Category Archives: healthcare

Leveraging healthcare data for consumer solutions

On April 23, 2016, over 300 developers from around the country descended on San Francisco for the weekend to tackle some of the hardest challenges facing the nation.  The event was called BayesHack, sponsored by the nonprofit Bayes Impact.  There were representatives from 7 cabinet-level federal agencies present to set up the 11 “prompts”, mentor the teams and judge the entries.  The prompts for the U.S. Department of Health and Human Services and the Department of Veterans Affairs asked challenging questions on how to leverage existing datasets…

  • How can data connect individuals with the health providers they need?
  • How can data get help to sufferers of opioid addiction?
  • How can data predict and prevent veteran suicide?
  • How can data tackle End Stage Renal Disease (ESRD) and Chronic Kidney Disease (CKD)?


As part of the judging process, the teams had to pitch their solutions to both agencies and private sector judges, such as partners at Andreessen Horowitz.  All teams submitted their code to the event’s github account, so that it could be used for judging, as well as ensuring that it will be available in the public domain.    For hackathons such as this one, it’s important to recognize that even if there are already similar commercial products, getting solutions into the public domain makes it possible for others build on later.  (Incidentally, this focus on actual working prototypes via GitHub is surprisingly lacking from many hackathons.  Bayes did a great job focusing on potential implementation beyond just the weekend.)

Of particular focus was the “How can data connect individuals with the health providers they need?” prompt, since this data has only recently become available, due to a regulatory requirement.  This data consisted of commercial healthcare provider networks for plans on ACA insurance marketplaces, including plan coverage, practice locations, specialties, copays and drug formularies.  There were 7 team submissions, most of which produced solutions focusing on usability for consumers and advanced analytics to policy makers.  Some teams expanded the scope to include not just insurance selection, but access to care in general.

To summarize some of the novel ideas in the solutions…

  • Simplified mobile-first user experience, resembling TurboTax for health selection
  • Visualizations and what-if analysis for policy makers
  • Voice recognition and NLP, as in Google freeform search instead of menus and buttons
  • Ranking algorithms and recommendation engines
  • Ingesting additional 3rd party information (such as Vitals, Yelp, and Part D claims) for consumers who need additional information before they can make an informed choice
  • Providing an API for other apps to leverage
  • Enabling self-reporting of network accuracy, like GasBuddy for health plan coverage

Here are some notable entries for this prompt:

The Hhs-marketplace team created an app that leverages chart visualizations to let a consumer compare plan attributes against benchmarks, such as state averages.  The example below shows a user entering a zip code and the specialists they’re interested in seeing.  The app finds the plans that meet those criteria, displays cost comparisons for them and a graphical comparison of the options.


The Fhir salamander team created mobile-first responsive web front end that takes the user through a series of simple menu choices to get them to recommended plans.  Along the way, for convenience and efficiency, it enables the user to click a button to place a telephone call to the plans (to ensure that the doctor they want is taking new patients from that plan) or to view the summary plan description files.

In working on the challenge the team transcribed the JSON provider network schema into a relational model.  They reported identifying data quality issues and therefore needing to clean up the raw data in order to use it for analytics.  They also generated state-level statistics to assist in comparison.  The app is written in Javascript, while the analytics are in Python.  They feel that the relational model, code to load it and the code to clean up the data could be reused elsewhere.  While the AWS website ( is no longer live, the deck is available (

The Hhs insights team produced an interactive provider density map.  Their approach was to target policy makers, rather than consumers.  For that purpose, they built aggregate analytics and related visualizations.  For example, their code uses DOL MSA (Metro Statistical Area) for GeoJSON calculations and visualizations.  In order to enable the needed analytics, they had to take on the challenge of normalizing the JSON schema of provider networks into a tabular format, as well as pre-calculating several aggregate metrics.

The Hhs marketplace finder team created an app that displays the pros-cons of the top 5 plan option for the
user, along with visualizations for making quantitative comparisons easy to understand.  Bad choices are suppressed to avoid screen clutter.  It starts with less than 10 simple questions.  Then adds a prediction of the user’s healthcare needs, which was determined based on statistics by age, gender, preexisting conditions and location.  Finally, it would eventually make it possible for a user to estimate their total cost based on different events, such as hospitalization or illness.
A data science team from Berkeley calling themselves Semantic Search, submitted an extremely ambitious

project.  Basically, creation of a Google Pagerank for healthcare decisions.  Instead of the menus and buttons of a traditional app UI, this solution used a freeform field for a user to indicate what they were looking for.  The goal is to let a consumer who is not tech saavy explain their situation in a natural way, without the interface and technology getting in the way.  Under the covers it uses natural language processing, ranking algorithms and a recommendation engine.  The user is ultimately presented with the top couple plans, along with explanations of why they were recommended.  To make the solution possible, this app has to collect behavioral data logs, use logistic regression to predict the probability that a certain plan would work, and leverage the LETOR ranking mechanism to provide answers.
As an interesting side note, a standard for U.S. health insurance networks has recently been adopted.  Eventually, medical groups and insurance companies can publish semantically tagged information directly to the web, bypassing the current single point of collection at CMS.  This would allow for a growth of relevant data that could be used by applications like this one.



Disclaimer: The challenge prompt used for HHS does not constitute the department’s official stance or endorsement of this activity.  It was used in an unofficial capacity only and intended to take advantage of data newly available from industry due to changes in regulations of the health insurance marketplace. publishes health plan and provider network schemas

Some good news on healthcare standards

I have been working with the Google semantic web group for many months to design several schemas that represent healthcare provider networks and health insurance plan coverage.  The good news is that these schemas have been officially published for use with  This is the first step towards a wider adoption for a more consistent designation for this type of information.  The schemas are:

Health Insurance Plan: List of health plans and their corresponding network of providers and drug formularies
Health Plan Network: Defines a network of providers within an health plan.
Health Plan Cost Sharing Specification: List of costs to be paid by the covered beneficiary.
Health Plan Formulary: Lists of drugs covered by health plan.

Now for the background…

In November 2015, the US health agency Centers for Medicare & Medicaid Services (CMS) enacted a new regulatory requirement for health insurers who list plans on insurance marketplaces. They must now publish a machine-readable version of their provider network directory and health plan coverage, publish it to a specified JSON standard, and update it at least monthly. Many major health insurance companies across the US have already started to publish their health plan coverage, provider directories and drug formularies to this standard.

The official schema is kept in a GitHub Repository:  This format makes it possible to see how which changes were made and when.  It also has an issues section to facilitate ongoing discussion about the optimal adoption of the standard.  There’s a website that goes into a more detailed explanation on the background of this effort:

This website also includes the “Machine-readable URL PUF” seed file” to the actual data that have been published by insurance company.  This file contains URLs that can be crawled to aggregate the latest plan and provider data.

In terms of adoption, U.S. health plans that participate in insurance markeplaces have published: *

  • 39 states
  • 398 health plans
  • ~26,000 URLs describing insurance coverage, provider networks, drug formularies

* Updated November 2016

A group of companies representing the provider, payer and consumer segments of healthcare convened to discuss the standard throughout 2015.  The considerations that went into formation of the standard can be found at:

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  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:   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 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 workflow.

Provider Network Directories on FHIR

FHIR logoI’ve done a lot of work on designing provider network directory schemas.  Much of it is described in this blog (“provider directories” tag) and in the related “Interoperability” entry on the DDOD website.  But so far, the effort has been focused on designing a standard data schema that could adequately represent the way the healthcare industry currently operates in terms of provider networks and health insurance coverage.  Now I’d like to highlight an important factor that’s been overlooked: the mechanics of moving this data between systems.

Simplified provider network directory modelIn their recent machine readability requirement for insurance issuers on health insurance marketplaces, CMS/CCIIO did not specify the transport mechanism for the QHP schema. The only requirement is to register the URL containing the data with HIOS (Health Insurance Oversight System). The URLs could be to a static page or to a dynamic RESTful query. I’d like to point out that CMS or third party services have an opportunity to provide significant value to both consumer applications and transaction oriented systems by adding a RESTful FHIR layer. Ideally, this would be done in front of globally aggregated datasets that have been registered in HIOS.  The resulting FHIR API would have resource types of Provider, Network and Plan, which correspond to the JSON files of the QHP provider directory schema.  The most relevant resource type

Much of the usefulness for machine readable provider network requirement is around enabling consumers to ask certain common questions when they need to select an insurance plan. (For example: Which insurance plans is my doctor in? Is she taking new patients at a desired facility under a particular plan? What plans have the specialists I need in a specific geographic region?) These questions could easily translate to FHIR queries using the Search interaction on any of the defined resource types.  With required monthly updates and potentially frequent changes in network and provider demographics, there are also use cases that benefit from availability of the History interaction, either as a type-level change log or an instance-level version view.  Additionally, by adding search parameters, response record count limits, and pagination in front of network directory datasets, load from traffic on aggregated data servers could be much more efficient.

NPPES on FHIR serverI set up a server with an example of a FHIR API implemented for provider directories, although limited to NPPES data model.  A big thanks to Dave McCallie for creating and sharing the original codebase: DavidXPortnoy/ nppes_fhir_demo.  You can find the live non-production sandbox version here:  Here are a few sample queries you can run against it:

I’m working on expanding the functionality of this server to accommodate the full provider network directory schema, including components of provider demographics, facilities, organizations, credentialing, insurance plans, plan coverage, and formularies.


Edit 10/2015: It should be said that my HHS Entrepreneur-in-Residence colleague, Alan Viars, has led an effort to build a robust API for NPPES for HHS IDEA Lab’s NPPES Modernization Project.  It’s designed to handle both efficient read access wanted by many applications and robust methods for making changes.  Although initially it focused on providing the simplest purpose built API possible, Alan is now looking at creating a version that would be based on FHIR practices.

Additional FHIR server implementations

The current FHIR server is quite simple.  It’s implemented using Python, Elasticsearch as document store for NPPES records, Flask as Python web server, and Gunicorn as WSGI web gateway.  Let’s call it the Flask-ElasticSearch implementation.   There are a couple other more popular alternatives.

It seems that the most active FHIR open source codebase is HAPI, located at  It’s managed by James Agnew at University Health Network.  This is a Java / Maven library for creating both FHIR servers and clients.  Its ability to easily bolt FHIR onto any database makes it ideal for extending the API to existing applications.  It also enables existing apps to connect to other FHIR servers as a client.  This codebase is quite full featured, supporting all current FHIR resource types, most operations, and both XML and JSON encodings.  Relative to other alternatives, it’s well documented as well.  There’s a live demo project available:

Finally, FHIRbase, located at, is a relational storage server for FHIR with a document API.  It uses PostgreSQL as the relational database engine and written in PLpgSQL.  FHIRplace, located at, provides a server that accesses FHIRbase.  It’s written in Clojure, Node.js, and JavaScript.  And like HAPI, it supports all current FHIR resource types, operations, and both XML and JSON encodings.

There are also a surprisingly large number of Windows-based FHIR servers that I haven’t considered, due to a desire to stay on non-proprietary platforms.  Although perhaps it shouldn’t be that surprising given the Windows heavy history of EHR and other healthcare apps.


Provider network directory standards

Here’s my most recent contribution to the effort around deploying data interoperability standards for use with healthcare provider network directories.  The schema proposed for use by QHP (Qualified Health Plans) on health insurance marketplaces can be found on GitHub:  Designing an improved model for the provider directory and plan coverage standards required analysis of:

The data model now looks like this:

Background info on this topic can be found in the related DDOD article.

Vision of healthcare provider network directories


There are four pieces of information that U.S. consumers need to make informed choices about their healthcare insurance coverage.

  1. Directory: What are the healthcare provider demographics, including specialty, locations, hours, credentialing?
  2. Coverage: Does the provider take a particular insurance plan?
  3. Benefits: What are the benefits, copays and formularies associated with my plan?
  4. Availability: Is the provider accepting new patients for this particular insurance plan and location?

Without having these capabilities in place, consumers are likely to make uninformed decisions or delay decisions.  That in turn has significant health and financial impacts.


Healthcare provider directories have historically been supplied by the NPPES database.  But it has been lacking in terms of being accurate, up to date, or even able to represent reality accurately.  First, the overhead of making changes is quite high and there hasn’t been an easy way for a provider to delegate ability to make changes.  Second, the incentives aren’t there.  There are no penalties for abandoning updates and many providers don’t realize how frequently NPPES data is downloaded and propagated to consumer-facing applications.  Third, the data model is fixed by regulation, but it cannot accurately represent the many-to-many relationships among practitioners, groups, facilities and locations.  It also doesn’t adequately reflect the ability to manage multiple specialties and accreditations.

Incidentally, my work in the area of provider directories has been driven by the needs of DDOD.  Specifically, there were at least five DDOD use cases that directly depended on solving the provider directory problems.  But the actual problem extends well past the use cases.  An accurate and standardized “provider dimension” is needed for any type of analytics or applications involving providers.  That could include having access to insurance coverage information to analytics on utilization, open payments, fraud and comparative effectiveness research.

Addressing consumers need to understand their options in terms of coverage and benefits has historically been a challenge that’s yet to be solved.  There are routine complaints of consumers signing up for new coverage, only to find out that their provider doesn’t take their new plan or that they are not accepting patients for their plan.  These problems have been the driver for Insurance Marketplaces (aka, FFMs) instituting a new rule requiring QHPs (Qualified Health Plans) to publish machine readable provider network directories that are updated on at least a monthly basis.  This rule, which is effective open enrollment 2015 and the technical challenges around it are described in detail in the related DDOD discussion on provider network directories.  (Note that although the rule refers to “provider directories”, in reality it includes all 4 pieces of information.)  CMS already collects all this information from QHPs during the annual qualifications process.  It asks payers to submit template spreadsheets containing information about their plans, benefits and provider networks.

The seemingly simple question as to whether a provider is taking new patients has been a challenge as well.  That’s because the answer is both non-binary and volatile.  The answer might be different depending on insurance plan, type of referral, location and even time of day.  It may also fluctuate based on patient load, vacations and many other factors.  The challenged becomes even harder when you consider the fact that providers often don’t have the time or financial incentive to update this information with the payers.


Aneesh Chopra and I put together an industry workgroup to help determine how to best implement the QHP rule.  The workgroup spans the full spectrum of industry participants, payers, payer-provider intermediaries, providers and consumer applications.  It should be noted that we have an especially strong representation from payers and intermediaries, representing a substantial portion of the market.  While looking at the best ways to implement the rule from a technical and logistical perspective, we identified a missing leg: incentives.

3 pillars needed to reach critical mass for a new standard to become sustainable
Technology Logistics Incentives

The QHP rule and the specified data schema provides a starting point for the technology.  Workgroup participants also suggested how to use their organizations’ existing systems capabilities to fulfill the rule requirements.  We discussed logistics of how data can get moved from its multiple points of origin to CMS submission.

Through this exercise, it became quite clear that the implementation of the QHP mandate could make significant progress towards achieving its stated goals if certain actions are taken in another area — Medicare Advantage (MA).  That’s because, much of the data in the proposed standard originates with providers, rather than payers.  Such data typically includes provider demographics, credentialing, locations, and whether they’re accepting new patients.  But at this point, marketplaces are able to only exert economic pressure on payers.  MA, on the other hand, can leverage the STAR rating system to establish incentives for providers as well, which typically get propagated into provider-payer contracts.  STAR incentives are adjusted every year.  So it should be well within CMS’s ability to establish the desired objectives.  They can also leverage the CAHPS survey to measure the level of progress these efforts are making towards providing the necessary decision making tools to consumers.  At the moment, marketplaces don’t have any such metric.

It’s worth noting that Original Medicare (aka, Medicare FFS or Fee for Service) has an even stronger ability to create incentives for providers and I’ve been talking with CMS’s CPI group about publishing PECOS data to the new provider directory standard.  PECOS enjoys much more accurate and up to date provider data than NPPES, due to its use for billing.  But the PECOS implementation is not as challenging as its QHP counterpart in that we’re effectively publishing coverage for only one plan.  So complexities around plan coverage and their mapping to provider networks don’t apply.  But consumers still benefit from up to date provider information.


If we create incentive-driven solutions in the areas of Marketplaces, Medicare Advantage, Managed Medicaid, and Original Medicare, we might be able to solve the problems plaguing NPPES without requiring new regulation or a systems overhaul.  We will be including the vast majority of the practitioners across the U.S., almost all payers and deliver the needed information for consumers to make decisions about their coverage.

Finally, we are partnering with Google to leverage the timing of the QHP rule with a deployment of a compatible standard on  Doing so would help cement the standards around provider directories and insurance coverage even further.  It empowers healthcare providers and payers to publish their information in a decentralized manner.  Since updating information is so easy, it can happen more frequently.  Third party applications could pull this information directly from the source, rather than relying on a central body.  And the fact that search engines correctly interpret and index previously unstructured data means faster answers for consumers even outside of specialized applications.

Record matching on mortality data

I’m looking forward to teaming up with my HHS Entrepreneur-in-Residence cohorts Paula Braun and Adam Culbertson.  We have a “perfect storm” coming up, where all three of our projects are intersecting.  Paula is working on modernizing the nation’s mortality reporting capabilities.  Adam has been working with the HIMSS (Heath Information Management Society and Systems) organization to improve algorithms and methods for matching patient records.  And I, for the DDOD project, have been working on a use case to leverage NDI (National Death Index) for outcomes research.  So the goals of mortality system modernization, patient matching and outcomes research are converging.Patient Matching Exercise

To that end, Adam organized a hackathon at the HIMSS Innovation Center in Cleveland for August 2015.  This event throws in one more twist: the FHIR (Fast Healthcare Interoperability Resources) specification.  FHIR is a flexible standard for exchanging healthcare information electronically using RESTful APIs.  The hackathon intends to demonstrate what can be accomplished when experts from different domains combine their insights on patient matching and add FHIR as a catalyst.  The event is broken into two sections:

Section 1:  Test Your Matching Algorithms
Connect matching algorithms to a FHIR resource server containing synthetic patient resources.  The matching algorithms will be updated to take in FHIR patient resources and then perform a de-duplication of the records.  A final list of patient resources should be produced.  Basic performance metrics can then be calculated to determine the success of the matching exercise.  Use the provided tools, or bring your own and connect them up.Section 2:  Development Exercise
Develop applications that allow EHRs to easily update the status of patients who are deceased. A synthetic centralized mortality database, such as the National Death Index or a state’s vital statistics registry, will be made available through a FHIR interface.  External data sources, such as EHRs, will be matched against this repository to flag decedents. The applications should be tailored to deliver data to decision makers. This scenario will focus on how different use cases drive different requirements for matching.

Matching algorithms for patient recordsPatient matching and de-duplication is an important topic in EHRs (Electronic Health Records) and HIEs (Health Information Exchanges), where identifying a patient uniquely impacts clinical care quality, patient safety, and research results.  It becomes increasingly important as organizations exchange records electronically and patients seek treatment across multiple healthcare providers.   (See related assessment titled “Patient Identification and Matching Report” that was delivered to HHS’s ONC in 2014.)

We’re looking forward to reporting on progress on all three initiatives and the common goal.

This topic is covered on the HHS IDEA Lab blog:

Appendix: Background on patient matching

Additional challenges occur because real-world data often has errors, variations and missing attributes.  Common errors could include misspellings and transpositions.  Many first names in particular could be written in multiple ways, including variations in spelling, formality, abbreviations and initials.  In large geographies, it’s also common for there to be multiple patients with identical first and last names.

Data set Name Date of birth City of residence
Data set 1 William J. Smith 1/2/73 Berkeley, California
Data set 2 Smith, W. J. 1973.1.2 Berkeley, CA
Data set 3 Bill Smith Jan 2, 1973 Berkeley, Calif.

Although there’s a broad range of matching algorithms, they can be divided into two main categories:

  • Deterministic algorithms search for an exact match between attributes
  • Probabilistic algorithms score an approximate match between records

These are often supplemented with exception-driven manual review.  From a broader, mathematical perspective, the concept we’re dealing with is entity resolution (ER).  There’s a good introductory ER tutorial that summarizes the work in Entity Resolution for Big Data, presented at KDD 2013.  Although it looks at the discipline more generically, it’s still quite applicable to patient records.  It delves into the areas of Data Preparation, Pairwise Matching, Algorithms in Record Linkage, De-duplication, and Canonicalization.  To enabling scalability, it suggest use of Blocking techniques and Canopy Clustering    These capabilities are needed so often, that they may be built into commercial enterprise software.  IBM’s InfoSphere MDM (Master Data Management) is an example.

Metrics for patient matchingWhen comparing multiple algorithms for effectiveness, we have a couple good metrics: precision and recall.  Precision identifies how many of the matches were relevant, while recall identifies how many of the relevant items were matched.  F-Measure combines the two.  It should be noted that the accuracy metric, which is the ratio of items accurately identified to the total number of items, should be avoided.  It suffers from the “accuracy paradox”, where lower measures of accuracy may actually be more predictive


  • Precision:     p = TP/(TP+FP)
  • Recall:    r = TP/(TP+FN)
  • F-Measure =  2 p r / (p + r)
  • Accuracy:   a = TP+TN/(TP+TN+FP+FN)

In the long run, the challenge can also be approached from the other side.  In other words, how can the quality of data entry and storage within an organization be improved.  This approach could reap benefits in downstream matching, reducing the need for complex algorithms and improving accuracy.  AHIMA published a primer on Patient Matching in HIEs, in which they go as far as calling for a nationwide standard that would facilitate more accurate matching.  They suggest standardizing on commonly defined demographic elements, eliminating use of free text entry except for proper names, and ensuring multiple values aren’t combined in single fields.

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 and 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 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,, 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, because this catalog includes all 3 types of access levels: public, restricted public, and non-public datasets.

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
    Health DevCamp logo
    Developer 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! 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.

Investment Model for Pharma

I had the opportunity to attend a presentation on “Entry and Investment Decisions in the
Pharmaceutical Industry”
by Anita Rao, PhD at Booth School of Business, University of Chicago. Transition between FDA approval years The concepts examined are applicable for any product that has lengthy periods of pre-launch R&D investment with a presence of competing products.  But there’s an aspect to this particular research that’s unique to pharmaceuticals: the uncertainty factor introduced by FDA’s drug approval process.  With that in mind, the paper analyzes historical data from FDA to infer how firms working on potentially competing products may respond each other’s actions prior to approval.

Quick side note…  I love what you can do by analyzing readily available historical data in a new way.  I think there’s an opportunity to improve on this model by leveraging valuable data that’s still buried deep within the FDA.  That’s exactly the kind of opportunity Demand-Driven Open Data (DDOD) was designed to address.

Investment model faster FDA approvalThere was one key question that it aimed to answer: What net effect would accelerating the drug approval process have on investment decisions and NPV (net present value) of each product.  The conclusion reached was that the increased incentive due to accelerated return on investment was significantly stronger than the disincentive due to risk of intensified competition.

Most immediately this model has potential to assist investors in making better decisions in regulated industries with substitute products and long investment time periods.  Investors in the areas of medical devices, agriculture and alternative energy might also be able to use this model.

But I’d love for this model to go beyond the use by investors and help inform public policy.  For that to happen, it needs to take into account a bigger picture, including the cost to the regulating body and by implication typically the taxpayer.  So in this particular case, we would need to assess the cost FDA bears in its current approval process, as well as estimating the likely increases due for accelerated approvals.

To take the concept further, there are certain questions that policymakers could address in order to maximize the total economic value to all participants.   For example, are there opportunities for the firms to fund or offset some of the additional cost from accelerating the approval process?  Is there an efficient way for the FDA to prioritize approvals more dynamically based on economic or public health value?  And is there a way to do so without significant conflicts of interest and minimal additional risk to consumers?