Conducting Online Expert panels: a feasibility and experimental replicability study

<p>Abstract</p> <p>Background</p> <p>This paper has two goals. First, we explore the feasibility of conducting online expert panels to facilitate consensus finding among a large number of geographically distributed stakeholders. Second, we test the replicability of panel findings across four panels of different size.</p> <p>Method</p> <p>We engaged 119 panelists in an iterative process to identify definitional features of Continuous Quality Improvement (CQI). We conducted four parallel online panels of different size through three one-week phases by using the RAND's ExpertLens process. In Phase I, participants rated potentially definitional CQI features. In Phase II, they discussed rating results online, using asynchronous, anonymous discussion boards. In Phase III, panelists re-rated Phase I features and reported on their experiences as participants.</p> <p>Results</p> <p>66% of invited experts participated in all three phases. 62% of Phase I participants contributed to Phase II discussions and 87% of them completed Phase III. Panel disagreement, measured by the mean absolute deviation from the median (MAD-M), decreased after group feedback and discussion in 36 out of 43 judgments about CQI features. Agreement between the four panels after Phase III was fair (four-way kappa = 0.36); they agreed on the status of five out of eleven CQI features. Results of the post-completion survey suggest that participants were generally satisfied with the online process. Compared to participants in smaller panels, those in larger panels were more likely to agree that they had debated each others' view points.</p> <p>Conclusion</p> <p>It is feasible to conduct online expert panels intended to facilitate consensus finding among geographically distributed participants. The online approach may be practical for engaging large and diverse groups of stakeholders around a range of health services research topics and can help conduct multiple parallel panels to test for the reproducibility of panel conclusions.</p

Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: The impact of a local calibration

<p>Abstract</p> <p>Background</p> <p>In the financing of a national health system, where pharmaceutical spending is one of the main cost containment targets, predicting pharmacy costs for individuals and populations is essential for budget planning and care management. Although most efforts have focused on risk adjustment applying diagnostic data, the reliability of this information source has been questioned in the primary care setting. We sought to assess the usefulness of incorporating pharmacy data into claims-based predictive models (PMs). Developed primarily for the U.S. health care setting, a secondary objective was to evaluate the benefit of a local calibration in order to adapt the PMs to the Spanish health care system.</p> <p>Methods</p> <p>The population was drawn from patients within the primary care setting of Aragon, Spain (n = 84,152). Diagnostic, medication and prior cost data were used to develop PMs based on the Johns Hopkins ACG methodology. Model performance was assessed through r-squared statistics and predictive ratios. The capacity to identify future high-cost patients was examined through c-statistic, sensitivity and specificity parameters.</p> <p>Results</p> <p>The PMs based on pharmacy data had a higher capacity to predict future pharmacy expenses and to identify potential high-cost patients than the models based on diagnostic data alone and a capacity almost as high as that of the combined diagnosis-pharmacy-based PM. PMs provided considerably better predictions when calibrated to Spanish data.</p> <p>Conclusion</p> <p>Understandably, pharmacy spending is more predictable using pharmacy-based risk markers compared with diagnosis-based risk markers. Pharmacy-based PMs can assist plan administrators and medical directors in planning the health budget and identifying high-cost-risk patients amenable to care management programs.</p

Relationship between efficiency and clinical effectiveness indicators in an adjusted model of resource consumption : a cross-sectional study

Background: Adjusted clinical groups (ACG®) have been widely used to adjust resource distribution; however, the relationship with effectiveness has been questioned. The purpose of the study was to measure the relationship between efficiency assessed by ACG® and a clinical effectiveness indicator in adults attended in Primary Health Care Centres (PHCs). Methods: Research design: cross-sectional study. Subjects: 196, 593 patients aged >14 years in 13 PHCs in Catalonia (Spain). Measures: Age, sex, PHC, basic care team (BCT), visits, episodes (diagnoses), and total direct costs of PHC care and co-morbidity as measured by ACG® indicators: Efficiency indices for costs, visits, and episodes (costs EI, visits EI, episodes EI); a complexity or risk index (RI); and effectiveness measured by a general synthetic index (SI). The relationship between EI, RI, and SI in each PHC and BCT was measured by multiple correlation coefficients (r). Results: In total, 56 of the 106 defined ACG® were present in the study population, with five corresponding to 44.5% of the patients, 11 to 68.0% of patients, and 30 present in less than 0.5% of the sample. The RI in each PHC ranged from 0.9 to 1.1. Costs, visits, and episodes had similar trends for efficiency in six PHCs. There was moderate correlation between costs EI and visits EI (r = 0.59). SI correlation with episodes EI and costs EI was moderate (r = 0.48 and r = −0.34, respectively) and was r = −0.14 for visits EI. Correlation between RI and SI was r = 0.29. Conclusions: The Efficiency and Effectiveness ACG® indicators permit a comparison of primary care processes between PHCs. Acceptable correlation exists between effectiveness and indicators of efficiency in episodes and costs

It is time to talk about people: a human-centered healthcare system

Examining vulnerabilities within our current healthcare system we propose borrowing two tools from the fields of engineering and design: a) Reason's system approach [1] and b) User-centered design [2,3]. Both approaches are human-centered in that they consider common patterns of human behavior when analyzing systems to identify problems and generate solutions. This paper examines these two human-centered approaches in the context of healthcare. We argue that maintaining a human-centered orientation in clinical care, research, training, and governance is critical to the evolution of an effective and sustainable healthcare system

How can we recognize continuous quality improvement?

Objective: Continuous quality improvement (CQI) methods are foundational approaches to improving healthcare delivery. Publications using the term CQI, however, are methodologically heterogeneous, and labels other than CQI are used to signify relevant approaches. Standards for identifying the use of CQI based on its key methodological features could enable more effective learning across quality improvement (QI) efforts. The objective was to identify essential methodological features for recognizing CQI. Design: Previous work with a 12-member international expert panel identified reliably abstracted CQI methodological features. We tested which features met rigorous a priori standards as essential features of CQI using a three-phase online modified-Delphi process. Setting: Primarily United States and Canada. Participants: 119 QI experts randomly assigned into four on-line panels. Intervention(s): Participants rated CQI features and discussed their answers using online, anonymous and asynchronous discussion boards. We analyzed ratings quantitatively and discussion threads qualitatively. Main outcome measure(s): Panel consensus on definitional CQI features. Results: Seventy-nine (66%) panelists completed the process. Thirty-three completers self-identified as QI researchers, 18 as QI practitioners and 28 as both equally. The features'systematic data guided activities,''designing with local conditions in mind' and'iterative development and testing' met a priori standards as essential CQI features. Qualitative analyses showed cross-cutting themes focused on differences between QI and CQI. Conclusions: We found consensus among a broad group of CQI researchers and practitioners on three features as essential for identifying QI work more specifically as'CQI.' All three features are needed as a minimum standard for recognizing CQI methods. © The Author 2013. Published by Oxford University Press in association with the International Society for Quality in Health Care

Statins enhance efficacy of venetoclax in blood cancers.

Statins have shown promise as anticancer agents in experimental and epidemiologic research. However, any benefit that they provide is likely context-dependent, for example, applicable only to certain cancers or in combination with specific anticancer drugs. We report that inhibition of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) using statins enhances the proapoptotic activity of the B cell lymphoma-2 (BCL2) inhibitor venetoclax (ABT-199) in primary leukemia and lymphoma cells but not in normal human peripheral blood mononuclear cells. By blocking mevalonate production, HMGCR inhibition suppressed protein geranylgeranylation, resulting in up-regulation of proapoptotic protein p53 up-regulated modulator of apoptosis (PUMA). In support of these findings, dynamic BH3 profiling confirmed that statins primed cells for apoptosis. Furthermore, in retrospective analyses of three clinical studies of chronic lymphocytic leukemia, background statin use was associated with enhanced response to venetoclax, as demonstrated by more frequent complete responses. Together, this work provides mechanistic justification and clinical evidence to warrant prospective clinical investigation of this combination in hematologic malignancies