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báo cáo khoa học: "An evidence-based health workforce model for primary and community care"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài:An evidence-based health workforce model for primary and community care | Segal and Leach Implementation Science 2011 6 93 http www.implementationscience.eom content 6 1 93 Implementation Science IMPLEMENTATION SCIENCE DEBATE Open Access An evidence-based health workforce model for primary and community care Leonie Segal and Matthew J Leach Abstract Background The delivery of best practice care can markedly improve clinical outcomes in patients with chronic disease. While the provision of a skilled multidisciplinary team is pivotal to the delivery of best practice care the occupational or skill mix required to deliver this care is unclear it is also uncertain whether such a team would have the capacity to adequately address the complex needs of the clinic population. This is the role of needs-based health workforce planning. The objective of this article is to describe the development of an evidence-informed needs-based health workforce model to support the delivery of best-practice interdisciplinary chronic disease management in the primary and community care setting using diabetes as a case exemplar. Discussion Development of the workforce model was informed by a strategic review of the literature critical appraisal of clinical practice guidelines and a consensus elicitation technique using expert multidisciplinary clinical panels. Twenty-four distinct patient attributes that require unique clinical competencies for the management of diabetes in the primary care setting were identified. Patient attributes were grouped into four major themes and developed into a conceptual model the Workforce Evidence-Based WEB planning model. The four levels of the WEB model are 1 promotion prevention and screening of the general or high-risk population 2 type or stage of disease 3 complications and 4 threats to self-care capacity. Given the number of potential combinations of attributes the model can account for literally millions of individual patient types each with a distinct clinical team need which can be used to estimate the total health .