Search

Examines the psychological factors in the aetiology of certain skin diseases. Physiological effects of certain emotions on the sweat-glands; Patterns of skin disorders in which psychological factors sometimes seem to play a part in pathogenesis; Importance of the variable effect of any particular kind of emotional experience upon different personality types.

Presents abstracts of various dermatological studies. "Acne Vulgaris," by J. T. Ingram; "Scabies Treated With One Application of Benzyl Benzoate," by J. A. Graham; "Treatment of Scabies by T.E.T.M.S.," by T. M. Clayton; "Acertasone in the Treatment of Pemphigus," by M. Oppenheim and D. Cohen.

Introduction: Osteoporotic fractures, especially of the hip and spine, entail significant morbidity and mortality, with a 50% increased risk of hip fracture following a prior fracture. Globally - and in Chile, a low rate of post-fracture osteoporosis treatment persists. The International Osteoporosis Foundation's "Capture the Fracture" initiative advocates optimal practices, including Fracture Liaison Services (FLS); however, in Chile, there are only two FLS, both in the private healthcare system., Objective: This study aims to implement the first FLS in the public domain, specifically at the Dra. Eloísa Díaz Clinical Hospital, at Santiago. The methodology is based on the Logic Model, emphasizing the importance of human resources, especially a coordinating nurse., Results: The findings identify the problem, necessary resources, key actions, and short, medium, and long-term goals. External factors such as the hospital environment and collaboration with existing programs are considered. A graphical representation of the Logic Model is presented, along with proposed evaluation indicators., Discussion: Addressing fragility fractures as a public health issue is pertinent, and hospital authorities' acceptance of our project for implementation is highlighted. This initiative is anticipated to inspire other public health centers in Chile to implement similar services, contributing to closing the treatment gap and enhancing patients' quality of life.

Competing Interests: Competing interests: None declared.

Background: The successful implementation of clinical smartphone apps in hospital settings requires close collaboration with industry partners. A large-scale, hospital-wide implementation of a clinical mobile app for health care professionals developed in partnership with Google Health and academic partners was deployed on a bring-your-own-device basis using mobile device management at our UK academic hospital. As this was the first large-scale implementation of this type of innovation in the UK health system, important insights and lessons learned from the deployment may be useful to other organizations considering implementing similar technology in partnership with commercial companies., Objective: The aims of this study are to define the key enablers and barriers and to propose a road map for the implementation of a hospital-wide clinical mobile app developed in collaboration with an industry partner as a data processor and an academic partner for independent evaluation., Methods: Semistructured interviews were conducted with high-level stakeholders from industry, academia, and health care providers who had instrumental roles in the implementation of the app at our hospital. The interviews explored the participants' views on the enablers and barriers to the implementation process. The interviews were analyzed using a broadly deductive approach to thematic analysis., Results: In total, 14 participants were interviewed. Key enablers identified were the establishment of a steering committee with high-level clinical involvement, well-defined roles and responsibilities between partners, effective communication strategies with end users, safe information governance precautions, and increased patient engagement and transparency. Barriers identified were the lack of dedicated resources for mobile change at our hospital, risk aversion, unclear strategy and regulation, and the implications of bring-your-own-device and mobile device management policies. The key lessons learned from the deployment process were highlighted, and a road map for the implementation of large-scale clinical mobile apps in hospital settings was proposed., Conclusions: Despite partnering with one of the world's biggest technology companies, the cultural and technological change required for mobile working and implementation in health care was found to be a significant challenge. With an increasing requirement for health care organizations to partner with industry for advanced mobile technologies, the lessons learned from our implementation can influence how other health care organizations undertake a similar mobile change and improve the chances of successful widespread mobile transformation., (©Ravi Aggarwal, Sheena Visram, Guy Martin, Viknesh Sounderajah, Sanjay Gautama, Kevin Jarrold, Robert Klaber, Shona Maxwell, John Neal, Jack Pegg, Julian Redhead, Dominic King, Hutan Ashrafian, Ara Darzi. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 08.02.2022.)

Background: There is an abundance of patient experience data held within health care organizations, but stakeholders and staff are often unable to use the output in a meaningful and timely way to improve care delivery. Dashboards, which use visualized data to summarize key patient experience feedback, have the potential to address these issues., Objective: The aim of this study is to develop a patient experience dashboard with an emphasis on Friends and Family Test (FFT) reporting, as per the national policy drive., Methods: A 2-stage approach was used-participatory co-design involving 20 co-designers to develop a dashboard prototype, followed by iterative dashboard testing. Language analysis was performed on free-text patient experience data from the FFT, and the themes and sentiments generated were used to populate the dashboard with associated FFT metrics. Heuristic evaluation and usability testing were conducted to refine the dashboard and assess user satisfaction using the system usability score., Results: The qualitative analysis from the co-design process informed the development of the dashboard prototype with key dashboard requirements and a significant preference for bubble chart display. The heuristic evaluation revealed that most cumulative scores had no usability problems (18/20, 90%), had cosmetic problems only (7/20, 35%), or had minor usability problems (5/20, 25%). The mean System Usability Scale score was 89.7 (SD 7.9), suggesting an excellent rating., Conclusions: The growing capacity to collect and process patient experience data suggests that data visualization will be increasingly important in turning feedback into improvements to care. Through heuristic usability, we demonstrated that very large FFT data can be presented in a thematically driven, simple visual display without the loss of the nuances and still allow for the exploration of the original free-text comments. This study establishes guidance for optimizing the design of patient experience dashboards that health care providers find meaningful, which in turn drives patient-centered care., (©Mustafa Khanbhai, Joshua Symons, Kelsey Flott, Stephanie Harrison-White, Jamie Spofforth, Robert Klaber, David Manton, Ara Darzi, Erik Mayer. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 03.02.2022.)

Objectives: The Friends and Family Test (FFT) is commissioned by the National Health Service (NHS) in England to capture patient experience as a real-time feedback initiative for patient-centred quality improvement (QI). The aim of this study was to create a process map in order to identify the factors that promote and limit the effective use of FFT as a real-time feedback initiative for patient-centred QI., Setting: This study was conducted at a large London NHS Trust. Services include accident and emergency, inpatient, outpatient and maternity, which routinely collect FFT patient experience data., Participants: Healthcare staff and key stakeholders involved in FFT., Interventions: Semi-structured interviews were conducted on 15 participants from a broad range of professional groups to evaluate their engagement with the FFT. Interview data were recorded, transcribed and analysed for using deductive thematic analysis., Results: Concerns related to inefficiency in the flow of FFT data, lack of time to analyse FFT reports (with emphasis on high level reporting rather than QI), insufficient access to FFT reports and limited training provided to understand FFT reports for frontline staff. The sheer volume of data received was not amenable to manual thematic analysis resulting in inability to acquire insight from the free text. This resulted in staff ambivalence towards FFT as a near real-time feedback initiative., Conclusions: The results state that there is too much FFT free text for meaningful analysis, and the output is limited to the provision of sufficient capacity and resource to analyse the data, without consideration of other options, such as text analytics and amending the data collection tool., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. We develop a novel predictive framework to understand the temporal dynamics of hospital demand., Methods: We compare and combine state-of-the-art forecasting methods to predict hospital demand 1, 3 or 7 days into the future. In particular, our analysis compares machine learning algorithms to more traditional linear models as measured in a mean absolute error (MAE) and we consider two different hyperparameter tuning methods, enabling a faster deployment of our models without compromising performance. We believe our framework can readily be used to forecast a wide range of policy relevant indicators., Results: We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. Our approach is able to predict attendances at these emergency departments one day in advance up to a mean absolute error of ±14 and ±10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively., Conclusions: Simple linear methods like generalized linear models are often better or at least as good as ensemble learning methods like the gradient boosting or random forest algorithm. However, though sophisticated machine learning methods are not necessarily better than linear models, they improve the diversity of model predictions so that stacked predictions can be more robust than any single model including the best performing one.

Background: Access to rapid diagnosis is key to the control and management of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Laboratory RT-PCR testing is the current standard of care but usually requires a centralised laboratory and significant infrastructure. We describe our diagnostic accuracy assessment of a novel, rapid point-of-care real time RT-PCR CovidNudge test, which requires no laboratory handling or sample pre-processing., Methods: Between April and May, 2020, we obtained two nasopharyngeal swab samples from individuals in three hospitals in London and Oxford (UK). Samples were collected from three groups: self-referred health-care workers with suspected COVID-19; patients attending emergency departments with suspected COVID-19; and hospital inpatient admissions with or without suspected COVID-19. For the CovidNudge test, nasopharyngeal swabs were inserted directly into a cartridge which contains all reagents and components required for RT-PCR reactions, including multiple technical replicates of seven SARS-CoV-2 gene targets ( rdrp1, rdrp2, e -gene, n -gene, n1, n2 and n3) and human ribonuclease P ( RNaseP ) as sample adequacy control. Swab samples were tested in parallel using the CovidNudge platform, and with standard laboratory RT-PCR using swabs in viral transport medium for processing in a central laboratory. The primary analysis was to compare the sensitivity and specificity of the point-of-care CovidNudge test with laboratory-based testing., Findings: We obtained 386 paired samples: 280 (73%) from self-referred health-care workers, 15 (4%) from patients in the emergency department, and 91 (23%) hospital inpatient admissions. Of the 386 paired samples, 67 tested positive on the CovidNudge point-of-care platform and 71 with standard laboratory RT-PCR. The overall sensitivity of the point-of-care test compared with laboratory-based testing was 94% (95% CI 86-98) with an overall specificity of 100% (99-100). The sensitivity of the test varied by group (self-referred healthcare workers 94% [95% CI 85-98]; patients in the emergency department 100% [48-100]; and hospital inpatient admissions 100% [29-100]). Specificity was consistent between groups (self-referred health-care workers 100% [95% CI 98-100]; patients in the emergency department 100% [69-100]; and hospital inpatient admissions 100% [96-100]). Point of care testing performance was similar during a period of high background prevalence of laboratory positive tests (25% [95% 20-31] in April, 2020) and low prevalence (3% [95% 1-9] in inpatient screening). Amplification of viral nucleocapsid (n1, n2, and n3) and envelope protein gene (e-gene) were most sensitive for detection of spiked SARS-CoV-2 RNA., Interpretation: The CovidNudge platform was a sensitive, specific, and rapid point of care test for the presence of SARS-CoV-2 without laboratory handling or sample pre-processing. The device, which has been implemented in UK hospitals since May, 2020, could enable rapid decisions for clinical care and testing programmes., Funding: National Institute of Health Research (NIHR) Imperial Biomedical Research Centre, NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with Public Health England, NIHR Biomedical Research Centre Oxford, and DnaNudge., (© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license.)