There is a storm brewing in Healthcare. Doctors have been in charge of healthcare for a long time, and have become comfortable, sometimes even arrogant, with their authority and power. But dumb data beats smart doctors every time. Forward thinking doctors are embracing data, with surprising grace and humility. Others are having much more trouble adjusting.
Doctors, historically, have been the “end of the discussion” on clinical matters. Doctors make the diagnosis, they make the calls in the surgery suite, they get to decide if someone is suffering enough to justify pain medications, they frequently decide whether someone is mentally incompetent or merely eccentric. Our society places a lot of trust in doctors, because they have the training needed to make really hard choices.
Doctors, as a group, have been in charge of how healthcare operates for centuries. In times past, the only way to determine whether a doctor was doing a good job was to become a doctor yourself, and then perform case reviews. Even in court, if you wanted to refute a doctor, you needed another doctor.
Recently, Health and Human Services (HHS) Centers for Medicare and Medicaid Services (CMS) released a request for public comment on how they should handle the reversal of an injunction that prevented them from revealing specific information about how doctors perform.
The mere request by the federal government for feedback on how best to use their new capacity to leverage Big Data to improve the healthcare system is a breath of fresh air. It is a welcome change from Snowden/NSA news and is a welcome indication that somewhere in the Federal Government there exists someone who understands technology policy well enough to not have “Aim gun at own toes” on their todo list. So we should all enjoy that for a moment.
Open source communities to help find the next blockbuster drug
Big drug companies are not what they used to be. It is harder to find new drug candidates, to test them, and to get them approved than ever before. Drugs that are “mere chemicals” are becoming more and more complex. Frequently, new drugs require DNA interaction, which requires them to be manufactured through a mostly automated cellular process rather than just mixing the right components in the right order. Just the changes to the refrigeration requirements for these new drugs represents a challenge to drug manufacturers, pharmacies and hospitals.
Combined, these difficulties create a combustible business environment that can ignited by the pressure of expiring patents. Experts estimate that the approval process ensures that a drug company actually gets only about 12 years of exclusivity before a 20-year patent wears off. So in pharma-land, the march of popular medications to generic status forces the original developers into the famous Innovators Dilemma. Most companies face competition from the generic versions of their own previous work.
A network graph approach to modeling the health-care system.
To achieve the the triple aim in healthcare (better, cheaper, and safer), we are going to need intensive monitoring and measurement of specific doctors, hospitals, labs and countless other clinical professionals and clinical organizations. We need specific data and specific doctors.
In 1979 a Federal judge in Florida sided with the AMA to prevent these kinds of provider-specific data sets violated doctor privacy. Last Friday, a different Florida judge reversed the 1979 injunction, allowing provider identified data to be released from CMS under FOIA requests. Even without this tremendous victory for the Wall Street Journal, there was already a shift away from aggregation studies in healthcare towards using Big Data methods on specific doctors to improve healthcare. This critical shift will allow us to determine which doctors are doing the best job, and which are doing the worst. We can target struggling doctors to help improve care, and we can also target the best doctors, so that we can learn new best practices in healthcare.
Evidence-based medicine must be targeted to handle specific clinical contexts. The only really open questions to decide are “how much data should we relese” and “should this be done in secret or in the open.” I submit that the targeting should be done at the individual and team level, and that this must be an open process. We need to start tracking the performance and clinical decisions of specific doctors working with other specific doctors, in a way that allows for public scrutiny. We need to release uncomfortably personal data about specific physicians and evaluate that data in a fair manner, without sparking a witch-hunt. And whether you agree with this approach or not, it’s already underway. The overturning of this court case will only open the flood gates further. Read more…
Which data formats should the DocGraph project support?
The DocGraph project has an interesting issue that I think will become a common one as the open data movement continues. For those that have not been keeping up, DocGraph was announced at Strata RX, described carefully on this blog, and will be featured again at Strata 2013. For those that do not care to click links, DocGraph is a crowdfunded open data set, which merges open data sources on doctors and hospitals.
As I recently described on the DocGraph mailing list, work is underway to acquire the data sets that we set out to merge. The issue deals with file formats.
The core identifier for doctors, hospitals and other healthcare entities is the National Provider Identifier (NPI). This is something like a Social Security number for doctors and hospitals. In fact it was created in part so that doctors would not need to use their Social Security numbers or other identifiers in order to participate in healthcare financial transactions (i.e. paid by insurance companies for their services). The NPI is the “one number to rule them” in healthcare and we want to map data from other sources accurately to that ID.
Each state releases none, one or several data files that can be purchased and also contain doctor data. But these file downloads are in “random file format X.” Of course we are not yet done with our full survey of the files and their formats, but I can assure you that they are mostly CSV files and a troubling number of PDF files. It is our job to take these files and merge them against the NPI, in order to provide a cohesive picture for data scientists.
But the data available from each state varies greatly. Sometimes they will have addresses, sometimes not. Sometimes they will have fax numbers, sometimes not, sometimes they will include medical school information, some will not. Sometimes they will simply include the name of the medical school, sometimes they will use a code. Sometimes when they use codes they will make up their own …
I am not complaining here. We knew what we were getting ourselves into when we took on the DocGraph project. The community at large has paid us well to do this work! But now we have a question? What data formats should we support? Read more…
An inside look at DocGraph, a data project that shows how the U.S. health care system delivers care.
At Strata RX in October I announced the availability of DocGraph. This is the first project of NotOnly Development, which is a Not Only For Profit Health IT micro-incubator.
The DocGraph dataset shows how doctors, hospitals, laboratories and other health care providers team together to treat Medicare patients. This data details how the health care system in the U.S. delivers care.
You can read about the basics of this data release, and you can read about my motivations for making the release. Most importantly, you can still participate in our efforts to crowdfund improvements to this dataset. We have already far surpassed our original $15,000 goal, but you can still get early and exclusive access to the data for a few more days. Once the crowdfunding has ended, the price will go up substantially.
This article will focus on this data from a technical perspective.
In a few days, the crowdfunding (hosted by Medstartr) will be over, and I will be delivering this social graph to all of the participants. We are offering a ransom license that we are calling “Open Source Eventually,” so participants in the crowdfunding will get exclusive access to the data for a full six months before the license to this dataset automatically converts to a Creative Commons license. The same data is available under a proprietary-friendly license for more money. For all of these “releases,” this article will be the go-to source for technical details about the specific contents of the file.
Look inside health data access and you'll see why "ownership" is inadequate for patient information.
Patients, doctors and providers have a unique set of privileges that do not line up exactly with a traditional concept of ownership.
This is an opportunity to rethink how health data flows.
In this digital world, health data that's 36-hours old can only be analyzed as a post-mortem. Health data that's 30-days old is already rotting.
Houston's healthcare community is deploying a Direct Project pilot.
Jim Langabeer, CEO of Greater Houston Healthconnect, discusses the implementation goals and hurdles related to a Direct Project pilot program.
A merging of artificial intelligence and healthcare is tougher than many realize.
People will eventually get better care from artificial intelligence, but for now, we should keep the algorithms focused on the data that we know is good and keep the doctors focused on the patients.