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Background: Children are one of the biggest users of emergency departments in the UK, sometimes utilising services when experiencing acute illnesses that can be managed at home. mHealth can be an efficacious way for parents/guardians to manage acute illnesses. It has also become a ubiquitous resource to promote public health interests within a resource constrained health system. However, to be effective, co-design is necessary with concepts such as usefulness, ease of use, trustworthiness and security underpinning the utility of mHealth interventions for acute childhood illness., Objective: The objective of this study was to conduct an environmental scan of mHealth apps for parental assessment and management of acute childhood illnesses in 0- to 5-year-olds, review the content and functionality of each identified app against the list of desired attributes identified in previous research and synthesise publicly available reviews from end users to demonstrate the quality of current apps in the marketplace and identify areas for improvement., Method: App characteristics were mapped to an a priori defined list of desirable attributes. End user reviews were mapped to a modified Technology Acceptance Model framework that included overarching themes of: Perceived Usefulness (PU), Perceived Ease of Use (PEU), Perceived Trustworthiness (PT) and Perceived Security (PS) using reflexive thematic analysis., Results: Overall, 15 apps were included in the analysis (Table 2). Fourteen of the 15 apps were available on both the iOS and Android platforms. 'Baby Illness, Prevention, Cure' was only available on the Android store. All apps were free to download and did not restrict access to information or features via in-app purchases. Baby and child first aid (British Red Cross) was the earliest app to be released (2014). This app was updated annually, with the most recent update being 2022. The most recent apps to be released (2018) were 'AskFirst' (formerly Ask NHS), 'Essex Child Health' and 'The Lullaby Trust Baby Check', updated in 2022, 2020 and 2019, respectively. No individual app met all the desirable attributes highlighted by parents in previous research. Both 'Healthier Together', and 'The Lullaby Trust Baby Check' included all but one desirable attributes (video), as did the ''CATCH (Common Approach to Children's Health), which did not utilise a traffic light system. Apps that were locality specific were not rated by users (Berkshire Child Health, Cheshire Child Health, Child Health Guide Newham, Child Health HMR, Oldham Child Illness and Walsall Healthy Child). All other apps were rated from 2/5 to 5/5 stars. When considering localities, the 'CATCH' app was only supported in Halton, Cheshire, Knowsley, Liverpool, St Helens and Vale Royal. Further investigation evidenced no correlation between inclusion of the desirable attributes and app rating., Conclusion: Overall, this research has highlighted several aspects of best practise when developing mHealth apps for the management of acute childhood illnesses, for increasing PU, PEU, PT and PS; and also, places importance on co-design., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: Clinical prediction models have the potential to improve the quality of care and enhance patient safety outcomes. A Computer-aided Risk Scoring system (CARSS) was previously developed to predict in-hospital mortality following emergency admissions based on routinely collected blood tests and vitals. We aimed to externally validate the CARSS model., Methods: In this retrospective external validation study, we considered all adult (≥18 years) emergency medical admissions discharged between 11/11/2020 and 11/11/2022 from The Rotherham Foundation Trust (TRFT), UK. We assessed the predictive performance of the CARSS model based on its discriminative (c-statistic) and calibration characteristics (calibration slope and calibration plots)., Results: Out of 32,774 admissions, 20,422 (62.3 %) admissions were included. The TRFT sample had similar demographic characteristics to the development sample but had higher mortality (6.1 % versus 5.7 %). The CARSS model demonstrated good discrimination (c-statistic 0.87 [95 % CI 0.86-0.88]) and good calibration to the TRFT dataset (slope = 1.03 [95 % CI 0.98-1.08] intercept = 0 [95 % CI -0.06-0.07]) after re-calibrating for differences in baseline mortality (intercept = 0.96 [95 % CI 0.90-1.03] before re-calibration)., Conclusion: In summary, the CARSS model is externally validated after correcting the baseline risk of death between development and validation datasets. External validation of the CARSS model showed that it under-predicted in-hospital mortality. Re-calibration of this model showed adequate performance in the TRFT dataset., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background: Acute illness accounts for the majority of episodes of illness in children under five years of age and is the age group with the highest consultation rate in general practice in the UK. The number of children presenting to emergency care is also steadily increasing, having risen beyond pre-pandemic numbers. Such high, and increasing, rates of consultation have prompted concerns about parents' level of knowledge and confidence in caring for their children when they are ill, and particularly when and how to seek help appropriately., Aim: The ASK SNIFF collaboration research programme identified parents' need for accurate and accessible information to help them know when to seek help for a sick child in 2010. This paper presents the resulting programme of research which aimed to co-develop an evidence-based safety netting intervention (mobile app) to help parents know when to seek help for an acutely ill child under the age of five years in the UK., Methods: Our programme used a collaborative six step process with 147 parent and 324 health professional participants over a period of six years including: scoping existing interventions, systematic review, qualitative research, video capture, content identification and development, consensus methodology, parent and expert clinical review., Results: Our programme has produced evidence-based content for an app supported by video clips. Our collaborative approach has supported every stage of our work, ensuring that the end result reflects the experiences, perspectives and expressed needs of parents and the clinicians they consult., Conclusion: We have not found any other resource which has used this type of approach, which may explain why there is no published evaluation data demonstrating the impact of existing UK resources. Future mobile apps should be designed and developed with the service users for whom they are intended., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future., Methods: This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions., Results: Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objective: To identify and discuss theory-based studies of large-scale health information technology programs in the UK National Health Service., Materials and Methods: Using the PRISMA systematic review framework, we searched Scopus, PubMed and CINAHL databases from inception to March 2022 for theory-based studies of large-scale health IT implementations. We undertook detailed full-text analyses of papers meeting our inclusion criteria., Results: Forty-six studies were included after assessment for eligibility, of which twenty-five applied theories from the information systems arena (socio-technical approaches, normalization process theory, user acceptance theories, diffusion of innovation), twelve from sociology (structuration theory, actor-network theory, institutional theory), while nine adopted other theories. Most investigated England's National Program for IT (2002-2011), exploring various technologies among which electronic records predominated. Research themes were categorized into user factors, program factors, process outcomes, clinical impact, technology, and organizational factors. Most research was qualitative, often using a case study strategy with a longitudinal or cross-sectional approach. Data were typically collected through interviews, observation, and document analysis; sampling was generally purposive; and most studies used thematic or related analyses. Theories were generally applied in a superficial or fragmentary manner; and articles frequently lacked detail on how theoretical constructs and relationships aided organization, analysis, and interpretation of data., Conclusion: Theory-based studies of large NHS IT programs are relatively uncommon. As large healthcare programs evolve over a long timeframe in complex and dynamic environments, wider adoption of theory-based methods could strengthen the explanatory and predictive utility of research findings across multiple evaluation studies. Our review has confirmed earlier suggestions for theory selection, and we suggest there is scope for more explicit use of such theoretical constructs to strengthen the conceptual foundations of health informatics research. Additionally, the challenges of large national health informatics programs afford wide-ranging opportunities to test, refine, and adapt sociological and information systems theories., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: The British Gynaecological Cancer Society (BGCS) has highlighted the disparity of ovarian cancer outcomes in the UK compared to other European countries. Therefore, cancer quality assurance audits and subspecialty training are important in improving the UK standard of care for these patients. The current workforce crisis afflicting the NHS creates difficulty in dedicating teams of clinicians to these audits. We present a single institution study to evaluate if NLP-generated code can improve the efficiency of ovarian cancer and subspeciality reaccreditations audits. We used the chat bot Google Bard to write Visual Basic Applications algorithms that utilise Excel files from electronic health records., Methods: Primary ovarian cancer data from 2019 to 2022 was retrospectively collected from the Cambridge University Hospital electronic health records. The surgical subspecialty reaccreditation audit analysed the 2022 surgical database. A modular coding approach with Google Bard was applied to generate audit algorithms. The time to complete these current audits was compared against the 2016 ovarian cancer and 2020 subspeciality reaccreditation audits., Results: The previous ovarian cancer audit conducted in 2016 required 3 clinicians for the 135 cases and data collection required 1800 min. Data analysis was completed in 300 min. The current ovarian cancer audit allocated 2 clinicians to the 600 surgical cases. Data collection was completed in 3120 min, 3360 min for code development and 720 min for testing. The 2020 subspecialty reaccreditation audit was completed in 360 min. The 2022 subspecialty reaccreditation audit was completed in 1680 min, with 960 min for code development, 240 for debugging and 480 min for testing., Conclusion: We have demonstrated that NLP-generated code can significantly increase the efficiency of surgical quality assurance audits by eliminating the need for manual data analysis. With the current trajectory of NLP development, increasingly complex algorithms can be developed with minimal programming knowledge., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Care pathways in renal transplantation involve multi-speciality coordination and administration of clinical data across organisational boundaries. The potential for information technology (IT) to support the service through data management, communication and national registration has been reported. However, no previous national-level evaluation on the current state of digital transformation has been undertaken., Methods: We undertook structured phone interviews with transplant coordinators at all 23 transplant centres in the United Kingdom (UK). The interview covered topics including clinical workflows during transplant referral and current data management processes. Based on established frameworks, we elicited IT capabilities, readiness and infrastructure to evaluate the digital maturity at each centre. We analysed interview data thematically and synthesised results across centres to identify common challenges and inform recommendations., Results: Transplant coordinators across centres reported similar care pathways with patients being referred by to transplant centres from regional renal referral centres. Analysis revealed that a key challenge revolved around the inability to access data across organisational boundaries. This resulted in dependence on postal or electronic mail to transfer clinical data, such as blood results or medication lists. Most centres had a hospital-wide electronic medical record, which was unable to meet the specific workflow requirements of transplantation. This was primarily because implemented solutions were unable to provide a single unified view of transplant-related data. As a result, transplant coordinators reported several workarounds to manage clinical data, such as paper folders, Excel© sheets and scanned files on hospital shared drives., Conclusions: Existing front-line IT solutions and wider infrastructure did not support the requirements of care pathways in renal transplantation. Digital transformation should focus on the need to surface patient data across organisational boundaries and provide specific views of data that complement the clinical workflow. This study highlights how regional interoperability remains a major priority to support multi-centre specialist services., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)