Biomedical Engineering Reference
In-Depth Information
have a limited amount of time to devote to pursuing AT. Clients may have
diculty making repeated trips to a clinic to try out multiple devices and
external deadlines, like school, training programs, or returning to work can
intrude to force a decision. Consequently, a device that has an extended
learning curve often loses out to a device that is easy to use quickly, even if
the more complicated device might be a better choice in the long run.
A final obstacle to effectively evaluating a client's needs and abilities in
the clinic is that both will change over time. A client's needs will change
as job responsibilities, schoolwork, or personal interests evolve over time. A
client's abilities can change due to the progression of their medical condition
or changes in medication, but can also change as their familiarity with their
AT increases.
6.4 Evaluating Technology in the World
Once a client has received an AT (or multiple AT), measuring the outcome
of the entire AT intervention is extremely dicult. The discipline of AT
Outcomes Measurement is still quite new and many fundamental questions
remain unanswered, including:
When do we measure? Immediately after the technology is received may
be too early. The client may not have had adequate time to integrate the
technology into his or her life. Do we wait until the technology has been in
use for a while? Maybe, but how long? And, if we do, how do we isolate the
AT intervention from everything else that has happened in the intervening
time?
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What do we measure? Measures of performance can't be compared across
device categories (e.g., mobility devices and communication devices). Even
within a single device category, comparing performance measures across
clients with different clinical goals (e.g., maximizing text entry rate vs.
minimizing pain) may be fruitless. Measures of quality or satisfaction, on
the other hand, can be dicult to define.
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How do we measure? Objective measures are attractive because they lend
themselves to statistical analysis, but objective measures for inherently
subjective concepts (e.g., quality of life) are not always readily available.
Subjective measures, on the other hand, raise issues such as inter-rater
reliability.
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Whom do we measure? The challenges presented by measuring AT out-
comes are exacerbated by the diversity and (relative) scarcity of AT
users. Identifying enough AT users to accommodate all of the potentially
confounding variables, and isolating a control group of similar non-AT
users, can be extremely dicult.
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