I gave this talk in the "Presidential Symposium" at the annual meeting of the American Association of Physicists in Medicine, in Annaheim, California. The President of AAPM, Dr. Maryellen Giger, wanted some people to give some visionary talks. She invited (I kid you not) Foster, Gates, and Obama. Fortunately Bill and Barack had other commitments, so I did not need to share the time with them.
Medicine is approaching a profound transition as the methods of molecular medicine start to transform the nature of health care.What is the significance of such methods? For the researcher, it is a paradigm shift, as the number of things that can be measured increases dramatically.
Researchers express a vision for a scientific revolution in health care, from the qualitative to the quantitative-- A revolution based on information and thus computing
However, even as we talk about transformation and revolution, we must recognize that computing is poorly used in health care today.These are the words of a recent National Research Council report.Thus, I will seek in my remarks today to shed light on three questions: how information technology is evolving, how this evolution may impact medicine, and how changes in medicine and health care will stress information techology.
The story of computers is one of exponentials
The story of computers is one of exponentials
The story of computers is one of exponentials
But things are not quite as bad as that
What does this mean for medicine?We will certainly continue to see increasingly sophisticated computer applications aiding the physician in their tasks of observing, diagnosing, and treating – what used to be solely the domain the human senses, the brain, and the hands.More accurate, higher resolution, and more automated data acquisition systems.Computer-aided diagnosis and treatment planning systems that use large-scale data analysis and computer simulations.Automated radiation treatment and surgery systems. However, I want to focus here on some larger systems issues relating to quantitative medicine.
Using gene expression microarrays, we find that these two diseases have quite different phenotypes—that quite different genes are expressed in the two conditions.Here, columns are patients; rows are genes.Not sure what is the significance of the Stage 1/Stage 2.”The beauty of gene expression profiling data is that it is quantitative and highly reproducible. Because of this, these data can be used to generate multivariate statistical models of the clinical behavior of cancer that have great predictive power.” -- http://lymphochip.nih.gov/Staudt_Adv_Immunol_2005.pdf
And of course, we must not forget image-based biomarkers, as used in computer aided diagnosis of breast cancer, or as shown here, in an attempt to identify biomarkers for traumatic brain injury.ROIs used in a study at UIC(A) forceps minor (green), cortico-spinal tract (purple), inferior frontal-occipital fasciculus (red), external capsule (yellow), sagittal stratum (blue) (B) anterior corona radiata (green), superior longitudinal fasciculus (red), posterior corona radiata (blue); (C) cingulum (red), corpus callosum body (blue), splenium (yellow), and genu (green), and forceps major (purple).
Then, by tracking the personalized treatment plan, we collect more patient data.Success demands that we integrate, to a far greater degree than previously possible, clinical practice, basic research, and clinical trials. A profound challenge for health care system and for information technology.
Collecting and managing the enormous quantities of data that are now feasible, and required for EBM, is a huge challenge.However, merely putting in place the systems required to collect large quantities of data is not enough.We then need to make sense of that data. A challenge both for the physician and the researcher.
These problems arise at multiple scales. E.g. …
What these (and other examples that we will not have time to review) have in common …
We cite [Rouse, Health Care as a CAS: Implications for Design… , NAE 2008] for the righthand side aprt.Must supportDynamic composition for a specific purposeEvolving community, function, environmentMessy data, failure, incomplete knowledgeNice, but insufficientData standardsPlatform standardsFederal policies
Another perspective on the problem. A few words of explanation. If we are deploying a hospital IT system, we are (hopefully) in the bottom left hand corner.“You can’t achieve success via central planning.” Quoted in Crossing the Quality Chasm, p. 312In our scenarios, we don’t have that ability to control.
What is the alternative? We can put in place mechanisms that facilitate groups with some common goal to form and function.Over time, things change, these groups evolve.If we are successful, they can expand, perhaps merge.Challenges: make this easy. Leverage scale effects.
These are issues that the grid community has been working on for many years. We call these groupings Virtual Organizations.In healthcare today, there are of course many such “VOs.”But they are hard to form, fragmented, …
Principles and mechanisms that has been under development for some years.First CS, then physical sciences, then biology, most recently biomedicine –
What are these grid mechanisms and concepts, then? Hard to say something sensible in a few minutes.But basically it is about separating out concerns in a way that reduces barriers to entry and permits flexible use.
API vs. protocol? “Illities”?
[Create an image here.]For example DICOM and HL7 combine messaging and data model in the same interoperability standard. People are contextualizing this problem at the data interoperability level. Systems interoperability often neglected. An area of differentiation, bringing in best practice in industry and science into health care space. Open source platform. Experience with systems interoperability standards: IETF, OASIS, W3C,
Scaling via automating data adaptersRepresentations of those things and semantics of those representations.Talk about how services are published, data modeling, etc.Publish data basesPublish servicesName published objects
Loose coupling and encapsulationInteroperability through integration based on data mediation Evolutionary in nature Set of scalable systems and methods Explicit in architecture – data integration layerDemonstrated in GSI, GridFTP, MDS, ECOG
Most images are never seen—and are not available—outside their originating institution