Tensions and paradoxes in electronic patient record research: a systematic literature review using the meta-narrative method

Background: The extensive and rapidly expanding research literature on electronic patient records (EPRs) presents challenges to systematic reviewers. This literature is heterogeneous and at times conflicting, not least because it covers multiple research traditions with different underlying philosophical assumptions and methodological approaches. Aim: To map, interpret and critique the range of concepts, theories, methods and empirical findings on EPRs, with a particular emphasis on the implementation and use of EPR systems. Method: Using the meta-narrative method of systematic review, and applying search strategies that took us beyond the Medline-indexed literature, we identified over 500 full-text sources. We used ‘conflicting’ findings to address higher-order questions about how the EPR and its implementation were differently conceptualised and studied by different communities of researchers. Main findings: Our final synthesis included 24 previous systematic reviews and 94 additional primary studies, most of the latter from outside the biomedical literature. A number of tensions were evident, particularly in relation to: [1] the EPR (‘container’ or ‘itinerary’); [2] the EPR user (‘information-processer’ or ‘member of socio-technical network’); [3] organizational context (‘the setting within which the EPR is implemented’ or ‘the EPR-in-use’); [4] clinical work (‘decision-making’ or ‘situated practice’); [5] the process of change (‘the logic of determinism’ or ‘the logic of opposition’); [6] implementation success (‘objectively defined’ or ‘socially negotiated’); and [7] complexity and scale (‘the bigger the better’ or ‘small is beautiful’). Findings suggest that integration of EPRs will always require human work to re-contextualize knowledge for different uses; that whilst secondary work (audit, research, billing) may be made more efficient by the EPR, primary clinical work may be made less efficient; that paper, far from being technologically obsolete, currently offers greater ecological flexibility than most forms of electronic record; and that smaller systems may sometimes be more efficient and effective than larger ones. Conclusions: The tensions and paradoxes revealed in this study extend and challenge previous reviews and suggest that the evidence base for some EPR programs is more limited than is often assumed. We offer this paper as a preliminary contribution to a much-needed debate on this evidence and its implications, and suggest avenues for new research

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

Trade costs, openness and productivity: market access at home and abroad

This paper discusses the channels between openness and productivity and trade hampering factors. The stylized facts from the heterogeneous firms literature suggests that firms face market entry costs for each new product they export and to each new export market. Transport costs, border costs and retail and wholesale distribution costs might add up to 170% of the export value, but formal import tariffs and duties are relatively unimportant. The results by the Observatory of European SMEs survey, which has firm-level data for the whole European Union confirm this result. Lack of knowledge on export markets and regulations in other countries are important trade barriers for European firms. From these outcomes it could be derived that EU trade policies should be directed to deep integration with other countries, preferably by implementing internal market policies for goods and services trade and foreign direct investment. These policies can deal with reducing regulations heterogeneity, non-tariff barriers and customs procedures. Providing public information on export markets (e.g. customers, contact, and distribution networks) could also be helpful, if well targeted

Initial experiences in developing e-health solutions across Scotland

The MRC funded Virtual Organisations for Trials and Epidemiological Studies (VOTES) project is a collaborative effort between e-Science, clinical and ethical research centres across the UK including the universities of Oxford, Glasgow, Imperial, Nottingham and Leicester. The project started in September 2005 and is due to run for 3 years. The primary goal of VOTES is to develop a reusable Grid framework through which a multitude of clinical trials and epidemiological studies can be supported. The National e-Science Centre (NeSC) at the University of Glasgow are looking at developing the Scottish components of this framework. This paper presents the initial experiences in developing this framework and in accessing and using existing data sets, services and software across the NHS in Scotland