Editorial: Non-themed issue: 2010

The decision to have regular non-themed issues of English Teaching: Practice and Critique was made by way of Board consultation some time ago. As a Board, we believe that the policy of having a panel of guest editors taking control of a “themed” issue has worked well. In many cases, guess editors have worked together for the first time in a common enterprise. In all cases, having panels of guest editors has expanded the reach of the journal, increasing its subscriber base and the number of distinct educational constituencies who view the journal as a desirable target for contributions. It has facilited the journal’s aim of providing “a place where authors from a range of backgrounds can identify matters of common concern and thereby foster professional communities and networks”

The Power of Trust: Designing Trustworthy Machine Learning Systems in Healthcare

Machine Learning (ML) systems have an enormous potential to improve medical care, but skepticism about their use persists. Their inscrutability is a major concern which can lead to negative attitudes reducing end users trust and resulting in rejection. Consequently, many ML systems in healthcare suffer from a lack of user-centricity. To overcome these challenges, we designed a user-centered, trustworthy ML system by applying design science research. The design includes meta-requirements and design principles instantiated by mockups. The design is grounded on our kernel theory, the Trustworthy Artificial Intelligence principles. In three design cycles, we refined the design through focus group discussions (N1=8), evaluation of existing applications, and an online survey (N2=40). Finally, an effectiveness test was conducted with end users (N3=80) to assess the perceived trustworthiness of our design. The results demonstrated that the end users did indeed perceive our design as more trustworthy

The abstraction transition taxonomy: developing desired learning outcomes through the lens of situated cognition

We report on a post-hoc analysis of introductory programming lecture materials. The purpose of this analysis is to identify what knowledge and skills we are asking students to acquire, as situated in the activity, tools, and culture of what programmers do and how they think. The specific materials analyzed are the 133 Peer Instruction questions used in lecture to support cognitive apprenticeship -- honoring the situated nature of knowledge. We propose an Abstraction Transition Taxonomy for classifying the kinds of knowing and practices we engage students in as we seek to apprentice them into the programming world. We find students are asked to answer questions expressed using three levels of abstraction: English, CS Speak, and Code. Moreover, many questions involve asking students to transition between levels of abstraction within the context of a computational problem. Finally, by applying our taxonomy in classifying a range of introductory programming exams, we find that summative assessments (including our own) tend to emphasize a small range of the skills fostered in students during the formative/apprenticeship phase

Continuous epidural infusion of morphine versus single epidural injection of extended-release morphine for postoperative pain control after arthroplasty: a retrospective analysis

Stephanie Vanterpool, Randall Coombs, Karamarie FechoDepartment of Anesthesiology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USABackground: This study retrospectively compared the continuous epidural infusion of morphine with a single epidural injection of extended-release morphine for postoperative pain control after arthroplasty.Methods: Medical records were reviewed for subjects who had total knee or hip arthroplasty (THA) under spinal anesthesia and received either a continuous epidural infusion of morphine (Group EPID; n = 101) or an extended-release epidural morphine (Group EREM; n = 109) for postoperative pain. Data were collected for three postoperative days (POD) on: pain scores; supplemental opioids; medications for respiratory depression, nausea, and pruritus, and distance ambulated during physical therapy.Results: Pain scores were similar until subjects were transitioned to another analgesic approach on POD 2; after that time, pain scores increased in Group EPID, although they decreased in Group EREM. Supplemental opioids were used more on POD1 in Group EREM than in Group EPID, although time to first opioid and total daily morphine equivalents were similar. Naloxone and antiemetics, not antipruritics, were used more in Group EREM. Distance ambulated after THA was greater in Group EREM than in Group EPID.Conclusions: These results suggest that EREM is associated with better postoperative ambulation and analgesia during the transition to oral or intravenous analgesics, although a higher incidence of side-effects was evident.Keywords: continuous epidural morphine infusion, extended-release epidural morphine, lower extremity arthroplasty, ambulation, postoperative pain, side-effect

To Use or Not to Use Artificial Intelligence? A Framework for the Ideation and Evaluation of Problems to Be Solved with Artificial Intelligence

The recent advent of artificial intelligence (AI) solutions that surpass humans’ problem-solving capabilities has uncovered AIs’ great potential to act as new type of problem solvers. Despite decades of analysis, research on organizational problem solving has commonly assumed that the problem solver is essentially human. Yet, it remains unclear how existing knowledge on human problem solving translates to a context with problem-solving machines. To take a first step to better understand this novel context, we conducted a qualitative study with 24 experts to explore the process of problem finding that forms the essential first step in problem-solving activities and aims at uncovering reasonable problems to be solved. With our study, we synthesize emerged procedural artifacts and key factors to propose a framework for problem finding in AI solver contexts. Our findings enable future research on human-machine problem solving and offer practitioners helpful guidance on identifying and managing reasonable AI initiatives

Continuous epidural infusion of morphine versus single epidural injection of extended-release morphine for postoperative pain control after arthroplasty: a retrospective analysis

Background:This study retrospectively compared the continuous epidural infusion of morphine with a single epidural injection of extended-release morphine for postoperative pain control after arthroplasty.Methods:Medical records were reviewed for subjects who had total knee or hip arthroplasty (THA) under spinal anesthesia and received either a continuous epidural infusion of morphine (Group EPID; n = 101) or an extended-release epidural morphine (Group EREM; n = 109) for postoperative pain. Data were collected for three postoperative days (POD) on: pain scores; supplemental opioids; medications for respiratory depression, nausea, and pruritus, and distance ambulated during physical therapy.Results:Pain scores were similar until subjects were transitioned to another analgesic approach on POD 2; after that time, pain scores increased in Group EPID, although they decreased in Group EREM. Supplemental opioids were used more on POD1 in Group EREM than in Group EPID, although time to first opioid and total daily morphine equivalents were similar. Naloxone and antiemetics, not antipruritics, were used more in Group EREM. Distance ambulated after THA was greater in Group EREM than in Group EPID.Conclusions:These results suggest that EREM is associated with better postoperative ambulation and analgesia during the transition to oral or intravenous analgesics, although a higher incidence of side-effects was evident

In-hospital resuscitation: opioids and other factors influencing survival

Karamarie Fecho1, Freeman Jackson1, Frances Smith1, Frank J Overdyk21Department of Anesthesiology, University of North Carolina, Chapel Hill, North Carolina, USA; 2Department of Anesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, South Carolina, USAPurpose: “Code Blue” is a standard term used to alertt hospital staff that a patient requires resuscitation. This study determined rates of survival from Code Blue events and the role of opioids and other factors on survival.Methods: Data derived from medical records and the Code Blue and Pharmacy databases were analyzed for factors affecting survival.Results: During 2006, rates of survival from the code only and to discharge were 25.9% and 26.4%, respectively, for Code Blue events involving cardiopulmonary resuscitation (CPR; N = 216). Survival rates for events not ultimately requiring CPR (N = 77) were higher, with 32.5% surviving the code only and 62.3% surviving to discharge. For CPR events, rates of survival to discharge correlated inversely with time to chest compressions and defibrillation, precipitating event, need for airway management, location and age. Time of week, witnessing, postoperative status, gender and opioid use did not influence survival rates. For non-CPR events, opioid use was associated with decreased survival. Survival rates were lowest for patients receiving continuous infusions (P < 0.01) or iv boluses of opioids (P < 0.05).Conclusions: One-quarter of patients survive to discharge after a CPR Code Blue event and two-thirds survive to discharge after a non-CPR event. Opioids may influence survival from non-CPR events.Keywords: code blue, survival, opioids, cardiopulmonary resuscitation, cardiac arrest, patient safet

Machine Learning Systems in Clinics – How Mature Is the Adoption Process in Medical Diagnostics?

In a world with a constantly growing and aging population, health is a precious asset. Presently, with machine learning (ML), a technological change is taking place that could provide high quality healthcare and especially, improve efficiency of medical diagnostics in clinics. However, ML needs to be deeply integrated in clinical routines which highly differs from the integration of previous health IT given the specific characteristics of ML. Since existing literature on the adoption of ML in medical diagnostics is scarce, we set up an explorative qualitative study based on a conceptual basis consisting of the technological-organizational-environmental framework (TOE) and the healthcare specific framework of non-adoption, abandonment, scale-up, spread, and sustainability (NASSS). By interviewing experts from clinics and their suppliers we were able to connect both frameworks and identify influencing factors specific to the adoption process of ML in medical diagnostics

Machine Learning Developments as Stimuli for Organizational Learning

Organizational learning is a fundamental process that defines organizational behavior and thereby strongly influences organizational performance. As organizations increasingly adopt machine learning (ML) systems in their routines, the need to illuminate the impact of learning machines on organizational learning processes becomes increasingly urgent. In particular, due to their highly interdisciplinary and collaborative nature, ML developments—as organizations’ activities aimed at creating productively usable ML systems—may hereby represent an important but not yet well understood mechanism for fostering organizational learning. To explore how ML developments affect organizational learning processes, we interviewed 42 experts who are frequently involved in ML developments. Our findings suggest that ML developments can enhance organizational learning by stimulating a variety of organizational learning processes that generate a wealth of explicit and tacit knowledge in organizations