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.

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:

1. Self-service data mining enabled by cohort definition tools, both vendor developed and open source
2. 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
3. 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.

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.

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.