Abstract
BackgroundChronic illnesses are significant to individuals and costly to society. When systematically implemented, the well-established and tested Chronic Care Model (CCM) is shown to improve health outcomes for people with chronic conditions. Since the development of the original CCM, tremendous information management, communication, and technology advancements have been established. An opportunity exists to improve the time-honored CCM with clinically efficacious eHealth tools.ObjectiveThe first goal of this paper was to review research on eHealth tools that support self-management of chronic disease using the CCM. The second goal was to present a revised model, the eHealth Enhanced Chronic Care Model (eCCM), to show how eHealth tools can be used to increase efficiency of how patients manage their own chronic illnesses.MethodsUsing Theory Derivation processes, we identified a “parent theory”, the Chronic Care Model, and conducted a thorough review of the literature using CINAHL, Medline, OVID, EMBASE PsychINFO, Science Direct, as well as government reports, industry reports, legislation using search terms “CCM or Chronic Care Model” AND “eHealth” or the specific identified components of eHealth. Additionally, “Chronic Illness Self-management support” AND “Technology” AND several identified eHealth tools were also used as search terms. We then used a review of the literature and specific components of the CCM to create the eCCM.ResultsWe identified 260 papers at the intersection of technology, chronic disease self-management support, the CCM, and eHealth and organized a high-quality subset (n=95) using the components of CCM, self-management support, delivery system design, clinical decision support, and clinical information systems. In general, results showed that eHealth tools make important contributions to chronic care and the CCM but that the model requires modification in several key areas. Specifically, (1) eHealth education is critical for self-care, (2) eHealth support needs to be placed within the context of community and enhanced with the benefits of the eCommunity or virtual communities, and (3) a complete feedback loop is needed to assure productive technology-based interactions between the patient and provider.ConclusionsThe revised model, eCCM, offers insight into the role of eHealth tools in self-management support for people with chronic conditions. Additional research and testing of the eCCM are the logical next steps.
Highlights
This report presents preliminary mortality data for the United States based on vital records for a substantial proportion of deaths occurring in 2011
The age-adjusted death rate increased for six leading causes of death: Chronic lower respiratory diseases, Diabetes mellitus, Influenza and pneumonia, Chronic liver disease and cirrhosis, Parkinson’s disease, and Pneumonitis due to solids and liquids
Preliminary data in this report are based on records of deaths that occurred in calendar year 2011, which were received from state vital statistics offices and processed by the Centers for Disease Control and Prevention’s National Center for Health Statistics (NCHS) as of June 12, 2012
Summary
This report presents preliminary mortality data for the United States based on vital records for a substantial proportion of deaths occurring in 2011. Preliminary data in this report are based on records of deaths that occurred in calendar year 2011, which were received from state vital statistics offices and processed by the Centers for Disease Control and Prevention’s National Center for Health Statistics (NCHS) as of June 12, 2012. A comparison of a) the number of 2011 death records received from the states for processing by NCHS with b) the state’s independent control counts of the number of deaths in 2011 indicates that demographic information from death certificates for the United States was available for an estimated 98.9 percent of infant deaths (under age 1 year) and 99.4 percent of deaths of persons aged 1 year and over occurring in calendar year 2011 (see Table I in the Technical Notes). Estimates of cause of death based on pre liminary mortality data may differ from statistics developed from the final mortality data (see Tables II and III in the Technical Notes). These measures typically are similar, they can differ because they have different denominators
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