Journal of evaluation in clinical practice
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To utilize lean six sigma (LSS) and failure model and effect analysis (FMEA) to prevent dispensing errors in a Chinese teaching hospital. ⋯ The combination of LSS and the FMEA tool can be an efficient approach for helping reduce MEs in pharmacy dispensing.
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Clinical reasoning in general practice is increasingly challenging because of the rise in the number of patients with multimorbidity. This creates uncertainty because of unpredictable interactions between the symptoms from multiple medical problems and the patient's personality, psychosocial context and life history. ⋯ Application of "systems thinking" tools such as causal loop diagrams allows the patient's problems to be viewed holistically and facilitates understanding of the complex interactions. We will show how complexity levels can be graded in clinical reasoning and demonstrate where and how systems thinking can have added value by means of a case history.
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Multimorbidity - the occurrence of two or more long-term conditions in an individual - is a major global concern, placing a huge burden on healthcare systems, physicians, and patients. It challenges the current biomedical paradigm, in particular conventional evidence-based medicine's dominant focus on single-conditions. Patients' heterogeneous range of clinical presentations tend to escape characterization by traditional means of classification, and optimal management cannot be deduced from clinical practice guidelines. ⋯ The underlying principles include non-linearity, tipping points, emergence, importance of initial conditions, contextual factors and co-evolution, and the presence of patterned outcomes. From a clinical perspective, complexity science has important implications at the theoretical, practice and policy levels. Three essential questions emerge: (1) What matters to patients? (2) How can we integrate, personalize and prioritize care for whole people, given the constraints of their socio-ecological circumstances? (3) What needs to change at the practice and policy levels to deliver what matters to patients? These questions have no simple answers, but complexity science principles suggest a way to integrate understanding of biological, biographical and contextual factors, to guide an integrated approach to the care of people with multimorbidity.
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Review
Improving quality in a complex primary care system-An example of refugee care and literature review.
Applying traditional industrial quality improvement (QI) methodologies to primary care is often inappropriate because primary care and its relationship to the healthcare macrosystem has many features of a complex adaptive system (CAS) that is particularly responsive to bottom-up rather than top-down management approaches. We report on a demonstration case study of improvements made in the Family Health Center (FHC) of the JPS Health Network in a refugee patient population that illustrate features of QI in a CAS framework as opposed to a traditional QI approach. ⋯ Meaningful improvement in primary care is more likely achieved when the impetus to implement change shifts from top-down to bottom-up.
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Meta Analysis
Methodological assessment of systematic reviews and meta-analyses on COVID-19: A meta-epidemiological study.
COVID-19 has caused an ongoing public health crisis. Many systematic reviews and meta-analyses have been performed to synthesize evidence for better understanding this new disease. However, some concerns have been raised about rapid COVID-19 research. This meta-epidemiological study aims to methodologically assess the current systematic reviews and meta-analyses on COVID-19. ⋯ The current systematic reviews and meta-analyses on COVID-19 might suffer from low transparency, high heterogeneity, and suboptimal statistical methods. It is recommended that future systematic reviews on COVID-19 strictly follow well-developed guidelines. Sensitivity analyses may be performed to examine how the synthesized evidence might depend on different methods for appraising evidence, assessing publication bias, and implementing meta-analysis models.