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BACKGROUND/RATIONALE:
Common symptoms are often under-recognized and undertreated, particularly in HIV care.
OBJECTIVE(S):
To develop and test the feasibility of an electronic medical record (EMR) -based clinical decision support to increase provider awareness of and response to common HIV symptoms.
METHODS:
We sought to develop a clinical decision support for symptom management within the Veterans Health Administration EMR that would minimize workflow interruption and maximize clinical relevance by incorporating the following design factors: elicitation (eliciting symptom information directly from patients), organization (organizing symptom information to maximize specificity), presentation (presenting symptom information to clinicians), and recommendation (recommending responses to symptom information). We then studied its feasibility by implementing it within a weekly HIV clinic, comparing a 4-week intervention period with a 4-week control period.
FINDINGS/RESULTS:
We have developed a clinical decision support for symptom management that incorporates our intended design factors. It uses portable electronic "tablets" to elicit symptom information at the time of check-in, filters and organizes this information into a concise and clinically-relevant EMR note available at the point of care, and facilitates clinical responses to this information. It appeared to be well accepted by patients and providers and did not substantially impact workflow. Although this pilot study was not powered to detect effectiveness, patients in the intervention group (N=28) reported greater perception of providers' awareness of their symptoms than control patients (N=34), with 94% versus 75% reporting that providers are "very aware" (p=0.07).
IMPACT:
We have developed a symptom support tool that may be feasible in care. The Veterans Administration EMR may be an effective "laboratory" for developing and testing decision supports.
PUBLICATIONS:
None at this time.
DRA:
Chronic Diseases, Health Services and Systems, Special (Underserved, High Risk) Populations
DRE:
none
Keywords:
none
MeSH Terms:
none
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