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

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

ACOs Are Learning to Use Big Data

Pathologists and clinical laboratory managers could find opportunity to add value as providers learn to use big data to improve patient outcomes and lower costs

Early adopter accountable care organizations (ACOs) are establishing data warehouses. This is a first step in collecting and analyzing healthcare big data. The move toward integrated care makes big data critical to an ACO’s success. Pathologists and clinical laboratory managers will want to follow the healthcare big data trend because laboratory test results will be a major component of that data.

The Goal of Healthcare Big Data

The goal for healthcare big data is twofold: 1) develop the ability to aggregate a vast amount of data from wide-ranging sources, and 2) use that data in real time to improve care and lower costs, a recent story published in Modern Healthcare (MH) reported. New performance-based reimbursement models mean that big data analytics will be essential for ACOs to succeed going forward. (more…)

;