Industry Perspectives: Genomic Data Delivery Must Span the Enterprise
By Joel Diamond, MD, FAAFP
For The Record
Vol. 31 No. 9 P. 8
Many in the industry laud precision medicine as the new standard of care.
And it stands to reason that having highly personalized information about patients, including their genetic/genomic profile, could lead to more accurate, effective diagnoses and treatments.
As with any transformation, however, questions remain about how to translate the promise of precision medicine into reality. Among the most pressing issues is how to manage the data generated by genetic and genomic testing and make them actionable within the provider’s standard workflow.
Generally speaking, this issue is not new. Since the advent of the EHR, health care has been struggling to make patient data convenient and useful. Decades later, we’ve not completely solved the interoperability issue—and we stand at the threshold of introducing yet another voluminous data set to the mix.
Siloed Data Limit Value
Physicians who have begun to order molecular tests typically receive the resulting genomic reports as a pages-long scanned document, not as discrete data. This means it is saved as an attached document in the EHR. But to incorporate this knowledge into their decision making, physicians first must be aware that the test was conducted and remember that the results are available somewhere in the EHR. Then, they must leave their normal workflow and hunt for the molecular lab report. Once they access it, physicians must interpret and apply the information as they consider other factors (such as comorbidities and consequent treatments) in the broader clinical record.
Besides being inconvenient and time-consuming, this approach greatly limits the value health care organizations can derive from genetic and genomic data.
First, only the physician ordering the genomic test knows the data are available. Others in the department—and across the broader organization—aren’t even aware the information exists.
Second, the genetic/genomic results aren’t integrated with the patient’s clinical information, so the provider doesn’t have a comprehensive view of how all factors might affect therapeutic decisions.
Third, it is likely the physician uses the test results only for the immediate clinical need, not realizing some of the insights available in the report can inform future decisions as well.
Results Must Be Searchable, Sharable, Reusable
To fully leverage the potential of genetic and genomic information, health care organizations must establish an informatics strategy that allows these data and resulting insights to be shared and used wherever appropriate.
Consider the following example:
A 47-year-old patient, Joanne, is being treated for breast cancer. Her oncologist, Dr. Clarke, performs genomic testing on the tumor tissue to better understand the disease and orders a targeted therapy shown to positively impact Joanne’s form of cancer. In the process, Dr. Clarke discovers that Joanne’s tumor markers indicate that she does not require chemotherapy following surgery and would benefit equally as well from hormone therapy.
Typically, an oncologist might simply prescribe tamoxifen in this situation. Instead, Dr. Clarke orders a pharmacogenomics test, a genetic panel that reveals which medications are most effective and which ones might not work well or carry toxicity risks.
Upon learning that Joanne does not metabolize tamoxifen well, he orders a different medication. The pharmacogenomics test indicates that this new medication interacts poorly with an antidepressant Joanne is taking, so Dr. Clarke reaches out to Joanne’s primary care physician (PCP) to share the results. Together, they determine which combination of hormone therapy and antidepressant will best manage both Joanne’s long-term cancer risk and her depression.
In addition, the PCP can use the pharmaceutics test results in the future to help determine the best therapies for diseases and conditions that might impact Joanne’s long-term health. If she were to develop high blood pressure, for instance, the PCP could use the test results to select the best drug for faster and better results.
If Joanne’s genomic results were locked in a scanned document that only Dr. Clarke could access, neither he nor Joanne’s PCP would be able to effectively treat her current conditions (cancer and depression) or her potential future issues (high blood pressure).
Look Beyond the EHR
As they begin to consider this burgeoning data management/data utilization issue, many health care systems are wondering whether their EHR provides the answer. However, EHRs may not offer a total solution. For example, while EHRs do accept genetic/genomic reports into the system as scanned documents, results are rarely saved as discrete data. Therefore, they can’t be integrated with other clinical information or analyzed for other purposes such as cohort identification to support population health initiatives.
In addition, EHR functionality was not originally developed to accommodate genomic data, which is returned in a unique vocabulary. Even standard EHR features such as family history functionality are not robust enough to collect and display insights that can help identify which patients could benefit from genomic testing for potential inherited diseases.
Health care professionals need to consider one additional factor when determining how to manage genomic data: All results from testing are not immediately actionable. While genomic science is advancing at an incredibly rapid rate, test results might include genetic variations that haven’t been fully explored. Researchers and clinicians might not yet know what these anomalies mean.
This raises questions around how to store this information so that providers can use it when its significance is better understood but at the same time not expose the organization to potential liability by maintaining data that could have tremendous impact on care decisions.
Checklist Guides Data Management Strategy
As the movement toward incorporating precision medicine data at the point of care intensifies, health care organizations need to carefully assess their data management strategy. The following checklist can assist in determining a successful go-forward plan:
• Establish a task force to develop a precision medicine strategy and execution plan. Make sure all stakeholders are represented, including clinicians and informaticists.
• Provide opportunities (such as trips to conferences and peer-to-peer networking opportunities) for the task force to learn more about the current state of precision medicine and the barriers to success.
• Empower the task force to learn which departments and specialty areas might already be conducting genetic and genomic testing (eg, oncologists or maternal-fetal physicians).
• Determine organizational priorities: If genomic data are already being gathered, where does leadership want to head next? If genomic testing is not yet being done, where will the organization start?
• Define objectives and benchmarks to measure success (eg, enhance patient outcomes, achieve higher quality scores).
• Assess the current IT informatics infrastructure to ensure the organization can support point-of-care precision medicine.
- Can leadership apply adequate governance to genetic and genomic ordering practices?
- Does the current system consume data from multiple molecular labs?
- Can the system ingest results as discrete data to be integrated with clinical information?
- Can providers access information from within their clinical workflows?
- Can all care teams across the enterprise leverage the information if appropriate?
- Will the organization be able to accommodate changes in genomic science as the field of precision medicine evolves?
• Communicate broadly both within the organization (eg, providers, IT team, support staff) and into the community (eg, patients, family members, residents) about the value of precision medicine and why leadership believes it can improve care delivery.
Precision medicine holds great promise; many factors are amplifying its role at the point of care. In these early days, health care leaders have an opportunity to build an infrastructure that avoids data management mistakes of the past, while ensuring valuable genomic data can be leveraged by all stakeholders, now and into the future.
— Joel Diamond, MD, FAAFP, is adjunct associate professor of biomedical informatics at the University of Pittsburgh, a diplomat of the American Board of Family Practice, and a fellow in the American Academy of Family Physicians. He cares for patients at Handelsman Family Practice in Pittsburgh and serves as chief medical officer for 2bPrecise.