http://nevadawellness.org/wp-content/uploads/2015/06/Diabetes-self-management-education-for-adults-with-type-2-diabetes-mellitus-A-systematic-review-of-the-effect-on-glycemic-control.pdf

Diabetes self-management education for adults with type 2 diabetes mellitus: A systematic review of the effect on glycemic control.

Diabetes is a chronic disease that requires a person with diabetes to make a multitude of daily self-management decisions and to perform complex care activities. Diabetes self-management education and support (DSME/S) provides the foundation to help people with diabetes to navigate these decisions and activities and has been shown to improve health outcomes (1–7). Diabetes self-management education (DSME) is the process of facilitating the knowledge, skill, and ability necessary for diabetes self-care. Diabetes self-management support (DSMS) refers to the support that is required for implementing and sustaining coping skills and behaviors needed to self-manage on an ongoing basis. (See further definitions in Table 1.) Although different members of the health care team and community can contribute to this process, it is important for health care providers and their practice settings to have the resources and a systematic referral process to ensure that patients with type 2 diabetes receive both DSME and DSMS in a consistent manner. The initial DSME is typically provided by a health professional, whereas ongoing support can be provided by personnel within a practice and a variety of community-based resources. DSME/S programs are designed to address the patient’s health beliefs, cultural needs, current knowledge, physical limitations, emotional concerns, family support, financial status, medical history, health literacy, numeracy, and other factors that influence each person’s ability to meet the challenges of self-management.

View this table:

TABLE 1. 
Key Definitions



It is the position of the American Diabetes Association (ADA) that all individuals with diabetes receive DSME/S at diagnosis and as needed thereafter (8). This position statement focuses on the particular needs of individuals with type 2 diabetes. The needs will be similar to those of people with other types of diabetes (type 1 diabetes, prediabetes, and gestational diabetes mellitus); however, the research and examples referred to in this article focus …

https://clinical.diabetesjournals.org/content/diaclin/34/2/70.full.pdf

Diabetes Self-management Education and Support in Type 2 Diabetes: A Joint Position Statement of the American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics

In 2005, approximately 400,000 people provided primary medical care in the United States. About 300,000 were physicians, and another 100,000 were nurse practitioners and physician assistants. Yet primary care faces a growing crisis, in part because increasing numbers of U.S. medical graduates are avoiding careers in adult primary care. Sixty-five million Americans live in what are officially deemed primary care shortage areas, and adults throughout the United States face difficulty obtaining prompt access to primary care. A variety of strategies are being tried to improve primary care access, even without a large increase in the primary care workforce.

Primary Care: Current Problems And Proposed Solutions

© 2015 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd. Background: Personalised care planning is a collaborative process used in chronic condition management in which patients and clinicians identify and discuss problems caused by or related to the patient's condition, and develop a plan for tackling these. In essence it is a conversation, or series of conversations, in which they jointly agree goals and actions for managing the patient's condition. Objectives: To assess the effects of personalised care planning for adults with long-term health conditions compared to usual care (i.e. forms of care in which active involvement of patients in treatment and management decisions is not explicitly attempted or achieved). Search methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO, ProQuest, clinicaltrials.gov and WHO International Clinical Trials Registry Platform to July 2013. Selection criteria: We included randomised controlled trials and cluster-randomised trials involving adults with long-term conditions where the intervention included collaborative (between individual patients and clinicians) goal setting and action planning. We excluded studies where there was little or no opportunity for the patient to have meaningful influence on goal selection, choice of treatment or support package, or both. Data collection and analysis: Two of three review authors independently screened citations for inclusion, extracted data, and assessed risk of bias. The primary outcomes were effects on physical health, psychological health, subjective health status, and capabilities for self management. Secondary outcomes included effects on health-related behaviours, resource use and costs, and type of intervention. A patient advisory group of people with experience of living with long-term conditions advised on various aspects of the review, including the protocol, selection of outcome measures and emerging findings. Main results: We included 19 studies involving a total of 10,856 participants. Twelve of these studies focused on diabetes, three on mental health, one on heart failure, one on end-stage renal disease, one on asthma, and one on various chronic conditions. All 19 studies included components that were intended to support behaviour change among patients, involving either face-to-face or telephone support. All but three of the personalised care planning interventions took place in primary care or community settings; the remaining three were located in hospital clinics. There was some concern about risk of bias for each of the included studies in respect of one or more criteria, usually due to inadequate or unclear descriptions of research methods. Physical health Nine studies measured glycated haemoglobin (HbA1c), giving a combined mean difference (MD) between intervention and control of -0.24% (95% confidence interval (CI) -0.35 to -0.14), a small positive effect in favour of personalised care planning compared to usual care (moderate quality evidence). Six studies measured systolic blood pressure, a combined mean difference of -2.64 mm/Hg (95% CI -4.47 to -0.82) favouring personalised care (moderate quality evidence). The pooled results from four studies showed no significant effect on diastolic blood pressure, MD -0.71 mm/Hg (95% CI -2.26 to 0.84). We found no evidence of an effect on cholesterol (LDL-C), standardised mean difference (SMD) 0.01 (95% CI -0.09 to 0.11) (five studies) or body mass index, MD -0.11 (95% CI -0.35 to 0.13) (four studies). A single study of people with asthma reported that personalised care planning led to improvements in lung function and asthma control. Psychological health Six studies measured depression. We were able to pool results from five of these, giving an SMD of -0.36 (95% CI -0.52 to -0.20), a small effect in favour of personalised care (moderate quality evidence). The remaining study found greater improvement in the control group than the intervention group. Four other studies used a variety of psychological measures that were conceptually different so could not be pooled. Of these, three found greater improvement for the personalised care group than the usual care group and one was too small to detect differences in outcomes. Subjective health status Ten studies used various patient-reported measures of health status (or health-related quality of life), including both generic health status measures and condition-specific ones. We were able to pool data from three studies that used the SF-36 or SF-12, but found no effect on the physical component summary score SMD 0.16 (95% CI -0.05 to 0.38) or the mental component summary score SMD 0.07 (95% CI -0.15 to 0.28) (moderate quality evidence). Of the three other studies that measured generic health status, two found improvements related to personalised care and one did not. Four studies measured condition-specific health status. The combined results showed no difference between the intervention and control groups, SMD -0.01 (95% CI -0.11 to 0.10) (moderate quality evidence). Self-management capabilities Nine studies looked at the effect of personalised care on self-management capabilities using a variety of outcome measures, but they focused primarily on self efficacy. We were able to pool results from five studies that measured self efficacy, giving a small positive result in favour of personalised care planning: SMD 0.25 (95% CI 0.07 to 0.43) (moderate quality evidence). A further five studies measured other attributes that contribute to self-management capabilities. The results from these were mixed: two studies found evidence of an effect on patient activation, one found an effect on empowerment, and one found improvements in perceived interpersonal support. Other outcomes Pooled data from five studies on exercise levels showed no effect due to personalised care planning, but there was a positive effect on people's self-reported ability to carry out self-care activities: SMD 0.35 (95% CI 0.17 to 0.52). We found no evidence of adverse effects due to personalised care planning. The effects of personalised care planning were greater when more stages of the care planning cycle were completed, when contacts between patients and health professionals were more frequent, and when the patient's usual clinician was involved in the process. Authors' conclusions: Personalised care planning leads to improvements in certain indicators of physical and psychological health status, and people's capability to self-manage their condition when compared to usual care. The effects are not large, but they appear greater when the intervention is more comprehensive, more intensive, and better integrated into routine care.

/pdf/personalised-care-planning-for-adults-with-chronic-or-long-4mhp9doih1.pdf

Personalised care planning for adults with chronic or long-term health conditions

In this research we explore the current state of chronic diseases in the United States, using data from the Centers for Disease Control and Prevention and applying visualization and descriptive analytics techniques. Five main categories of variables are studied, namely chronic disease conditions, behavioral health, mental health, demographics, and overarching conditions. These are analyzed in the context of regions and states within the U.S. to discover possible correlations between variables in several categories. There are widespread variations in the prevalence of diverse chronic diseases, the number of hospitalizations for specific diseases, and the diagnosis and mortality rates for different states. Identifying such correlations is fundamental to developing insights that will help in the creation of targeted management, mitigation, and preventive policies, ultimately minimizing the risks and costs of chronic diseases. As the population ages and individuals suffer from multiple conditions, or comorbidity, it is imperative that the various stakeholders, including the government, non-governmental organizations (NGOs), policy makers, health providers, and society as a whole, address these adverse effects in a timely and efficient manner.

/pdf/an-empirical-study-of-chronic-diseases-in-the-united-states-55t1sf8xah.pdf

An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health

The U.S. chronic illness burden is increasing and is felt more strongly in minority and low-income populations: in 2005, 133 million Americans had at least one chronic condition. Prevention and management of chronic disease are best performed by multidisciplinary teams in primary care and public health. However, the future health care work-force is not projected to include an appropriate mix of personnel capable of staffing such teams. To prepare for the growing chronic disease burden, a larger interdisciplinary primary care workforce is needed, and payment for primary care should reward practices that incorporate multidisciplinary teams.

https://www.commed.vcu.edu/Chronic_Disease/2009/The_future_jan09.pdf

Confronting The Growing Burden Of Chronic Disease: Can The U.S. Health Care Workforce Do The Job?

PURPOSE Peer health coaches offer a potential model for extending the capacity of primary care practices to provide self-management support for patients with diabetes. We conducted a randomized controlled trial to test whether clinic-based peer health coaching, compared with usual care, improves glycemic control for low-income patients who have poorly controlled diabetes. METHOD We undertook a randomized controlled trial enrolling patients from 6 public health clinics in San Francisco. Twenty-three patients with a glycated hemoglobin (HbA1C) level of less than 8.5%, who completed a 36-hour health coach training class, acted as peer coaches. Patients from the same clinics with HbA1C levels of 8.0% or more were recruited and randomized to receive health coaching (n = 148) or usual care (n = 151). The primary outcome was the differ- ence in change in HbA1C levels at 6 months. Secondary outcomes were propor- tion of patients with a decrease in HbA1C level of 1.0% or more and proportion of patients with an HbA1C level of less than 7.5% at 6 months. Data were ana- lyzed using a linear mixed model with and without adjustment for differences in baseline variables. RESULTS At 6 months, HbA1C levels had decreased by 1.07% in the coached group and 0.3% in the usual care group, a difference of 0.77% in favor of coaching (P = .01, adjusted). HbA1C levels decreased 1.0% or more in 49.6% of coached patients vs 31.5% of usual care patients (P = .001, adjusted), and levels at 6 months were less than 7.5% for 22.0% of coached vs 14.9% of usual care patients (P = .04, adjusted). CONCLUSIONS Peer health coaching signifi cantly improved diabetes control in this group of low-income primary care patients.

https://www.annfammed.org/content/annalsfm/11/2/137.full.pdf

Impact of Peer Health Coaching on Glycemic Control in Low-Income Patients With Diabetes: A Randomized Controlled Trial

Background: Lack of concordance between medications listed in the medical record and taken by the patient contributes to poor outcomes. We sought to determine whether patients who received health coaching by medical assistants improved their medication concordance and adherence. Methods: This was a nonblinded, randomized, controlled, pragmatic intervention trial. English- or Spanish-speaking patients, age 18 to 75 years, with poorly controlled type 2 diabetes, hypertension, and/or hyperlipidemia were enrolled from 2 urban safety net clinics and randomized to receive 12 months of health coaching versus usual care. Results: Outcomes included concordance between medications documented in the medical record and those reported by the patient and adherence based on the patient-reported number of days (of the last 7) on which patient took all prescribed medications. The proportion of medications completely concordant increased in the coached group versus the usual care group (difference in change, 10%; P = .05). The proportion of medications listed in the chart but not taken significantly decreased in the coached group compared with the usual care group (difference in change, 17%; P = .013). The mean number of adherent days increased in the coached but not in the usual care group (difference in change, 1.08; P Conclusions: Health coaching by medical assistants significantly increases medication concordance and adherence.

The Impact of Health Coaching on Medication Adherence in Patients With Poorly Controlled Diabetes, Hypertension, and/or Hyperlipidemia: A Randomized Controlled Trial

Objectives Health coaches can help patients gain knowledge, skills, and confidence to manage their chronic conditions. Coaches may be particularly valuable in resource-poor settings, but they are not typically reimbursed by insurance, raising questions about their budgetary impact. Study design The Health Coaching in Primary Care (HCPC) study was a randomized controlled trial that showed health coaches were effective at helping low-income patients improve control of their type 2 diabetes, hypertension, and/or hyperlipidemia at 12 months compared with usual care. Methods We estimated the cost of employing 3 health coaches and mapped these costs to participants. We tested whether the added costs of the coaches were offset by any savings in healthcare utilization within 1 year. Healthcare utilization data were obtained from 5 sources. Multivariate models assessed differences in costs at 1 year controlling for baseline characteristics. Results Coaches worked an average of 9 hours with each participant over the length of the study. On average, the health coach intervention cost $483 per participant per year. The average healthcare costs for the coaching group was $3207 compared with $3276 for the control group (P = .90). There was no evidence that the coaching intervention saved money at 1 year. Conclusions Health coaches have been shown to improve clinical outcomes related to chronic disease management. We found that employing health coaches adds an additional cost of $483 per patient per year. The data do not suggest that health coaches pay for themselves by reducing healthcare utilization in the first year.

Costs for a health coaching intervention for chronic care management.

Context: The 15-minute office visit to primary care clinicians cannot meet the health care needs of patients. Innovation is needed to address this limitation, but practice redesign is challenging in clinical settings.

Objective: Here we describe the implementation of a practice innovation, the teamlet model, in a San Francisco safety-net clinic. The teamlet consists of a clinician and “health coach” who expand the traditional medical visit into previsit, visit, postvisit, and between-visit care.

Design: Teamlet implementation is occurring in phases. Phase 1 is evaluated using plan-do-study-act improvement cycles and interviews with a few patients, clinicians, and coaches. Phase 2 is evaluated using a pre- and postevent questionnaire, focused interviews, and focus groups with patients, faculty, clinicians, and coaches.

Main outcome measures: Phase 1: Plan-do-study-act cycles generate ideas to improve implementation. Phase 2 evaluation will query demographics, satisfaction, knowledge of self-management support, access, teamwork, and benefits/challenges of the teamlet model. Future research would measure objective clinical outcomes.

Results: Phase 1 of the teamlet project led to useful adaptations, with anecdotal evidence that patients and clinicians were satisfied overall with practice improvements. Logistic problems made implementation of the innovation challenging. Phase 2 is currently underway, with results expected in 2008.

Conclusions: Primary care innovation requires multiple perspectives and constant revision. Traditional randomized controlled trials and quantitative evaluation designs are not appropriate for assessing practice-improvement pilot projects because projects must change and develop in their early stages. Despite numerous challenges, the teamlet practice redesign has the potential for improving on the traditional 15-minute physician's office visit.

Introducing the “Teamlet”: Initiating a Primary Care Innovation at San Francisco General Hospital

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