An Electronic Linkage System for Health Behavior Counseling

Department of Family Medicine, Virginia Commonwealth University, Richmond, Virginia 23298-0251, USA.
American journal of preventive medicine (Impact Factor: 4.28). 11/2008; 35(5 Suppl):S350-8. DOI: 10.1016/j.amepre.2008.08.010
Source: PubMed

ABSTRACT A variety of factors limit the ability of clinicians to offer intensive counseling to patients with unhealthy behaviors, and few patients (2%-5%) are referred to the community counseling resources that do offer such assistance. A system that could increase referrals through an efficient collaborative partnership between community programs and clinicians could have major public health implications; such was the subject of this feasibility evaluation.
At nine primary care practices, an electronic linkage system (eLinkS) was instituted to promote health behavior counseling and to automate patient referrals to community counseling services. Patients were offered 9 months of free counseling for weight loss, smoking cessation, and problem drinking at a choice of venues: group counseling, telephone counseling, computer care, and usual care. The delivery of behavioral counseling, measured by the 5A's (ask, address, advise, assess, agree, arrange) and patients' reported experiences with eLinkS, was examined.
For 5 weeks eLinkS was used, until high referral volumes depleted counseling funds. Of the 5679 patients visiting the practices, 71% had an unhealthy behavior. Of these patients, 10% were referred for intensive counseling from a community program, most often for weight loss. Counseling and referrals occurred regardless of visit type--wellness, acute, or chronic care. eLinkS was used more often for middle-aged adults and women and by more-experienced clinicians.
The intervention increased the rate at which patients were referred for intensive behavioral counseling compared to current practice norms. Given the evidence that intensive counseling is more effective in promoting behavior change, implementing eLinkS could have substantial public health benefits.


Available from: Alex Krist, May 03, 2015
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective: To use qualitative methods to explore how clinicians approach weight counseling, including who they counsel, how they bring up weight, what advice they provide, and what treatment referral resources they use. Methods: Thirty primary care physicians, physician assistants, and nurse practitioners from four multi-clinic community health center systems (CHCs) in the state of Georgia (U.S.) completed one-on-one semi-structured interviews. Interviews were digitally recorded, transcribed verbatim, and coded. Results: Clinicians report addressing weight with those who have weight-related chronic conditions, are established patients, or have a change in weight since the previous visit. Most clinicians address weight in the context of managing or preventing chronic conditions. Clinicians report providing detailed dietary advice to patients, including advice about adding or avoiding foods. Many clinicians base advice on their own experiences with weight. Most report no community-based resources to offer patients for weight loss. In the absence of resources, clinicians develop or use existing brochures, refer to in-house weight programs, or use online resources. Conclusion: Clinicians use a variety of approaches for addressing weight, many of which are not evidence-based. Linkages with weight loss resources in the health care system or community are not widely reported. Implications for practice: Clinicians and others from the primary care team should continue to offer weight-related counseling to patients with obesity, however, evidence-based treatment approaches for weight loss may need to be adapted or expanded for the CHC practice environment.
    Patient Education and Counseling 06/2014; 97(1). DOI:10.1016/j.pec.2014.05.026 · 2.60 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: There has been little discussion of or research on the key translational issue of how to integrate patient self-management programs across multiple primary care clinics within an HMO. The purpose of this study was to summarize our experiences and lessons learned in trying to integrate information from a web-based diabetes self-management program into primary care and the electronic health record (EHR). We describe plans, implementation, adaptations made, and data on patient and physician reactions to the My Path diabetes self-management program provided to 331 adult primary care patients. Mixed methods results revealed that, despite the availability of a state-of-the-art EHR, the intervention was not well integrated into primary care. Information from health-promotion and disease management programs, even within the same organization and with advanced EHR systems, is challenging to integrate into busy primary care.
    09/2012; 2(3):313-21. DOI:10.1007/s13142-012-0109-8
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background The effectiveness of lifestyle interventions in reducing diabetes incidence has been well established. Little is known, however, about factors influencing the reach of diabetes prevention programs. This study examines the predictors of enrolment in the Sydney Diabetes Prevention Program (SDPP), a community-based diabetes prevention program conducted in general practice, New South Wales, Australia from 2008–2011. Methods SDPP was an effectiveness trial. Participating general practitioners (GPs) from three Divisions of General Practice invited individuals aged 50–65 years without known diabetes to complete the Australian Type 2 Diabetes Risk Assessment tool. Individuals at high risk of diabetes were invited to participate in a lifestyle modification program. A multivariate model using generalized estimating equations to control for clustering of enrolment outcomes by GPs was used to examine independent predictors of enrolment in the program. Predictors included age, gender, indigenous status, region of birth, socio-economic status, family history of diabetes, history of high glucose, use of anti-hypertensive medication, smoking status, fruit and vegetable intake, physical activity level and waist measurement. Results Of the 1821 eligible people identified as high risk, one third chose not to enrol in the lifestyle program. In multivariant analysis, physically inactive individuals (OR: 1.48, P = 0.004) and those with a family history of diabetes (OR: 1.67, P = 0.000) and history of high blood glucose levels (OR: 1.48, P = 0.001) were significantly more likely to enrol in the program. However, high risk individuals who smoked (OR: 0.52, P = 0.000), were born in a country with high diabetes risk (OR: 0.52, P = 0.000), were taking blood pressure lowering medications (OR: 0.80, P = 0.040) and consumed little fruit and vegetables (OR: 0.76, P = 0.047) were significantly less likely to take up the program. Conclusions Targeted strategies are likely to be needed to engage groups such as smokers and high risk ethnic groups. Further research is required to better understand factors influencing enrolment in diabetes prevention programs in the primary health care setting, both at the GP and individual level.
    BMC Public Health 09/2012; 12(1). DOI:10.1186/1471-2458-12-822 · 2.32 Impact Factor