News, Analysis, Trends, Management Innovations for
Clinical Laboratories and Pathology Groups

Hosted by Robert Michel

News, Analysis, Trends, Management Innovations for
Clinical Laboratories and Pathology Groups

Hosted by Robert Michel
Sign In

Former FDA Commissioner Scott Gottlieb to Headline Artificial Intelligence in Healthcare and Diagnostics Conference

Gottlieb will speak about the state of AI in healthcare at the event May 11-12

Medical technicians in clinical laboratories and pathology groups may worry that artificial intelligence (AI) will eventually put them out of their jobs.

However, that’s not likely to be the case, according to former Food and Drug Administration (FDA) Commissioner Scott Gottlieb. He was just announced as a top speaker at the Artificial Intelligence in Healthcare and Diagnostics (AIHD) Conference, which takes place May 10-11 in San Jose, Calif.

Instead, expect AI in healthcare to help labs better aggregate and analyze an ever-growing repository of clinical data.

“As we start to digitize more of this information, build out bigger repositories, and correlate more of this information with experimental evidence that’s also captured digitally, it’s going to become an immensely powerful tool,” Gottlieb said during a 2021 webinar hosted by Proscia, which develops pathology software embedded with AI.

Scott Gottlieb, former FDA commissioner
Former FDA Commissioner Scott Gottlieb said AI in healthcare will “become an immensely powerful tool.” (Photo courtesy of: Worldwide Speakers Group)

“[AI is] going to be a predictive tool,” he continued. “So, now you start to think about digital data from traditional pathology, digital data from characterizing tumors to sequencing, alongside digital data capture through electronic health records. And you start to have a really powerful, robust set of information.”

Writing for MobiHealthNews last year, Liz Kwo, MD, also noted the potential of AI to deal with unstructured data—in other words, information that is not in a pre-set data model and thus difficult to analyze.

“In many cases, health data and medical records of patients are stored as complicated unstructured data, which makes it difficult to interpret and access,” wrote Kwo, who is Deputy Chief Clinical Officer at insurer Anthem and Faculty Lecturer at Harvard Medical School.

AI can seek, collect, store, and standardize medical data regardless of the format, assisting repetitive tasks and supporting clinicians with fast, accurate, tailored treatment plans and medicine for their patients instead of being buried under the weight of searching, identifying, collecting and transcribing the solutions they need from piles of paper formatted EHRs,” she added.

AIHD conference to explore the state of artificial intelligence in healthcare

At AIHD, Gottlieb will take part in a fireside chat and also contribute to a panel discussion with other keynote speakers.

“There’s no better individual than Dr. Gottlieb to address AIHD participants about the state of artificial intelligence, where it’s going, how it’s regulatory oversight will unfold, and what’s likely to be the most surprising contribution of AI in patient care,” said Robert Michel, founder of AIHD, Executive Director of the Precision Medicine Institute, and Editor-in-Chief of clinical lab intelligence publication The Dark Report.

The event will bring together senior-level representatives from AI companies, hospitals, physician offices, and diagnostic providers.

Gottlieb promoted greater use of digital tools for clinicians

Gottlieb is a well-known advocate for digital tools in healthcare, including AI. In 2019, he outlined a framework the FDA would start using to promote the development of safe medical devices that use advanced AI algorithms.

“I can envision a world where, one day, artificial intelligence can help detect and treat challenging health problems, for example by recognizing the signs of disease well in advance of what we can do today,” Gottlieb stated at the time. “These tools can provide more time for intervention, identifying effective therapies and ultimately saving lives.”

During and after his tenure at the FDA, he has been a prolific commentator about the SARS-CoV-2 pandemic and steps public health agencies have taken to curb COVID-19.

As Dark Daily previously reported, Gottlieb has also shown interest in technologies used to combat COVID-19, such as laboratory-developed tests created under emergency use authorizations.

Gottlieb is currently a Senior Fellow at the American Enterprise Institute, a public policy think tank. He is also partner at venture capital firm New Enterprise Associates and serves on the boards of Pfizer and Illumina.

—Scott Wallask

Related Resources:

Artificial Intelligence in Healthcare and Diagnostics Conference

Future Ready Pathology by Proscia

What is unstructured data?

Top 10 Use Cases for AI in Healthcare

Statement from FDA Commissioner Scott Gottlieb, M.D. on steps toward a new, tailored review framework for artificial intelligence-based medical devices

FDA Issues its First Emergency Use Authorization for an Antigen-based Diagnostic as Top IVD Manufacturers Race to Supply Medical Laboratories with COVID-19 Tests

Recent Acquisitions by Roche Highlight the Importance of Structured Data and Concerns for Diagnostics Providers and Pathology Laboratories

Data generated by medical laboratories and diagnostic providers takes an increasing role in treatment and precision medicine and allows greater analysis of data and integration of data into the care process

Most anatomic pathologists recognize that the unstructured data that makes up most pathology reports also represents a barrier to more sophisticated use of the information in those pathology reports. One solution is for pathology groups to adopt synoptic reporting as a way to get a pathology report’s essential data into structured fields.

The healthcare marketplace recognizes the value of structured data. In 2012, venture capitalists funded a new company called Flatiron Health. Flatiron’s goal was to access the medical records of cancer patients specifically to extract the relevant—and generally unstructured—data and put it into a structured database. This structured database could then be used to support both research and clinical care for cancer patients.

How valuable is structured healthcare data? Just this February, Roche paid $1.9 billion to acquire Flatiron. At that point, Flatiron had assembled information about the health records of two million cancer patients.

But Roche (ROG.S), recognizing the value of data, was not done. In July, it entered into an agreement to pay $2.4 billion for the remaining shares of cancer-testing company Foundation Medicine that it did not own. Foundation Medicine sequences tumors and uses that genetic data to assist physicians in diagnosing cancer, making treatment decisions, and identifying cancer patients who qualify for specific clinical trials.

Anatomic pathologists play a central role in the diagnosis, treatment, and monitoring of cancer patients. It behooves the pathology profession to recognize that generating, storing, analyzing, and reporting the data generated from examinations of tumor biopsies is a critical success factor moving forward. Otherwise, other players and stakeholders will move past the pathology profession and stake their own claim to capturing, owning, and using that data to add value in patient care.

How Lack of Standards Impact Transfer of Patient Data

DATAMARK Inc., a business process outsourcing (BPO) company headquartered in El Paso, Texas, reports that analysts from Merrill Lynch, Gartner, and IBM estimate unstructured data comprises roughly 80% of the information in the average electronic medical record. This data could be the key to improving outcomes, tailoring precision medicine treatments, or early diagnosis of chronic diseases.

From narrative descriptions of biopsies to dictated entries surrounding preventative care appointments, these entries hold data that might have value but are difficult to collate, organize, or analyze using software or reporting tools.

To further complicate matters, each service provider in a patient’s chain of care might hold different standards or preferred methods for recording data.

“At this point, [standards] are not to a level that helps with the detailed clinical data that we need for the scientific questions we want to ask,” Nikhil Wagle, MD, Assistant Professor of Medicine, Dana-Farber Cancer Institute, Harvard Medical School, and Associate Member, Broad Institute, told the New York Times.

An oncologist at the Dana Farber Cancer Institute in Boston, Wagle and his colleagues are creating a database of metastatic breast cancer patients capable of linking medical records, treatments, and outcomes with their genetic backgrounds and the genetics of their tumors. Despite best efforts, they’ve only collected 450 records for 375 patients in 2.5 years.

Nikhil Wagle, MD

Nikhil Wagle, MD (above), Assistant Professor of Medicine, Dana-Farber Cancer Institute, Harvard Medical School, and Associate Member, Broad Institute, is building databases that link patient outcomes and experiences with their EHRs. But sharing that information has proved problematic, he told the New York Times. “Patients are incredibly engaged and excited,” he said, “[But] right now there isn’t a good solution. Even though the patients are saying, ‘I have consented for you to obtain my medical records,’ there is no good way to get them.” (Photo copyright: Dana-Farber Cancer Institute.)


Additionally, once records are obtained, the information—sometimes spanning hundreds of faxed pages—must still be processed into data compatible with Dana-Farber’s database. And updating and maintaining the database requires a full-time staff of experts that must review the information and accurately enter it as required.

When critical concerns arise—such as a cancer diagnosis—information that could yield valuable clues about treatment options and improve outcomes might be held in any number of data silos in any number of formats.

This doesn’t account for the complexity of organizing such information for researchers who are developing new treatments, applying data to less targeted approaches, or dealing with privacy concerns between care providers.

Moving forward, those who can create and interact with data in a way that requires minimal human touch to make it suitable for analysis, further processing, or archiving, could communicate data more effectively and glean value from the growing trove of data silos created by laboratories around the world.  

Big Pharma Making Big Bets on Structured Data

These are all the reasons why the recent moves by Roche show the importance and perceived value of structured medical records data as it takes an increasingly important role in precision medicine treatments and diagnosis.

With its acquisition of both Flatiron Health and Foundation Medicine, Roche has secured the ability to generate data, convert said data into a structured format to drive decisions, improve core data-related services, and promote the value of their offerings. This positions Roche to maximize the value of its data for internal use and marketing to researchers and other interested parties.

For clinical laboratories, pathology groups, and other diagnostics providers generating untold amounts of data daily, this highlights a critical opportunity to stay ahead of future trends and position themselves as valuable sources of information as healthcare data continues to play an essential role in modern healthcare.

—Jon Stone

Related Information:

New Cancer Treatments Lie Hidden under Mountains of Paperwork

Unstructured Data in Electronic Health Record Systems: Challenges and Solutions

Pharma Giant Roche Just Made a $2.4 Billion Bet on Cancer Data

Roche to Buy Flatiron Health for $1.9 Billion to Expand Cancer Care Portfolio

Why Drug Giant Roche’s $1.9 Billion Deal to Buy Data Startup Flatiron Health Matters

Roche Acquires the Outstanding Shares of Foundation Medicine for $2.4Bn

New Solutions for Unstructured Data May Help with Clinical Laboratory and Anatomic Pathology Data

Unstructured Data Is a Target for New Collaboration Involving IBM’s Watson Health and Others; Could Help Pathologists and Radiologists Generate New Revenue

If this medical imaging collaborative develops a way to use the unstructured data in radiology images and anatomic pathology reports, it could create a new revenue stream for pathologists

Unstructured data has been regularly recognized as one Achilles heel for the anatomic pathology profession. It means invaluable information about the cancers and other diseases diagnosed by surgical pathologists are “locked up,” making it difficult for this information to be accessed in efforts to advance population health management (PHM) or conduct clinical studies.

Similarly, medical imaging has an essential role in the diagnosis of cancer and other diseases. And, like most anatomic pathology reports, medical imaging also is considered to be “unstructured” by data experts because it is not easily accessible by computers, reported Fortune magazine.

Unstructured Data in Anatomic Pathology and Radiology

Now one of the world’s largest information technology companies wants to tackle the challenge of unstructured data in radiology images. IBM (NYSE: IBM) Watson Health launched a global initiative involving 16 health systems, radiology providers, and imaging technology companies.

The Watson Health medical imaging collaborative is working to apply cognitive computing of radiology images to clinical practice. IBM aims to transform how physicians use radiology images to diagnose and monitor patients. (more…)

New Solutions for Unstructured Data May Help With Clinical Laboratory and Anatomic Pathology Data

Existing unstructured anatomic pathology reports would directly benefit from novel word disambiguation approach under development at MIT

Unstructured medical laboratory data is widely recognized to be one significant hurdle on the path toward the universal electronic health record (EHR). This is particularly true for anatomic pathology reports. Despite advances in synoptic reporting, to date, few pathology groups and clinical laboratories have developed ways to resolve this problem.

Now there is news of a different approach toward unstructured healthcare data. Researchers at the Massachusetts Institute of Technology (MIT) have developed a system for algorithmically distinguishing words with multiple possible meanings. The new approach could help find useful information buried in electronic medical records (EHR). (more…)