For most of modern medicine, treatment has been based on averages — the drug that works best for most people, the dosage that suits the typical patient, the protocol that produces the best outcomes across the population. Precision medicine reverses that logic, tailoring treatment to the individual patient based on their genetic makeup, biomarkers, environment, and lifestyle. It’s the shift from “what works for most patients” to “what works for this patient” — and it creates entirely new requirements for healthcare IT systems.
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Precision medicine (also called personalized medicine) is an approach to patient care that uses an individual’s genetic, genomic, environmental, and lifestyle data to guide decisions about their prevention, diagnosis, and treatment. Instead of applying one-size-fits-all protocols, precision medicine matches patients with therapies that are most likely to be effective — and least likely to cause adverse effects — based on their unique biological profile.
The concept gained national visibility through the Precision Medicine Initiative launched by President Obama in 2015 (now the All of Us Research Program under NIH), which aims to build a research cohort of 1+ million participants to advance individualized care.
For healthcare IT, precision medicine introduces new data types, new integration challenges, and new clinical workflows. Genomic data must be captured, stored, and made accessible to clinicians. Pharmacogenomic results must inform prescribing decisions in the EHR. Tumor molecular profiling must connect to clinical trial matching. Biomarker data must flow between lab systems, clinical decision support engines, and specialty applications — all while maintaining HIPAA compliance for some of the most sensitive data in medicine.
In simple terms: Precision medicine is healthcare tailored to the individual — using genomic and molecular data to choose the right treatment, at the right dose, for the right patient — and it demands health IT systems that can handle data types and workflows traditional EHRs were never designed for.
Precision medicine operates through a cycle of molecular testing, data integration, clinical interpretation, and treatment selection.
Genomic and molecular testing. The process starts with molecular analysis — whole genome sequencing, whole exome sequencing, targeted gene panels, pharmacogenomic testing, or tumor molecular profiling. These tests generate structured reports identifying genetic variants, biomarkers, and molecular signatures relevant to the patient’s condition or treatment plan.
Data integration into clinical systems. Genomic test results must flow into the clinical workflow. Results from molecular laboratories arrive as structured reports — often in HL7v2 ORU messages with LOINC-coded observations and coded variant data. The results need to reach the EHR where the ordering clinician can review them in the context of the patient’s clinical history, medications, and diagnoses.
Pharmacogenomics (PGx). One of the most immediate precision medicine applications. PGx testing identifies genetic variants that affect how a patient metabolizes specific drugs. A patient who is a CYP2D6 poor metabolizer may need a different opioid. A patient with a specific HLA variant may have a severe adverse reaction to carbamazepine. PGx results must integrate with the EHR’s prescribing workflow — triggering clinical decision support alerts when a clinician prescribes a drug affected by the patient’s genomic profile.
Oncology tumor profiling. In cancer care, molecular profiling of the tumor (next-generation sequencing, biomarker panels) identifies mutations that may respond to targeted therapies. A lung cancer patient with an EGFR mutation is a candidate for specific tyrosine kinase inhibitors. A breast cancer patient with HER2 amplification benefits from trastuzumab. These molecular results must be documented, tracked longitudinally, and connected to treatment decisions and clinical trial eligibility.
Clinical trial matching. Precision medicine generates molecular profiles that may qualify patients for clinical trials targeting specific mutations or biomarkers. Trial matching services compare a patient’s molecular and clinical data against trial eligibility criteria — genomic variants, diagnosis, prior treatments, lab values — and surface potential matches to the care team. This requires structured data in standardized vocabularies (SNOMED CT, ICD-10, LOINC, HGNC for gene nomenclature).
Population-level genomic analytics. Beyond individual patient care, precision medicine data supports population health research — identifying genetic predispositions across patient panels, measuring treatment response rates by genotype, and building predictive models that incorporate genomic risk factors alongside clinical and social determinant data.
HL7 has published FHIR-based implementation guides for genomic data exchange. The Genomics Reporting IG defines FHIR profiles for representing genetic variants, diagnostic implications, therapeutic implications, and pharmacogenomic results using Observation, DiagnosticReport, and MolecularSequence resources. The IG uses established genomic nomenclature (HGVS for variant naming, HGNC for gene symbols) and coded clinical implications.
LOINC provides codes for genomic observations — specific gene panels, variant analyses, and pharmacogenomic test results. LOINC codes enable genomic results to be exchanged and recognized across systems using the same coding framework that lab results use.
ClinVar (maintained by NCBI) is the public archive linking human genetic variants to clinical significance — pathogenic, likely pathogenic, benign, uncertain significance. ClinGen provides expert-curated gene-disease and variant-disease associations. Precision medicine clinical decision support systems reference these databases to interpret variant pathogenicity and clinical relevance.
FHIR provides the emerging standard for genomic data exchange in clinical systems. The MolecularSequence resource represents raw sequence data. Observation profiles represent interpreted variants and their clinical implications. DiagnosticReport bundles genomic findings into clinical reports. SMART on FHIR apps can launch within the EHR to provide specialized genomic data visualization and interpretation.
The FDA requires companion diagnostic tests for certain targeted therapies — tests that must confirm the presence of a specific biomarker before the therapy can be prescribed. Precision medicine IT systems must track companion diagnostic requirements, ensure testing is completed before therapy initiation, and document results for compliance.
Precision medicine implementation requires new data types, new integrations, new clinical workflows, and heightened privacy considerations.
Genomic data storage and scale. A whole genome sequence generates approximately 100–200 GB of raw data per patient. Even processed variant call files (VCFs) can be tens of megabytes. Traditional EHR databases aren’t designed for this volume. Organizations need dedicated genomic data repositories or specialized platforms that store, index, and serve genomic data alongside clinical data.
EHR integration is the bottleneck. Most EHR systems have limited native support for genomic data. Structured pharmacogenomic results can be mapped to standard lab result formats (HL7 ORU messages with LOINC codes), but complex genomic reports with multiple variants, uncertain significance findings, and therapeutic implications often exceed what EHR lab result modules can display. Specialized genomic viewers launched through SMART on FHIR provide a workaround — displaying rich genomic data within the EHR context.
Clinical decision support for PGx. Pharmacogenomic CDS is one of the most actionable precision medicine use cases — but it requires maintaining a current knowledge base of gene-drug interactions (CPIC guidelines, PharmGKB database), mapping the patient’s PGx results to the knowledge base, and triggering alerts through CDS Hooks or native EHR alerting when a clinician prescribes an affected medication.
Genetic privacy and consent. Genomic data carries unique privacy implications — it reveals predispositions for diseases the patient may not know about, has implications for biological relatives, and cannot be de-identified in the traditional sense (a genome is inherently identifying). Federal and state genetic privacy laws (GINA, state genetic testing laws) impose additional protections beyond HIPAA. Consent management for genomic data must address research use, data sharing, and return of incidental findings.
Interoperability across molecular labs and clinical systems. Molecular lab results must flow from specialized reference laboratories into ordering provider EHR systems. Interface standards are still maturing — some molecular labs deliver results as PDFs rather than structured data. Building structured HL7/FHIR interfaces with molecular labs is essential for clinical decision support and longitudinal data management.
Clinical trial matching automation. Manual trial matching is time-consuming and incomplete. Automated matching services that compare structured patient data (molecular profile + clinical history) against trial eligibility criteria can surface opportunities that manual review misses. This requires structured data in standardized vocabularies and API connectivity to trial registries.
At Taction, our team builds precision medicine data infrastructure, EHR integrations, and clinical applications for healthcare organizations and genomics companies.
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