Health Information Exchange: Realizing the Value of Health Information Technology

I had the privilege of presenting on health information exchange (HIE) this morning to Prof. Rema Padman’s Health Information Technology class. My goal was to put HIE in context, explain its history, challenges, and provoke discussion as to the future form of exchanging electronic health information.

When Congress allocated $35bn to support the adoption and implementation of information technology in healthcare, America made a down payment on promise of health information technology to increase the efficiency, affordability, safety, and quality of care of healthcare in America.

The result of that $35bn in HIT investment has been largely to transform silos of paper-based health information into silos of electronic health information. Electronic records have improved quality and efficiency within healthcare organizations, and possibly been a driving factor behind the consolidation that has characterized healthcare over the last decade. Much of the promise of health information technology remains out of reach, however, until such time that healthcare providers are meaningfully connected and exchanging health information.

The result of $35bn in health IT investment has largely been to transform paper silos to digital silos
The result of $35bn in health IT investment has largely been to transform paper silos to digital silos

Health information exchange addresses this need. Health information exchange is both the verb of (mass) exchange of health information between healthcare organizations, and also the noun for the entity (usually a non-profit entity, often with state or federal grant support) that provides the infrastructure and connectedness supporting such an exchange.

Health information exchange has many obvious benefits, but those benefits accrue primarily to patients and health plans (at present), and to the community and population (in the idealized HIE realization). As currently implemented and under current reimbursement schemes, healthcare providers seldom directly benefit form HIE (except to the extent that they value providing affordable, quality care), but are nonetheless expected to foot the bill for HIE (based on the currently popular business models for HIE entities).

I presented three (out of many) cases where HIE reduces costs or improves care:

  1. Reduction in duplictative tests and services
  2. Improvement in the quality of emergency care
  3. Reduction of preventable hospital readmissions

The takeaways from today’s talk:

  1. Health information exchange contains the potential to improve the quality and affordability of healthcare
  2. HIE faces considerable barriers to adoption, including gaining provider trust, matching provider incentives, and creating sustainable business models for the HIE organization
  3. Health Information Exchanges are one of many competing mechanisms for the exchange of health information. A decade ago, the Personal Health Record was the darling child of electronic health exchange. Today, the PHR is dead, and HIE is the current contender. HIE faces competition however, from direction (vendor mediated) connections, from upstart “HIE like” private companies aggregating pharmacy benefit and lab result information, and from future distruptions in the market.

There’s no guarantree of the future success of HIE. Although HIE has far greater adotpion than the PHR ever did, the future success of HIE depends on its ability to overcome obstacles to adoption, while silmutaneously increasding the quantity and quality of data available to its members through connections with laboratories, PBMs, and payers

The slides from my talk today are available below:

CMU Health Information Systems Presentation: Health Information Exhcnage: Realizing the Promise of HIT
Click to download CMU Health Information Systems Presentation: Health
Information Exchange: Realizing the Promise of HIT

Data Science Careers

I came across a quote yesterday in Cathy O’Neil and Rachel Schutt’s Doing Data Science that really resonates:

The best minds of my generation are thinking about how to make people click ads… That sucks.

~Jeff Hammerbacher

One surprise about data science is that most data science jobs exist within the marketing departments of large corporations. Marketing departments have “big data” on their potential customers, a clear business case for hiring smart people to mine those insights, and budgets with which to pay those smart people. But I can’t help but agree with Mr. Hammerbacher.

I’m grateful to my Heinz College public policy peers for their constant reminder of the broader, more interesting world in which data science has just as much to offer. Data and funding are greater challenges in this broader world than in the corporate world, but so too is the potential for impact.

Pittsburgh Bus Wait Times

2016-04-13-pittsburgh-bus-wait-times
A simple website displaying wait times between buses

It’s a bit messy (time constraints!) but I recently put together a simple web page that displays the average time between bus arrivals for any PAAC Route 61A/61B/61C/61D stop. It also shows the average wait time, and the excess wait times caused by variance in arrival time spacing.

This website can be accessed at the following link:

http://pittsburgh-bus-wait-times.appspot.com/

Human Stories vs Data

I came across an insightful (and indicting) quote tonight in a data visualization paper:

I think people have begun to forget how powerful human stories are, exchanging their sense of empathy for a fetishistic fascination with data, networks, patterns, and total information. … Really, the data is just part of the story. The human stuff is the main stuff, and the data should enrich it.

~Jonathan Harris

The quote led me to Jonathan Harris’s website, which profiles his work as a digital artist. My immediate impression is of an artist of the first rate, who uses programming and data as his materials and media. Check him out.

Location and Activity Data

As a fun exercise in my Data Science Pipeline class, I used my smartphone to  collect location data for approximately three weeks. A built-in algorithm also attempted to determine my activity (e.g. riding in vehicle, walking, etc.). By combining my location data with my timestamped activity data, I was able to produce a map of travels and modes of transportation:

Around Home / Squirrel Hill

mark-egge-around-home

Around Pittsburgh
mark-egge-around-pittsburgh

Around Pennsylvania
mark-egge-around-pennsylvania

For more, view the live version of the project here: http://mark-where-and-what.appspot.com/

Pittsburgh Bus Bunching Project Features on CMU Students for Urban Data Analytics Website

Bus ClusterWhat began as a casual observation that Pittsburgh’s buses, when running late, often arrive in pairs turned into a data warehouse and empirical investigation. Fellow students Bhavna, Ranjana, Rohita, Enbo and I built a data warehouse to capture the real-time bus location data published by the Port Authority of Allegheny County. Our analysis of the data revealed that, indeed, buses do tend to “bunch.”

I’m excited to share that this project and its results are currently features on the CMU Students for Urban Data Analytics website.

Check out the post here: http://suds-cmu.org/2016/02/05/do-pittsburghs-buses-bunch/

The post was also picked up in today’s issue of Eat That, Read This:

SUDS Post Featured in Eat That, Read This

A big thank you to my classmates who I had the pleasure of working with on this project, and to Students for Urban Data Analytics for featuring the project!