Category Archives: Demand-Driven Open Data

Open Referral standard

Open_ReferralThe DDOD program is currently assisting the proponents of a new open standard for publishing human services, called Open Referral.  In order for us to be able to justify the promotion of this standard and publication of data to it, we’re first looking to develop clear and concise use cases.

The Background

Open Referral is a standard that originally came out of a Code for America initiative a couple years ago, with the goal of automating the updating of human services offered across many programs.  Doing so would not only make offered services more discoverable, but also lower the cost of administration for the service providers and referring organizations.

The Problem: A landscape of siloed directories

It’s hard to see the safety net. Which agencies provide what services to whom? Where and how can people access them? These details are always in flux. Nonprofit and government agencies are often under-resourced and overwhelmed, and it may not be a priority for them to push information out to attract more customers.

So there are many ‘referral services’ — such as call centers, resource directories, and web applications — that collect directory information about health, human, and social services. However, these directories are all locked in fragmented and redundant silos. As a result of this costly and ineffective status quo:

  • People in need have difficulty discovering and accessing services that can help them live better lives.
  • Service providers struggle to connect clients with other services that can help meet complex needs.
  • Decision-makers are unable to gauge the effectiveness of programs at improving community health.
  • Innovators are stymied by lack of access to data that could power valuable tools for any of the above.  

– Source: Open Referral project description

For potential use cases, there have been a small handful of government programs identified as potential pilots.  These include:

 

The Competition

Open Referral is not without competing standards.  In fact, the AIRS/211 Taxonomy is already widely used among certified providers of information and referral services, such as iCarol.  However, AIRS/211 has two drawbacks in comparison with Open Referral.  

First, it’s not a free and open standard.  While there are sample PDFs available for parts of the taxonomy, a full spec requires a subscription.

“If you wish to evaluate the Taxonomy prior to subscribing, you can register for evaluation purposes and have access to the full Taxonomy for a limited period of time through the search function. ”  – Source: UAIRS/211 Download page and Subscription page

The taxonomy also requires an annual license fee, which could be a challenge to continue funding in perpetuity for government and nonprofit organizations.

“Organizations need a license to engage in any use of the Taxonomy.”
— Source: AIRS/211 Subscription page

Second, the AIRS/221 taxonomy if highly structured and extensive.  While that has advantages for consistency and interoperability, it raises other challenges.  It leads to a high learning curve and therefore sets potentials barriers for organizations without technical expertise.  Open Referral states that it is a more lightweight option.

It should also be noted that there’s a CivicServices schema defined for use with  Schema.org.  Its approach is to embed machine-readable “Microdata” throughout human-readable HTML web pages.  Schema.org standards are intended to be interpreted by web engines like Google, Bing and Yahoo when indexing a website.  That said, the degree of adoption for CivicServices in particular – from either search engines or information publishers – is unclear at this point.

 

Onward!

In concept, the Open Referral standard would lower the cost and lag time for organizations to update relevant services for their constituents.  The standard is being evangelized by Greg Bloom, who has started with Code for America and has been reaching out to organizations who would be consuming this data (such as Crisis Text Line, Purple Binder and iCarol) for the purpose of defining a compelling use case.

There’s a DDOD writeup on this topic at “Interoperability: Directories of health, human and social services”, intended to facilitate creation of practical use cases.

 

 

Further reading…

Additional information on Open Referral can be found at:

DDOD featured on Digital Gov

DDOD logoThe Demand-Driven Open Data (DDOD) program has recently been featured on DigitalGov.  (See DigitalGov article.)

It should be added, that a major project in the works is the merging of DDOD tools and methodologies into the larger HealthData.gov program.  The effort seeks to maximize the value of existing data assets from across HHS agencies (CMS, FDA, CDC, NIH, etc.).  Already planned are new features to enhance data discoverability and usability.

We’re also looking into how to improve the growing knowledge base of DDOD use cases by leveraging semantic web and linked open data (LOD) concepts.  A couple years ago, HHS organized the Health Data Platform Metadata Challenge – Health 2.0.  The findings from this exercise could be leveraged for both DDOD and HealthData.gov.

DDOD featured on DigitalGov

Plans for Demand-Driven Open Data 2.0

Demand-Driven Open Data (DDOD) is a component HHS’s Health Data Initiative (HDI) represented publicly by  HealthData.gov.  DDOD is a framework of tools and methods to provide a systematic, ongoing and transparent mechanism for industry and academia to tell HHS more about their data needs.  The DDOD project description has recently been updated on the HHS IDEA Lab website: http://www.hhs.gov/idealab/projects-item/demand-driven-open-data/.   The writeup includes the problem description, background and history, the DDOD solution and process, and future plans.

In November 2015, the project has undergone an extensive evaluation of the activities and accomplishments from the prior year.  Based on the observations, plans are in place to deploy DDOD 2.0 in 2016.  On the process side, the new version will have clearly defined SOPs (standard operating procedures), better instructions for data requesters and data program owners, and up-front validation of use cases.  On the technology side, DDOD will integrate with the current HealthData.gov platform, with the goals of optimizing data discoverability and usability.  It will also include dashboards, data quality analytics, and automated validation of use case content.  These features help guide the operations of DODD and HealthData.gov workflow.

Using DDOD to identify and index data assets

Part of implementing the Federal Government’s M-13-13 “Open Data Policy – Managing Information as an Asset” is to create and maintain an Enterprise Data Inventory (EDI).   EDI is supposed to catalog government-wide SRDAs (Strategically Relevant Data Assets).  The challenge is that the definition of an SRDA is subjective within the context of an internal IT system, there’s not enough budget to catalog the huge number of legacy systems, and it’s hard to know when you’re done documenting the complete set.

Enter DDOD (Demand-Driven Open Data).  While it doesn’t solve these challenges directly, its practical approach to managing open data initiatives certainly can improve the situation.  Every time an internal “system of record” is identified for a DDOD Use Case, we’re presented with a new opportunity to make sure that an internal system is included in the EDI.  Already, DDOD has been able to identify missing assets.

DDOD helps with EDI and field-level data dictionary

But DDOD can do even better.  By focusing on working one Use Case at a time, we provide the opportunity to catalog the data asset to a much more granular level.  The data assets on HealthData.gov and Data.gov are catalog at the dataset level, using the W3C DCAT (Data Catalog) Vocabulary.  The goal is to catalog datasets associated with DDOD Use Cases at the field-level data dictionary level.  Ultimately, we’d want to get attain a level of sophistication at which we’re semantically tagging fields using controlled vocabularies.

Performing field-level cataloging all this has a couple important advantages.  First, in enables better indexing and more sophisticated data discovery on HealthData.gov and other HHS portals.  Second, it identifies opportunities to link across datasets from different organizations and even across different domains.  The mechanics of DDOD in relation to EDI, HealthData.gov, data discoverability and linking is further explained at the Data Owners section of the DDOD website.

Note: HHS EDI is not currently available as a stand-alone data catalog.  But it’s incorporated into http://www.healthdata.gov/data.json, because this catalog includes all 3 types of access levels: public, restricted public, and non-public datasets.

Obtaining data on cost of FDA drug approval process

To follow up on the post describing Investment Model for Pharma…   We’re working on obtaining data on cost of FDA drug approval process via DDOD (Demand-Driven Open Data).  Use Case 34: Cost of drug approval process describes this effort.  It identifies the drivers and value of obtaining this data in informing policy.  The writeup identifies several data sources and how to go about using them.  The information provided has come from discussions with FDA’s CDER Office of Strategic Programs (OSP).

Data sources identified:

  • IND activity: Distinct count of new INDs (Investigational New Drug) received during the calendar year and previously received INDs which had an incoming document during the same period: INDs with Activity page
  • PDUFA reports: The Prescription Drug User Fee Act (PDUFA) requires FDA to submit two annual reports to the President and the Congress for each fiscal year: 1) a performance report and 2) a financial report
  • FTE reports: Statistics on number of FDA employees and grade levels
  • ClinicalTrials.gov might provide glimpses into drug approval activity, although it’s not complete (especially for Phase 1 trials) and mixes in non-IND trials.
  • Citeline has counts of active compounds under development, including breakdown by Phase

As more users come forward to identify specifics of how they need to use the data, there’s an opportunity to refine the use case and focus efforts on obtaining data not yet available.

DDOD Love from Health Datapalooza 2015

Health Datapalooza

Demand-Driven Open Data (DDOD) has gotten a lot of coverage throughout Health Datapalooza 2015.  I participated in 4 panels throughout the week and had the opportunity to explain DDOD to many constituents.

  • Developer HealthCa.mp
    Health DevCamp logo
    Developer HealthCa.mp is a collaborative event for learning about existing and emerging APIs that can be used to develop applications that will help consumers, patients and/or beneficiaries achieve better care through access to health data, especially their own!Areas of focus include:
    • Prototype BlueButton on FHIR API from CMS
    • Project Argonaut
    • Privacy on FHIR initiative
    • Sources of population data from CMS and elsewhere around HHS
  • Health Datapalooza DataLab
    EVENT DETAILS HHS has so much data! Medicare, substance abuse and mental health, social services and disease prevention are only some of the MANY topical domains where HHS provides huge amounts of free data for public consumption. It’s all there on HealthData.gov! Don’t know how the data might be useful for you? In the DataLab you’ll meet the people who collect and curate this trove of data assets as they serve up their data for your use. But if you still want inspiration, many of the data owners will co-present with creative, insightful, innovative users of their data to truly demonstrate its alternative value for positive disruptions in health, health care, and social services.

    Moderator: Damon Davis, U.S. Department of Health & Human Services

    Panelists: Natasha Alexeeva, Caretalia; Christina Bethell, PhD, MBA, MPH, Johns Hopkins; Lily Chen, PhD, National Center for Health Statistics; Steve Cohen, Agency for Healthcare Research & Quality; Manuel Figallo, Sas; Reem Ghandour, DrPH, MPA, Maternal and Child Health Bureau; Jennifer King, U.S. Department of Health & Human Services; Jennie Larkin, PhD, National Institutes of Health; Brooklyn Lupari, Substance Abuse & Mental Health Services Administration; Rick Moser, PhD, National Cancer Institute; David Portnoy, MBA, U.S. Department of Health & Human Services; Chris Powers, PharmD, Centers for Medicare and Medicaid Services; Elizabeth Young, RowdMap

  • No, You Can’t Always Get What You Want: Getting What You Need from HHS
    EVENT DETAILSWhile more data is better than less, pushing out any ol’ data isn’t good enough.  As the Data Liberation movement matures, the folks releasing the data face a major challenge in determining what’s the most valuable stuff to put out.  How do they move from smorgasbord to intentionally curated data releases prioritizing the highest-value data?  Folks at HHS are wrestling with this, going out of their way to make sure they understand what you want and ensure you get the yummy data goodies you’re craving.  Learn how HHS is using your requests and feedback to share data differently.  This session explores the HHS new initiative, the Demand-Driven Open Data (DDOD): the lean startup approach to public-private collaboration.  A new initiative out of HHS IDEA Lab, DDOD is bold and ambitious, intending to change the fundamental data sharing mindset throughout HHS agencies — from quantity of datasets published to actual value delivered.

    Moderator: Damon Davis, U.S. Department of Health & Human Services

    Panelists: Phil Bourne, National Institute of Health (NIH); Niall Brennan, Centers for Medicare & Medicaid Services; Jim Craver, MMA, Centers for Disease Control & Prevention; Chris Dymek, EdD, U.S. Department of Health & Human Services; Taha Kass-Hout, Food & Drug Administration; Brian Lee, MPH, Centers for Disease Control & Prevention; David Portnoy, MBA, U.S. Department of Health & Human Services

  • Healthcare Entrepreneurs Boot Camp: Matching Public Health Data with Real-World Business Models
    EVENT DETAILSIf you’ve ever considered starting something using health data, whether a product, service, or offering in an existing business, or a start-up company to take over the world this is something you won’t want to miss.  In this highly-interactive, games-based brew-ha, we pack the room full of flat-out gurus to get an understanding of what it takes to be a healthcare entrepreneur.  Your guides will come from finance and investment; clinical research and medical management; sales and marketing; technology and information services; operations and strategy; analytics and data science; government and policy; business, product, and line owners from payers and providers; and some successful entrepreneurs who have been there and done it for good measure.  We’ll take your idea from the back of a napkin and give you the know-how to make it a reality!

    Orchestrators: Sujata Bhatia, MD, PhD, Harvard University; Niall Brennan, Centers for Medicare & Medicaid Services; Joshua Rosenthal, PhD, RowdMap; Marshall Votta, Leverage Health Solutions

    Panelists: Michael Abate, JD, Dinsmore & Shohl LLP; Stephen Agular, Zaffre Investments; Chris Boone, PhD, Health Data Consortium; Craig Brammer, The Health Collaborative; John Burich, Passport Health Plan; Jim Chase, MHA, Minnesota Community Measurement; Arnaub Chatterjee, Merck; Henriette Coetzer, MD, RowdMap; Jim Craver, MAA, Center for Disease Control; Michelle De Mooy, Center for Democracy and Technology; Gregory Downing, PhD, U.S. Department of Health & Human Services; Chris Dugan, Evolent Health; Margo Edmunds,PhD, AcademyHealth; Douglas Fridsma, MD, PhD, American Medical Informatics Association; Tina Grande, MHS, Healthcare Leadership Council; Mina Hsiang, US Digital Services; Jessica Kahn, Center for Medicare & Medicaid Services; Brian Lee, MPH, Center for Disease Control; David Portnoy, MBA, U.S. Department of Health & Human Services; Aaron Seib, National Association for Trusted Exchange; Maksim Tsvetovat, OpenHealth; David Wennberg, MD, The Dartmouth Institute; Niam Yaraghi, PhD, Brookings Institute; Jean-Ezra Yeung, Ayasdi

 

There were follow-up publications as well.  Among them, was HHS on a mission to liberate health data from GCN.

GCN article on DDOD
HHS found that its data owners were releasing datasets that were easy to generate and least risky to release, without much regard to what data consumers could really use. The DDOD framework lets HHS prioritize data releases based on the data’s value because, as every request is considered a use case.It lets users — be they researchers, nonprofits or local governments — request data in a systematic, ongoing and transparent way and ensures there will be data consumers for information that’s released, providing immediate, quantifiable value to both the consumer and HHS.

My list of speaking engagements at Palooza is here.

Healthcare Provider Registries

As I’ve been reviewing Use Cases for DDOD (Demand-Driven Open Data), I’m realizing how much the industry depends on an up-to-date, reliable source of healthcare providers (aka, physicians, groups, hospitals, etc.).  Although some people may also call such an effort “NPI registry”, the actual need identified encompasses much more than even the fields and capabilities of the existing NPPES database.

Here are just the Use Cases that directly mention NPPES and other existing registries.

And besides these, there are at least a dozen more that would benefit from this repository, since they rely on the “provider” dimension for their analytics.  For example, most analysis on provider quality, utilization, and fraud depend on this dimension.

The most obvious improvements needed are around:

  • More realistic association between provider, group, and location, recognizing that these are many-to-many relationships that change with time
  • More accurate specialty taxonomy
  • More up to date information (since NPPES entries are rarely updated)
  • Easier method to query this information (rather than relying on zip file downloads)

But there are challenges on the “input” side of the equation as well.  There also seems to be some confusion in terms of assigning rights for modifying registries.  For example, it’s not easy for a provider group to figure out how to delegate update rights for all of its physicians to third party administrator.

There’s a growing list of companies and non-profits (including the American Medical Association) that have been trying to capitalize on the opportunities for a better solution.  As we go about working on the use cases mentioned here, I’d be looking to build a body of knowledge that would contribute to solving the core problems identified.


Related post:  CMS is enabling streamlined access to NPPES, PECOS, EHR to 3rd parties