How AI Is Helping Anonymize Clinical Trial Submissions 🧪
Hello,
We’re back on schedule with 2 monthly newsletter editions published in September and October!
I’ve been traveling the past month — attending a CDAO conference, talking at a leadership summit, and meeting with clients and network connections.
I’ve been thoroughly enjoying the in-person vibes. The brilliant fall colors on the East Coast around this time of the year have been a nice plus as well. 🙂
This newsletter will take you about 4 minutes to read.
I. Spotlight: How AI Is Helping Anonymize Clinical Trial Submissions 🧪
We’re seeing a record number of data breaches over the pandemic. A recent IBM report found that data breach costs have also shot up.
Healthcare tops the list of most impacted industries, with an average data breach costing $9.2 million per incident. Sensitive customer data was the most common type of information exposed in these breaches.
Pharmaceutical and healthcare firms operate under stringent guidelines that mandate the protection of patient data; hence any breach can prove costly. For example, throughout the drug discovery phase, firms collect, process, and store personally identifiable information (PII). When firms make clinical submissions at the end of the trials, they must protect patient privacy in the published results.
Regulations such as the European Medicines Agency’s (EMA) 0070 and Health Canada’s Public Release of Clinical Information (PRCI) lay out specific recommendations for anonymization of data to minimize the risk of re-identifying patient details from the results.
In addition to advocating data privacy, these regulations mandate sharing of trial data to enable the community to build upon the research. This poses a dilemma for companies.
How can pharma firms balance data privacy with transparency while publishing results in a timely and cost-efficient manner? Here’s where artificial intelligence (AI) can help by potentially saving over 97% effort in the submission process.
Why anonymization of clinical study results (CSRs) is tough
There are three key challenges firms run into while anonymizing clinical submissions:
1. It’s tricky to handle unstructured data: A significant part of clinical trial data is unstructured. Study results have large volumes…
II. Industry Roundup:
1. Article: Your Data Initiatives Can’t Just Be for Data Scientists
09 minutes | HBR | Thomas C. Redman
Companies need to see *all* employees as a part of their data strategy solution. Every data project should be clear on who it affects and how to get them involved. They are vital to the project’s successful implementation & adoption. Data teams should equip employees with the necessary tools to formulate & solve their own problems.
2. Article: AI Could Help Increase the IVF Success Rate
05 minutes | IEEE Spectrum | Joanna Goodrich
Many decisions related to IVF are made on a doctor’s gut feeling resulting in a low success rate. Instead, AI models can learn from patient information, treatment plans, and outcomes to provide more scientific recommendations to doctors. This data-backed decision-making has been shown to increase the success rate of IVF.
3. Article: A Better Way to Put Your Data to Work
14 minutes | HBR | Veeral Desai, Tim Fountaine, and Kayvaun Rowshankish
Companies that treat data like a product reduce the implementation time of new use cases by 90% reports this article. It introduces the concept of a data product and shows how to develop one. The authors share the five common modes of data consumption and explain why data prepared as a product can best address most business application needs.
III. From my Desk:
1. Article: How To Implement Decision Intelligence in Your Enterprise With A Semantic Layer
7 min | AtScale
Did you know that 79% of firms end up with zero or negative returns on their data analytics investments? This article introduces Decision Intelligence and shares the three key disciplines of DI. I explain each of these with a real-world example and highlight how a semantic layer can help.
2. Post: I asked an AI to write an abstract for a talk on Real Estate and AI I delivered this week. The results spooked me 😮
2 min | LinkedIn
I used an open-source algorithm to write me an abstract for a keynote talk on Real Estate and AI. I was surprised with the result because the abstract I had prepared a few weeks earlier was close to this! In this post, I share the final output that surprised me and share the tool I tried.
Having a hard time picking your wardrobe today👗? Why not ask this AI?!
Thank you for subscribing and reading the newsletter. I appreciate your attention,
Ganes.
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I’m Ganes Kesari. I publish ‘Data-Driven Future’ to help understand how data shapes our world, explore key trends, and explain what they mean for you today. I speak and write to demystify data science for decision-makers and organizations.
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