Pharmacogenomics creates complex data that need special ways to manage. Genetic data is different from usual health data like vital signs or lab tests because it is large, detailed, and needs to be kept for a person’s whole life.
For example, genetic results about blood thinners like warfarin or drugs like clopidogrel can help avoid bad drug reactions and make treatments work better if used the right way in clinics.
Even though pharmacogenomics is useful, no popular EHR system in the United States fully includes genetic data to help with medicine decisions automatically. This makes it hard to use genetic tests well, even though groups like the Clinical Pharmacogenetics Implementation Consortium (CPIC) have made guidelines to help doctors understand gene and drug interactions.
EHR systems face some problems that make it hard to include pharmacogenomic information smoothly:
To handle these problems, healthcare groups use external systems that work alongside or connect with EHR platforms.
External systems built for genomics and pharmacogenomic data offer several benefits when connected with EHRs.
The eMERGE Network, a federally funded group of nine institutions, has pointed out key features needed to support genomic medicine in clinics. These features include storing structured genetic data, using standard ways to exchange data, linking clinical data with genetic info, integrating rules-based CDS, and showing data clearly to clinicians. External systems help provide many of these features where regular EHRs do not.
Many places in the Clinical Sequencing Exploratory Research (CSER) group have made their own tools outside main EHRs to bring in genomic data. They create reports that humans can read and are structured, working as passive CDS inside the EHR. Only a few have started making active CDS with live alerts, showing that the field is still new and hard to put into practice.
Because of this variety, teamwork among these sites and IT vendors is needed to build systems that work well together and support pharmacogenomics across many health groups.
Clinical decision support (CDS) helps share pharmacogenomic information in useful ways. CDS can be two kinds:
Right now, active CDS for pharmacogenomics is rare because it is hard to link gene variant databases with EHR triggers. But as external systems improve, they create chances to test and use active CDS more easily. Their modular design lets CDS tools and gene-drug rules be updated without waiting for EHR companies to release new versions.
Putting pharmacogenomic features into EHR systems needs effort from many people in a healthcare group:
Marc S. Williams from the Genomic Medicine Institute at Geisinger Health System says success needs many stages including figuring out needs, testing, using, and checking back. This helps healthcare groups shape tech that fits how they work instead of using fixed one-size-fits-all systems. This is important because each practice runs differently in the United States.
Artificial intelligence (AI) and workflow automation help close gaps in using pharmacogenomic info with EHRs and external systems.
Simbo AI is a company that uses AI mainly for automating office phone tasks and answering services. Their technology shows how AI tools can make clinical work run more smoothly. Similar AI tools could help with patient talks about pharmacogenomic tests — for example, scheduling tests, answering questions about results, and helping with medicine changes from genetic alerts.
The U.S. government has supported doctors and hospitals to use EHRs but has not yet pushed full genetic data integration. Since the Centers for Medicare & Medicaid Services (CMS) EHR Incentive Program started, $14.3 billion has helped promote meaningful EHR use. Still, no commercial EHR system fully supports genetic data for daily medicine decisions.
This shows a need for external pharmacogenomic systems and workflow automation that work with current EHRs. Practice managers and IT staff must look at tech options carefully to fit federal incentives and rules to get the best value.
Also, legal and ethical concerns about genetic data privacy and fair access are very important. Practices should keep data safe, teach staff about genetics, and help patients understand their rights about genetic info.
For healthcare groups in the U.S. wanting to use pharmacogenomics in normal care, current commercial EHR systems offer limited built-in support. Using only these EHRs might cause pharmacogenomic test results to be unused and decision support to be weak or missing.
Connected external systems can fix these problems by improving data storage, sharing, decision support, and clear displays. These systems let practices offer precision medicine more widely and make medicine use safer and more effective.
Using AI and automation adds more help by lowering the mental and work load on doctors and staff. Practice leaders should focus on involving all stakeholders, trying out tech carefully, and following healthcare IT rules and federal programs.
By planning carefully and using these external tools and technology, practice administrators, owners, and IT managers can help their groups provide better pharmacogenomic care in the changing U.S. healthcare environment.
The purpose is to utilize clinical decision support (CDS) to address implementation challenges, streamline pharmacotherapy, and improve patient care by providing clinicians with relevant pharmacogenomic information at the point of care.
CDS provides point-of-care guidance that helps clinicians use pharmacogenomics effectively, ensuring that individual patient genetic profiles are considered when making medication decisions.
Important considerations include clinical workflows, identification of alert triggers, and tools for interpreting results to ensure seamless integration into EHR.
Passive CDS delivers information without direct clinician intervention, while active CDS prompts clinicians with alerts or recommendations, enhancing their decision-making process.
Challenges include the growing volume of pharmacogenomic knowledge, enduring test results, and the complexity of interpreting this data within clinical workflows.
CPIC provides resources and recommendations that help clinicians integrate pharmacogenomic data and support the interpretation of gene-drug interactions in clinical practice.
Precision medicine leverages individual genetic information, including pharmacogenomics, to tailor treatment strategies, optimizing drug efficacy and reducing adverse effects.
Integrating ancillary systems outside the EHR can augment its capabilities, providing additional support for interpreting pharmacogenomic data and improving clinical decision-making.
Effective pharmacogenomic CDS can lead to improved medication safety, reduced adverse drug reactions, and better therapeutic outcomes through personalized treatment plans.
Recommendations related to gene-drug pairs should be summarized and provided in a user-friendly manner that integrates seamlessly into the clinician’s existing workflow.
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