INFS 838


Cory presents on open access publication and peer review… holy cow! Then he gets to rest and listen to Ruba and Matt.

Quick note: during Q&A, Ruba observes that maybe we miss important phenomena by focusing on reviewing all the A-lit and not reading the trade journals.

Ruba develops ontology and model management further (refer to earlier presentation).

  • Based on existing semantic Web tech: use what you’ve got
  • Standardization is a big deal! allows widespread adoption
  • Remember Krishnan and Chari (2000) give the really good background on model management
  • distributed model management needed (models just like info; gotta be able to share)
  • model management and Service-Oriented Architectures synergy
  • models are like services: they have inputs, processes, and outputs
  • ontologies are applied to services… maybe do same to models?
  • RDF: Resource Description Framework: mechanism for describing Web resources; effort at standardization of metadata
  • then came RDF-S, OWL (more complex), SWRL/SQWRL (these are pronounced swirl and squirrel)…W3C can explain further
  • a lot of this is about helping machines reason through rules, figure out things like “The brother of Bob’s son is another one of Bob’s sons.”
  • We’re trying to incorporate semantic info (that’s hard!) along with syntactic info (that’s easy) and apply it to decision-making models
  • Three levels, from abstract to concrete:
    1. modeling paradigm: capture constructs
    2. model schema: model indep. of data
    3. model instance: particular problem with data
  • Again, the service-model analogy is a big deal. Models can be thought of as services, services can be thought of as models….
  • [Warning: the acronyms are growing to, in one instance, six letters long. Could the length of acronyms could be an indicator of ripeness for a paradigm shift?… check that: eight letters. And a hyphen.]
  • I ask whether there is a field where we might get good empirical validation of the model; Deokar mentions semantic wikis in academic setting, useful also as portal for industry to register their own models in collaboration with other organizations, maybe security….

Matt Wills returns with electronic health records (EHR) and performance assessment (see Feb presentation). Tonight: some further investigation and proposal

  • Objectives:
    1. estab. strong theoretical foundation for model of clinical decision task performance with EHR
    2. redefine EHR in context of ERP systems
    3. explore/further define clinical decision task and EHR characteristics
    4. propose research protocol for data analysis and collection
  • Task-technology fit (TTF) theory (Goodhue and Thompson 1995)
    • nutshell: IT will have pos. impact when tech cap. matches task demands
  • EHR as ERP?
    • third-gen (3G) similar in many ways: basically, EHR can be the ERP for health care industry, can cover much the same business process aspects as ERP; applying the ERP model makes sense
    • TTF proposed for eval of entire IS infrastructure, not isolated tech
    • unlike EHR, ERP extensively studied, can guide this research
  • Four major themese in clinical decision theory:
    1. Pattern recognition (lowest level: requires no specific depth of knowledge; experience-based; basis of majority of decisions)
    2. Algorithms/heuristics (rules of thumb still grouping like pattern recognition, but requires more knowledge)
    3. Event-driven (no existing rules of thumb or patterns help; focus shifts to immediate therapeutic options, not driven by diagnosis or deep info)
    4. Hypothetical deduction (highest level of deduction)
  • utilization may drop from Matt’s approach to the TTF model, since EHR ut. will likely be mandated: nothing to study there! Performance is the only outcome variable of interest

…plus a quick gripe about IEEE style!

Here’s something to kick around: move all scholarly IS research, review, and publication online. We’re IS geeks; we ought to be able to use IS to optimize our own processes. (more…)

Ruba Aljafari speaks tonight (with contributions from Drs. Deokar and El-Gayar) on model management. (more…)

Eric D. Brown presents tonight: “Applications of Storytelling in Knowledge Management” [I love serendipity!]

Three papers:

  1. Bhardwaj, M., and Monin, J. (2006). Tacit to explicit: Interplay shaping organization knowledge.
  2. Swap, W.,  Leonard, D., Shields, M., and Abrams, L. (2001). Using mentoring and storytelling to transfer knowledge in the workplace.
  3. Nielsen and Madsen (2006). Using storytelling to reflect on IT projects. (more…)

Hey! Get that intermediate draft done! We better see at least 5 pages by Wednesday:

  1. Title and abstract
  2. ~1.5 page/couple parags intro:
    1. set up the problem (what’s the context?)
    2. hook the readers; don’t make ’em wait!
  3. ~2-3 pages literature review
  4. at least half a page of what you’re going to do about the problem!
  5. write in journal fashion, not magazine fashion! pay close attention to every sentence’s logical place in the argument
  6. support everything with sources (you’re a grad student: you won’t use too many references), but make sure those sources relate to what you are saying
  7. be “succinct yet meaningful”
  8. got writer’s block? Amit likes to look at3-4 model papers, see how they built their argument. He also goes for a walk or drive to think through the argument he’s after. (That’s funny: that’s how I avoid writing.)

So I wonder: can I jump ahead and hammer on design?

Deokar will be unavailable the 9th and 10th. I note with a wry grin that I will be unavailable the 6th and 7th.

Whew! And then I get to present on DSS (and a little KM) in e-government! Download the slides here, but if you want the audio, you’ll have to give me a call. ;-)

Laszlo, K.C., and Laszlo, A. (2002). Evolving knowledge for development: The role of knowledge management in a changing world. Journal of Knowledge Management, 6(4), 400-412.

[I had tons of fun reading the article and writing this summary. Democratizing KM, creating a global learning community… Wowza!]

Laszlo and Laszlo (2002) trace the logical progression of knowledge management through two stages, from a focus on internal processes (“business knowledge of the first kind”) to a broadening of focus to include knowledge of “one’s market, one’s industry, one’s consumers” in the scope of knowledge whose management can add value to an organization (“business knowledge of the second kind,” p. 401). The authors label the first stage as atomistic and the second as egocentric and critique both as grounded in a mechanistic, reductionistic paradigm mirroring traditional science. Laszlo and Laszlo recommend moving away from a paradigm that views business through the metaphorical lens of conflict and urge knowledge management researchers to realign their efforts with a more global, cooperative mindset. To support the replacement of the business-as-machine metaphor with a business-as-organism metaphor, the authors describe what they view as the next logical direction for knowledge management: advancing to a focus on the creation and sharing of “evolutionary business knowledge” (p. 401). To a great extent, Laszlo and Laszlo’s “business knowledge of the third kind” is a shift from descriptive to normative research, less about business and more about society:

In a highly interconnected world, the field of knowledge management faces the challenge of making concrete and relevant contributions for the betterment of society and not only for the promotion of competitive advantage of business. This involves a research agenda through which, first, KM can foster business knowledge of the third kind for the expansion of a corporate citizenship agenda and the emergence of evolutionary learning corporations; and, second, KM can make significant contributions for the creation of human and social capital required for evolutionary development (p. 402)

Laszlo and Laszlo’s discussion of evolutionary development and the sciences from which that concept takes its cues (e.g. chaos theory, nonlinear thermodynamics, autopoietic theory, “universal flow” toward complexity in everything physical and biological) verges occasionally into New-Age-like fuzziness. At base, though, Laszlo and Laszlo prescribe a clear expansion of the realm of knowledge management upward from the base of data, information (the “know-what” of KM), and knowledge (KM’s “know-how”) to include the understanding and wisdom (the “know-why”) necessary to encompass the concepts of global citizenship and sustainability.

Methodologically, accessing the reason, values, intellect, intuition, and love (!) that turn know-what and know-how into know-why entails a practical shift from quantitative reaserch to more qualitative, participatory research (p. 405). They draw their participatory systemic research paradigm from “four interdependent ways of knowing” (from Heron and Reason, 1997). Integral to this model is practical knowing, which turns experiential, presentational, and propositional knowing into value-adding actions (p. 406).

That practical action should manifest itself in research that looks into the creation of evolutionary learning communities, communities that support not simply the imitation of previously acquired knowledge but the empowerment of learners throughout society who can better learn and adapt to new social and environmental conditions (p. 407).  Laszlo and Laszlo still see economic benefits as a reasonable goal for KM research and implementations, but they envision working through the framework of Learning Regions Theory to find ways to use knowledge management to promote not just competitive advantage for individual firms but also economic development for regions scaling up to the entire global community (p. 409). Laszlo and Laszlo identify numerous research areas where such lofty goals may be pursued, including research on facilitating corporate citizenship, developing design methods and programs to expand participation of the global population in policy-making and regional development, and building a “global learning society” (p. 411). In the most direct terms, Laszlo and Laszlo’s profoundly democratic research agenda calls for a “big picture” knowledge management that equips all of humanity with greater access to existing knowledge and expands humanity’s ability build new knowledge and meaning in response to economic, environmental, and political problems.

Tonight, some health IT, with contributions from our very own Matt Wills, GA.

El-Gayar, Deokar, Wills (2008), “Current Issues and Future Trends of Clinical Decision Support Systems (CDSS).” written for an encyclopedia…

Matt says the paper was written mainly to map the field and identify gaps.

Serious issues in safety and access that CDSS can address! Scarce resources, spiraling costs — these are all issues that drive better CDSS. Matt mentions the 1999 Institute of Medicine report that found 48K-98K die each year as a result of medical error. The financial cost of those errors: $6B per year.

ULAM: a liver allocation model! Ew!

Dang! Matt says there’s a lack of research that says CDSS resuls in clear clinical benefits. The research isn’t saying CDSS produces no results; the problem is develping clear metrics and baselines and generalizing results. The existing research is mostly anecdotal.

One hindrance to CDSS research: clinician throughput! They don’t want participation in an experiment with some new tech tht would cut down the number of patients they could process each day.

Another problem: physician autonomy. They don’t want a machine telling them what to do. So maybe there’s an opening for CDSS in medical school first, as a pedagogical tool. Test it out in the low-risk area, with interns who still have doctors supervising them, with students who are confronting simpler problems with clearer answers. Apply the CDSS to simulations, benchmark with established student norms and medical knowledge.

Physicians need transparency in the CDSS! They want to know how the diagnosis or recommendation was arrived at.

Matt says we need better Natural Language Processing (NLP) technology, especially to read all the “unstructured text” that physicians generate — just another example of how this is a very young field with a lot remaining to be done.

Matt would like to see more research on collaboration in the clinical setting. Lots of interesting telemedicine issues to be studied here — Matt finds a gap in the lit there!

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