Posts Tagged ‘ human factors ’

The Knowing and Agency of Information Need

There is a fuzzy distinction between “information” and “knowledge” that is strongly emphasized in Wilson’s article “On User Studies and Information Needs”.  Information exists as a subcategory of knowledge; in terms of the models we’ve previously discussed—in particular, Nonaka and Cook & Brown—knowledge encompasses both the property of information and context, and the activity of interpretation (or “knowing”).  Wilson describes this in his figure for the “Universe of Knowledge” (661).  An alternative interpretation of this model would be to consider the concentric circles as “bodies of knowledge”, and the intersecting lines between “users”, “information systems”, and “information resources” as the action or practice of “knowing”.

The distinction between “information” and “knowledge” becomes fuzzy the instant you introduce agency into the equation—particularly, human agency.  As soon as we begin thinking of people accessing, transmitting, and creating information, we also have to start thinking about processes and motivation.  The concept of “information needs”, then, is epistemological; as Wilson describes it, an information need arises from a more basic “human need” that may have a physiological, affective or cognitive source, implying that a person must know something before seeking information (663).  That initial knowing or knowledge might be implicit or tacit.  You might feel hungry and, knowing implicitly that you must eat to resolve this physiological need, you might seek information about the nearest restaurant or supermarket.  How you go about doing that would be categorized as “information seeking behaviour”, and would be influenced by context—for instance, what you already know about what restaurants or supermarkets look like, what neighborhood you are in, what kind of restaurant or food you could afford and how much money you have in your purse or wallet, what information resources are most easily available to you, etc, etc.  If you have an iPhone, you might simply locate the nearest restaurant using GIS technology.  If not, you might consult a nearby map or directory, or simply look for signs of restaurants.  Or you might ask someone.  All of these represent different behaviours designed to fulfill an information need.  Once you have located a restaurant, you have fulfilled the information need required to fulfill your physiological need—hunger.  You have acquired information—namely, where to find the nearest restaurant from your starting point.  But you have also acquired a great deal of additional, potentially useful knowledge about the neighborhood, about other businesses you came across that were not restaurants, about how to find restaurants in general, and so on and on.  What you now know is not limited to the restaurant itself and the meal you are about to have, but includes every new piece of information that you came across throughout the information seeking process.  Including the process itself.  And this knowledge will be available to you the next time you have an information need.

Wilson identifies three definitions of “information” in user studies research (659):

1. Information as a physical entity (a book, a record, a document).

2. Information as a medium, or a “channel of communication” (oral and written).

3. Information as factual data (the explicit contents of a book, or record, or document).

These definitions are useful, but need to be expanded.  In his analysis, Wilson only discusses information as being transmitted orally or in writing.  There are, however, a number of alternative means for acquiring information.  Taking my previous example, you might smell cooked food before you see the marquee above a restaurant.  Or you might first notice the image of a hamburger on a sign before reading the words printed underneath.  Both of these examples—visual and olfactory information media—demonstrate that messages are transmitted in a variety of ways.  Additionally, we cannot forget the context.  If I am on a diet, I might ignore the building that smells of French fries and hamburgers.  If I am allergic to certain foods, an image of the type of fare served in a particular establishment might turn me off of it.  And it is possible that I miss these messages entirely; if I have a cold, maybe I won’t smell the hamburgers, and walk past that particular restaurant, unaware that it could satisfy my need.

Knowledge seeking can also be considered in terms of communication.  When I look at a sign, a message containing information is being transmitted to me.  Simplistically, this is the “conduit” metaphor for communication, which usually disregards or downplays the notions of context, influence and noise.  The communication process is far more complex, but conceptually the metaphor is useful for highlighting the roles of transmitter/speaker, message and receiver/listener.  Thomas, Kellogg and Erickson explore this idea in their article by suggesting the alternative “design-interpretation” model.  They argue that “getting the right knowledge to people” is only part of the equation, and that “people need to engage with it and learn it.” (865)  Thomas, et al. describe the model as follows:

The speaker uses knowledge about the context and the listener to design a communication that, when presented to and interpreted by the listener, will have some desired effect. (865)

The application of existing knowledge about the environment and the target audience by the speaker (or transmitter) is important to understand.  When I see the image of the hamburger, I can assume that the restaurant owners put some thought into presenting an appetizing, attractive product that will draw the most clientele.  If the image makes my mouth water, the message is received—and if I am then motivated to enter the restaurant, the owners achieved the desired effect.  If, however, I find the image unappealing, the message has failed; not because I don’t understand the information it contains, but because the restaurant owners failed to appropriately apply their knowledge about what potential customers want.  Perhaps they lacked the information they needed in order to do this successfully.

Cited References

Thomas, J. C., Kellog, W. A. and Erickson, T. (2001). The knowledge management puzzle: Human and social factors in knowledge management. IBM System Journal, 40(4), 863-884.

Wilson, T. D. (2006). On User Studies and Information Needs.  Journal of Documentation, 62(6), 658-670.

Designing Visual Differentiators

On Friday, November 20, as a guest of Humanities Computing colloquia, Sandra Gabriele (professor of Design, York University) presented “Visualization Differentiation in Look-alike Medication Names: Evaluating Design in Context”.

The problem that inspired Gabriele’s study is a troubling one: 7.5 % of patients admitted for acute care experience one or more adverse events; 24% of these are drug-related.  Meaning that, all too often, the wrong medication is administered to patients.  Why?

Gabriele identified two sources for design errors in hospital drug-selection:

  • orthographic similarities of drug names
  • phonetic similarities of drug names

Drug names come in two varieties: the generic name (or type), and the brand name (or unique name).  Gabriele showed us examples of how medication is stored in hospital pharmacies, presenting pictures of uniform bins of drugs organized alphabetically by name, usually regardless of its intended purpose.  One bin contained similarly named blood pressure medications, one for high blood pressure, one for low blood pressure, their names orthographically similar and the labels uniform as well; as a layperson, certainly, I would have been unable to tell the difference at a glance.

Gabriele’s project was to find better ways of designing drug labels for hospital pharmacies.  What was most interesting to me was the framework she chose in approaching this problem, asking what was required in effective drug-labelling:

1. Attention: that is, what makes the label distinctive.  Some of the designs she used in user tests were changing the colour and weight of the text, or using white text on solid black.  User tests showed drug names that were printed as white text on solid black made for the most attention-getting label.

2. Perception: or, legibility issues (cutting down the possibility of confusing orthographically similar drug names), establishing a visible hierarchy of data included on the label, and visual cueing (“chunking”, typographic styles, spatial cues, and mark cues).  Gabriele proposed to change the font, so that there was a clearer distinction between upper and lowercase letters, and cleaner font weight.  Interestingly enough, users in her test group responded negatively to this change; most drug labels use a Tallman font (which does pose legibility issues like those mentioned), and it seemed that the users (all hospital nurses) were conditioned to using it, when a layperson would have had more difficulty determining minor differences in names.  There seems to be some debate over the use of Tallman; a 2006 study in Glasgow indicated that Tallman was actually more effective in reducing name-related errors when selecting drugs (Filik et al.).

3. Understanding: Making sure a user can identify and understand all the data available on the label at a glance.  Gabriele’s presentation did not delve too deeply into this part of her study, but I would have found this probably the most interesting step in her research. How do users make sense of the labels?  Does the reorganization and stratification of data (in the “perception” stage) make a positive difference for comprehension with the trained professional?  It seems like, while errors do sometimes occur, changing labels that would avoid errors for a layperson might in fact cause more errors for someone trained to use the current labels in place.

Works cited

Filik, R., Purdy, K., Gale, A., and Gerrett, D. (2006).  “Labeling of Medicines and Patient Safety: Evaluating Methods of Reducing Drug Name Confusion.” Human Factors: The Journal of the Human Factors and Ergonomics Society, 48. pp. 39-47.