Abstract Models—mathematical frameworks that facilitate estimation of the consequences of health care decisions—have become essential tools for health technology assessment. Evolution of the methods since the first ISPOR Modeling Task Force reported in 2003 has led to a new Task Force, jointly convened with the Society for Medical Decision Making, and this series of seven articles presents the updated recommendations for best practices in conceptualizing models; implementing state-transition approaches, discrete event simulations, or dynamic transmission models; and dealing with uncertainty and validating and reporting models transparently. This overview article introduces the work of the Task Force, provides all the recommendations, and discusses some quandaries that require further elucidation. The audience for these articles includes those who build models, stakeholders who utilize their results, and, indeed, anyone concerned with the use of models to support decision making.
This parameter was developed by the Joint Task Force on Practice Parameters, representing the American Academy of Allergy, Asthma & Immunology (AAAAI); the American College of Allergy, Asthma & Immunology (ACAAI); and the Joint Council of Allergy, Asthma & Immunology (JCAAI). The AAAAI and the ACAAI have jointly accepted responsibility for establishing “Food Allergy: A practice parameter update—2014.” This is a complete and comprehensive document at the current time. The medical environment is a changing one, and not all recommendations will be appropriate for all patients. Because this document incorporated the efforts of many participants, no single individual, including those who served on the Joint Task Force, is authorized to provide an official AAAAI or ACAAI interpretation of these practice parameters. Any request for information about or an interpretation of these practice parameters by the AAAAI or ACAAI should be directed to the Executive Offices of the AAAAI, ACAAI, and JCAAI. These parameters are not designed for use by pharmaceutical companies in drug promotion.
Abstract Background Budget impact analyses (BIAs) are an essential part of a comprehensive economic assessment of a health care intervention and are increasingly required by reimbursement authorities as part of a listing or reimbursement submission. Objectives The objective of this report was to present updated guidance on methods for those undertaking such analyses or for those reviewing the results of such analyses. This update was needed, in part, because of developments in BIA methods as well as a growing interest, particularly in emerging markets, in matters related to affordability and population health impacts of health care interventions. Methods The Task Force was approved by the International Society for Pharmacoeconomics and Outcomes Research Health Sciences Policy Council and appointed by its Board of Directors. Members were experienced developers or users of BIAs; worked in academia and industry and as advisors to governments; and came from several countries in North America and South America, Oceania, Asia, and Europe. The Task Force solicited comments on the drafts from a core group of external reviewers and, more broadly, from the membership of the International Society for Pharmacoeconomics and Outcomes Research. Results The Task Force recommends that the design of a BIA for a new health care intervention should take into account relevant features of the health care system, possible access restrictions, the anticipated uptake of the new intervention, and the use and effects of the current and new interventions. The key elements of a BIA include estimating the size of the eligible population, the current mix of treatments and the expected mix after the introduction of the new intervention, the cost of the treatment mixes, and any changes expected in condition-related costs. Where possible, the BIA calculations should be performed by using a simple cost calculator approach because of its ease of use for budget holders. In instances, however, in which the changes in eligible population size, disease severity mix, or treatment patterns cannot be credibly captured by using the cost calculator approach, a cohort or patient-level condition-specific model may be used to estimate the budget impact of the new intervention, accounting appropriately for those entering and leaving the eligible population over time. In either case, the BIA should use data that reflect values specific to a particular decision maker’s population. Sensitivity analysis should be of alternative scenarios chosen from the perspective of the decision maker. The validation of the model should include at least face validity with decision makers and verification of the calculations. Data sources for the BIA should include published clinical trial estimates and comparator studies for the efficacy and safety of the current and new interventions as well as the decision maker’s own population for the other parameter estimates, where possible. Other data sources include the use of published data, well-recognized local or national statistical information, and, in special circumstances, expert opinion. Reporting of the BIA should provide detailed information about the input parameter values and calculations at a level of detail that would allow another modeler to replicate the analysis. The outcomes of the BIA should be presented in the format of interest to health care decision makers. In a computer program, options should be provided for different categories of costs to be included or excluded from the analysis. Conclusions We recommend a framework for the BIA, provide guidance on the acquisition and use of data, and offer a common reporting format that will promote standardization and transparency. Adherence to these good research practice principles would not necessarily supersede jurisdiction-specific BIA guidelines but may support and enhance local recommendations or serve as a starting point for payers wishing to promulgate methodology guidelines.
Background:This study evaluated the views of the cardiology community on the clinical use of coronary intravascular imaging (IVI).Methods and Results:A web-based survey was distributed to 31,893 individuals, with 1,105 responses received (3.5% response rate); 1,010 of 1,097 respondents (92.1%) self-reported as interventional cardiologists, 754 (68.7%) with >10 years experience. Overall, 96.1% had personal experience with IVI (95.5% with intravascular ultrasound [IVUS], 69.8% with optical coherence tomography [OCT], and 7.9% with near-infrared spectroscopy); 34.7% of respondents were from Europe and 52.0% were from Asia (45.4% from Japan). The most commonly reported indications for IVI were optimization of stenting (88.5%), procedural/strategy guidance (79.6%), and guidance of left main interventions (77.0%). Most respondents reported perceived equipoise regarding choice between IVUS and OCT for guidance of coronary intervention. High cost (65.9%) and prolongation of the procedure (35.0%) were the most commonly reported factors limiting use. IVI was used more frequently (>15% of cases guided by IVI) in Japan than Europe (96.6% vs. 10.4%, respectively; P<0.001) and by operators with longer interventional experience.Conclusions:In a sample of predominantly experienced interventional cardiologists, there was a high rate of personal experience with IVI in clinical practice. The most commonly identified indications for IVI were optimization of stenting, procedural/strategy guidance, and guidance of left main interventions. Variability in practice patterns is substantial according to geographic region and interventional experience.
BACKGROUND: Estimates suggest that approximately 25% of requests for pathology tests are unnecessary. Even in diabetes, for which international guidance provides recommended testing frequency, considerable variability in requesting practice exists. Using the diabetes marker, Hb A(1c), we examined (a) the prevalence of under- and overrequesting, (b) the impact of international guidance on prevalence, and (c) practice-to-practice variability. METHODS: We examined Hb A(1c) requests (519 664 requests from 115 730 patients, January 2001 to March 2011) processed by the Clinical Biochemistry Department, University Hospital of North Staffordshire, and prevalence of requesting outside guidance from intervals between requests was calculated. Requests were classified as "appropriate," "too soon," or "too late." We also assessed the effect of demographic factors and publication of guidance, along with between-practice variability, on prevalence. RESULTS: Only 49% of requests conformed to guidance; 21% were too soon and 30% were too late. Underrequesting was more common in primary care, in female patients, in younger patients, and in patients with generally poorer control (all P < 0.001); the reverse generally was true for overrequesting. Publication of guidance (e.g., American Diabetes Association, UK National Institute for Health and Clinical Excellence) had no significant impact on under- or overrequesting rates. Prevalence of inappropriate requests varied approximately 6-fold between general practices. CONCLUSIONS: Although overrequesting was common, underrequesting was more prevalent, potentially affecting longer-term health outcomes. National guidance appears to be an ineffective approach to changing request behavior, supporting the need for a multisystern approach to reducing variability. (C) 2012 American Association for Clinical Chemistry
Funding bodies have recently introduced a requirement that data sharing must be a consideration of all funding applications in genomics. As with all new developments this condition has had an impact on scientific practice, particularly in the area of publishing and in the conduct of research. We discuss the challenges that must be addressed if the full benefits of data sharing, as envisaged by funders, are to be realized.
Aim: To explore the effectiveness and feasibility of implementing the two clinical dimensions of the Careful Nursing Philosophy and Professional Practice Model (c) (Careful Nursing) in an acute care hospital. Background: Implementation of a professional practice model by nurses in hospitals supports nurses' control over their practice and enhances the quality of their contribution to patient care. Implementing such change is complex and initially best approached on a small scale. Methods: A mixed methods exploratory design was used. Data were sought from 23 professional nurses practising in a 26-bed acute medical ward for older persons. Quantitative data were collected on nurses' control over and documentation of their practice. Qualitative data were collected on nurses' perceptions of their practice. Result: Nurses' control over practice and adherence to practice documentation standards increased. Overall, the nurses perceived Careful Nursing-guided practice positively. Feasibility issues were identified and addressed. Conclusion: Exploratory evidence suggests that Careful Nursing could influence nurses' practice and overall perception of practice positively; its implementation is feasible.