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Clinical Trial
. 1999 Mar;37(3):270-84.
doi: 10.1097/00005650-199903000-00007.

Patient decision support intervention: increased consistency with decision analytic models

Affiliations
Clinical Trial

Patient decision support intervention: increased consistency with decision analytic models

M Holmes-Rovner et al. Med Care. 1999 Mar.

Abstract

Background: Patient Decision Support (PDS) tools assist patients in using medical evidence to make choices consistent that are with their values and in using evidence about consequences of medical alternatives.

Objective: To evaluate a PDS intervention for perimenopausal hormone replacement therapy. We assessed the impact of the PDS on (1) consistency between the decision to take estrogen replacement therapy (ERT) or progesterone/estrogen replacement therapy (PERT) and the expected utility of treatment and (2) likelihood to take ERT and PERT pre- and postintervention.

Design: Content of the PDS was standardized. Randomized trial of three intensities of intervention: (1) brochure; (2) lecture/discussion; and (3) active decision support.

Subjects: Participants were perimenopausal community volunteers between the ages of 40 and 65 (n = 248).

Measures: (1) Consistent with values (correlation between expected utility (EU) and likelihood of taking hormones); and (2) Likelihood to take hormone replacement therapy.

Results: (1) The brochure group was less consistent with the decision analytic model than the lecture/discussion and active decision support groups. (2) Influence on decisions: PDS tools increased the number of women certain about whether or not to take hormones. There were no differences among experimental groups. Of 99 women uncertain about ERT pre-PDS, 65% changed. Twenty-one (32%) decided against ERT and 44 (68%) decided for ERT. (3) More intensive interventions produced modest gains in a normative direction.

Conclusions: PDSs using any of 3 formats reduce uncertainty and assist women to make informed decisions. Increased consistency with decision analytic models appears to be driven by better estimates of likelihood of outcomes.

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