Personalized Medicine – How Big Data Can Help Mitigate the Risks
Personalized medicine is being heralded as the future. Harnessing big data in medicine yields valuable insights and advances knowledge.
Better utilization of big data in medicine could generate up to $100 billion annually across the US healthcare system, according to the McKinsey Global Institute.
In Europe, the pharmaceutical industry is big business. The European Commission established the big data value public-private partnership in October 2014, and has earmarked €500 million. Investments are expected to reach €2.5 billion by 2020. In Germany alone, companies have generated €6 billion through big data solutions in the past year.
Personalized medicine promises improved health and better outcomes by utilizing genetic testing to identify predisposition to disease and effective treatment. Manufacturers are increasingly taking interest, conducting research into the power of big data in order to improve the potential of personalized medicines. According to BioPharm International, the number of personalized medicine products more than quadrupled between 2006 and 2012, and the U.S. market will double from $9.2 billion in 2013 to $18.2 billion in 2019.
But personalized medicine is not without controversy. With the increased ability to predict and cure disease comes increased patient demand. Spiralling expectations mean patients are seeking cures for conditions previous generations endured. Equipped with information gleaned from the Internet, they arrive at their clinician’s practice armed to the hilt with new-found knowledge.
The risks of personalized medicine include:
Public policy and legal issues – with implications for both healthcare providers and patients
Scientific uncertainty – this is an evolving field
Social and economic concerns
Possible healthcare discrimination
The need for training of doctors in genetics
Legal liability – for not recommending or performing genetic testing
This last point is perhaps one of the most pressing. Patients may sue if they feel they haven’t been offered genetic tests. The healthcare profession is advised to start acting now to prevent personalized medicine lawsuits.
The healthcare industry has always been awash with data. New advances mean it is springing up in new areas, beyond traditional information silos and archived records. Big data now travels fluidly between healthcare stakeholders and partners:
Electronic health records are deployed across systems
Clinical trial data is increasingly shared
Genome sequencing technologies yield data
Social media and online patient and clinician communities allow information sharing
Smartphones and fitness devices provide real-time data
Cloud-based platforms allow for the centralization of big data in healthcare, releasing the potential of personalized medicine. You can gain valuable insights into patients, helping to identify serious illness. By driving data towards providing answers, you will improve patient outcomes. Big data in healthcare can also drive forward research and clinical trials.
In a data-driven age, where every step, every calorie can be recorded, the possibilities for big data in healthcare are exciting. The key to your progress lies in how big data is analyzed by your company, and how the information gleaned can help mitigate risk and increase efficiency and productivity. Whether you choose to do this in-house or contract out to data professionals, your investment now will mark you out as being at the cutting edge of personalized medicine.
The key steps to harnessing big data, as outlined by McKinsey and Company involve:
Breaking down silos and enabling a single authoritative source.
Tip: Targeting important data first means you get straight to the hub of information.
Communicating better with partners in research, development, commercialization and delivery.
Tip: Create a communications strategy that covers information exchange. Mitigate legal, regulatory and intellectual property risks.
IT-enabled portfolio management
Allows data-driven decisions to be made quickly.
Tip: Use a visual dashboard to lay out key priorities.
Cutting edge technologies
Prioritizing technologies that produce data quickly.
Tip: Connect genotypes to clinical trial results.
Real-world data gathering
Gather information through remote monitoring of patients.
Tip: Use data in R&D, drug efficacy and to record patient reactions to ensure correlation with intended outcomes.
Improve clinical trials
More efficient data exchange and smarter devices will enable better analysis of small subgroups of patients.
Tip: Target specific populations in smaller trials that cost less.
Improved risk management
Rare adverse events can be spotted using big data
Tip: Physician communities and patient commentary can help flag up safety issues, facilitating early response and the alleviation of concerns.
Providing data in this field can assist with closer targeting of specific patient groups.
Tip: the FDA has incentivized research into health economics and outcomes.
Tackle information silos and become more data-centric.
Foster a new information sharing culture across your organization.
Scrutinize the in-house capabilities regarding new technologies and analytics. Consider outsourcing.
Tackle entrenched mindsets, for example fear of regulators.
Flag up the advantages of utilizing big data to bolster flagging R&D.