Posts Tagged ‘BigData’
Clinical Analytics – Teaching and Learning
September 5, 2013
Recently I had a blog posted on “Teaching Clinical Informatics” on the HIMSS Clinical and Business Intelligence blog. It is a topic that has not received enough attention in HIT but it is the logical next step as more hospitals, health systems and practices implement EMRs.
Also from HIMSS, I will be speaking at a virtual event, Transforming Care by Improved Decision Making: Deriving Meaning from Big Data on September 18. My topic will be “Developing a Centralized Repository Strategy: The Top Three Critical Success Factors.’
On September 9, I will be on a panel at the Midwest Hospital Cloud Forum in Chicago. The panel is titled, “Closing the Loop in Healthcare Analytics – Correlating Clinical and Administrative Systems with Research Efforts to Deliver Clinical Efficiency in Real Time.” I have posted slides on my initial thoughts on national trends related to the topic.
On a side topic, I was interviewed by Deanna Pogorelc of MedCityNews (a Cleveland news outlet) on the topic of “A healthcare innovator’s guide to must-know tech terms for the next decade of medicine.” I address the topics of Artificial intelligence/algorithm medicine and the Internet of Things
More on analytics and innovation in future weeks.
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Data Mining as an Essential Informatics Skill Set
November 29, 2012
Clinical Integrated Data Repositories are now become common at academic medical centers. With tools like i2b2 and RemedyMD, plus a broad range of analytic tools, access to large volumes of clinical data for research and population management is coming to maturity. The opportunities for use of this data in enabling clinical trials and accelerating research are promising. Quality and patient safety can also be enhanced through use of electronic medical records; a recent New England Journal of Medicine article by Dean Sittig details how to “Use EHRs to Monitor and Improve Patient Safety.” “Organizations must leverage EHRs to facilitate rapid detection of common errors (including EHR-related errors), to monitor the occurrence of high-priority safety events, and to more reliably track trends over time.”
To maximize these opportunities, physicians and other health professionals must develop skills in understanding and utilizing this data. Medical informatics has been successful in developing tools for data mining, but translating raw data into research questions and disease trends requires training medical professionals in new ways of thinking. Understanding clinical workflow in an EMR does not directly translate into this type of research. One must understand how the data is organized and coded to create disease cohorts for analysis. Informaticists are key in training a new generation of physicians in this skill. Because of the complexity of this clinical data, there are three approaches to this data mining and analysis:
- Self-service data mining enabled by cohort definition tools, both vendor developed and open source
- Analyst provided data – skilled data analysts can pull relevant data sets based on their understanding of the research question and the data. However, there are limitations on the number of experienced data analyst any organization can afford to meet the coming demand
- Predictive analytics – this is the realm of the biostatistician who will be key consumers of large data sets to create predictive models to be used in clinical practice. This is also a limited resource, so prioritizing predictive modeling projects which major impact is key
Data mining and analytics should be taught in medical schools for the next generation of providers. Data visualization will be helpful in exploring this complex, big data. More on this in a future post.
Share this:Medical Innovation – Big Data and Patient Engagement
October 31, 2012
At the Cleveland Clinic Medical Innovation Summit, there was a discussion about big data in health care which moved to the issue of patient engagement and the need for not only transparency of data but also providing tools to manage and interpret data. Two panelists had important inputs – 23&Me and Dr. Harris, CIO of the Cleveland Clinic. What is needed includes PHRs, like MyChart, but also interpretation of results, such as offered by 23&Me. The tools must provide actionable results.
IBM Watson was also featured with a new initiative with Cleveland Clinic to “send Watson to Medical School” using medical students and others to improve paths for medical decisions. This exciting prospect is an experiment in human-computer interaction and machine learning. See this video for more details:
Finally, the top ten innovations announced at the conference including everything from handheld imaging to bariatric surgery.
Share this:Everyone is Talking about Big Data
July 20, 2012
Several new publications about Big Data in healthcare are showing up with good analysis of this emerging field.
First, an article from PharmExec called “Super-Size Me: Optimizing the Information Explosion” which came out in May. They note new sources of information including:
- Electronic Medical Records
- Social Media
- Real world evidence
- Personalized medicine
- Track and trace systems
They see significant potential value in big data:
» Uncover unmet needs
» Assess the feasibility of clinical trial designs and recruit trial subjects
» Demonstrate product value
» Conduct pharmacovigilance
» React more quickly to market changes via real-time market measurement and sophisticated KPIs
» Enhance commercial activities and enable more personalized messaging
» Deploy predictive capabilities rather than retrospective analytics
And next they note the layers of technology required:
- Collection, Aggregation, and Storage
- Analytics
- Reporting.
- “Nowcasting,” real-time data analysis, and pattern recognition will surely get better.
- The good of Big Data will outweigh the bad. User innovation could lead the way, with “do-it-yourself analytics.”
- Open access to tools and data “transparency” are necessary for people to provide information checks and balances. A re they enough?
- The Internet of Things will diffuse intelligence, but lots of technical hurdles must be overcome.
- Humans, rather than machines, will still be the most capable of extracting insight and making judgments using Big Data. Statistics can still lie.
- Respondents are concerned about the motives of governments and corporations, the entities that have the most data and the incentive to analyze it. Manipulation and surveillance are at the heart of their Big Data agendas.
2012 Predictions – Analytics
December 29, 2011
There are many top 10 predictions for 2012 out there. I could probably add my own for eHealth and mHealth.
These 10 Business Intelligence Trends for 2012 from Tableau Software apply to healthcare as much as any business.
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