Discoveries advance on a regular basis as to how cannabidiol and related therapeutics can heal or at least relieve the pain associated with health conditions. From cancer and opioid addiction to chronic pain and glaucoma, medicinal cannabis shows great promise. As with other efforts to address a patient’s condition, this field will morph toward a more personalized set of therapy regimes. The broader precision medicine field has a significant head start, though, because of the years of both longitudinal and historical data studies. The medicinal cannabis field must leap ahead in this direction.
The proliferation of different, proprietary data sets is seen slowing the growth and penetration of more ‘traditional’ personalized medicine. Each pharmaceutical company, bio bank and research organization has already collected large amounts of data from clinical trials, patients, providers and other sources. But the data an organization owns might not contain the insights it needs to achieve a breakthrough in personalized medicine.
As medicinal cannabis providers collect and combine data sets, the key will be ensuring that those sets are correctly linked and that the data itself provides enough depth to yield real insights. That points to the need to set consistent standards for collecting data – a challenge in the fragmented world of health care providers generally and certainly in the medicinal cannabis space. The industry will benefit, however, through adoption of a common vocabulary with respect to therapies, conditions, and dosing. The broader world is practically swimming in data, but for the medicinal cannabis field to forge ahead, there is the need to focus on consistency and quality of data, not just quantity.
To the extent that ‘big data’ in this field exists or can be developed, the same analytical tools applied to other precision medicine studies can be leveraged for interpretation of the data. The importance of returning to first principles in data analytics. The advent of big data has helped enable the growth of personalized medicine. But if machine learning and analytics are to truly help transform health care, it won’t be through bigger data, but through harmonized, smarter data. Big data is a means to an end, and we need to think about the end points so we can harmonize the data via statistical analysis.
Creating a collaborative database obviously requires caution and significant protections for any identifiable patient data. Beyond the privacy concerns, there is considerable cost and effort to developing a valuable data set, and that data will be subject to business models and monetization before firms will contribute data freely into any consolidated database.
Such a collaborative database could also help standardize the language around data analytics – another important step to achieving significant adoption, and to unleashing the power of personalized cannabis-based therapies.