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: 4.53). 11/2008; 35(5 Suppl):S350-8. DOI: 10.1016/j.amepre.2008.08.010
Source: PubMed


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.

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Available from: Alex Krist, Oct 05, 2015
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    • "However, the delivery of preventive services is often a low priority in family practices [6]. It has been stated that practice systems and processes are not optimally designed to address the care of individuals with preventive needs, for instance the delivery of behavioural counselling [7,8]. Despite various approaches to improve structure and organisation of services delivered in primary care [9,10] little is known about the relationship between structural and organisational practice characteristics and the quality of care [11], especially as it relates to preventive care. "
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    ABSTRACT: Prevention of cardiovascular diseases (CVD) is a major health issue worldwide. Primary care plays an important role in cardiovascular risk management (CVRM). Guidelines and quality of care measures to assess CVRM in primary care practices are available. In this study, we assessed the relationship between structural and organisational practice characteristics and the quality of care provided in individuals at high risk for developing CVD in European primary care. An observational study was conducted in 267 general practices from 9 European countries. Previously developed quality indicators were abstracted from medical records of randomly sampled patients to create a composite quality measure. Practice characteristics were collected by a practice questionnaire and face to face interviews. Data were aggregated using factor analysis to four practice scores representing structural and organisational practice features. A hierarchical multilevel analysis was performed to examine the impact of practice characteristics on quality of CVRM. The final sample included 4223 individuals at high risk for developing CVD (28% female) with a mean age of 66.5 years (SD 9.1). Mean indicator achievement was 59.9% with a greater variation between practices than between countries. Predictors at the patient level (age, gender) had no influence on the outcome. At the practice level, the score 'Preventive Services' (13 items) was positively associated with clinical performance (r = 1.92; p = 0.0058). Sensitivity analyses resulted in a 5-item score (PrevServ_5) that was also positively associated with the outcome (r = 4.28; p < 0.0001). There was a positive association between the quality of CVRM in individuals at high risk for developing CVD and the availability of preventive services related to risk assessment and lifestyle management supported by information technology.
    Implementation Science 03/2013; 8:27. DOI:10.1186/1748-5908-8-27 · 4.12 Impact Factor
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    • "The low rates of engagement by GPs may reflect the fact that the program was a trial and not an ongoing service. Research suggests that better system support is required to engage general practice in prevention including adequate funding and reimbursement systems, the use of staff such as practice nurses and managers and referral brokers (who act to facilitate referrals between GPs and services), along with better practice systems such as patient registration and recall and reminder systems [37-41]. Our findings also highlight the importance of appropriately briefing GPs on prevention programs so they can encourage participation amongst their high risk patients. "
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    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 · 2.26 Impact Factor
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    The Annals of Family Medicine 03/2010; 8(2):100-7. DOI:10.1370/afm.1099 · 5.43 Impact Factor
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