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GNS Healthcare - a Cambridge, MA-based precision medicine firm - received $23 million in a funding round led by Cigna Ventures, which looks to utilize GNS' machine learning tech to bring personalized predictive care to Cigna's 86 million customers, per FierceHealthcare.
GNS employs a novel approach to machine learning it claims is capable of discovering new causal relationships between data: It states that by modeling diseases and millions of data points - including geographic, imaging, clinical, and genetic data - it can accurately predict patient outcomes before care is applied.
Here's what it means: Precision medicine akin to GNS' could deliver care with pinpoint accuracy and lower costs - if it works.
For payers like Cigna, predictive precision medicine is the holy grail for cutting costs. Precision medicine like GNS' offering could lead to remarkable savings by allowing health plans to "confidently make intervention recommendations" on the kinds of care their members should be receiving.
For example, the tech could allow clinicians to experiment with drug delivery - testing via simulation which medications their patient would best respond to and cut down on the $136 billion spent on adverse drug reactions in the US each year.
But I (Zach) have reservations about whether GNS can deliver on its lofty claims. All machine learning software - even the "causal" machine learning models that GNS utilizes - must be based on underlying data sets and certain assumptions about how variables interact with one another.
For its simulations to assess whether a patient will respond better to treatment A or treatment B without analyzing documented real-world outcomes - as is the norm - GNS would likely need to model not only the targeted disease, but also how every other relevant health factor interacts with that disease.
There are two major hurdles that could hamper precision medicine firms from delivering clinical improvements:
- Healthcare's massive data interoperability barriers make data accumulation on the scale necessary to power precision medicine incredibly difficult. Healthcare data is notoriously difficult to move and share, with only 20% of hospitals using data from outside their organization. Furthermore, 25% of small hospitals and rural hospitals aren't using electronic methods at all for receiving patient care records, per a 2017 ONC report. The herculean effort required to unlock the insights of nondigital records and tap into the huge amounts of data siloed away across individual health systems could hinder the development of robust predictive care models.
- And the healthcare industry lacks the data necessary to account for basic differences across patient populations such as race and ethnicity. While ethnic and racial minorities account for 39% of the US population, they only make up between 2% and 16% of clinical trial participants. And this data discrepancy persists despite findings from the FDA that show 20% of drugs approved between 2009 and 2015 affected patients differently across race and ethnic groups, per MedCity News.
So, if critical patient data is either inaccessible or doesn't exist, how reliable can GNS' models really be for predicting clinical outcomes? Perhaps GNS is onto something revolutionary, but precision medicine has yet to fare well in clinical applications: No more than 8% of precision medicine interventions in cancer treatment have been successful, per The New York Times.
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