Informatics in Primary Care (BCS, The Chartered Institute for IT)
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Managing health IT risks: reflections and recommendations
No full textHealth information technology (IT) offers exciting opportunities for providing novel services to patients, and for improving the quality and safety of care. However, the introduction of IT can lead to unintended consequences, and create opportunities for failure, which can have significant effects on patient safety. In this paper I argue that many health IT patient safety risks are probably quite predictable, but are often not considered at the time. This puts patients at risk, and it threatens the successful adoption of health IT. I recommend that healthcare providers focus on strengthening their processes for organisational learning, promote proactive risk management strategies, and make risk management decisions transparent and explicit.
Learning health systems need to bridge the ‘two cultures’ of clinical informatics and data science
Full text linkBackground UK health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational “Big Data”. Learning health systems require not only data, but feedback loops of knowledge into changed practice. This depends on knowledge management and application, which in turn depend upon effective system design and implementation. Biomedical informatics is the interdisciplinary field at the intersection of health science, social science and information science and technology that spans this entire scope. Issues In the UK, the separate worlds of health data science (bioinformatics, “Big Data”) and effective healthcare system design and implementation (clinical informatics, “Digital Health”) have operated as ‘two cultures’. Much NHS and social care data is of unusably poor quality. Substantial research funding is wasted on ‘data cleansing’ or by producing very weak evidence. There is not yet a sufficiently powerful professional community or evidence base of best practice to influence the practitioner community or the digital health industry. Recommendation The UK needs increased clinical informatics research and education capacity and capability at much greater scale and ambition to be able to meet policy expectations, address the fundamental gaps in the discipline’s evidence base and mitigate the absence of regulation.Independent evaluation of digital health interventions should be the norm, not the exception. Conclusions Policy makers and research funders need to acknowledge the existing gap between the ‘two cultures’ and recognise that the full social and economic benefits of digital health and data science can only be realised by accepting the interdisciplinary nature of biomedical informatics and supporting a significant expansion of clinical informatics capacity and capability
In this issue: Digital disparities, complexity and patient safety
No full text In this issue we publish articles about how digital health and the skills needed to use it may increase disparities
Genesis of a UK Faculty of Clinical Informatics at a time of anticipation for some, and ruby, golden and diamond celebrations for others
Full text linkThis Editorial marks the launch of the UK Faculty of Clinical Informatics (FCI) at the time when non-clinically qualified informaticians are anticipating the lauch of the Federation of Informatics Professionals in Health and Care (Fed-IP).
In this issue: Prerequisites for precision medicine are genomics, computerised medical record systems, and big data analytics
Full text linkWho adopts a patient portal?: An application of the diffusion of innovation model
No full textBackground: Patient portals have emerged as an important tool through which patients can access online health information and engage in their health care. However, we know little about how patients perceive portals and whether patient perceptions might influence portal adoption.Objective: Apply the diffusion of innovation (DOI) theory to assess perceptions of adopters and non-adopters of a patient portal.Methods: We conducted a cross-sectional survey of adopters and non-adopters of the portal. Our survey consisted of perceived attributes from the DOI theory, socio-demographic characteristics and patient perceptions of technology adoption.Results: Three factors representing perceived attributes from DOI theory accounted for 73% of the variance in the data: Factor 1 – Relative Advantage (27%); Factor 2 – Ease of Use (24%) and Factor 3 – Trialability (22%). Adopters perceived greater Relative Advantage [mean (SD)] = 3.8 (0.71) versus 3.2 (0.89), p < 0.001, Ease of Use = 4.1 (0.71) versus 3.3 (0.95), p < 0.001 and Trialability = 4.0 (0.57) versus 3.4 (0.99), p < 0.001 than non-adopters. In multivariate modelling, age [OR = 3.75, 95% CI: (2.17, 6.46), p < 0.001] and income [OR = 1.87, 95% CI: (1.17, 3.00), p < 0.01] predicted adoption of the portal. Among DOI factors, Relative advantage predicted adoption of the portal [OR = 1.48, 95% CI: (1.03, 2.11), p < 0.05].Conclusion: Patients will adopt a patient portal if they perceive it to offer a relative advantage over existing practices such as telephoning or visiting the doctor’s office. Organisations seeking to increase the adoption of patient portals should implement strategies to promote the relative advantage of portals as, for example, through posters in waiting and exam rooms. A digital divide in the adoption of patient portals may exist with respect to age and income
Development of a National Core Dataset for the Iranian ICU Patients Outcome Prediction; a Comprehensive Approach
Full text linkObjective: To define a core dataset for ICU Patients Outcome Prediction in Iran. This core data set will lead us to design ICU outcome prediction models with the most effective parameters.Methods: A combination of literature review, national survey and expert consensus meetings were used. First, a literature review was performed by a general search in PubMed to find the most appropriate models for intensive care mortality prediction and their parameters. Secondly, in a national survey, experts from a couple of medical centers in all parts of Iran were asked to comment on a list of items retrieved from the earlier literature review study. In the next step, a multi-disciplinary committee of experts was installed. In 4 meetings each data item was examined separately and included/excluded by committee consensus.Results: The combination of the literature review findings and experts’ consensus resulted in a draft dataset including 26 data items. 92% percent of data items in the draft dataset were retrieved from the literature study and the others were suggested by the experts. The final dataset of 24 data items covers patient history and physical examination, chemistry, vital signs, oxygenations and some more specific parameters. Conclusions: This dataset was designed to develop a nationwide prognostic model for predicting ICU mortality and length of stay. This dataset opens the door for creating standardized approaches in data collection in the Iranian intensive care unit estimation of resource utility
The characteristics and capabilities of the available open source health information technologies supporting healthcare: A scoping review protocol
Full text linkINTRODUCTION: Due to the many advantages of open source software (OSS), including reduced cost of licensing, more flexibility in terms of customisation and redistribution, better quality and no vendor lock-in, OSS in healthcare is increasingly gaining importance. Various open source health information technologies (OS-HITs) are continuously being designed and developed for different areas of healthcare to increase organisational efficiencies and quality of care at minimum costs. The objective of this scoping review is to identify the kinds of existing OS-HITs, their characteristics (e.g. functions) and capabilities (e.g. advantages/disadvantages) for various healthcare stakeholders (physicians and patients) and healthcare sectors (e.g. clinical, administrative).METHODS: We will conduct a scoping review to identify the range of available OS-HITs in international literature from 1980 to September 2018. Searches will be conducted in six major international databases, namely: Cumulative Index to Nursing and Allied Health Literature Plus, Excerpta Medica Database, Global Health, Library Information Science and Technology Abstracts, Medline and Web of Science to identify relevant published research. We will also search the Google search engine and Google Scholar for on-going and unpublished work and the grey literature. Searches will be peer-reviewed by two independent reviewers and will not be limited by methodology or language. Next, selected references will be tabulated for study characteristics by author affiliation, country of origin, the name of OS-HIT, healthcare area/sector, system requirements, stakeholders, complete solution and web link. Furthermore, functions, benefits/advantages, disadvantages and outcomes (e.g. usability) of OS-HITs will be extracted. Narrative and interpretative synthesis of data will be undertaken.RESULTS: We will report our findings in a peer-reviewed journal