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Policy

Putting Data To Work To Improve Health

Published by HealthAffairs.org

The health data movement started with a call for open health data. In 2010, the Institute of Medicine and U.S. Department of Health & Human Services convened the Health Data Initiative to explore the potential of open government health data to fuel discovery by private sector entrepreneurs to improve health care. What started as a gathering of 45 people in 2010 has now grown to be Health Datapalooza, an annual event hosted by Health Data Consortium with more than 2,000 participants. This year’s gathering—which I encourage you to attend—will take place in Washington, DC from May 31 to June 3.

Since that time, we’ve seen progress, with more and more public and private data sets becoming publicly available, spurring innovations, new companies, and a burgeoning industry based on the use of data to improve health and health care. While much has been accomplished in a relatively short time, this is just the start to a greater movement.

In recent years we’ve seen great examples of how data can be used to change the world around us, from predicting election results to a child’s success based on the neighborhood in which he or she grew up. As incredible as these examples can be, we know this is just a start. In health, an unquantifiable amount of data exists to garner insights to improve prevention efforts, aid in early and accurate diagnosis, guide treatment decision making, identify gaps in care, and answer questions about the health care system itself.

But data doesn’t work if you can’t use it. Currently, we face several barriers to the full utilization of data to improve health.

Using Data to Improve Health

Regulatory frameworks and policies are struggling to keep up with the pace of innovation. While legislative initiatives such as 21st Century Cures are starting to address these gaps, such as improved data sharing for NIH-funded research and interoperability, more work is needed, including updates to HIPAA. Questions around privacy remain, particularly as it relates to consumer-facing applications, devices, and platforms that exist outside of the regulatory framework of HIPAA — with a need to both protect IP and encourage investment, while also safeguarding the democratic nature of open data.

And finally, a lack of standards has made it difficult for people and organizations to access data. Stakeholders across tech and health care, including government agencies, patient advocacy groups, and organizations such as Health Data Consortium are working to address these barriers and identify solutions, but too often this work takes place in silos.

Fortunately, we have strong examples of data’s potential to improve health and health care. RowdMap is using data to create transparency around cost and value by tracking and analyzing provider spending. iConquerMS has demonstrated the ability to engage patients to not only share their data, but to play a pivotal role in driving and governing research by enabling patients to securely share their health information online and submit ideas for research topics important to them.

PurpleBinder, brought into the spotlight last year at Health Datapalooza, is making strides in community health by breaking down barriers to access to public services through the development of apps that aggregate the public services available to create clear and concise referral systems for communities, health systems and payers. And the Centers for Disease Control and Prevention (CDC), long known for using data to improve population health by identifying and tracking infectious disease outbreaks such as influenza, is using data to address environmental health issues, such as creating interactive maps for people to identify cooling centers during heat waves.

These examples showcase a wide range of approaches in the types of data already available and the public health and health care system improvements possible when data is accessible, in formats that can translate across systems.

Advancing Health Data

We all have a role to play in advancing the health data movement. In order to allow innovation to continue at an accelerated pace, three things need to happen. First, to liberate information and put it in the hands of data innovators to enable new discoveries data should be open. Health Data Consortium defines open data as data that is accurate, securely maintained, and made available in a manner which promotes productive use by others and respects the privacy interests of individuals.

Second, data access or data liquidity is needed to ensure a free flow of data so innovators can put the data to work. Moving beyond the public availability of data, data access speaks to the need for measures such as interoperability standards to enable a free flow of data.

Third, protecting privacy and creating systems that enable data access and open health data while maintaining privacy must be central to all efforts. As Health Data Consortium Advisory Council member Deven McGraw wrote, “The privacy issue, too long seen as a barrier to electronic health information exchange, can be resolved through a comprehensive framework that implements core privacy principles, adopts trusted network design characteristics, and establishes oversight and accountability mechanisms.”

Each year at Health Datapalooza, leaders in technology, health care, venture funding, research, government, advocacy, and policy share their ideas about how we capture insights from data in order to develop solutions for our most pressing health care challenges. And each year, we see more examples of the promise-turn-reality of the power of data to improve lives.

The goal of Health Datapalooza is to fuel this movement by connecting and inspiring those leading the charge for the free flow of data to improve the health and health care of our nation.

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Addressing Sustainability and Informatics Challenges for Clinical Data Registries

Published by Avalere.com

Summary

In the spring 2014 edition of the Journal of Health Information Management (JHiM), Avalere’s Chris Boone published an article addressing modern-day challenges to clinical data registries (CDR).

Please note: This is an archived post. Some of the information and data discussed in this article may be out of date. It is preserved here for historical reference but should not be used as the basis for business decisions. Please see our main Insights section for more recent posts.

Chris outlines the ways in which CDR act as the main vehicle for generating comparative effectiveness research (CER) evidence using electronic clinical data. He states that the industry uses both CDR and CER to aggregate data and generate reports on patient care, while explaining that much interest exists among healthcare stakeholders to leverage CDR data for research and policy analysis. According to Chris, these stakeholders face two very specific challenges: 1) unsustainable business models and 2) technical challenges associated with the collection, linkage and secure transmission of patient data from a variety of clinical data sources.

Chris’ qualitative, multi-cased, study analyzes the major barriers and lessons learned from establishing CDRs. In particular, he highlights the distinct business models of four U.S.-based CDR programs, addressing how each responds to these market-based challenges to CDR. Chris conducted 20 interviews with different stakeholders, such as program representatives, physicians, policy makers, technology experts and industry personnel.

View Chris’ complete JHiM article.

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