Promoting decision aid use in primary care using a staff member for delivery

Cecil G Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, NC 27599, USA.
Patient Education and Counseling (Impact Factor: 2.6). 06/2011; 86(2):189-94. DOI: 10.1016/j.pec.2011.04.033
Source: PubMed

ABSTRACT To determine the feasibility and effectiveness of in-clinic decision aid distribution using a care assistant.
We identified potentially eligible patients scheduled for upcoming appointments in our General Internal Medicine Clinic (n=1229). Patients were deemed eligible for two decision aids: prostate cancer screening and/or weight loss surgery. Patients were approached to view the decision aid in-clinic. Our primary measures were the proportion of decision aids distributed to eligible patients, and the proportion of decision aids viewed.
Among 913 patients who attended their scheduled appointments, 58% (n=525) were approached and eligibility was assessed by the staff member. Among the 471 who remained eligible, 57% (n=268) viewed at least a portion of the target decision aid. The mean viewing time for patients who watched less than the complete decision aid was 13 min.
In clinic viewing of decision aids may be a feasible and effective distribution method in primary care.
In clinic distribution requires an electronic health information system to identify potentially eligible patients, and a staff member dedicated to DA distribution. Brief decision aids (less than 10 min) are needed so patients can complete their use prior to the visit to facilitate patient-physician decision making.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: INTRODUCTION: Screening for colorectal cancer can reduce incidence and death, but screening is underused, especially among vulnerable groups such as Medicaid patients. Effective interventions are needed to increase screening frequency. Our study consisted of a controlled trial of an intervention designed to improve colorectal cancer screening among Medicaid patients in North Carolina. METHODS: The intervention included a mailed screening reminder letter and decision aid followed by telephone support from an offsite, Medicaid-based, patient navigator. The study included 12 clinical practices, 6 as intervention practices and 6 as matched controls. Eligible patients were aged 50 years or older, covered by Medicaid, and identified from Medicaid claims data as not current with colorectal cancer screening recommendations. We reviewed Medicaid claims data at 6 months and conducted multivariate logistic regression to compare participant screening in intervention practices with participants in control practices. We controlled for sociodemographic characteristics. RESULTS: Most of the sample was black (53.1%) and female (57.2%); the average age was 56.5 years. On the basis of Medicaid claims, 9.2% of intervention participants (n = 22/240) had had a colorectal cancer screening at the 6-month review, compared with 7.5% of control patients (n = 13/174). The adjusted odds ratio when controlling for age, comorbidities, race, sex, and continuous Medicaid eligibility was 1.44 (95% confidence interval, 0.68-3.06). The patient navigator reached 44 participants (27.6%). CONCLUSION: The intervention had limited reach and little effect after 6 months on the number of participants screened. Higher-intensity interventions, such as use of practice-based navigators, may be needed to reach and improve screening rates in vulnerable populations.
    Preventing chronic disease 05/2013; 10:E82. DOI:10.5888/pcd10.120221 · 1.96 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Two decades of research has established the positive effect of using patient-targeted decision support interventions: patients gain knowledge, greater understanding of probabilities and increased confidence in decisions. Yet, despite their efficacy, the effectiveness of these decision support interventions in routine practice has yet to be established; widespread adoption has not occurred. The aim of this review was to search for and analyze the findings of published peer-reviewed studies that investigated the success levels of strategies or methods where attempts were made to implement patient-targeted decision support interventions into routine clinical settings. An electronic search strategy was devised and adapted for the following databases: ASSIA, CINAHL, Embase, HMIC, Medline, Medline-in-process, OpenSIGLE, PsycINFO, Scopus, Social Services Abstracts, and the Web of Science. In addition, we used snowballing techniques. Studies were included after dual independent assessment. After assessment, 5322 abstracts yielded 51 articles for consideration. After examining full-texts, 17 studies were included and subjected to data extraction. The approach used in all studies was one where clinicians and their staff used a referral model, asking eligible patients to use decision support. The results point to significant challenges to the implementation of patient decision support using this model, including indifference on the part of health care professionals. This indifference stemmed from a reported lack of confidence in the content of decision support interventions and concern about disruption to established workflows, ultimately contributing to organizational inertia regarding their adoption. It seems too early to make firm recommendations about how best to implement patient decision support into routine practice because approaches that use a 'referral model' consistently report difficulties. We sense that the underlying issues that militate against the use of patient decision support and, more generally, limit the adoption of shared decision making, are under-investigated and under-specified. Future reports from implementation studies could be improved by following guidelines, for example the SQUIRE proposals, and by adopting methods that would be able to go beyond the 'barriers' and 'facilitators' approach to understand more about the nature of professional and organizational resistance to these tools. The lack of incentives that reward the use of these interventions needs to be considered as a significant impediment.
    BMC Medical Informatics and Decision Making 01/2013; 13 Suppl 2:S14. DOI:10.1186/1472-6947-13-S2-S14 · 1.50 Impact Factor

Full-text (2 Sources)

Available from
Jun 3, 2014