Implementing Pharmacy Informatics in College Curricula: The AACP Technology in Pharmacy Education and Learning Special Interest Group

Many professional organizations have initiatives to increase the awareness and use of informatics in the practice of pharmacy. Within education we must respond to these initiatives and make technology integral to all aspects of the curriculum, inculcating in students the importance of technology in practice. This document proposes 5 central domains for organizing planning related to informatics and technology within pharmacy education. The document is intended to encourage discussion of informatics within pharmacy education and the implications of informatics in future pharmacy practice, and to guide colleges of pharmacy in identifying and analyzing informatics topics to be taught and methods of instruction to be used within the doctor of pharmacy curriculum

A meta-narrative review of electronic patient records

This session comprises four papers that consider how systematic review methods may be developed in order to make the best use of complex evidence in education and health. The methods and approaches reflected upon in these papers are not drawn from a single research tradition, but share a common goal of broadening the methodological scope of systematic reviews and better understanding the utilisation of knowledge produced in this way. The first paper (Henry Potts) reports an ongoing review using a meta-narrative approach to make sense of the diverse sources of knowledge regarding electronic patient records. The review method has stressed the importance of understanding knowledge from within the research tradition in which it was produced; it is argued that this has important implications for the way that evidence is utilised in the policy making process. The second paper (Geoff Wong) reflects upon the experience of using an explicit realist approach in the synthesis of the evidence in Internet based learning. This realist synthesis offers a method of making sense of the highly heterogeneous and context dependent evidence which exists in this field thus enabling greater insights into what makes such educational interventions ‘work’. The third paper (Rod Sheaff) reports a review of the predominantly qualitative research literature on organisational structures and their impacts upon policy outcomes in health systems. A scoping study found 14389 relevant papers of which 1568 were selected for review. These studies were very variable in the amount and quality of the qualitative data, hence 'evidence', which they reported. The paper describes an attempt to adapt realist methods so as to synthesise such bodies of research in ways which take account of this variation in the strength of qualitative evidence. The fourth paper (Mark Pearson) draws upon the work of Donald Campbell and colleagues in order to gain a fuller understanding of how systematic reviews are utilised in the policy making process. It is argued that interpretive approaches to understanding policy making (such as rhetorical analysis) need to be tempered with a more nuanced understanding of research validity. The case is made that interpretive approaches not only can, but should, be melded with research validity to increase understanding of the policy making process

The New Role of Academia in Drug Development

Recommends ways for government, nonprofits, and academic institutions to work with the private sector to develop drugs and bring them to market more efficiently, including establishing models for intellectual property and technology transfer processes

Creating a new education paradigm to prepare nurses for the 21st Century

Nurse educators are accountable to keep baccalaureate education responsive to the ever changing healthcare delivery environment. The changing context of healthcare delivery requires focusing on population health and social determinants, providing interprofessional, team-based care, advancing innovation, and preparing practice ready baccalaureate nursing graduates. To be practice ready, nursing graduates must be agile and think and reason on their feet due to increasing care complexity beyond the hospital walls, changing care needs of individuals and families, advancing technology, shifting settings of care delivery, and managing multiple transitions. The purpose of this paper is to consider these healthcare changes and share a new baccalaureate nursing curriculum that radically shifts the paradigm from caring for patients to caring for people, and transforms from a diseased-based, acute care focused curriculum to one promoting a culture of health and multiple new and emerging roles of registered nurses

Requirements: The Key to Sustainability

Software's critical role in society demands a paradigm shift in the software engineering mind-set. This shift's focus begins in requirements engineering. This article is part of a special issue on the Future of Software Engineering

Librarians as Members of Integrated Institutional Information Programs: Management and Organizational Issues

published or submitted for publicatio

Bioinformatics and the politics of innovation in the life sciences: Science and the state in the United Kingdom, China, and India

The governments of China, India, and the United Kingdom are unanimous in their belief that bioinformatics should supply the link between basic life sciences research and its translation into health benefits for the population and the economy. Yet at the same time, as ambitious states vying for position in the future global bioeconomy they differ considerably in the strategies adopted in pursuit of this goal. At the heart of these differences lies the interaction between epistemic change within the scientific community itself and the apparatus of the state. Drawing on desk-based research and thirty-two interviews with scientists and policy makers in the three countries, this article analyzes the politics that shape this interaction. From this analysis emerges an understanding of the variable capacities of different kinds of states and political systems to work with science in harnessing the potential of new epistemic territories in global life sciences innovation

The science of clinical practice: disease diagnosis or patient prognosis? Evidence about "what is likely to happen" should shape clinical practice.

BACKGROUND: Diagnosis is the traditional basis for decision-making in clinical practice. Evidence is often lacking about future benefits and harms of these decisions for patients diagnosed with and without disease. We propose that a model of clinical practice focused on patient prognosis and predicting the likelihood of future outcomes may be more useful. DISCUSSION: Disease diagnosis can provide crucial information for clinical decisions that influence outcome in serious acute illness. However, the central role of diagnosis in clinical practice is challenged by evidence that it does not always benefit patients and that factors other than disease are important in determining patient outcome. The concept of disease as a dichotomous 'yes' or 'no' is challenged by the frequent use of diagnostic indicators with continuous distributions, such as blood sugar, which are better understood as contributing information about the probability of a patient's future outcome. Moreover, many illnesses, such as chronic fatigue, cannot usefully be labelled from a disease-diagnosis perspective. In such cases, a prognostic model provides an alternative framework for clinical practice that extends beyond disease and diagnosis and incorporates a wide range of information to predict future patient outcomes and to guide decisions to improve them. Such information embraces non-disease factors and genetic and other biomarkers which influence outcome. SUMMARY: Patient prognosis can provide the framework for modern clinical practice to integrate information from the expanding biological, social, and clinical database for more effective and efficient care

MORMED: towards a multilingual social networking platform facilitating medicine 2.0

The broad adoption of Web 2.0 tools has signalled a new era of "Medicine 2.0" in the field of medical informatics. The support for collaboration within online communities and the sharing of information in social networks offers the opportunity for new communication channels among patients, medical experts, and researchers. This paper introduces MORMED, a novel multilingual social networking and content management platform that exemplifies the Medicine 2.0 paradigm, and aims to achieve knowledge commonality by promoting sociality, while also transcending language barriers through automated translation. The MORMED platform will be piloted in a community interested in the treatment of rare diseases (Lupus or Antiphospholipid Syndrome)

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur