Blockchain Papers

Adugna Negussie, Abay Sisay, Agumas Shibabaw, Edosa Kebede · 9 authors

No abstract is available for this record.

Adugna Negussie, Abay Sisay, Agumas Shibabaw, Edosa Kebede · 9 authors

Abstract Background Medical laboratory professionals play vital role in healthcare. The growing demand for quality laboratory services and emerging technologies underscore the crucial need for Continuing Professional Development (CPD). However, there is limited information on CPD programs in Ethiopia. Thus, this study aimed to assess medical laboratory professionals’ perceptions, attitudes, and challenges towards CPD and improve engagement to enhance diagnostic service quality. Methods this cross-sectional study enrolled 228 medical laboratory professionals in Ethiopia from July to October 2023. Using a mixed-methods approach that combined quantitative data from an online survey and qualitative data from interviews. SPSS version 28 was used for data analysis. Results the average age of the study participants was 32.6 ±6.4 (SD) years, the majority were men (88.6%), and 44.3% have worked for more than ten years. Of the participants, 51% never had CPD training. About three-fourth of the participants perceived CPD as essential to their professional career. About 45.2% of the study participants perceived that the purpose of CPD course is to renew their license and gain knowledge and skills that are not covered in basic training. While the majority of participants had good attitudes towards CPD, about 10% of them stated that it is not important in their career growth. The majority of the study participants were not in support of the decentralized CPD system. A notable problem with finance, insufficient manpower, unsupportive employers, a lack of awareness by regulatory bodies, inadequate access to training close to their working area, were identified as significant challenges of the CPD program. Conclusion the study highlights the perception that CPD is crucial for enhanced laboratory practices and career advancement. The study highlights the need for targeted strategies to address the identified problems and increase the engagement of medical laboratory professionals in the CPD program.

Gary Gaddis

In 2000, the first Academic Emergency Medicine consensus conference was convened. The topic, “Errors in Emergency Medicine,” was interposed between two well-known Institute of Medicine (IOM) reports of direct relevance: To Err is Human: Building a Safer Health System1 (1999) and Crossing the Quality Chasm: A New Health System for the 21st Century2 (2001). This foreshadowed the great degree of relevance and timeliness that would characterize future consensus conferences, each of which has as its primary goal the development of a research agenda that will guide the science of the topic at hand. This year, Academic Emergency Medicine convened its 11th consensus conference, “Beyond Regionalization: Integrated Networks of Emergency Care,” in conjunction with the annual meeting of the Society for Academic Emergency Medicine in Phoenix, Arizona. The proceedings will appear in the December issue of this journal. In this fashion, another timely research agenda will be developed, refined, and disseminated. These consensus conferences have not occurred in a vacuum. They have, to a demonstrable degree, had their intended effect of stimulating research. This month’s issue of the journal is the first to be devoted primarily to dissemination of research and concepts that have grown from prior consensus conferences. There is every reason to believe that a new and useful tradition has been established. The topics of the prior AEM consensus conferences have been: 2000: Errors in Emergency Medicine 2001: The Unraveling Safety Net 2002: Assuring Quality 2003: Disparities in Emergency Care 2004: Emergency Medicine Information Technology 2005: Ethical Conduct of Resuscitation Research 2006: The Science of Surge 2007: Knowledge Translation in Emergency Medicine: Establishing a Research Agenda and Guide Map for Evidence Uptake 2008: The Science of Simulation in Health Care: Defining and Developing Clinical Expertise 2009: Public Health in the Emergency Department: Surveillance, Screening, and Intervention 2010: Beyond Regionalization: Integrated Networks of Emergency Care A number of the consensus conferences have received significant financial support from Canadian, American federal, other public, and various private sources. Focusing on the conferences of 2007, 2008, and 2009 is illustrative. The 2007 conference received financial support from the Agency for Healthcare Research and Quality (AHRQ), and among the featured speakers who took time from their busy schedules to join us in Chicago was Carolyn Clancy, the director of the AHRQ since 2003. Support also came from the National Center for Research Resources, an organization promoting the Clinical and Translational Science Awards program within the United States National Institutes of Health, and the Knowledge Translation Workshops and Symposia Grant Program of the Canadian Institutes of Health.3 The 2008 conference received primary financial support from AHRQ, the Josiah Macy, Jr. Foundation, MedEdPORTAL (an electronic publication of the American Association of Medical Colleges), and the Risk Management Foundation of the Harvard Medical Institutions. Over 30 other medical organizations and academic departments nationwide provided smaller degrees of support. In addition, unrestricted educational grants were received from major manufacturers of medical simulator hardware and software.4 The 2009 conference was primarily supported by a grant from AHRQ and the National Institute on Drug Abuse of the National Institutes of Health, with secondary support from the National Institute of Mental Health; the National Institute on Alcohol Abuse and Alcoholism; the Substance Abuse and Mental Health Services Administration (SAMHSA); “Join Together” of the Boston University School of Public Health; and the Departments of Emergency Medicine of Boston University, Brown University, Emory University, George Washington University, Johns Hopkins University, the University of Maryland, the University of Michigan, and Yale University. This conference featured participation from Dr. Richard Denisco from the National Institute on Drug Abuse, Dr. Ralph Hingson from the National Institute on Alcohol Abuse and Alcoholism, Dr. Amy Goldstein from the National Institute of Mental Health, Dr. James Heffelfinger from the Centers for Disease Control and Prevention, Dr. Richard Saitz from Boston University, and Dr. Jack Stein, director of SAMHSA’s Center for Substance Abuse Treatment.5 Given the significant and continuing financial support of the agencies and foundations that have supported our consensus conferences, it seems prudent to provide them with proof that their efforts have been followed by meaningful responses to the numerous calls for research contained within the consensus conference summary statements and proceedings. In other words, this issue of our journal can be thought of as the first installment of a series of “itemized receipts” that will document how the financial support provided to enable the consensus conferences has been worthwhile. The first several consensus conferences did not enjoy such external financial support, but as the size and scope of the sessions grew, so did the need for funding. Our journal will continue to present these annual consensus conferences into the foreseeable future, and they will require the expenditure of significant resources of time and finances. To be able to document, from the pages of our own journal, that prior consensus conferences have had the desired effect of stimulating relevant research may increase the likelihood that the organizers of future conferences can continue to achieve similar external financial support. Looking forward, it is hoped that an even greater number of research efforts will be inspired by our consensus conferences and that much of that research will appear in our journal. It must be noted that not all of this month’s articles were submitted by authors who had the express intent that they appear in this special consensus conference follow-up issue. The Associate Editors and Senior Associate Editors, as well as Kathleen Seal, our Technical Editor, have referred a number of manuscripts submitted to the journal, but not submitted specifically for appearance in this issue, to the attention of the consensus conference guest editors group. In other words, our work group had to “prime the pump” somewhat. It is hoped that this will not be necessary in the future and that a greater number of manuscripts will be submitted with the goal that they appear in the consensus conference follow-up proceedings. A suggestion going forward relates to the probability that the research agendas set forth at a given consensus conference may eventually become “dated.” Toward that end, it is possible that future consensus conference follow-up issues might only accept manuscripts inspired by consensus conferences of the past 3 to 5 years. This issue contains a total of 15 manuscripts, with the following representation: 2002: one manuscript 2003: two manuscripts 2004: two manuscripts 2005: zero manuscripts 2006: two manuscripts 2007: three manuscripts 2008: one manuscripts 2009: four manuscripts I would be remiss not to thank several people without whom this edition of the journal would not have been possible. The Guest Assistant Editors, who functioned most capably as decision editors, included Gail D’Onofrio, Nicole Deiorio, Lynne Richardson, and Terri Schmidt. I am indebted to them for their efforts. It would be misleading to state that this process evolved exactly as first envisioned, and I must also thank these Guest Assistant Editors for their patience with the evolving processes. David Cone gave me the opportunity to more truly understand the extreme degree of knowledge and organization he brings to his role as Editor-in-Chief. Finally, but most importantly, I thank Kathleen Seal for her most capable guidance and patience. I have learned to an even greater degree than before what a valuable resource she is to our journal.

Jaideep J. Pandit, Steve Yentis

Doctors are expected to keep up to date with the literature, and to change their practice on the basis of what they have read. We believe that the skills needed for evaluating and interpreting scientific papers are often lacking. Even when they are taught, the emphasis is often focused on technical aspects of a paper without consideration of its value, a term we use here to encompass not only the quality of a paper's methods, but also its context and importance. We argue that critical appraisal, the term used to describe the assessment of a paper's methodological niceties [1], should never take place in isolation but must always occur in parallel with assessment of its value, since a paper may be methodologically sound but contribute little to a better understanding of the subject. Here, we discuss various aspects of scientific papers that contribute to or detract from their value, and suggest a stepwise approach for assessing them. The purpose of a scientific publication, of which there are numerous types, is to communicate information. Editorials are summary or personal views, perhaps commenting on a specific paper. Reviews are longer, in-depth analyses of the literature; they are most persuasive when their analysis is objective but their conclusions (as with editorials) are often subjective and personal. Specific forms of supposedly objective reviews are quantitative or systematic reviews, or meta-analyses[2]. Case reports describe one (or more) specific clinical cases, either so unusual as to be of interest, or from which it is hoped some particular lesson can be learned. A letter (correspondence) is a comment on, or criticism of, another's published paper, or seeks answers to a question. A letter may also convey information or data that do not constitute a full study but nonetheless are thought worthy of dissemination. Finally, there are experimental investigations. Broadly, these attempt to answer a question by the use of the ‘scientific method’, which involves the following process [3, 4]. First, the researcher formulates a hypothesis. This hypothesis may represent current ideology (the current theory, or paradigm), or it may be an idea developed de novo (e.g. suggested by preliminary observations). The hypothesis leads to an experimental prediction: if a certain experiment is conducted, then this predicted result should obtain if the hypothesis is correct. A result consistent with the prediction supports the hypothesis, but does not ‘prove’ it (thus scientific proof is very different from mathematical proof). If, on the other hand, the experimental result is not as was predicted, then either the hypothesis is incorrect (so disproved) or the conduct of the experiment was flawed. There are many types of experimental investigations: clinical studies or trials (the terms are often used interchangeably, although the latter is sometimes restricted to investigations of a treatment's efficacy); laboratory investigations; mathematical modelling of data; and some observational studies and audits. We focus here on experimental investigations since these are the papers that advance the knowledge base the most. There are generally two aspects to excellence in an experimental study: first, the study's conduct (of which handling of the data is an inherent part), and second, its presentation. Important aspects of presentation are covered by journals' instructions to authors and by specific publications [5, 6], and we will not discuss these in detail here. First, the study must consider a clear hypothesis that is stated unambiguously, ideally, illustrated by the results that would be expected if the hypothesis were true. The final results and conclusion of the study should relate to this prediction and must either refute or be consistent with the hypothesis. Second, the study must have appropriate ethical approval (or conform to animal research guidelines). We will not consider this issue further, since it has been addressed recently [7]. Third, the technical conduct of the experiment must be sound. Particular attention should be given to the avoidance of surrogate measures, appropriate measurement tools, proper randomisation and blinding, appropriate use of control groups, and appropriate application and interpretation of statistics. We consider each of these below. Surrogate measures or end-points have serious limitations. For example, a study of the control of minute ventilation may not actually involve measurement of this outcome at all, but instead may try to derive conclusions based on the measurement of another, related variable (say, arterial Pco2). The problem is that other factors may influence the related variable (e.g. ventilation is not the only factor influencing Pco2). Furthermore, measures that seem superficially related may not be; for example, although flecainide reduces cardiac arrhythmias, it increases mortality – a much more relevant end-point [8]. Surrogate measures are sometimes used in clinical studies because of the difficulty in measuring the desirable end-point, such as long-term survival. They might also give crude estimates of trends over time for certain variables [9], but they have very little (if any) place in studies that seek to question or overturn fundamental hypotheses in the underlying science. Measuring devices and assessment tools must be valid (measure what they are supposed to measure), accurate (measure the true quantity), and reliable (different users obtaining the same results) – aspects that often escape attention in manuscripts. This is not restricted to technical measurements; for example, assessment of ‘maternal satisfaction’ with the use of a simple visual analogue scale continues widely despite little evidence to support it [10]. When technology is used, coefficients of variation should be given, but rarely are. Further, there ought to be some confidence as to how the technology works. For example, much of the growing literature on the bispectral index (BIS) as a monitor of ‘awareness’ raises concerns. Since it is not known precisely what is being measured by the BIS [11], it can never be known whether an unexpected result has arisen because the BIS is invalid or inaccurate, or because the hypothesis being tested is incorrect [12]. Randomisation and blinding are intended to minimise the influence of bias. The hope with randomisation is that all ‘confounding factors’ (both known and unknown) that might influence the outcome will be distributed equally amongst the groups. If any differences are found they can therefore be attributed to the sole factor – the treatment under study – that has not been ‘shared out’ in this way. However, even with proper randomisation, groups may be unequal: chance alone might result in one group's subjects being older, heavier, younger or just luckier than those in the other group. Even when groups appear equal, small inequalities might combine to influence the results. For example, in a study by Greif et al. [13] into the possible anti-infective effect of peri-operative oxygen therapy, patients randomly allocated to receive extra oxygen were by chance more likely to be fitter and less likely to be smokers, to have inflammatory bowel disease, and to undergo rectal surgery than those in the ‘no oxygen’ group. Could these factors have combined to contribute to the dramatic reduction in infection seen in the ‘oxygen’ group, such that a subsequent study obtained completely the opposite results [14]? In addition, the human urge to guess or manipulate treatment allocations, or otherwise interfere with proper randomisation in studies, is well reported [15]. Blinding is present when the person treating the patient, or making the assessments, does not know which patients receive which treatment. Some studies fail to take even the simplest steps to ensure blinding, while others go to extraordinary lengths (for an example of the latter, see Smith and Thwaites's [16] commendable study comparing intravenous with inhalational anaesthesia). Occasionally, blinding is impossible (e.g. when comparing two different laryngoscope blades or bougies [17]), but the results of a blinded study are always more persuasive. Indeed, studies with insufficient blinding tend to report greater treatment effects than those with proper blinding procedures [15]. Control groups may be inappropriate because of poor randomisation or blinding. Even if these are sound, though, there may be other reasons why treatment effects may be masked or exaggerated by problems with control groups: lack of consideration of other possible ‘confounding factors’; use of historical, rather than contemporaneous, controls; comparison of a treatment against placebo instead of standard practice, or against an inappropriate treatment; or (at worst) lack of a control group at all. Much of the above might give the impression that the testing of hypotheses by close attention to the study's conduct will always give a clear-cut result. Unfortunately, this is not the case, because we can never achieve certainty; the best we can do is use statistical analysis to indicate the degree of uncertainty [18]. A full discussion of statistical methods is dealt with elsewhere [19], and here we consider only two related areas that commonly cause difficulties: significance and power. Significance. If, in a study, one drug appears more effective than another, the traditional approach is to ask the question: ‘What is the likelihood (or probability) that this result is a chance finding, and that these two drugs are in fact equivalent?’ This approach (testing the equivalence, as opposed to testing the difference) is known as testing the null hypothesis. It is important to stress that this null hypothesis assessed by the statistical test may not always be exactly the same as the underlying scientific hypothesis being examined by the study as a whole: the result of the former will help interpret the latter. Many statistical tests ultimately generate a ‘p-value’: the lower the p-value, the less likely it is that the null hypothesis is correct. A p-value of 0.03 indicates that if the two drugs are indeed equivalent, chance alone would be expected to yield the observed results three out of every 100 times one conducted the study. Conventionally, a p-value of < 0.05 is taken to represent ‘statistical significance’, although this value can and should be adjusted in certain circumstances, for example if multiple comparisons are made [20, 21]. Confidence intervals can be used as an alternative to testing the null hypothesis [22, 23]; nonetheless, the conclusions reached by using confidence intervals are invariably the same. The real problem lies in how p-values are interpreted rather than how they are calculated [24]. An entirely different approach is to interpret a study's results mathematically in the context of prior knowledge (Bayesian statistics) [25, 26]. Regardless of the method of calculating or presenting statistical significance, the smaller the p-value, or the further away from zero the difference in confidence intervals, the less likely the result is to be a ‘chance’ finding and therefore the more likely it is that the difference between the two groups is indeed ‘genuine’. But such a chance finding is still possible, albeit unlikely; as Counsell et al. [27] point out, chance ‘…doesn’t get the credit it deserves'. Furthermore, a low p-value does not exclude poor methodology in the conduct of the study. Power. If the p-value in a drug study is, say, 0.07, does this mean there is genuinely no difference between the two drugs? Or does it mean that there might be a true difference, but that the study has simply failed to show it? It is specifically to help answer such questions that a power analysis is useful. The power analysis estimates how likely it is that a negative result can be ‘believed’. One emerging problem is that some researchers (or their critics) are placing far too much emphasis upon power analysis [28]. One example demonstrates the type of misplaced faith in power analysis: ‘…at least 400 patients would be required to prove there is no statistically significant difference between the groups…’[29] (our emphasis). Such statements reveal a poor understanding of the scientific method and of the concept of scientific proof. One reason for our concern is that the concept of power analysis itself has very serious limitations. Power analyses are only crude estimates of a sample size (indeed, the word ‘crude’ is emphasised by statisticians [30]). For example, two main elements that contribute to power for normally distributed continuous data are the difference between the means that is deemed important and the expected standard deviation (SD) of the measure of interest. Both of these are subject to serious shortcomings. The choice of what constitutes an ‘important difference’ is almost entirely subjective, and small but arbitrary adjustments to its value can have a great impact upon a study's calculated power. Where no previous data exist, the expected SD is usually taken from a pilot study, often without a control group, and by definition always smaller and less robust than the planned substantive study. In reality, the power analysis itself is probably best expressed in terms of a confidence interval: for example, ‘Power analysis indicated that we would require 20–60 subjects to be 70–90% confident of detecting a difference between the means of 10–50 s’– although this is rarely done. The crudeness of the power analysis as a tool is reflected in the different sample sizes yielded by different methods. If we assume that for a hypothetical study, the important difference is 1.0 arbitrary units, with a standard deviation of 0.8 arbitrary units, then various calculations give a sample size per group (with 80% power at p < 0.05) of 10 [30], 11 [31], 13 [32] and 18 [33]. So at best, power analysis only gives an approximate estimate of sample size. 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Many authors have how is in such a So while critical of papers by an (or by a might to one by may to a different The for a in this is to consider the to which it to (or of from We have various aspects of a paper that can contribute to its We here by an approach to a paper, based on the a hypothetical of with the the most papers and the the The is to go each paper and at the of the be to place it in the appropriate of the above factors – aspects of the study's conduct of other aspects of its methodology not covered the of the question and the answers the of the evidence and any persuasive factors – should be at intervals when a paper, and each has the to the paper up or to the impression The steps are in this process the paper can be or many times up in its final in two First, every paper will have some value, even if it up in the any a paper should be for what is all a considerable in the current Second, different may place the same paper into different final – but this is a and appropriate of value in any of the paper with or the should be reasons for the paper to a particular and for being in various (or by all aspects of the paper. 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K. Kwiatkowski, Patrícia Flatley Brennan, David L. DeMets, Karen Dahlen · 5 authors

The University of Wisconsin-Madison Health Sciences Schools are currently in the planning stage of developing an Integrated Advanced Information Management System (IAIMS). The planning phase of this project attends to the unique opportunities that are found at the flagship campus of a large state university system. Statewide teaching and research initiatives and accelerated campus-level capital development challenge the planners to create an IAIMS plan that anticipates an emerging health science environment. Additionally, UW-Madison has an organizational culture with a strong tradition of faculty governance, which provides a very desirable and flexible decision-making environment for a cross-discipline collaborative information management initiative. Development of a shared IAIMS vision conflicts with a governance model that most directly supports intradepartmental decision-making. The challenge presented here for an IAIMS initiative has less to do with hard wiring a technical infrastructure and more to do with increased stakeholder cooperation in a highly decentralized organization with autonomous information systems.