Liveblog: Big Data: Interpretations for Youth + Tech + HIV

Matthew Holt – @boltyboy – Health 2.0 

“When I first came to this conference I was surprised to hear that MSM = men who have sex with men, because in my world it meant mainstream media.”

We have tons and tons of data, more than we ever did. Data is collected at the point of care, processed, and spit back out on “unplatforms”, which are the many places where we interact with folks needing health services (and is kind of an odd term so people don’t use it a lot).

What we do in Health 2.0 is we literally have something on the order of 50 people doing 4.5 minute demos of health applications – I want to show you  some of these that focus on big data.

  • Fantastic data from Community Commons – all kinds of data – makes it easy to make maps, to upload your own data set & compare to national data sets, etc.
  • Fount.in – this is a way of taking data out of Twitter where people say things about sickness to make maps and other real time images about health risk.
  • Phytel – population management system – takes very simple kinds of data created by a clinic and allows us to see patients visually, and then allows us to take an intervention program/campaign and launch it based on the risk factors.

Patrick Sullivan – Emory University School of Public Health, Epidemiology – AIDSvu

AIDSvu is an interactive online map showing the AIDS epidemic and its social determinants in the US.

It has:

  • National, State, and local maps of  persons living with HIV, social determinants.
  • HIV Testing Site Locator
  • Downloadable resources including high resolution maps & data set on slides – all available for free

What does AIDSVu.org reveal about big data for health?

  • make it visual – big charts are useful to some people but most people can’t understand them.
  • make it interactive – people want to ask the questions that they want to ask
  • make it comparable – use comparable methods in each state, know that different states collect different info.
  • make it mashable/reusable – people want to use different data to work on the problem
  • make it accessible to different types of users – we’ve made infographics as well as maps and text so that more people can capture what we are trying to show with the data

How can you use AIDSVu?

  • Determine where there are highest populations of people in need of treatment/prevention
  • Illustrate context of work to funders
  • Download data & explore hypotheses
  • Help clients locate services that are convenient to them

@aidsvu / email: info AT aidsvu.org

Fard Johnmar – Enspektos LLC @enspektosllc

Big Data can be defined as:

  • volume: small data sources with high complexity with many variables, or larger data sources with many many entries
  • velocity: we are able to capture information quickly.
  • versioning: many sources of data colliding that we have to make sense of.

Data used wisely: collect, analyze, and share data to change the way people think or behave!

A special infographic for YTH Live (available through April 9): http://digihealth.info/YTHLIVE

Enmobius: we have a technology that collects digital breadcrumbs of Web/Social Media Users – it enables us to ask and answer questions about how their health behaviors are changing.

digihealthpulse: we are running a study of e-Patients, folks who search for health info on the internet.  we looked at whether this info helped them say they would engage in safer sex. We looked at Chris Brown & Rhianna on gossip websites, graphic sexual content, and all kinds of other places that people interact with sexual info, and we tracked data about comments on safer sex behaviors and STDs.

But the bad news: only about 18% of the people who said yes followed through, and they felt that digital content did not have a high impact on their behaviors.

Some tips for using big data:

  1. have goals for your data – behaviors you want to change, health issues you want to work on.
  2. be open-minded about data – One virtue of big & complex data is that they will surprise you. You never know what connections you’ll make. don’t be afraid to explore and stay open to patterns that may emerge.
  3. Focus on the one data point that matters most; data can lead down many wrong paths if you are not focused
  4. visualize your data – it can be helpful to draw something even if it’s on a napkin. putting data into pictures will help you tell a story that makes sense.
  5. be ethical – data has significant implications for civil rights & society – handle with care & be responsible, protect the privacy of people in our studies
  6. Never stop learning  – we’re collecting more data faster, never stop learning & exploring & changing your mind.

 


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