Population Health and Evidence-Based Practice Sample

ByLeon Kiprotich Leon Updated on

Population Health and Evidence-Based Practice Sample

In 2015, chronic diseases such as heart disease, diabetes, and cancer were among the top seven causes of death in the United States (Centers for Disease Control and Prevention [CDC], 2017). Many health care organizations have focused their resources on controlling and preventing chronic diseases among community members through population health management (PHM) strategies (see Appendix, Terms and Definitions). In response to the prevalence of chronic diseases, the Gilbert-Hopes Family Health Center (GHFHC) in Southern Arizona has created a population health improvement plan based on PHM strategies to improve one pervasive health concern in its community—Type 2 Diabetes Mellitus (T2DM). Type 2 Diabetes Mellitus in American Indian (AI) communities is the focus of the plan. The initiatives implemented under the health improvement plan will use the best available evidence on Southern Arizona’s AI communities gained through the evaluation of demographic, epidemiological, and environmental data. Additionally, the plan will apply strategies for communicating health improvement goals with AI communities and health care professionals in an ethical, culturally sensitive, and inclusive way.

Environmental and Epidemiological Data About American Indian Communities

According to 2012 data, diabetes is a serious chronic disease affecting 29.1 million people in the United States. It can lead to conditions such as kidney failure, blindness, and heart disease. Diabetes also makes patients vulnerable to infections that require amputation (CDC, 2014). In Arizona, which has the third largest population of AIs in the country, almost 16% of AIs reported having diabetes, especially T2DM (Bass, Bailey, Gieszl, & Gouge  2015). In Southern Arizona, the CDC estimates that about 24.1% of adult AIs have diabetes. The state’s distribution of T2DM is caused by a combination of genetic and environmental factors.

Behavioral risk factors such as smoking, alcoholism, sedentary lifestyles, weight gain, and poor diets can be classified as environmental factors of T2DM and were observed among Navajo Nation and Pima Indians (Arizona Department of Health Services, Bureau of Tobacco and Chronic Disease [AZDHS], 2011; Murea, Ma, & Freedman, 2012). Exposure to pollutants is another environmental factor that can be associated with T2DM because pollutants affect insulin sensitivity and glucose metabolism (Eze et al., 2015). Genetic factors include a family history of obesity or diabetic vascular complications. Individuals with such a family history are at high risk of getting Type 2 diabetes (Murea, Ma, & Freedman, 2012).

The evaluation of epidemiological and environmental data about T2DM in AI communities has revealed several gaps in knowledge. To begin with, most epidemiological data about AIs by federal agencies such as the CDC do not have information on populations living in Indian reservations as reservations are independent governmental entities (AZDHS, 2011). Moreover, further evaluation is needed on the effects of exposure to environmental pollutants; most studies tend to focus on behavioral risk factors. These gaps in knowledge can cause health disparities among urban AIs and those living in reservations, thereby making it difficult to identify chronic disease patterns.

Furthermore, there is a need for further evaluation of sociocultural and linguistic factors that often prevent people from accessing health care. The concept of cultural competence (see Appendix, Terms and Definitions) is imperative if the GHFHC wishes to successfully implement a population health improvement plan that will address the various needs of AI communities.

Health Improvement Plan to Address Diabetes Among American Indians

Among the many models adopted into PHM efforts, the collaborative chronic care model (CCM) framework is successful at managing diabetes and other chronic diseases among affected populations. There are six elements that are essential to the CCM: (a) health systems, (b) delivery system design, (c) decision support, (d) clinical information systems, (e) community resources and policies, and (f) self-management support (Improving Chronic Illness Care, 2003; see Appendix, Terms and Definitions). The GHFHC’s will follow the CCM for its health improvement plan based on certain assumptions about the plan. These assumptions are that the plan (a) needs to be sustained for a long time, (b) needs to comply with evidence-based guidelines for patient care, (c) needs to focus on patient education and lifestyle improvement, (d) needs to provide affordable and cost-effective care for AIs, and (e) needs to be culturally sensitive and equitable for disadvantaged community members.

The CCM’s six elements complement these assumptions and when the model is implemented in a PHM, the CCM will allow an informed, active community to productively interact with a proactive, prepared clinical team to achieve improved outcomes. Key components of the plan that are consistent with the CCM elements and the GHFHC’s assumptions are as follows: (a) establishing a system for collecting data and tracking health outcomes among AI patients; (b) establishing an operational leadership that will change staff management policies to ethnically match ethnicity and language of AI patients; (c) training all health care professionals on the CCM and cultural and linguistic competence; (d) sharing reports, lab-work, and epidemiological data with local health systems; (e) identifying local resources such as community health centers, YMCAs, religious centers, and senior centers that can help connect patients with the GHFHC; and (f) planning regular meetings for all stakeholders to resolve issues, discuss outcomes, and make recommendations.

The different components in the plan will enable health care professionals in diagnosing widespread diabetes in the AI community and ensure that cultural competence is deployed at the patient, health care professional, organizational, and systems levels. The next section will discuss why the CCM was selected over other community-based population health management models. Relevant evidence and examples will be provided.

Value and Relevance of the Chronic Care Model

Since its inception more than 15 years ago, the chronic care model has been for diabetes care in health care organizations across the United States with positive outcomes (Baptista et al., 2016). Most of the evidence supporting the model comes from randomized control trials (RCTs), qualitative reviews, meta-analyses, and systematic reviews of articles on the CCM in health care organizations. The results of one systematic review of 16 studies on the CCM application revealed better diabetes management programs in several health organizations (Stellefson, Dipnarine, & Stopka, 2013). Organizational leaders used the CCM to initiate system-level changes that improved delivery of diabetes care to patients. The organizations introduced disease registries and electronic records to establish patient-centered goals, educate patients on self-management, and train health care professionals in evidence-based care.

Another study evaluated the success of Project Dulce, a CCM-based diabetes care program developed by the Scripps Whittier Diabetes Institute in collaboration with San Diego County, San Diego State University, and federally qualified health centers (Philis-Tsimikas & Gallo, 2014). The project used specially trained teams and peer educators to implement the CCM elements in an ethnically and racially diverse community. The results showed significant cost-effectiveness hospitalizations and emergency visits reduced.

While the CCM has many merits, there are conflicts in the data provided by the aforementioned studies. Health care organizations implement only one or two elements such as delivery design systems or self-management rather than the combined implementation of all six elements. As a result, it is difficult to determine the overall impact of the CCM or identify the combinations of elements that are ideal (Davy et al., 2015). Other conflicting problems are related to the study process of RCTs: participants were sometimes aware of their participation in trials, follow-up periods and sample sizes were insufficient, and study nurses were inadequately trained (Baptista et al., 2016).

Despite these problems, the CCM remains a popular model compared to the acute care model of case management. While the two are similar in terms of care coordination and cost-effective strategizing, the CCM is more overarching, community and population-based, and more proactive in health improvement. Acute care, on the other hand, is case-specific, client-centered, and episodic (Huber, 2017). Because of the reach and magnitude of GHFHC’s health improvement plan, evaluating its outcomes can become a complex task. As the plan follows already established population health management strategies, evaluative criteria will correspondingly borrow from PHM theories of outcomes measurement.

Criteria to Evaluate Achievement of Plan Outcomes

Population health improvement plans are the convergence of different health care roles and resources. To devise an effective PHM plan, GHFHC must identify, define, and assess standards that can evaluate its plan in its entirety. One of the leading organizations conducting research and development on population health programs is Care Continuum Alliance (CCA). The CCA identified key components of PHM (Care Continuum Alliance [CCA], n.d.-a; see Appendix, Terms and Definitions) in its population health improvement model. The model proposed an additional five measures of population health plan outcomes: (a) optimal clinical indicators, including process and outcomes measures; (b) assessment of patient satisfaction with health care; (c) economic and health care utilization indicators; (d) functional status and quality of life; and (e) impact on known population health disparities (CCA, n.d.-a).

These five measures can be supplemented with evaluative criteria adapted from the CCA’s six components of disease management (DM) programs. The DM evaluation criteria can help determine whether the plan objectives are completed and detail the performance indicators or methods used in the process (Huber, 2017; CCA, n.d.-b; see Appendix, Terms and Conditions). The combination of both sets of criteria can provide a well-rounded evaluation and simultaneously consider different components and methods within the GHFHC’s health improvement plan.

Other evaluative criteria that were considered, but rejected, belonged to the RAND Corporation’s DISMEVAL project for chronic disease management. The RAND evaluation is organized under broad categories—input measures, process measures, output measures, outcome measures, and other impacts—with specific dimensions to be selected based on the design and goals of the health intervention (Nolte et al., 2012). However, the criteria do not adequately address sociocultural impacts from the health care intervention. As the health improvement plan specifically targets a vulnerable community, the plan will benefit from an evaluative framework that presupposes cultural disparities and promotes specific steps for optimal social outcomes.

Implementation and evaluation of the population health improvement plan depend on a concrete communication strategy. To coordinate care across different times, settings, providers, and community members, the structure and challenges of its communication plan become even more relevant.

Culturally Competent Communication Strategies for the PHM Plan

Communication is central to achieving each component of the population health improvement plan. Therefore, communication is a core task for GHFHC’s health care professionals. The biggest communication barriers to be expected in care coordination and self-management are distrust, misunderstanding due to language differences, inappropriate educational methods, a lack of cultural competence, and low levels of interaction between health care providers and patients brought on by language barriers (Tiedt & Sloan, 2015; Ghosh & Spitzer, 2014).

One strategy to remove these barriers is training health care professionals, especially nursing professionals who are primary caregivers, in cultural and linguistic competence. The strategy includes enlisting the expertise of interpreters and hiring staff from AI backgrounds to achieve language concordance (Dauvrin, Lorant, & d’Hoore, 2015). Peer specialist-led interventions are instrumental in communication because they connect with patients through their shared experiences. Peer specialists are individuals who have personal experiences with a health issue such as T2DM and who may belong to the same ethnicity or community as the patient (Cabassa et al., 2015; Dauvrin, Lorant, & d’Hoore, 2015). Cultural tailoring of resources and facilities is a part of this strategy. For example, educational materials and content-based resources for patients can depict substantial graphic content instead of plain text to avoid ambiguity.

Another strategy is to improve interaction between health care providers and patients. Establishing regular contact through telephonic calls and emails, conducting meetings at public or community spaces, and arranging for clinic-to-home services for patients are appropriate communication methods (CCA, 2012). Face-to-face patient follow-ups can be set up on a daily, weekly, or monthly basis determined by the severity and risk of the case (Cabassa et al., 2015). Additionally, interactions are simplified by setting up clinical information systems for sharing all patient-related data among care providers and care coordinators because health care professionals who can answer patients’ queries are more trusted (Dauvrin, Lorant, & d’Hoore, 2015).

Despite these strategies, there is potential for challenges in communication. For instance, many community members may be spread across relatively isolated rural areas and reservations. They may not be able to procure access to health interventions, or health care providers may not be able to go to them. The solution will be enlisting the help of local community leaders and medical centers to bridge the communication gap and execute the communication strategies. Another challenge can arise if health professionals do not involve patients’ families in the health management process. Families play an important role in enforcing behavioral and lifestyle changes and sustaining those changes to improve health outcomes. It is impossible to plan for each problem, communication-related or other, as health care is a high-risk environment. Instead, health care professionals must focus on the professional guidelines on patient-centered care and cultural competence that will help them solve problems as they arise. Health care professionals focused on proactive care delivery are part of what makes a population improvement plan effective.

Conclusion

As chronic diseases such as type 2 diabetes become more widespread, health care professionals and communities must work together to create focused health interventions. However, interventions cannot ignore the cultural contexts that shape individuals and their experiences in health care. Models such as the chronic care model are successful in customizing care for ethnic and racial communities. Yet, experts have voiced a need for improving such models and PHM guidelines that focus on the specific and unique needs of multicultural patients. The GHFHC, through its health improvement plan, will join other health care organizations in promoting research and innovation in an important health care field.

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