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I visualize data buried in non-proprietary healthcare databases
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Quantifying the true cost of obesity...

5/12/2015

 

The impact of obesity on lifelong health and risk for disease is significant. I continue to randomly grab learning objectives from the internet to encourage a collaborative discussion of where we might be missing the point. The data is out there--are we looking at the right information? I have gathered data resources into a small print or e-book. For the price of a latte or a bowl of soup you can have the data at your fingertips that you need to review to make data informed decisions for funding, proposals, or research. Observe below how real world data can reveal better solutions to managing patients with obesity.
Review these learning objectives pulled from the internet to see where you might be able to improve your process...
  • Discuss the basics of neuroendocrine regulation of body weight, and why it is difficult for obese patients to lose weight.

I do not agree with objectives driven by discussion. Engaging subjective discussion does nothing to highlight the influence of networks (see short video) or focus on the data to measure facilitators or barriers for weight management. Additionally, this LO is actually 2. 
  • Describe the basics of the clinical evaluation and management of patients with obesity.

This learning objective has not qualified the data that should be consulted to determine the "basics". Evidence-based (preferably meta-analyses) and real world evidence should inform clinical evaluation and management. Additionally, this LO is actually 2
  • Assess the basics of pharmacologic management of obesity including the currently available medical treatments for obesity.

I don't understand the "basics" as being the low bar for managing patients with obesity. We should look at the economics and outcomes to determine the value of "currently available medical treatments". What about lifestyle interventions?
  • Discuss the management of the bariatric surgery patient.

This would be relevant if LO highlighted EBM identified algorithms on how to decide which patients should be selected for an invasive and risky procedure.
Click on the video and wait for the dynamic shift in the relationships that evolve.
The networks point to a significant driver of obesity that is not correlated to "biology". The status quo is not creating a significant transformation in costs or outcomes, medical education programs are misguided, and opportunities to shift the conversation remain largely ignored.

The maps below (generated by data sources freely available) show interesting trends that should generate novel community solutions to integrate the "networks" into lifestyle intervention strategies to expand transformation and influence patient outcomes.



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Current levels of obesity in the United States represent a large-scale loss in quality of life, deterioration in population health, and an enormous drain on financial resources. How can the cost of obesity be quantified, beyond the direct medical costs incurred by the obese population? How many dollars are lost because of reduced productivity, taxes forgone and increased Social Security Disability Insurance benefits attributable to obesity? How much do these costs accrue over the course of a lifetime?--Brookings Institute

How do we answer these questions without meaningful data? stay tuned...
Thoughtful discussions about content development and outcomes analytics that apply the principles and frameworks of health policy and economics to persistent and perplexing health and health care problems


freelance medical writer, healthcare policy and health economics analytics and reporting

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  • Data & Donuts (thinky thoughts)
  • COLLABORATor
  • Data talks, people mumble
  • Cancer: The Brand
  • Time to make the donuts...
  • donuts (quick nibbles)
  • Tools for writers and soon-to-be writers
  • datamonger.health
  • The "How" of Data Fluency