People have unique identities, preferences, and lifestyle. It makes intuitive sense that medical treatment should provide individualized care, instead of a ‘one-size-fits all’ approach. Until recently, technological limitations have forced medicine to focus on treating illness using broad epidemiology, that is, large populations of patient response, using statistical averages. Today, patient-specific genetic data makes it possible for healthcare to correlate patient trends, creating intelligent insights across sub-populations of patients, to truly personalize medical care.
Genetic differences among populations mean medications may work for some patients, and not for others. Research shows prescription drugs on the market will only work for half of the patients who take them.
Personalized medicine may be the answer to increasing the percentage of patient efficacy. This medical approach takes into account a patient’s unique genetic make-up (i.e. his or her genes and gene networks) along with external factors, such as diet and exercise, to determine biological traits, specifically around medication tolerance. Personalized medicine proponents know that the benefits greatly outweigh that of traditional, generalized medicine. The advantages can be felt on the patient’s side (avoiding drug or drug combinations with negative side effects for the patient’s constitution), the provider’s side (customizing disease-prevention drugs), and even from the drug manufacturer’s perspective (such as reducing time, cost, and failure rates in pharmaceutical clinical trials).
As genetic testing becomes more readily available and less cost prohibitive, medicine will move from a ‘one-size-fits-all’ model to a tailored approach, creating more effective treatments.
Interoperability at the forefront
However, while the benefits are obvious and genetic testing is more accessible, interoperability amongst healthcare providers remains an ongoing issue. As massive amounts of data—specifically around genetic testing—enter into the healthcare realm, clinicians need intelligent ways to access data for an individual patient (within the medical record or portal). Systems to make analytical evaluations for uncovering trends in patient populations is the next step. For personalized medicine to truly be revolutionary in the medical community, providers need to have access to healthcare information and the analytical interpretation of that data.
The human biology is a complex system. While geneticists and scientists continue to generate new insights into how genetic complexity drives health and disease, providers and healthcare professionals will need better tools to store and mine the data. Big data, machine learning (ML) and artificial intelligence (AI) applications, such as data governance, advanced analytics, and augmented analytics will all play a central role in pushing the industry forward, ultimately saving and improving the lives of patients.
Recent advances in the field, easier access to genetic testing and customer demand for more individualized care have made a personalized medical approach a more common practice for healthcare providers. However, adoption remains low by clinicians. Clear guidelines have yet to be established for pharmacogenetics testing and personalized medicine and stakeholders continue to carefully examine its ramifications.
When it comes to improving the quality of patient care, healthcare leaders must act now to address the opportunity provided by the burgeoning data influx. The industry needs to prepare for the evolution of the changing landscape using new methods to develop, market, and price medications for patients. The ecosystem of patients, providers, pharmacists, and the biopharmaceutical companies must be tighter than ever.
The key for a successful medication revolution starts with a strong view into the future:
Big Data and ML (Machine Learning)
Data is the driving force behind personalized medicine. It’s the glue that makes the idea stick. By 2020, 1.7MB of new information will be generated per second for every human. To turn raw data, such as a patient’s genetic mapping, into intelligent and useable information, powerful tools for predictive analytics and statistical analysis will become table stakes. These insights will only be scalable with artificial intelligence and machine learning to derive meaning from the data.
To move to the next stage of adopting a personalized medicine approach, industry leaders need connected machines and computers to learn, evolve, and improve the care providers own learning. Genetic information can then be transformed from raw data into intelligent information to reveal key trends, ultimately improving care quality across populations.
Build infrastructure with DevOps
To create a future in which personalized medicine is a reality for all patients, drug producers must be ready and able to create an infrastructure to operationalize research requirements and create a sustainable process. Legacy systems may be unable to handle the flexibility and stability needed to support the rapid growth in this highly specialized field. Working with a DevOps partner like SoftServe will create a dramatically faster development cycle to optimize the outcomes of treatments.
Ensure data integrity and privacy
With more data comes more responsibility. Protected health information is one of the most critical areas in need of security investment, especially as pharmaceutical companies will require information down to the individual genetic code. Protecting this valuable data effectively will mitigate the risk of potential breaches. Incident detection and response will share common elements of all data and software security: people, process, and technology. This makes data strategy and governance an even greater imperative for healthcare.
Always move forward
Pharmaceutical companies have begun investing heavily into personalized medicine, with a clear focus on gene testing, as leading life sciences companies continue to double and triple spending in the next five years. Even with all the advances thus far, achieving this vision?? relies on creating a global healthcare ecosystem that allows all stakeholders to access copious amounts data in a secure, automated, and analytical system. For more information on the recent advances in pharmacogenetics, read our whitepaper, Personalized medicine and the big data challenge.