🧐 Healthcare's Hidden Gold

Good morning! 

Welcome to Healthcare AI News, your weekly dose of the latest developments and headlines in the world of Healthcare AI.

In this issue, we explore:

✅ Headlines: Humans are biased. Generative AI is even worse

✅ Industry: What will Chief AI Officers do? 4 healthcare leaders weigh in

✅ Feature: Dark Data

✅ Tech:  How health system CIOs, CISOs collaborate

✅ Deal Flow: Novartis AG to acquire Chinook Therapeutics for $3.5 billion

Be sure to read on to see this week's Top headlines, Industry, Tech, and M&A news.

Let's dive in.

HEADLINE ROUNDUP

• Humans are biased. Generative AI is even worse (Read More)

• New “AI doctor” predicts risk of death with 85% accuracy (Read More)

• Stanford Medicine and Institute for Human-Centered AI announces RAISE-Health, an initiative focused on responsible AI innovation (Read More)

• Mark Cuban says healthcare has to have more of a 'Netflix model' (Read More)

• 42% of CEOs say AI could destroy humanity in five to ten years (Read More)

• Exploring the benefits and drawbacks of integrating ChatGPT into Healthcare (Read More)

• ChatGPT fails American Urological Association self-assessment exam (Read More)

• Is too much AI causing mental health problems? (Read More)

💡 Keep reading to catch up on Industry, Tech & Deal flow

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INDUSTRY NEWS

• What will Chief AI Officers do? 4 healthcare leaders weigh in (Read More)

• Oracle trains generative AI to create model for medical, 1st responders (Read More)

• Meet the health tech founder using algorithms to tackle Mental Illness (Read More)

• The missing steps in the application of AI on remote health monitoring (Read More)

• ML provides personalized Hypertension treatment recommendations (Read More)

• Obesity changes the brain, with ‘no sign of reversibility,’ expert says (Read More)

• 4 hacking groups targeting healthcare (Read More)

• Report shows how big pharma Is price gouging cancer patients (Read More)

THE FEATURE

Dark Data

Healthcare is full of dark data. Here are 3 steps to leveraging yours.

If you’ve taken a basic biology class, you’ve probably come across this estimate: Only about 1 percent of DNA codes proteins. The rest of the genome is noncoding—once referred to as “junk DNA.” But now, scientists know many noncoding sequences do serve functional roles.

It turns out, the data produced across industries is a lot like DNA. A vast majority (about 90 percent) of it is unused—otherwise known as dark data.

Healthcare is no exception. Our industry abounds with dark data, from unstructured patient information to health system financial details. And with the acceleration of artificial intelligence, more and more ways of using that dark data are coming to light.

But dark data isn’t just an opportunity for our industry. Analyzing it must be an urgent priority. Unexplored, siloed data can cause miscommunications, friction, poor interoperability, and inefficiencies across healthcare.

Today, we’re exploring 3 action steps you can (and should) take on your organization’s dark data—to uncloak a more data-informed, secure, and efficient healthcare operation.

1. Identify your dark data

Yes, de-siloing data is key for seamless collaboration in any healthcare organization.

But before you can overhaul your organization’s data architecture, it’s important to identify where you are collecting and storing dark data.

Take the example of patient information. You need to:

• Distinguish between SSNs, demographic information, and health information.

• Determine who has access to it and who needs access to it.

• Locate where that data lives—from EHRs to employee workstations.

Tip: When possible, opt to not store data. Certain data must be stored for a certain period of time, but chances are you’re collecting and storing data you don’t need to be, creating more dark data to sift and stumble through.

2. Audit your own data architecture & cybersecurity

In the U.S., the FTC has begun cracking down on data privacy, fining digital health companies like Premom and GoodRx for unauthorized data sharing.

Let’s avoid these kinds of consequences when managing your dark data. Ask yourself these questions:

• Does your organization have the right IT infrastructure to support the massive amounts of data you’re collecting and storing?

• Do you have processes for normalizing and translating unstructured data, such as with the HL7 FHIR standard widely used in healthcare data analytics?

• And even if your IT’s security is up to snuff, what about the third-party vendors your organization interfaces with? Don’t let interoperability become a liability.

You need to understand how your current dark data is stored and how you intend to store it once it’s cleaned, segmented, and analyzed.

Remember: Wrangling your dark data isn’t a one-time project, but front-loaded planning will make it easier down the road.

3. Using AI to analyze your dark data

The vast majority of dark data in healthcare is unstructured, including call records, emails, and medical imaging. This is where AI-enabled analytics get to shine.

When you’re managing text-based data, rely on natural language processing (NLP) techniques for:

• Keyword extraction

• Spam detection

• Sentiment analysis

• Intent classification

This will help turn, for example, your disparate customer interaction data into Voice of Customer data for marketing.

Media processing techniques are your friends when it comes to non-text data like images, video, and audio. These approaches include speech-to-text transcription and optical character recognition (OCR).

Choosing the right AI-enabled techniques and vendor solutions is key to making sense of your dark data—and maximizing your ROI.

To get you inspired, here are a few healthcare NLP and media processing startups to watch:

• Emtelligent — medical text NLP processing

• Plasticity — clinical dialogue analysis and extraction

• Navina — actionable patient data organizing for primary care

• MedInReal — voice recognition and transcription for clinicians

• PathAI — AI-enabled digital pathology slide analysis

4. Using AI to analyze your dark data

Need more inspiration? Here’s how healthcare organizations are already using AI to make their dark data actionable:

• Health insurance: Improving customer onboarding and KYC processes by automating form digitization via OCR, driving up to 80% cost savings for these processes.

• Medical imagining: Improving classification of screening images for accelerated and precise diagnosis of diseases like lung cancer.

• Medical research & public health: NLP analysis of conversational transcripts with clinical trial participants for the more efficient identification of eligible participants.

In the future, exploring dark data will lead us to medical and corporate insights we’re beginning to see now—from patient-centered health data APIs to precision population data analysis.

The Healthcare AI News Perspective

When it comes to handling your dark data, we urge you to take action sooner rather than later.

Not only may your bottom line benefit from the business insights you unearth, but you may uncover areas of security vulnerability lurking within your business.

How do you think exploring your dark data may improve your own healthcare enterprise? Reply and let us know!

TECH NEWS

• Leveraging vector databases for better healthcare outcomes (Read More)

• Two different seasonings for the soup: How health system CIOs, CISOs collaborate (Read More)

• How NLP can provide deeper, actionable data insights for all healthcare stakeholders (Read More)

• Self-healing code is the future of software development (Read More)

• Salesforce launches AI Cloud to bring models to the enterprise (Read More)

• Orca: A 13-Billion parameter model that outperforms other LLMs by learning from GPT-4 (Read More)

• The Prompt: 5 generative AI platform pillars + execs compare generative AI to past disruptions (Read More)

DEAL FLOW

• Chinook Therapeutics enters into an agreement to be acquired by Novartis AG for $3.5 billion (Read More)

• Sun Pharma to acquire Concert Pharmaceuticals for $576 million, advancing the potential treatment of Alopecia Areata (Read More)

• BurstIQ acquires business intelligence platform from Olive AI (Read More)

• Merit Medical expands portfolio with acquisitions of Dialysis Catheter Portfolio, BioSentry® Biopsy Tract Sealant System, and Surfacer® Inside-Out® Access Catheter System (Read More)

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