Tag Archives: Analytics

Great #BigData JAMA Image Missing Some Data Sources

JAMA image data

When I saw this article and image in JAMA, I was really excited.  It’s a good collection of structured and unstructured data sources.  It reminded me of Dr. Harry Greenspun’s tweet from earlier today which points out why this new thinking is important.

 

But, it also made me think about this image and what was missing.  The chart shows all the obvious data sources:

  • Pharmacy
  • Medical
  • Lab
  • Demographic
  • EMR / PHR

It even points out some of the newer sources of data:

  • Facebook
  • Twitter
  • Online communities
  • Genetics

But, I think they missed several that I think are important and relevant:

  1. Structured assessments like the PHQ-9 for depression screening or the Patient Activation Measure.
  2. Communications data like:
    • How often do they call the call center?
    • What types of questions do they have?
    • Do they respond to calls, e-mails, SMS, letters, etc?
    • Have they identified any barriers to adherence or other actions (e.g., vaccines)?  Is that stored at the pharmacy, call center, MD notes?
  3. Browser / Internet data:
    • This could be mobile data from my phone.
    • What searches I’ve done to find health information.  What have I read?  Was it a reliable source?
  4. Device data (e.g., FitBit):
    • What’s my sleep pattern?
    • What am I eating?
    • How many steps do I walk a day?
  5. Income information or even credit score type data

These things seem more relevant to me than fitness club memberships (which doesn’t actually mean you go to the fitness club) or ancestry.com data which isn’t very personalized (to the best of my knowledge).

In some cases, just simply understanding how consumers are using the healthcare system might be revealing and provide a perspective on their health literacy.

  • Do they call the Nurseline?
  • Do they go to the ER?
  • Do they have a PCP?
  • Do they use the EAP?

We’d like to think this was all coordinated (and sometimes scared into believing that it is), but the reality is that these data silos exist with limited ability to track a patient longitudinally and be sure that the patient is the same across data sources without a common, unique identifier.

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