Health 2.0: Data — how to organize, access, and use it
Not the collection of data — we have plenty of that and the means to do it. This year was focused on the need to aggregate data and make it more organized and accessible so we can start using it to improve patient outcomes and the overall condition of healthcare in the United States.
For years, we have been collecting increasing amounts of healthcare data and storing it in vast silos. Because of technological limits and patient privacy concerns, we have yet to organize this data in a format that will make it available and useful to healthcare providers and patients. One of the goals of the conference was to discuss both emerging and improved technologies that can be used to arrange this data to inform healthcare decisions for individual patients as well as provide insights into how to treat and prevent different disease states. This discussion can serve as a basis from which to inform policy as we adjust to new norms within the healthcare industry.
Some of the main problems were distilled down and articulated in a talk by Tariq Dastagir, MD, Lead Medical Director of Transcend Insights/Humana. He focused on four areas where we need to improve as a next step to solving the issues associated with the accessibility and use of data within the healthcare industry:
- Bridging silos
We need to find a consistent means to share data between various individual organizations so that we can use it to its full potential.
- Refining the analytics process
We have lots of data, now what can we do with it? Healthcare analytics is a relatively new field, but as it gains steam and proves its value, the potential of using data to improve patient outcomes increases significantly.
- Leveraging big data
How do we leverage data at the population level to help at the individual patient level?
- Improving collaboration
Technology alone is not the answer. How do we integrate technology, data and care providers to co-create tomorrow’s solutions?
An example of these insights being put into practice was discussed in a talk by Mark A. Tripodi, Chief Development Officer of Venebio Group. He explained the Venebio’s focus on using data and analytics to help prevent opioid overdoses. The American opioid crisis is well documented, but so far few have come up with viable solutions to the problem. Restricting quantities or access to the medication has made it more difficult for those who truly need the drugs and has created the unintended side effect of pushing addicts toward illegal drugs like fentanyl and heroin, essentially making the problem worse.
The Venebio Group is working toward a solution by creating data analysis algorithms intended to assess risk factors of individual patients or entire patient populations. Pulling data from individual health records, insurance databases and state-run prescription-drug monitoring databases for controlled substances, Venebio can start to provide individualized risk factor profiles that can be used to support a physician’s clinical decision making at the moment they are crafting a treatment plan.
Another example of using data and analytics to improve patient outcomes was discussed in an interview with Iya Khalil, Co-Founder and Chief Commercial Officer at GNS Healthcare. She spoke about leveraging the power of causal machine learning to identify the right interventions for patient treatment.
With the advent of new technology and tools, we can measure what’s happening in individual patients at a very detailed level. From specific blood indicators to a patient’s entire genome, we can gather multiple discreet data points from disparate sources within the body. While machine learning can help us crunch the numbers and look for patterns where layers of data cross over, causal machine learning goes one step further by interpreting big data in a statistically meaningful way. This means that by overlaying statistics on detected patterns, we can determine the cause (or causes) of a patient’s infirmity with increasing confidence. Once the cause is discovered, the same set of algorithms can be used not only to generate hypotheses for treatment, but also to test those hypotheses against the available data.
In many respects, this is exactly what an experienced physician does when they are assessing a patient, but with the added benefit of being able to instantly recall and synthesize millions of points of data. These are just two of the many examples of how the best minds in the industry are attempting to put theory into practice when using data to help improve the overall state of healthcare.
Companies introduce exciting technologies
The conference also supported a whole host of companies and institutions that are looking at healthcare from another perspective, prevention. Fitbit gave us a sense of the potential appetite for data collection and personal analysis. It was an unmitigated success for a few years, then tapered off as individuals determined that they had little use for the data other than to satisfy curiosity. At the conference, there were several new companies offering both hardware and software platforms to collect data and use it in ways to actively enhance the well-being of the user, whether through the attainment of better sleep, deeper meditation, faster relaxation or even vital sign capture and analysis driven by AI.
Ovia is a new company that presented their offering at the conference. The goal of the company is to provide a suite of apps for women in different stages of parenting. For those who are considering having a child, Ovia offers an app that will help track fertility. Once pregnant, Ovia offers an app to not only help track your physical health data, but to also provide insight on what to expect as one’s body changes over the course of the pregnancy. Once the baby is delivered? You guessed it, there’s an app for that too. Not only do the apps help the user to track physical changes, they also provide valuable insights and advice based on where the user is in the process.
A new blood pressure monitor watch by Omron was also presented at the conference. It combines the utility of a smartwatch with an FDA- approved blood pressure measuring device (sphygmomanometer, being the technical term). One can actively track their blood pressure throughout the day, which can provide deeper insight into how the user’s blood pressure changes under different circumstances. A spike right after a cup of coffee might inform the wearer to hold back on caffeine. A lower blood pressure because of working out might help keep the user motivated to get those numbers down.
At this year’s Health 2.0 conference, there was quite a bit of optimism that with an increased focus on the collection, aggregation and processing of health data, healthcare can enter a new era of better patient outcomes and, perhaps more importantly, get a better grasp on prevention. Soon, the healthcare industry will be able to use the massive amounts of data that are already available to help doctors come up with new treatment protocols that are specialized for individuals. Leveraging new technologies such as artificial intelligence (AI) and the ubiquitous smartphone, people can get better information about their own health and reduce some of the burden on the healthcare system, ultimately bringing down costs while allowing for continued investment into research and development