The Manchester Framework For The Evaluation of Emergency Department Pharmacy Services

Background: Many countries, including the United Kingdom, have established Emergency Department (ED) pharmacy services where some ED pharmacists now work as practitioners. They provide both traditional pharmaceutical care and novel practitioner care i.e. clinical examination, yet their impact on quality of care is unknown.Aim: To develop a framework of structures, processes and potential outcome indicators to support evaluation of the quality of ED pharmacy services in future studies.Method: Framework components (structures, processes and potential outcome indicators) were identified in three ways, from a narrative review of relevant international literature identified through systematic searches; a panel meeting with ED pharmacists; and a panel meeting with other ED healthcare professionals. Structures and processes were collated into categories developed iteratively throughout data collection, with outcome indicators collated into six domains of quality as proposed by the Institute of Medicine. These raw data were then processed e.g. outcome indicators screened for clarity i.e. those which explicitly stated what would be measured were included in the framework.Results: A total of 190 structures, 533 processes, and 503 outcome indicators were identified. Through data processing a total of 153 outcome indicators were included in the final framework divided into the domains safe (32 outcome indicators), effective (50), patient centred (18), timely (24), efficient (20) and equitable (9). Fewer potential outcome indicators were identified for the patient centred, efficient and equitable domains than others. Conclusion: Whilst frameworks to support evaluation of general ED care exist, this is the first framework specific to ED pharmacy services. Although included in the framework, potential outcome indicators require further development prior to their use in evaluation studies. To that end, evaluation teams should be multidisciplinary and ideally involve researchers with expertise in outcome measurement. Finally, evaluation should not neglect some domains of quality at the expense of others, as previously found by the Institute of Medicine. High quality health services are not only safe, effective and timely, but also patient centred, efficient and equitable.

What are the chances? How descriptions of probability affect risky decisions

<p>People make different decisions when they know the odds of an event occurring, (e.g. told 10% chance of an earthquake that year) than when they draw on only their own experience (e.g. living in a city with, on average, one earthquake every 10 years). It may be that when we make decisions based on our past experience (decisions from experience) we are more likely to choose a risky option when it can lead to the biggest win and avoid it when it can lead to the biggest loss, this effect is called the Extreme-Outcome rule. Across three Experiments we tested the Extreme-Outcome rule by having participants make repeated choices between either safe or risky options which had the same expected value. In each experiment, we varied the magnitude of the reinforcer’s participants could win in both an Experience condition and a condition that had both description and experience information. In Experiment 1 where we had two reinforcer sizes (small and large) we found an Extreme-Outcome effect in the Experience condition, but not the Description-Experience condition. In Experiment 2 we tested a prediction of the Extreme-Outcome rule that participants would be sensitive to the best and worst outcome by adding another reinforcer size (reinforcers were small, medium and large) and therefore on some trials neither alternative included an extreme outcome. We also removed zero as a potential outcome to investigate whether zero aversion might be driving the effect of reinforcer magnitude in the Experience condition. We did not find response patterns consistent with an Extreme-Outcome rule in the Experience condition. Instead, participants were least risk seeking when the reinforcer was small, but there was no difference in levels of risk seeking between the medium and large reinforcer trials. In other words, there was an effect of the low-extreme outcome but not the high-extreme outcome. Like Experiment 1, in the Description-Experience condition risk preference was not influenced by reinforcer size, but the absolute levels were higher. To investigate whether this increase in risk preference was due to removing the zero, in Experiment 3 we manipulated whether zero was present or absent. When zero was absent, risk preference was not influenced by the size of the reinforcer in the Description-Experience condition, but there was an effect of the low-extreme outcome when zero was present. We also found an effect of the low extreme outcome in the Experience condition regardless of whether zero was present or absent. Overall, these findings suggest the Extreme-Outcome rule needs to be modified to take into account the effect of the low extreme but not the high extreme outcome.</p>

What are the chances? How descriptions of probability affect risky decisions

<p>People make different decisions when they know the odds of an event occurring, (e.g. told 10% chance of an earthquake that year) than when they draw on only their own experience (e.g. living in a city with, on average, one earthquake every 10 years). It may be that when we make decisions based on our past experience (decisions from experience) we are more likely to choose a risky option when it can lead to the biggest win and avoid it when it can lead to the biggest loss, this effect is called the Extreme-Outcome rule. Across three Experiments we tested the Extreme-Outcome rule by having participants make repeated choices between either safe or risky options which had the same expected value. In each experiment, we varied the magnitude of the reinforcer’s participants could win in both an Experience condition and a condition that had both description and experience information. In Experiment 1 where we had two reinforcer sizes (small and large) we found an Extreme-Outcome effect in the Experience condition, but not the Description-Experience condition. In Experiment 2 we tested a prediction of the Extreme-Outcome rule that participants would be sensitive to the best and worst outcome by adding another reinforcer size (reinforcers were small, medium and large) and therefore on some trials neither alternative included an extreme outcome. We also removed zero as a potential outcome to investigate whether zero aversion might be driving the effect of reinforcer magnitude in the Experience condition. We did not find response patterns consistent with an Extreme-Outcome rule in the Experience condition. Instead, participants were least risk seeking when the reinforcer was small, but there was no difference in levels of risk seeking between the medium and large reinforcer trials. In other words, there was an effect of the low-extreme outcome but not the high-extreme outcome. Like Experiment 1, in the Description-Experience condition risk preference was not influenced by reinforcer size, but the absolute levels were higher. To investigate whether this increase in risk preference was due to removing the zero, in Experiment 3 we manipulated whether zero was present or absent. When zero was absent, risk preference was not influenced by the size of the reinforcer in the Description-Experience condition, but there was an effect of the low-extreme outcome when zero was present. We also found an effect of the low extreme outcome in the Experience condition regardless of whether zero was present or absent. Overall, these findings suggest the Extreme-Outcome rule needs to be modified to take into account the effect of the low extreme but not the high extreme outcome.</p>

Discovery of Genes Positively Modulating Treatment Effect Using Potential Outcome Framework and Bayesian Update

BackgroundThe recent explosion of cancer genomics provides extensive information about mutations and gene expression changes in cancer. However, most of the identified gene mutations are not clinically utilized. It remains uncertain whether the presence of a certain genetic alteration will affect treatment response. Conventional statistics have limitations for causal inferences and are hard to gain sufficient power in genomic datasets. Here, we developed and evaluated an algorithm for searching the causal genes that maximize the effect of the treatment.MethodsThe algorithm was developed based on the potential outcome framework and Bayesian posterior update. The precision of the algorithm was validated using a simulation dataset. The algorithm was implemented to a cBioPortal dataset. The genes discovered by the algorithm were externally validated within CancerSCAN screening data from Samsung Medical Center.ResultsSimulation data analysis showed that the C-search algorithm was able to identify nine causal genes out of ten. The C-search algorithm shows the discovery rate rapidly increasing until the 1500 number of data. Meanwhile, the log-rank test shows a slower increase in performance. The C-search algorithm was able to suggest nine causal genes from the cBioPortal Metabric dataset. Treating the patients with the causal genes are associated with better survival outcome in both the cBioPortal dataset and the CancerSCAN dataset which is used for external validation.ConclusionsOur C-search algorithm demonstrated better performance to identify causal effects of the genes than multiple rog-rank test analysis especially within a limited number of data. The result suggests that the C-search can discover the causal genes from various genetic datasets, where the number of samples is limited compared to the number of variables.

Causation and causal inference

This chapter provides an introduction to causal inference theory for public health research. Causal inference can be viewed as a prediction problem, addressing the question of what the likely outcome will be under one action vs. an alternative action. To answer this question usefully requires clarity and precision in both the statement of the causal hypothesis and the techniques used to attempt an answer. This chapter reviews considerations that have been invoked in discussions of causality based on epidemiologic evidence. It then describes the potential-outcome (counterfactual) framework for cause and effect, which shows how measures of effect and association can be distinguished. The potential-outcome framework illustrates problems inherent in attempts to quantify the changes in health expected under different actions or interventions. The chapter concludes with a discussion of how research findings may be translated into policy.

Core Outcome Measures for Palliative and End-of-Life Research After Severe Stroke: Mixed-Method Delphi Study

Background and Purpose: Stroke is the second commonest cause of death worldwide and a leading cause of severe disability, yet there are no published trials of palliative care in stroke. To design and evaluate palliative care interventions for people with stroke, researchers need to know what measurable outcomes matter most to patients and families, stroke professionals, and other service providers. Methods: A multidisciplinary steering group of professionals and laypeople managed the study. We synthesized recommendations from respected United Kingdom and international consensus documents to generate a list of outcome domains and then performed a rapid scoping literature review to identify potential outcome measures for use in future trials of palliative care after stroke. We then completed a 3-round, online Delphi survey of professionals, and service users to build consensus about outcome domains and outcome measures. Finally, we held a stakeholder workshop to review and finalize this consensus. Results: We generated a list of 36 different outcome domains from 4 key policy documents. The rapid scoping review identified 43 potential outcome measures that were used to create a shortlist of 16 measures. The 36 outcome domains and 16 measures were presented to a Delphi panel of diverse healthcare professionals and lay service users. Of 48 panelists invited to take part, 28 completed all 3 rounds. Shared decision-making and quality of life were selected as the most important outcome domains for future trials of palliative care in stroke. Additional comments highlighted the need for outcomes to be feasible, measurable, and relevant beyond the initial, acute phase of stroke. The stakeholder workshop endorsed these results. Conclusions: Future trials of palliative care after stroke should include pragmatic outcome measures, applicable to the evolving patient and family experiences after stroke and be inclusive of shared decision-making and quality of life.

Block What You Can, Except When You Shouldn’t

Several branches of the potential outcome causal inference literature have discussed the merits of blocking versus complete randomization. Some have concluded it can never hurt the precision of estimates, and some have concluded it can hurt. In this article, we reconcile these apparently conflicting views, give a more thorough discussion of what guarantees no harm, and discuss how other aspects of a blocked design can cost, all in terms of estimator precision. We discuss how the different findings are due to different sampling models and assumptions of how the blocks were formed. We also connect these ideas to common misconceptions; for instance, we show that analyzing a blocked experiment as if it were completely randomized, a seemingly conservative method, can actually backfire in some cases. Overall, we find that blocking can have a price but that this price is usually small and the potential for gain can be large. It is hard to go too far wrong with blocking.