Archive for November, 2012
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:
Games for Health Europe
November 14, 2012
Lucien Engelen of the Radboud ReShape Innovation Center in the Netherlands, had an extensive interview at this innovative conference. In this interview he discusses how his center at Radboud Medical Center will have have a game consultant visit weekly to consult with physicians, patients and others to discuss games for health.
Twitter stream on the conference is at #GFH12EU
Share this:
Kent State University Health Informatics Program
November 1, 2012
I am pleased to be officially Adjunct Faculty for the Kent State University Health Informatics Masters and Certificate program. This unique online program is under the school of Information Architecture and Knowledge Management, meaning it has close ties with both library science and nursing. I will be teaching Clinical Analytics in the May-June 2013 time frame and until then designing the course.
The program description states, “This integrated discipline features specialty domains in management science, management engineering principles, health care delivery and public health, patient safety, information science and computer technology.”
The program is completely online which will be a new challenge for me but is certainly becoming common for health and medical informatics programs appealing to those already in the field. The course fits well with my recent experiences of book chapters in Health Informatics and Wireless Health, the latter of which I developed video lectures. Also, my upcoming presentation at AMIA on Clinical Research Informatics Infrastructure.
And since clinical analytics is my daily focus in clinical research informatics, I am looking forward to condensing my knowledge into this course. Convergence within one’s career is not common – I am fortunate to have these opportunities come together.
Share this:
