Stan Huff: Current HIT Paradigm Isn’t Working

Intermountain Healthcare has devoted an enormous amount of resources for ground-up development of decision support algorithms and protocols to fire against its patient population. That effort has paid off in decreased mortality and marked improvements in clinical outcomes for segments of that population.

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Comments (3)
If humanity is really serious about the development and use of clinical decision support algorithms (evidence-based guidelines/pathways/protocols/checklists/etc.)--especially if they are personalized and designed to evolve continually--then we need a new paradigm that focuses on "sharing and playing with models in loosely-coupled collaborative networks. I discussed this new paradigm in 2006 in a series of posts at http://curinghealthcare.blogspot.com/search?q=playing+with+models.

People who collaborate in loosely-coupled social networks cross organizational and regional boundaries to share wide diversities of knowledge, ideas and points of view, which gives everyone access to a large collection of intellectual resource, and offers access to a greater variety of non-redundant information and knowledge on which to base decisions. A tightly-coupled network, on the other hand, limits participation to people within the same discipline, organization, region, etc., which means they have access to the same information sources and share similar knowledge and experiences. The loosely-coupled social networks, therefore, provide the greatest opportunities for stimulating multifaceted discussions, out-of-the box thinking, and creative solutions.

When people these networks share and play with decision-support models, they examine clinical and financial data to build testing hypotheses and assumptions to build decision support algorithms; they compare models and test them for their ability to reflect reality accurately; they manipulate the models to represent different scenarios, such as "what if" scenarios about the probability of future occurrences; and they discuss the assumptions and results the models produce.

When they find models that disagree or generate invalid results, they examine the fundamental assumptions built into the models, looking for logical flaws and inconsistencies, questioning the authors' perception of reality, and debating about the assumptions and practical value of the model. By challenging their assumptions, useful counterintuitive insights often emerge, innovative thought is sparked, new questions arise, relationships are developed, the influence of an organization's culture and politics are revealed, and compelling and unexpected management issues are discovered.

This paradigm should be applied nation-wide and world-wide to improve today's clinical decision support efforts!
Posted by Stephen B | Friday, June 20 2014 at 9:11AM ET
@Stephen. I believe the scenario you describe will not come about until we have a database centered on the patient, capable of storing ALL data about a patient; personal, clinical, social, devices, etc. Without a centralized database of patient records, most what-if scenarios will be hampered by the lack of complete data. Many outcomes are influenced by factors not typically captured in today's systems.
Posted by Tim S | Monday, June 23 2014 at 5:39PM ET
I think there may be a limit to how much algorithms can inform a clinician and patient to decide how to solve a problem. Here's an article that asks for a paradigm pivot:

http://www.bmj.com/content/348/bmj.g3725
Posted by Sandra R | Sunday, August 03 2014 at 6:41PM ET
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