Showing posts with label big data. Show all posts
Showing posts with label big data. Show all posts

Thursday, March 5, 2015

Big Data and You

El Mundo, one of the most prestigious newspapers in Spain, created a daily supplement on innovation and entrepreneurship (title Innovadores) and as an advisor to Bankinter's Future Forum I wrote this article on "Big Data and You."
Here is the translation:

Big Data and You

We all accept that there is an abundance of data that are used to define everything about us. The data that are collected are then harvested by all types of entities and put together in ways that communicate more to others than we may want to share. But with the promise that the data cannot be traced back to the individual we release much of what is private about our lives. Nowhere is this more evident than in the health care setting where data are being merged from different sources to produce predictive models for deciding upon the type of treatment an individual should receive.

The use of big health data has been heralded as a giant leap forward in that information from different people can be used to give a fuller picture of what are the risk factors that we need to pay attention to. With these aggregated data and the risk profiles they provide health care providers at every level are equipped with an array of protocols about how to treat a patient. These data can also provide new and compelling alternatives to what would be considered the usual standard of care.

But we must proceed with great caution.  Recent research[1] funded by the National Heart Blood Lung Institute in the U.S. documented that commonly used risk assessments of atherosclerotic cardiovascular disease (ASCVD) overestimated the risk by 37 to 154% in men and from 8 to 67% in women. That translates into a huge amount of people getting treatment they did not need.

Big data are the results of combining lots of smaller bits of information. How big data are gathered and how they are merged can become a problem. This is made worse if at the point of care risk scores replace talking to the patient or the ever elusive listening to what the patient says. The promise of health care providers to “Do no harm” can be compromised by the overreliance on tools that are meant to add to the clinical conversation and not dominate them.


Jane L Delgado, PhD, MS,
President and CEO
National Alliance for Hispanic Health
Washington, DC
janeonhealth@gmail.com



[1] Andrew P. DeFilippis, MD, MSc*; Rebekah Young, PhD*; Christopher J. Carrubba, MD; John W. McEvoy, MB, BCh, BAO; Matthew J. Budoff, MD; Roger S. Blumenthal, MD; Richard A. Kronmal, PhD; Robyn L. McClelland, PhD; Khurram Nasir, MD, MPH; and Michael J. Blaha, MD, MPH. An Analysis of Calibration and Discrimination Among Multiple Cardiovascular Risk Scores in a Modern Multiethnic Cohort. Ann Intern Med. 2015;162(4):266-275. doi:10.7326/M14-1281


Tuesday, December 23, 2014

It’s Not About the Base— It’s About Quality

Quality health outcomes are not just about the denominator, big data, or public health data; it is about the individual. The allure of numbers is strong because numbers can be analyzed and the implication is that the data are objective. Nevertheless, our quest for precise data and measurements is often futile as too many times visually intoxicating three-dimensional charts tell us nothing about what is happening at the point of care.

Our measurement system must be radically different from what it is today. We should stop using the medical, economic, or business derived terms of “patient” or “consumer”  and focus on the person or individual. For the first time, technology and individual use of powerful computing devices (smartphones) give us the capacity to move beyond groups to individual focus.  

At the very least quality health measures should be publicly reported and include more mortality and morbidity outcomes. Additionally measures must be sufficiently nimble to:

  1. Rapidly reflect new science.  
  2. Rapidly include new technologies.
  3. At a minimum be analyzed by race, ethnicity, and gender.
  4. Encourage care that is tailored to the individual.
  5. Avoid the tyranny of big data when caring for the individual.
  6. Address that quality is defined in different ways by each person. 
Our future care depends on having measures that matter to each one of us.