A New Perspective on Precision Medicine

Hoda Sayed-Friel Executive Vice President,
MEDITECH Professionals Services

As we move into this next era of precision medicine, it is important to reexamine our perspective of what that actually means and especially how healthcare IT can make the promise of personalized treatment even more personal. We’ll start by looking at current treatment protocols, why they need to change, and highlight how MEDITECH Expanse intersects social determinants of health, genetic indicators, and remote patient monitoring into one transformative platform to power precision medicine.

Current treatments are predicated on evidence-based medicine; however, these “best practices” are often based on research and conclusions that do not reflect the breadth and scope of the entire population. While effort is made to account for population variability across age, gender, and ethnicity criteria for most clinical research, the reality is most of our historical clinical guidelines (and even current ones) have been informed by a smaller subset of the general population, resulting in a one-size-fits-all set of protocols.^1-3 Physicians and caregivers need to move away from this traditional practice by evaluating all the variables that make each patient unique. While the starting point can be a traditional protocol, emphasis should be placed on the efficacy of the protocol and whether it will work for my patient or will achieve the desired outcomes. 

How do we move beyond age, gender, weight, and routine lab results? What other types of variables do we have access to and which should we start with? What if we could review a variety of important variables together in the EHR, at the time of care, to help guide treatment decision-making?

Technology in healthcare is evolving at a rapid pace, allowing for access to more data to help with decision-making. Molecular genetic testing has evolved in its scope while simultaneously decreasing in cost, thus providing the ideal atmosphere to optimize comprehensive diagnostic and prognostic information from these genetic test results. The improved understanding of causative genetic mutations has also guided specific drug development for treatments targeting pathologic genetic mutations. These two conditions, along with early tumor detection used in combination with other data such as remote patient monitoring and social and environmental determinants, can give us a better “picture” of a patient and what is truly causing their pathophysiology. Unifying this information into a central place, like the EHR, is a noble yet achievable quest. Let’s review the data we have at our disposal today.

Genetic information most frequently comes to mind when speaking of precision medicine. Genetic testing is the use of a laboratory test to examine an individual’s DNA for variations, typically performed in the context of medical care, ancestry studies, or forensics. In a medical setting, the results of a genetic test can be used to confirm or rule out a suspected genetic disease. Results may also be used to determine the likelihood of parents passing on a genetic mutation to their offspring. Genetic testing may be performed prenatally or postnatally. Genetic testing is also used to study the genomes of tumors in cancer cases. It can be a powerful diagnostic and predictive tool that can help people understand more about the biological basis of a health condition they may already have or may be at an increased risk of developing in the future.⁴ Genetic testing can also tell us how well a patient will metabolize a particular medication and predict a drug's effectiveness or drugs to avoid (pharmacogenomics); done more routinely, it can be used to reduce or prevent hospital readmissions.⁵

Pharmacogenomics (PGx) is the study of how variations in a patient’s genetic profile determine their body’s responses to specific medications, both in terms of what a drug does to the body and what the body does to a drug. Variants may impact drug metabolizing enzymes, transporters and targets, ultimately resulting in clinical differences in patient response to medications either improving or limiting a drug’s effectiveness or a drug's toxicity. Significant challenges and barriers specific to PGx and comprehensive medication management include a lack of consistency in the patient care processes, when PGx testing is necessary, what patients to test, what types of PGx tests are to be used, and lastly the application of the PGx results to overall patient care.⁶

For example, if codeine is given to a patient who is a Poor Metabolizer, based on their CYP2D6 phenotype, the patient is unable to convert enough of the drug into its active form to obtain relief and may be accused of drug-seeking if they complain of inadequate analgesia. If the patient is a Rapid Metabolizer or Ultra Metabolizer, they can experience the opposite effect, and a resultant higher than expected level of sedation, addiction, and other systemic side effects at lower doses than normal metabolizers.^7-8 Clinical decision support at the time of medication ordering will alert and advise providers of conflicts so alternative actions can be taken.

Somatic genetic testing of any tissue or organ developing an acquired mutation can both diagnose and guide treatment in the oncology patient population and inform the best selection of oncology treatment options. Even beyond the traditional BRCA breast cancer gene mutations, and the common biomarker indicators such as HR status (hormone receptor) or HER2 status (Herceptin^Ⓡ receptors), oncology treatment protocols require even more precise genetic information. For example, in women with advanced metastatic breast cancer who have failed previous treatments, 40% of them will have a PIK3CA mutation; it is critical to know this information, because there is targeted therapy for advanced breast cancer against this genetic mutation specificallywith a CDK46 inhibitor that is now the “next line” therapy. Without that additional genetic knowledge, the specific life-saving therapy cannot be instituted. Less expensive testing methodologies now mean greater access for patients at large.  Codification of this data from testing labs along with interoperability to EHRs powers clinical decision-making. Consuming codified data directly from the testing lab and focusing on the pertinent markers and mutations become an important part of choosing the ideal treatment.

Now let’s look at another class of data: Social determinants of health (SDOH). Food security, transportation, housing, and other SDOHs play pivotal roles in the health of individuals and populations. A landmark 2016 study published by the American Journal of Preventive Medicine found that socioeconomic factors, health behaviors, and the physical environment determine more than 80% of health outcomes, with clinical care accounting for only 16% of health outcomes.⁹ Although SDOH data takes time to collect, it will help determine which barriers need to be addressed for treatment to be effective. Dividing those long questionnaires across appropriate caregivers, then unifying that data in the EHR, will encourage efficient data collection while still providing a comprehensive picture. Referrals to the appropriate social/behavioral or other services can then begin to address any barriers. Identifying these areas and dealing with shortcomings should be part of health equity initiatives.

Let’s also factor in more data. Periodic healthcare encounters are mere snapshots in time and are incomplete reflections of health status. As recently as 2018, research from the Center for Disease Control reported that over 50% of adults in the study suffer from at least one of 10 selected diagnosed chronic conditions.¹⁰ Why not use remote patient monitoring to help with this problem? Remote physiologic monitoring (RPM) involves the collection and analysis of patient physiologic data that can be used to develop and manage a treatment plan related to a chronic and/or acute health illness or condition. It allows patients to be monitored remotely while in their homes, and for providers to track patients’ physiologic parameters (e.g., weight, blood pressure, and glucose) and implement changes to treatment as appropriate.¹¹ Medical devices and reliable apps, as well as clinical trials vetted with real world data analysis, are the best sources for RPM information. They must meet the FDA’s definition of medical device¹², however, they do not have to be FDA-cleared/registered. Small, inexpensive, easy to wear, and noninvasive are preferred by patients. Devices reimbursed by insurance plans also ease the financial burden of RPM. Now that CMS reimburses clinician time for setup, education, and review of data, RPM is more likely to be used as a clinical tool. We’ve found that data volume from remote monitoring can be voluminous, but algorithms and patient-specific parameters can filter that volume so only relevant data is highlighted.

In the past, even if all of this data was collected, it was scattered across many places, even when using a single EHR. Wouldn’t it be great to present all of this data together? What if an EHR could harmonize all of this data and provide it in a single view? Here is where MEDITECH Expanse comes in. Release of the Genomics application, coupled with efficient SDOH and RPM collection, interoperates, codifies, and unifies all of this data to give clinicians a comprehensive view of the patient — setting the stage for a better informed and truly personalized treatment plan. This new perspective on personalized medicine aided by the MEDITECH EHR demonstrates the power that data brings to healthcare. 

MEDITECH Professionals Services consultants have both the knowledge and skills to harness the power of this data and this new perspective on precision medicine.

Caught your interest?  Ask me for more information at hsayedfriel@meditech.com

References:

[1] Antman K, Amato D, Wood W, et al. Selection bias in clinical trials. J Clin Oncol. 1985;3(8):1142-1147. doi:10.1200/JCO.1985.3.8.1142

[2] Miller KD, Rahman ZU, Sledge GW Jr. Selection bias in clinical trials. Breast Dis. 2001;14:31-40. doi:10.3233/bd-2001-14105

[3] Sharpe N. Clinical trials and the real world: selection bias and generalisability of trial results. Cardiovasc Drugs Ther. 2002;16(1):75-77. doi:10.1023/a:1015327801114

[4] NIH National Human Genome Research Institute. https://www.genome.gov/genetics-glossary/Genetic-Testing. Updated June 1, 2022.  

[5] David SP, Singh L, Pruitt J, et al. The Contribution of Pharmacogenetic Drug Interactions to 90-Day Hospital Readmissions: Preliminary Results from a Real-World Healthcare System. J Pers Med. 2021;11(12):1242. Published 2021 Nov 23. doi:10.3390/jpm11121242 https://doi.org/10.3390/jpm11121242

[6] Morreale AP, McFarland MS. Legal and liability implications of pharmacogenomics for physicians and pharmacists. The Journal of Precision Medicine. 2021;7(4). https://www.thejournalofprecisionmedicine.com/wp-content/uploads/legal-liability-implications-pharmacogenomics.pdf

[7] Kirchheiner J, Schmidt H, Tzvetkov M, et al. Pharmacokinetics of codeine and its metabolite morphine in ultra-rapid metabolizers due to CYP2D6 duplication. Pharmacogenomics J. 2007;7(4):257-265. doi: 10.1038/sj.tpj.6500406 Epub 2006 Jul 4. PMID: 16819548.

[8] Agarwal D, Udoji MA, Trescot A. Genetic Testing for Opioid Pain Management: A Primer. Pain Ther. 2017;6(1):93-105. doi:10.1007/s40122-017-0069-2
 
[9] Hood CM, Gennuso KP, Swain GR, Catlin BB. County Health Rankings: Relationships Between Determinant Factors and Health Outcomes. Am J Prev Med. 2016;50(2):129-135. doi:10.1016/j.amepre.2015.08.024

[10] Boersma P, Black LI, Ward BW. Prevalence of Multiple Chronic Conditions Among US Adults, 2018. Prev Chronic Dis. 2020;17:E106. Published 2020 Sep 17. doi:10.5888/pcd17.200130 https://www.cdc.gov/pcd/issues/2020/20_0130.htm.

[11] 2021 Medicare Coverage of Remote Physiologic Monitoring. American Association of Medical Colleges. https://www.aamc.org/media/55306/download

[12] How to Determine if Your Product is a Medical Device: 201(h) of Federal, Food, Drug, and Cosmetic Act. U.S. Food and Drug Administration. Published 12/16/2019. https://www.fda.gov/medical-devices/classify-your-medical-device/how-determine-if-your-product-medical-device