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ATOMS
Project 2002-2003: Assumptions, Projects & Discoveries v1.3
ATOMS Project 2002-2003: Assumptions,
Projects & Discoveries (PDF,
0.49MB)
Roger O. Smith, PhD, OT & Kathy
Longenecker Rust, MS, OTR
As the ATOMS Project activities progress, we
have been watching our preliminary and interim findings. To summarize the
soft and empirical findings, we created two documents. The first document
relates to the ATOMS Project assumptions, combining where we were as our
work commenced and the assumptions that we have identified during our investigations. The
second document summarizes the ATOMS Project discoveries as they link to
some of our specific research activities. They are presented here consecutively.
The charge from NIDRR provides the backdrop for
the assumptions and discoveries. NIDRR suggested that the research a) perform
a needs assessment pertaining to outcomes measurement in AT, b) explore available
and new outcome measures and strategies for AT outcomes, and c) perform abandonment
investigations related to the previous activities.
Assumptions
- Assistive Technology (AT) devices serve as one intervention for people
with disabilities within a set of many interventions they typically receive.
- In a natural environment, AT use is often used concurrently with a variety
of other interventions and services
- Devices and services are two different components of AT interventions.
- There also exists a group of AT users that are not part of a service
system. Collecting outcome data for this group would be particularly challenging.
They are, however, stakeholders of AT outcomes.
- Despite a federal law mandating the consideration of AT, little evidence
suggests that all students with disabilities have access to AT.
- AT devices and services cross service delivery systems, including the
vocational rehabilitation, the educational, the medical and the independent
living systems.
- The context in which the device is used and the AT services obtained
are covariates that can confound and even reverse the outcomes of AT interventions.
- A variety of outcome dimensions contribute domains to the overall outcome.
These include self-satisfaction of products and services, costs, participation
in activities, task performance, goal achievement, AT device use, and quality
of life.
- By convention and by definition, AT device use can result in a negative
outcome.
- There are many measurement and research methodologies that are not typically
used for AT outcomes (e.g., goal attainment scaling, dynamic norming subjective
elicitation of data, MAU and Bayes) that we need to understand for their
potential contribution to an outcomes system.
ATOMS Project Activities & Discoveries
2002-2003
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Projects
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Discoveries
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Field Scans
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AT Instrument Update and
Review
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While dozens of assistive technology (AT) measurement instruments
exist, few have been devised with outcomes in mind. Most have been
created as part of the process to identify and select devices to
match a need to an individual AT consumer.
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Treatment of AT in Current/Emerging
Health & Rehabilitation Outcome Measures
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Health & rehabilitation functional performance & related
outcome measures rarely include AT as a co-variate. Many treat AT
as an impairment that lowers performance scores, and even fewer instruments
isolate the impact of AT in the outcome score (Rust & Smith,
2004a, 2004b).
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Outcome Measures Used in
AT Research & Development
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Product developers of AT devices report substantial interest in
AT outcomes, measurements and potential use of valid outcome measurement
instruments (Rust & Smith, 2004b, 2003a).
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Next Generation Data Collection
Technology
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Handheld computers provide a dynamic and efficient mechanism for
collecting large & individualized amounts of outcomes-related
data. The newer hardware & software components available open
many doors for naturalistic data collection (Kennedy, 2003).
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As AT outcomes & data will likely require a multi-dimensional
representation of data, new multi-dimensional data displays must
be considered as a part of AT outcomes instrumentation.
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Use Multi-attribute (MAU)
and Bayes Approaches in Outcomes Data Collection
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Decision analysis data collection & models, such as Bayesian
estimation & multi-attribute utility techniques are heavily used
in related fields. These may provide new strategies for measuring
key components of AT outcomes.
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Review of Taxonomies of AT
Outcomes Instruments
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Considering traditional measurement theory & methods, the task/activity
may provide the best conceptual vehicle for efficiently measuring
AT outcomes.
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Legal Implications of AT
Outcomes Instruments
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Legal issues surrounding AT outcomes data collection & application
are significant & more integral to AT outcomes instrument development
than initially considered. Further attention must be targeted on
the ethical & legal implication of AT outcomes (Mendelsohn, Schwanke, & Smith,
2004).
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History of AT Outcomes
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AT outcomes research & outcomes measurement research are relatively
new areas of inquiry. Interest has only been documented over the
past 20 years or so (Smith, Rust, Lauer, & Boodey, 2004).
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Methods to Identify AT Device
Use
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AT outcomes research to date has not identified a method to identify
the frequency and intensity of AT device use (Whyte, Smith, Fennema-Jansen, & Edyburn,
2003).
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Implications of Qualitative
Research Methods and Qualitative Data on AT Outcomes
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Qualitative data provides a depth of information in technology acquisition
and may have important applications within a future AT outcome measurement
system (Harris, 2004).
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Comparison of Cost Outcome
Methods
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Where cost analysis methods are maturing in health care, applications
and strategic methods that specifically address AT are still in early
conceptual development (Harris & Sprigle, 2003; Sprigle & Harris,
2004).
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Negative Aspects of AT
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The use of multi-focal/bifocal eyeglasses and walkers may have a
negative impact on gait speed & quality, suggesting that AT may
be a significant contributor to falls (Joerger, 2003).
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Satisfaction with AT
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"Satisfaction" should never be used without a qualifier. In
the field of AT there can be satisfaction with the device, satisfaction
with the service, or satisfaction with performance. Only the latter
appears to be outcome. The first two appear to be outcome-precursor
variables (Rust & Smith, 2004c).
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Focus Groups
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Service Program Administrators
Focus Group
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Service program directors identify 10 primary areas of outcomes
consistent with previous literature.
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Consumer Focus Groups
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Consumers of AT devices hold a unique perspective on what AT outcomes
mean. AT "outcomes" depict terminology and a concept created by the
service delivery and funding stakeholders (Taugher, 2003, 2004).
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Town Halls
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Legal Issues Town Hall
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This topic specific town hall identified 27 issues that all met
a high priority when ranked. The discussion was not able to generate
consensus on priorities. Issues were specific to service delivery
models.
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AT Outcomes Town Halls
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Participants voiced the universal need for better AT outcomes measurement
instruments and reporting systems regardless of AT service perspective.
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Database Projects
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Service Delivery System
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Service and delivery program records and data contain little to
no outcomes data. Service delivery records & data remain widely
variable & inconsistent from program to program (Schwanke & Smith,
in press; Schwanke & Smith, 2004a).
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Vocational Rehabilitation
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The Rehabilitation Services Administration database (named 911)
exists as one of the largest disability-related databases that contains
relevant AT device & service information for outcomes analysis.
This database might provide a foundation for examining AT outcomes
in the vocational rehabilitation sector (Schwanke & Smith, 2004b).
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NHIS
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The NHIS (National Health Information Survey-Disability) provides
a large database that might serve as a basis for AT outcomes analysis.
However, the NHIS database contains numerous problems in its design,
reducing the potential usefulness of the database to measure AT outcomes
(Moser, 2004a; Moser, 2004b).
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Ohio Schools
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Public school systems have a significant need for tracking AT outcomes.
A web-based centralized system seems to be a feasible data collection
medium (Fennema-Jansen, 2004a, 2004b; Wilson, Smith, Fennema-Jansen, & Edyburn,
2003).
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References
Fennema-Jansen, S. A. (2004a). Technical report-The assistive technology
infusion project (ATIP) database (1.0). University of Wisconsin-Milwaukee.
Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Fennema-Jansen, S. A. (2004b). Measuring AT outcomes using the student performance
profile: Analysis and recommendation. RESNA 27th International Conference
on Technology & Disability: Research, Design, Practice & Policy.
Harris, F. (2004). Technical report-Qualitative research discussions,
April 29, 2003 (1.0). University of Wisconsin-Milwaukee. Retrieved,
from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Harris, F., & Sprigle, S. (2003). Cost analyses in assistive technology
research. Assistive Technology, 15(1), 16-27.
Joerger, T. F. (2003). Risk of falling: The relationship between assistive
technology use and the quality and speed of gait. Unpublished master's
thesis, University of Wisconsin, Milwaukee.
Kennedy, B. L. (2003). Ecological electronic diary for outcomes measurement. RESNA
26th International Conference on Technology and Disability: Research, Design,
Practice and Policy.
Mendelsohn, S. B., Schwanke, T. D., & Smith, R. O. (2004). Overview
of legal issues in assistive technology outcomes measurement. RESNA 27th
International Conference on Technology & Disability: Research, Design,
Practice & Policy.
Moser, C. S. (2004a). The 1995 and 1995 NHIS Phase II Disability Followback
Survey-Child Questionnarie: A critical analysis of the data relating to AT
and its implications for future AT survey research. Unpublished doctoral
dissertation, University of Wisconsin-Milwaukee.
Moser, C. S. (2004b). Technical report-Data base analysis: 1994 and
1995 NHIS Phase II Disability Followback Survey, Child Questionnaire (1.0).
University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Rust, K. L., & Smith, R. O. (2004a). Technical report - The inclusion
of assistive technology outcomes in current health and rehabilitation outcome
measures (1.0). University of Wisconsin-Milwaukee. Retrieved, from
the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Rust, K. L., & Smith, R. O. (2004b). Technical report-Outcome measures
used in AT research and development (1.0). University of Wisconsin-Milwaukee.
Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Rust, K. L., & Smith, R. O. (2004c). Satisfaction with assistive technology: What
are we measuring? RESNA 27th International Conference on Technology & Disability: Research,
Design, Practice & Policy.
Rust, K. L., & Smith, R. O. (2003a). Outcome data needs for assistive
technology research and development. RESNA 26th International Conference
on Technology and Disability: Research, Design, Practice and Policy.
Rust, K. L., & Smith, R. O. (2003b). Treatment of Assistive Technology
Interventions in Health and Rehabilitation Outcome Assessments. RESNA
26th International Conference on Technology and Disability: Research, Design,
Practice and Policy.
Schwanke, T. D., & Smith, R. O. (in press). Assistive technology outcomes
in work settings, WORK: A Journal of Prevention, Assessment &Rehabilitation.
Schwanke, T. D., & Smith, R. O. (2004a). Technical report-Service
programs database (1.0). University of Wisconsin-Milwaukee. Retrieved,
from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Schwanke, T. D., & Smith, R. O. (2004b). Technical report-Vocational
rehabilitation database analysis: RSA-911 case service report and database
linking (1.0). University of Wisconsin-Milwaukee. Retrieved, from the
World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Smith, R. O., Rust, K. L., Lauer, A., & Boodey, E. (2004). Technical
report-History of assistive technology outcomes (1.0). University of
Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Sprigle, S., & Harris, F. (2004). Technical report-Comparison of
cost outcome methods (1.0). University of Wisconsin-Milwaukee. Retrieved,
from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Taugher, M. (2003). Available information and outcomes of assistive technology:
A consumer perspective focus group. RESNA 26th International
Conference on Technology and Disability: Research, Design, Practice and Policy.
Taugher, M. (2004). Focus groups on assistive technology use and outcomes:
A consumer perspective (1.0). University of Wisconsin-Milwaukee. Retrieved,
from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Whyte, F. L., Smith, R. O., Fennema-Jansen, S. A., & Edyburn, D. L.
(2003). Assistive Technology Device Use Inventory: The need and development
of a conceptual model. RESNA 26th International Conference on Technology
and Disability: Research, Design, Practice and Policy.
Wilson, S., Smith, R. O., Fennema-Jansen, S. A., & Edyburn, D. L. (2003).
Launching a large scale assistive technology service delivery and outcome
tracking system in the Public schools. RESNA 26th International Conference
on Technology and Disability: Research, Design, Practice and Policy.
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