The healthcare industry is experiencing an explosion of demand for new technologies designed to help patients and clinicians understand the value of leveraging genetic data to more precisely diagnose, prevent and treat disease.
From cancer to cardiology, discoveries are made monthly that tie specific inherited genes (germ line) to disease or identify genetic mutations in tumor cells (somatic) that would enable certain drugs or treatments to destroy those cells.
While the genetic testing results are useful, invariably the information is received by clinicians in paper form or downloaded PDFs. Standalone documents are a somewhat useful reference, but offered in a limited way rather than being able to access discrete data items that are integrated into an electronic medical record used for order entry or clinical decision support right at the point of care.
As genetic information grows and changes, repeated genetic test results generated over many months or years are impossible to synthesize into a coherent representation of the patient’s condition without using a discrete, comprehensive or end to end technology workflow to support care.
To address these and other needs, healthcare providers, academic medical centers and research institutes need the following from EHR vendors:
1. Store discrete genetic markers in EHR. Generally speaking, a clinician’s understanding of genetic data is limited when compared to oncologists, genetic counselors, molecular pathologists and researchers who are the most knowledgeable about this specialty. Community physicians and other specialists are just beginning their learning in this complex field. Genetic results that contain clinically actionable variants need to be filtered from variants of unknown significance and discretely interfaced into the electronic medical record. The data needs to be stored chronologically and linked to other genetic test results in order to be most effective. This proposed requirement is especially important for somatic test results that shift over time as cancer tumors develop drug resistance and continue to mutate. Germ line test results are much more constant over a patient’s lifetime but the relation of tumor specific markers to inherited markers is becoming more important in drug selection and targeted therapies. To achieve reliable EMR integration, genetic testing labs must accept inbound electronic orders from providers’ EHRs and return not only rich textual summaries that contain annotations, metadata and literature references but also discrete genetic variant specific measurements and attributes.
2. Actionable rule-based alerts. Once the actionable variants are stored discretely in the EHR database, they can enable decision support rules designed to guide clinician selection of diagnostic testing, tailoring of prevention advice and creation of treatment plans. The patient specific rules can also tailor drug ordering such as preventing options that would be less effective for patients based on their genetic profile. Some examples of this pharmacogenomics use case include blood thinner selection after invasive cardiology procedures or nicotine delivery method (patch vs. pill) based on nicotine metabolization rate. The rules are complex and require an easy way to maintain them as genetic science evolves.
3. Support for external report abstraction. Given the rapidly growing number of genetic testing labs surfacing, not all labs will likely be able to build electronic order and result interfaces. For this reason EHRs need to provide an easy way to abstract discrete data from textual reports for storage in the EHR’s discrete data model.
4. Make best clinical trials available. Often the ideal treatment for a specific patient is to be enrolled in a clinical trial that may not be known to the clinical care team. To do this, the EHR needs to provide a seamless way to match the patient’s genetic profile, past treatment, current performance status and other clinical data to an up-to-date database of open clinical trials. Then the results of that match need to be presented to the clinician and ideally the patient via a portal. The list of trials should be sortable by selection criteria such as geographic location, applicability to the current patient, likelihood of improving overall survival, and other important indicators.
5. Patient-finding features. Another helpful capability for clinicians and tumor boards is the ability to quickly find other patients within an institution that are a clinical and genetic match to similar patients currently in need of treatment support. This enables clinicians to identify treatment protocols that have worked in the past for patients with a similar disease burden. In time this capability should be extended to searching de-identified, semantically harmonized and consolidated databases across multiple EHRs.
6. Ability to share report data with patients. Just check any patient-centered cancer forum on the Internet and you will quickly see that patients are learning the science of genetics very quickly. They often know more about a specific genetic marker than their clinicians do. To assist in their efforts and to enable patients and providers to work together the patient portal of the EHR must also make genetic result data available to patients in ways that help them interpret and understand the usefulness of the results.
7. Ongoing monitoring of all patients for new actionable variants. Perhaps the most complex challenge we need to face is that the EHR needs to enable the clinical significance of genetics to change over time. As science uncovers the actionability of variants, the EHR needs the power to constantly look back across historic results for patients that may now be eligible for new treatments and tests. Additionally patients and provider organizations need the ability to opt-in or opt-out of being alerted about these new discoveries.
The list of requirements above is far from trivial but they are all key to realizing the power of genetic information at the point of care.
Providers, vendors and patients all need to work toward enabling and validating the power of these capabilities to improve patient outcomes. We cannot afford to miss the opportunity to personalize care or eradicate killer diseases.
By building the right software, the healthcare industry can improve the precision of care which benefits, patients, payers and providers.
Brian Wells is associate vice president of health technology and academic computing at Penn Medicine.
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