Personalised medicine: How big data can help mitigate the risks
Words written by EtQ life science product manager Alexandre Alain.
Personalised medicine is being heralded as the future. Harnessing big data in medicine yields valuable insights and advances knowledge.
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.
Personalised medicine promises improved health and better outcomes by utilising 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 personalised medicines. According to BioPharm International, the number of personalised 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 personalised 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 personalised 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
§ Funding issues
§ 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 personalised medicine lawsuits.
Alleviating Risk
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 centralisation of big data in healthcare, releasing the potential of personalised 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 analysed 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 personalised medicine.
The key steps to harnessing big data, as outlined by McKinsey and Company involve:
Integration
Breaking down silos and enabling a single authoritative source.
Tip: Targeting important data first means you get straight to the hub of information.
Collaboration
Communicating better with partners in research, development, commercialisation 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
Prioritising 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.
Real-world outcomes
Providing data in this field can assist with closer targeting of specific patient groups.
Tip: the FDA has incentivised research into health economics and outcomes.
Takeaways:
§ Tackle information silos and become more data-centric.
§ Foster a new information sharing culture across your organisation.
§ Scrutinise the in-house capabilities regarding new technologies and analytics. Consider outsourcing.
§ Tackle entrenched mindsets, for example fear of regulators.
§ Flag up the advantages of utilising big data to bolster flagging R&D.