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BACKGROUND/RATIONALE:
Information pervades modern healthcare organizations. With advances in technology, information has become exponentially abundant and increasingly accessible, providing greater opportunities for informed decision-making. However, the increased availability of information is not always associated with improved performance. It has been extensively documented that improper use of information often engenders information overload, a particularly pronounced phenomenon in data-intensive clinical environments where time is a critically scarce resource. If this issue is not addressed properly, heavily invested technologies and tools could be underutilized or abandoned; or could result in decreased efficiency, negative responses, and other unintended consequences detrimental to the quality of care and patient safety. The goal of the Diabetes Cube is to "provide clinicians and managers tools to improve care by identifying populations of patients at higher risk who may require additional follow-up and to evaluate patterns of care delivery". While this powerful tool offers convenient access to enormous amounts of patient data at a scale that has never been achieved before, whether and how it can be successfully integrated into clinicians' day-to-day practice has not been explored.
OBJECTIVE(S):
The objective of the proposed research is to determine how best to integrate data from the Diabetes Cube (and other VA databases) into clinicians' day-to-day practice. It draws on techniques from human-computer interaction (HCI) and cognitive science to study the Diabetes Cube in the social and cultural contexts where the tool will be used. Through this research, we aim to identify an 'optimal' information-user-task fit, which speaks for delivering minimum (efficient) information in a desired format to appropriate recipients at the right moment when the information is most needed.
METHODS:
The proposed research is a descriptive study that will identify usable information representations from the Diabetes Cube and take the first step for effectively integrating that information into clinicians' routine practice. Key research methods we plan to employ include an ethnographic study delineating the current practice of use of aggregated information in diabetes care; semi-structured interviews followed by participatory design and a triangulating card sorting survey to identify appropriate data representations; and a pilot study evaluating a tool to measure clinicians' cognitive load. The study will be conducted in the primary clinics at three Veterans Affairs facilities, namely Ann Arbor, Battle Creek, and Cleveland, and will involve the participation of primary care providers, including physicians, nurse practitioners, RNs, LPNs, physician assistants, and pharmacists.
FINDINGS/RESULTS:
None yet.
IMPACT:
It is expected that this research will ultimately improve the quality of care provide to VA patients with diabetes, by increasing clinicians' use of data from VA databses, thereby improving the clinical decision-making process.
PUBLICATIONS:
None at this time.
DRA:
none
DRE:
none
Keywords:
none
MeSH Terms:
none
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