Points of Failure: A Systematic Review of information-flow using Medication Use Cases

Background: Medication errors pose a significant problem in the clinical environment, causing adverse events which impact patient safety. Problem: The introduction of electronic information and clinical systems have reduced medication errors but have also been identified as creating new types of errors. Method: Using the previously developed Hermon model, this research aimed to identify and understand medication errors due to clinical information-flow in the Australian General Practice (primary care) setting. The research used existing general practice medication error report cases from the Threat to Patient Safety (TAPS) Study to map against the Hermon model, and validated this mapping through consultations with general practitioners. Findings: The findings informed the refinement of the Hermon Model, and assisted in identifying medication errors points of information-flow failure in general practice information-flow. Impact: This study has significance to improve patient safety and inform the development of general practice desktop systems through identification and understanding of information-flow points of failure which result in medication errors

A Study on Information Induced Medication Errors

Preventable medical adverse events are a serious concern for healthcare. Medication errors form a significant part of these concerns and it is evident that these errors can have serious consequences such as death or disability. Many medication errors are a consequence of information failure. Therefore to prevent such adverse events, the associated information flow must be understood. This research used a systematic review methodology to conduct an analysis of medication error as a result of information failure. Its aim was to suggest solutions on reducing information induced medication errors. The results indicate that is apparent that human error such as slips or lapses can occur due to stress, tiredness and interruptions within the clinical process. Numerous information flow problems are evident within the clinical culture and it is this clinical culture that allows human error which results in medication errors. By changing the clinical culture and establishing effective information flow, clinical errors may be reduced. Thus, recommendations for reducing information flow induced medication errors include a change in clinical culture and the design of a framework which can establish uniformity of communication between healthcare providers. Finally, a major concern for patients is lack of patient information such as medication histories and allergies. Reconciliation of medication histories and a data base of patient information can assist practitioners in identifying any allergies to medications and thus prevent patient allergy related medication errors. Patient health summaries should be shared, for instance using the Australian national eHealth record in order to reduce errors in transcription and to reduce the time spent on collecting medication history

A study on information induced medication errors

The electronic health record (eHR) system has recently been considered one of the biggest advancements in healthcare services. A personally controlled electronic health record (PCEHR) system is proposed by the Australian government to make the health system more agile, secure, and sustainable. Although the PCEHR system claims the electronic health records can be controlled by the patients, healthcare professionals and database/system operators may assist in disclosing the patients’ eHRs for retaliation or other ill purposes. As the conventional methods for preserving the privacy of eHRs solely trust the system operators, these data are vulnerable to be exploited by the authorised personnel in an immoral/unethical way. Furthermore, issues such as the sheer number of eHRs, their sensitive nature, flexible access, and efficient user revocation have remained the most important challenges towards fine-grained, cryptographically enforced data access control. In this paper we propose a patient centric cloud-based PCEHR framework, which employs a homomorphic encryption technique in storing the eHRs. The proposed system ensures the control of both access and privacy of eHRs stored in the cloud database

Software as a Medical Device (SaMD): Useful or Useless Term?

Software as a medical device is a relatively new and expanding field in which patient safety must be a key concern. Regulation and standards regarding software as a medical device (subsequently referred to as “SaMD”) must incorporate all components that could potentially influence SaMD, both in its development and implementation. However, SaMD has been varyingly defined by organisations and individuals within the literature, therefore there is no clear boundary as to what is or is not SaMD, consequently, no clear definition of SaMD exists. Without a clear definition it therefore becomes impossible to create standards to regulate SaMD. Ultimately, this results in increased risks to patient safety. The purpose of this study was to identify SaMD concepts through a Scoping Review to establish the boundaries of SaMD. This has significant impact on new technology applications to support healthcare monitoring and healthcare service delivery. This will ultimately affect how new technology can be regulated in healthcare and will impact innovation and design in this field

Big data in healthcare: What is it used for?

Big data analytics is a growth area with the potential to provide useful insight in healthcare. Whilst many dimensions of big data still present issues in its use and adoption, such as managing the volume, variety, velocity, veracity, and value, the accuracy, integrity, and semantic interpretation are of greater concern in clinical application. However, such challenges have not deterred the use and exploration of big data as an evidence source in healthcare. This drives the need to investigate healthcare information to control and reduce the burgeoning cost of healthcare, as well as to seek evidence to improve patient outcomes. Whilst there are a number of well-publicised examples of the use of big data in health, such as Google Flu and HealthMap, there is no general classification of its uses to date. This study used a systemic review methodology to create a categorisation of big data use in healthcare. The results indicate that the natural classification is not clinical application based, rather it falls into four broad categories: administration and delivery, clinical decision support (with a sub category of clinical information), consumer behaviour, and support services. Further, the results demonstrate that the use of big data in all examples in the literature is not singular in its approach and each study covers multiple use and application areas. This study provides a baseline to assess the proliferation of the use of big data in healthcare and can assist in the understanding the breadth of big data applications

I care by...

The Care research group at the Royal College of Art (RCA) was conceived in the last week of June 2020, a month after the killing of George Floyd by police in Minnesota, an act which catalysed global protests on systemic racism and police brutality. In the UK, tens of thousands of protesters took to the streets to show solidarity with demonstrators in the US. Coinciding with the easing of the lockdown restrictions imposed to manage the coronavirus, the marches shone a light on the government’s failure to protect Black, Asian and Minority Ethnic people from the disproportionate risk posed by COVID- 19, and on the police’s increased use of stop and search in areas with large BAME populations. The pandemic has shone the harshest of lights on the question of care in the age of neoliberalism: who gets it; who needs it; who does it; who controls it. The Care research group, comprising staff and postgraduate researchers within the School of Arts and Humanities at the RCA, works in this light. Over the course of a year, as the inequalities of the virus were becoming all too clear, the group regularly came together via Zoom to reflect on: the question of how to care for the human body in the technical-patriarchal societies the virus has re-inscribed; the ‘un-doing’ of what Judith Butler describes as the binary of vulnerability and resistance; the politically-transformative potential of prioritising care (rooted in empathy, solidarity, kinship) over capitalist gain; the activation of creative research practices (including but by no means limited to writing, looking, painting, drawing, filming, performing, collecting, assembling, curating, making public) as means of caring/transforming. The group’s activities through the year of trying, failing, and trying again to care for its work and members are gathered in a co-authored Declaration of Care, published here, and expanded upon with attention to some of the methods group members developed in their research through practice. The Declaration was recited in a participatory performance with invited artist Jade Montserrat on 10 March 2021. Over the course of a two-hour webinar, participants including members of the public were invited to draw alongside Montserrat with whatever materials they had to hand as they listened to texts on the vulnerabilities of bodies, the structuring of care within institutions, and the tactile, sensory, healing qualities of creative practice. This book includes a selection of the participants’ drawings, a Reader comprising the texts that were shared, and Montserrat’s drawings created through the performance. Ahead of the performance, Montserrat delivered an address to the Care research group which looked back on a lifetime of calling for a kind of care that was never provided. Excerpts from Montserrat’s address are included here too, alongside a text and image which reflect on the group’s affective reactions to the experience of listening to it, titled Episode. The Declaration is a list of methods (approaches, processes, techniques), an enumeration of how Care research group members have worked, and would like to work: ‘I care by…’. This is a statement which has reverberated throughout the year, which bears repeating, which resounds still. Gemma Blackshaw, Care research group convenor, 2020–202

Lamina propria macrophage phenotypes in relation to Escherichia coli in Crohn's disease

Background: Abnormal handling of E. coli by lamina propria (LP) macrophages may contribute to Crohn’s disease (CD) pathogenesis. We aimed to determine LP macrophage phenotypes in CD, ulcerative colitis (UC) and healthy controls (HC), and in CD, to compare macrophage phenotypes according to E. coli carriage. Methods: Mucosal biopsies were taken from 35 patients with CD, 9 with UC and 18 HCs. Laser capture microdissection was used to isolate E. coli-laden and unladen LP macrophages from ileal or colonic biopsies. From these macrophages, mRNA was extracted and cytokine and activation marker expression measured using RT-qPCR. Results: E. coli-laden LP macrophages were identified commonly in mucosal biopsies from CD patients (25/35, 71 %), rarely in UC (1/9, 11 %) and not at all in healthy controls (0/18). LP macrophage cytokine mRNA expression was greater in CD and UC than healthy controls. In CD, E. coli-laden macrophages expressed high IL-10 & CD163 and lower TNFα, IL-23 & iNOS irrespective of macroscopic inflammation. In inflamed tissue, E. coli-unladen macrophages expressed high TNFα, IL-23 & iNOS and lower IL-10 & CD163. In uninflamed tissue, unladen macrophages had low cytokine mRNA expression, closer to that of healthy controls. Conclusion: In CD, intra-macrophage E. coli are commonly found and LP macrophages express characteristic cytokine mRNA profiles according to E. coli carriage. Persistence of E. coli within LP macrophages may provide a stimulus for chronic inflammation

Big data in healthcare: and what is it used for?

Big data analytics is a growth area with the potential to provide useful insight in healthcare. Whilst many dimensions of big data still present issues in its use and adoption, such as managing the volume, variety, velocity, veracity, and value, the accuracy, integrity, and semantic interpretation are of greater concern in clinical application. However, such challenges have not deterred the use and exploration of big data as an evidence source in healthcare. This drives the need to investigate healthcare information to control and reduce the burgeoning cost of healthcare, as well as to seek evidence to improve patient outcomes. Whilst there are a number of well-publicised examples of the use of big data in health, such as Google Flu and HealthMap, there is no general classification of its uses to date. This study used a systemic review methodology to create a categorisation of big data use in healthcare. The results indicate that the natural classification is not clinical application based, rather it falls into four broad categories: administration and delivery, clinical decision support (with a sub category of clinical information), consumer behaviour, and support services. Further, the results demonstrate that the use of big data in all examples in the literature is not singular in its approach and each study covers multiple use and application areas. This study provides a baseline to assess the proliferation of the use of big data in healthcare and can assist in the understanding the breadth of big data application

Software as a Medical Device (SaMD): Useful or Useless Term?

Software as a medical device is a relatively new and expanding field in which patient safety must be a key concern. Regulation and standards regarding software as a medical device (subsequently referred to as “SaMD”) must incorporate all components that could potentially influence SaMD, both in its development and implementation. However, SaMD has been varyingly defined by organisations and individuals within the literature, therefore there is no clear boundary as to what is or is not SaMD, consequently, no clear definition of SaMD exists. Without a clear definition it therefore becomes impossible to create standards to regulate SaMD. Ultimately, this results in increased risks to patient safety. The purpose of this study was to identify SaMD concepts through a Scoping Review to establish the boundaries of SaMD. This has significant impact on new technology applications to support healthcare monitoring and healthcare service delivery. This will ultimately affect how new technology can be regulated in healthcare and will impact innovation and design in this field