Quote Of The Year

Quote Of The Year - Paul Shetler - "Its not Your Health Record it's a Government Record Of Your Health Information"

Friday, April 20, 2012

Data Mining and Big Data Are Combining with Some Interesting Outcomes.

We had two interesting and related reports appear recently.
First we have:

Start-ups want to help hospitals harness big data

By gshaw
Created Mar 13 2012 - 12:12pm
As the healthcare industry wakes up and smells the potential of big data, hospitals are experimenting with ways to harness it--and two new start-ups want to help them do so.
Charité University of Medicine Berlin, Europe's largest university hospital, is using increasingly large stores of complex information not only to improve quality and aid clinicians and researchers but also helps improve senior management processes, according to a case study [1] in Forbes magazine.
Deputy CIO Martin Peuker told Forbes that more than 700 hospital employees have access to a central data warehouse that holds both financial and operational information. Every senior manager has ready access to data about operations, scheduling, patient care, and patient records. The entire repository of information stored by the hospital exceeds 1.6 petabytes.
A McKinsey Global Institute report released last year said that effective and creative use of big data could create more than $300 billion in value for the U.S. health system every year. Two-thirds of that would be in the form of reducing US healthcare expenditure by about 8 percent, according to the report [2].
All that big data potential has inspired Cincinnati Children's Hospital Medical Center to create a new startup, QI Healthcare, according to MedCity News article [3].
QI Healthcare's first product is called Surgical Outcomes Collection System (SOCS). The application aggregates data from various hospital systems, including electronic medical records, to enable "institution-wide analyses of cases to identify opportunities to improve patient care," according to a QI statement [4].
"The real power of this software is in the ability to analyze every significant patient case," Frederick Ryckman, professor of surgery and senior vice president for medical operations at Cincinnati Children's, said. "Before SOCS we spent countless hours manually gathering data. SOCS improves the process through automation and enhanced analytics--and it frees up clinical resources to focus on quality improvement."
To learn more:
- read the Forbes magazine
case study [1]
- see the McKinsey
report [2] on the potential of big data
- see the MedCity News articles on
QI Healthcare [3] and Health Care DataWorks [5]
- read the QI Healthcare
announcement [4]
- read the PC Magazine
article [6] on the problems with big data
- get more info on Chopra's
talk [8] at GigaOM
And second we have this work reported in Nature:

Drug data reveal sneaky side effects

Mining of surveillance data highlights thousands of previously unknown consequences when drugs are taken together.
14 March 2012
An algorithm designed by US scientists to trawl through a plethora of drug interactions has yielded thousands of previously unknown side effects caused by taking drugs in combination.
The work, published today in Science Translational Medicine1, provides a way to sort through the hundreds of thousands of 'adverse events' reported to the US Food and Drug Administration (FDA) each year. “It’s a step in the direction of a complete catalogue of drug–drug interactions,” says the study's lead author, Russ Altman, a bioengineer at Stanford University in California.
Although clinical trials are often designed to assess the safety of a drug in addition to how well it works, the size of the trials needed to detect the full range of drug interactions would surpass even the large, late-stage clinical trials sometimes required for drug approval. Furthermore, clinical trials are often done in controlled settings, using carefully defined criteria to determine which patients are eligible for enrolment — including other conditions they might have and which medicines they can take alongside the trial drug.
Once a drug hits the market, however, things can get messy as unknown side-effects pop up. And that’s where Altman’s algorithm comes in.
“Even if you show a drug is safe in a clinical trial, that doesn’t mean it’s going to be safe in the real world,” says Paul Watkins, director of the Hamner–University of North Carolina Institute for Drug Safety Sciences in Research Triangle Park, North Carolina, who was not involved in the work. “This approach is addressing a better way to rapidly assess a drug’s safety in the real world once it is approved.”
Lots more detail here:
It looks to me that these two trends are gaining some real momentum and that their use can only grow. Well worth following the links to see the variety of things that are now being done.

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