Getting Electronic Health Records Right—Preparing for Genomics and Health Data Analytics

January 19, 2016

LMI Staff

Today’s electronic health record (EHR) systems cannot properly handle genomic data. Interpreting these huge and complex data, particularly in a visual manner, is challenging. Even when EHR systems can access these data, few standards exist for how to structure them to ensure seamless system integration, interoperability, and interpretation. Most medical schools do not teach doctors how to interpret genetic data, and local-level care centers require training on proper data storage and network security.

Precision medicine predicts, prevents, and treats diseases at the patient level. Its growth has created the need for internationally recognized genomic EHR standards and policies, which would protect individuals by ultimately improving patient outcomes. We need to prepare for a future in which medicine is more personalized and better able to evaluate genomic data.

Our Work Is Inspired by Real Stories

Recently, I met a colleague whose daughter is suffering from a genetic condition known as Stargardt disease. Sadly, her daughter is rapidly losing her vision. This disease, a form of juvenile macular degeneration, can only appear in children when both parents carry the mutated gene. If the gene had been identified at an early stage, medical practitioners would have had more time to investigate new drug therapies and gene-editing technologies to treat my colleague’s daughter. As part of her interoperable medical genetic record, physicians at research institutions who were also working on her case could have then viewed and collaborated by using this critical information. Hitting close to home, this is one of many stories that inspire us to prepare for the widespread application of precision medicine and genomic data analysis.

Making Genomic Data Useful for Medical Practitioners

The future of patient care requires connecting large external data sets with electronic healthcare records. Precision medicine will customize treatments down to a patient’s genes and behavior. By analyzing genetic data across thousands of people, scientists will discover preventative treatments and cures for challenging health issues.

Given the complexity of health and genomic data, one can analyze the same data in different ways and achieve different outcomes. “Well-designed data visualization could help doctors interpret the data more rapidly, arriving at more challenging diagnoses in less time,” says Erin Gordon, data visualization trainer and graphic facilitator at LMI.

Before developing a framework for integrating and analyzing disparate health data sets, we test our models for validity. “The quality of our medical data models has a direct impact on patient outcomes and daily operations in medical facilities,” says Brent Auble, a consultant with the Intelligence Programs group at LMI. To support LMI’s research into healthcare data management, our team set up a Hadoop cluster, which is a group of servers designed to quickly analyze massive quantities of structured and unstructured data.

Building the Future of Healthcare Analytics

To meet the growth in precision medicine and the use of health data analytics, future EHR systems need to

  • automatically generate comparisons of multiple genomes,
  • identify and match genetic variants based on known diseases,
  • ensure patient data privacy, and
  • integrate and search medical publications and scientific research for relevant patient data.

Our clients choose us because we have expertise across many skill sets—from data analysis to change management to cyber and data security. We advise healthcare information technology (IT) teams and bring impartial, big-picture knowledge of how healthcare analytics deliver innovative results, especially when integrated with a robust EHR system.

LMI is keenly aware of the dramatic changes and needs in IT systems development and health systems technology. We can help you to be prepared.

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