The Institute of Medicine is calling for a “Knowledge Network of Disease”. By bringing together diverse data on health, new discoveries can be made. Healthcare can get smarter, faster, and more evenly distributed.
Reporting on developments in The Atlantic, David A. Shaywitz highlights the tension facing all data aggregators: whether it's possible to create a single, coherent and correct data model, or whether it's better to use a more ad hoc approach. He quotes system biologist Eric Schadt: “The grand data model of everything in biology has been tried over and over again and ultimately it comes crashing down because once you lock in a model, you constrain the types of questions you can ask of the data. The information half life is short, and we know and understand so little about what the data can actually tell us that we don't really understand the questions that ultimately will be the most useful.”
How do we build a knowledge structure when we don't know what we want to know? It's a problem we face in business too. However, the problem can be attacked in an incremental way. There are levels of detail at which healthcare professionals will agree. They'll agree that there's such a thing as a patient, for example. They are also likely to agree on the major biological systems and organs. They will also probably agree on a core set of signs and symptoms.
There will be a point at which interested parties can no longer agree on definitions of items or relationships. But that doesn't mean the modeling approach should be abandoned. We have to tolerate fuzziness and ambiguity at the depths and fringes of our knowledge structures. That's how we learn.
As healthcare data sets become larger, researchers will use analytics to discover new types and constellations of facts. These discoveries will sharpen the resolution of the outlying fuzzy areas. So the model will become more detailed and more effective in response to its use in the field.
The idea that standards need to be all-or-nothing is one of the myths of information management. Standards exist to facilitate action, not constrain it. You don't have to have a grand data model. But you need a model. Instead of thinking that a model or standard “locks in” users, we need to manage standards as evolving tools that learn from users as well as supporting them. Standards must lock step with the communities they serve. Database for Medical Knowledge