Ideology with over 16 years of experience in developing and executing data management strategies, Business Intelligence methodologies, and complex analytic solutions.