By Rich Herrington, SoftServe
Personalized medicine is moving healthcare forward, but is the industry prepared for the change?
Healthcare providers must prepare to meet the collaborative demands of highly informed, tech savvy patients. The one-size-fits all approach to traditional medicine has never been ideal and advancements in healthcare have presented more sophisticated, individualized options. Precision medicine—customizing treatment based on environmental factors, genetic testing, pharmacogenetics (PGx), and more—is fast gaining adoption as the benefits of personalized treatment plans gain general awareness.
However, as patient requests for genetic testing and personalized care increase, the industry will amass vast amounts of data. To deliver this next advancement in personalized care, the onus is on healthcare providers to organize, manage, and leverage this data for the benefit of their patients.
Providers will require a strong data management strategy and governance as well as supportive technology to manage the influx of data and ensure its accessibility and security. Artificial intelligence (AI) and machine learning (ML) capabilities also will be needed to analyze patient data and automate personalized treatment. AI and ML should (and will) be a standard product requirement for electronic health record (EHR) and electronic medical record (EMR) systems. Healthcare providers can act by communicating this need to EHR vendors to ensure system readiness.
Making Medicine Personal
Personalized medicine applies to both internal factors (such as gene testing for medication tolerance or PGx) as well as external factors, such as diet and exercise. In this personalized approach, additional data—down to the genetic level—is progressively collected and stored for the patient. This is used to determine how a clinician can provide better care for the patient based on meaningful insights, creating a more tailored care plan and increasing medication effectiveness.
PGx is a burgeoning facet of precision medicine leveraging gene testing for better medical care. Ongoing clinical trials and research into gene testing for medication effectiveness offer better options for patients who need specific medications, such as cancer-related or depression drugs.
This field of pharmacogenetics—using genetic testing to discover medication responses to specific genes, is rapidly evolving. According to the Genetic Testing Registry, there are nearly 60,000 tests for 11,505 conditions and 18,600 discoverable genes. Providers often report better compliance and adherence within their patient populations, due to improved patient concordance. Patients are more likely to continue taking medications as prescribed when they experience fewer negative side-effects, such as dizziness or nausea.
There are still challenges on the road ahead to full adoption of this new approach, and clinicians must be able to meet informed patient requests for advanced testing. While providers understand current recommendations for medication dosing, new prescription requirements will continue to evolve as drugs are found to be more efficient in connection with PGx testing.
Genetic Testing For Medication Tolerance
Genetic testing has gained popularity as speed increases and costs decrease. In 2001, sequencing a human genome cost about $100M. 2014 began with an announcement of a new machine that could sequence 16 human genomes in three days at a cost of approximately $1,000 per genome. Healthcare providers should be encouraged by this progress as once DNA sequencing is completed, the results will never change. A patient can have his or her entire genome sequenced once and have usable data for the rest of their life. The question then becomes: how will this data be stored and leveraged in future years?
There are approximately three billion base pairs of genomes for each person, or nearly 200 gigabytes of raw data. Certain health conditions are linked to a single letter of code, while others are linked to combinations spread throughout. Given the size of this data, widespread adoption of DNA-based medicine will require management and optimization of Big Data and cloud that empower AI-driven technologies. Utilizing this vast amount of patient data would be impossible without superior technologies to drive it forward.
Instead of sequencing the entire genome for a patient, genetic testing for specific treatment decisions are often more useful for caregivers. Using AI and ML built into the electronic health record, a clinician will be notified as the EHR system alerts him or her to disease correlations, based on population trends, the patient’s lifestyle, and familiar diseases. PGx can be incorporated into the care plan for disease discovery, as well as medication tolerance.
Better Care, By Request
Healthcare information, including research studies and self-diagnosis websites, have become readily available in recent years. With patients taking a more active role in their personal healthcare ecosystem, it is expected that Millennials and younger generations will make educated, previously unprecedented requests of their healthcare professionals. This includes asking for genetic testing and medication tolerance testing. Clinicians need to respond with advanced tools to manage the data, along with AI/ML abilities to take action on the data.
Genetic testing has direct cost and adherence implications, which are often seen by patients themselves. In a 2015 study of 13,000 behavioral-health patients, those who had genetic testing received fewer drugs, and saved an average of $1,036 in annual prescription costs compared to patients who were not tested. The tested patients were also 17 percent more likely to keep taking their medications as prescribed.
Personalized medicine has many other uses, beyond influencing which medications a healthcare provider should prescribe. A few of the most beneficial examples include:
While looking at personalized medicine from a single patient view is exciting, translating the results across populations to uncover variants in disease process is even more fascinating. Programs like MyCode, launched in Pennsylvania, USA, is a network of health centers working to scan the DNA of their patient populations for similar gene types related to cancer or cardiovascular (CV) disease. While this is one example of an ongoing initiative to improve the overall patient population, regulatory bodies still greatly restrict applications in an attempt to mitigate misleading information.
Regulatory compliance aside, as more populations continue to grow and collect genetic data, storage and management requirements will become more complex. In addition, the ability to process and analyze data to make meaningful insights will be the deciding factor whether
personalized medicine advances quickly or lags behind.
To meet the ongoing requests from a more informed patient population and realize trends across patient populations, healthcare professionals need to ensure they have the necessary technical capabilities built into their EHR from the start. Managing the ever-growing amounts of protected personal information (PPI) is the beginning, ensuring actionable results and better care are the end results.