Part I: ePapers最新文献

7 Healthcare staff perceptions on using artificial intelligence predictive tools: a qualitative study 7医疗保健人员对使用人工智能预测工具的看法:一项定性研究

Part I: ePapers Pub Date : 2022-11-01 DOI: 10.1136/bmjhci-2022-fciasc.7

N. Hassan, R. Slight, S. Slight

{"title":"7 Healthcare staff perceptions on using artificial intelligence predictive tools: a qualitative study","authors":"N. Hassan, R. Slight, S. Slight","doi":"10.1136/bmjhci-2022-fciasc.7","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-fciasc.7","url":null,"abstract":"ObjectiveArtificial intelligence (AI) predictive tools can help inform the clinical decision-making process by, for example, detecting early signs of patient deterioration or predicting the likelihood of a patient developing a particular disease or complications postsurgery. However, it is unclear how acceptable or useful clinicians find these tools in practice. This project aims to explore healthcare staff’ perceptions on the benefits and challenges of using AI tools to inform clinical decision-making in practice.MethodsHealthcare staff (physicians, pharmacists and nurses) working in different departments at one large teaching hospital in the North East were invited to participate in semi-structured interviews. Interviews were conducted between August and November 2021 by zoom videoconferencing, with questions focused on what AI predictive tools they currently use, how they guide daily tasks around diagnosis, management, prevention, prognosis and screening, and what challenges they face with their use. All transcribed files were checked for accuracy. Thematic saturation guided the volume of qualitative data collection. Qualitative data analysis and development of themes was performed for each interview using Nvivo 12 software. Ethical approval was obtained (20/EM/0183, IRAS 280077).ResultsTen healthcare staff were interviewed (physicians (n=7), pharmacists (n=1), surgeons (n=2)) from different medical specialities (e.g., Oncology, Endocrinology, Cardiology, Head and Neck, and transplant surgery). Five themes emerged, including the meaning of the term AI, the usefulness of AI predictive tools in informing clinical decision-making, features that healthcare staff found helpful, and challenges around their use. Healthcare staff recognised the benefits of AI predictive tools in being able to ‘detect deterioration quicker than you would currently do’(05-ID), which informed decisions around patient discharge: ‘can you safely send them home (...) or do you want to keep them, in case they do deteriorate’ (05-ID). They found AI predictive tools useful when explaining the potential risk of cardiovascular events to patients and encouraging medication adherence ‘it does help so much convincing the patient to actually adhere to the medication’ (07-Endo).During COVID-19, AI prediction tools helped identify patients that might potentially need mechanical ventilation and ICU admission. Healthcare staff also felt it was important that AI predictive tools provided reliable information, that was easy to understand, and integrated with the current systems. A concern raised around the use of AI predictive tools was whether they might ‘mislead junior doctors or doctors who would not have that much of a clinical sense and would totally depend on it’ (07-Endo).ConclusionThis study demonstrated opportunities for the application of AI predictive tools in clinical practice. Concerns raised around the use of these tools should be considered by developers. We recognise that ","PeriodicalId":116980,"journal":{"name":"Part I: ePapers","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114387557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

6 A national survey of the current provisions, perceptions and challenges regarding digital health education in the UK medical undergraduate curriculum 对英国医学本科课程中关于数字健康教育的现行规定、看法和挑战的全国性调查

Part I: ePapers Pub Date : 2022-11-01 DOI: 10.1136/bmjhci-2022-fciasc.6

Nishita Gadi, M. Utukuri, B. Osei-Boadu, Y. Aung, E. Le, A. Deighton, C. Dibblin, M. Ferry, F. D'Souza, J. Hirniak, B. Agboola, M. Abedi, A. Axiaq, C. Chand, C. Patel, M. Pitt, B. Harris, M. Byrne, R. Sethi

{"title":"6 A national survey of the current provisions, perceptions and challenges regarding digital health education in the UK medical undergraduate curriculum","authors":"Nishita Gadi, M. Utukuri, B. Osei-Boadu, Y. Aung, E. Le, A. Deighton, C. Dibblin, M. Ferry, F. D'Souza, J. Hirniak, B. Agboola, M. Abedi, A. Axiaq, C. Chand, C. Patel, M. Pitt, B. Harris, M. Byrne, R. Sethi","doi":"10.1136/bmjhci-2022-fciasc.6","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-fciasc.6","url":null,"abstract":"ObjectiveDigital health (DH) is the integration of technologies to tackle challenges in healthcare. Its applications include mobile health, remote & wireless healthcare, artificial intelligence, and robotics. Digital technologies are increasingly being used to deliver routine care, whilst simultaneously patients are increasing their uptake of DH solutions (e.g. wearables).With the adoption of DH increasing across the NHS, there is a growing need for a digitally literate workforce. However, there are no national standards on DH education for UK medical students. Consequently, this study sought to assess the current provisions, perceptions and challenges regarding DH education in the undergraduate medical curriculum.MethodsAn anonymous cross-sectional online survey was developed following a literature search and by collecting iterative feedback from both researchers and external collaborators. The survey consisted of questions in 6 areas: (a) understanding of DH;(b) existing provision of DH education;(c) interest in DH education;(d) preferred means of delivering and assessing DH education;(e) impact of the COVID-19 pandemic on DH;and (f) demographic information.The survey was administered via Qualtrics from March to October 2021, and disseminated to UK medical students via university mailing lists, social media and student representatives. Quantitative and qualitative data were collected pertaining to demographics, attitudes, preferences, and current provisions regarding DH education. Qualitative responses underwent thematic analysis. For quantitative analysis, R (version 3.5.0) and R Studio (version 1.1a) were used.Results514 complete responses were received from 39 UK medical schools in 2021. 57.2% of respondents were female, with a mean age of 22.9 ± 3.2. 65.8% of students considered DH ‘extremely important’ to future clinical practice, particularly the domains of electronic patient records, telehealth and smartphone applications. However, only 18.1% felt aware of the DH competencies required in clinical medicine. 70.2% of students reported receiving some DH education, with the highest proportion being in the form of lectures or seminars (30.5%, n=157), e-learning modules (28.6%, n=147) and ad hoc teaching during clinical placements (22.8%, n=117). However, only 25.7% felt satisfied with these provisions. Themes for student satisfaction related to a practical teaching approach, delivery of content appropriate for their training stage and coverage of topics in student interest. Conversely, student dissatisfaction originated from inadequate teaching, and subsequent fears of falling behind. 56.1% preferred DH education to be mandatory rather than elective, ideally through hands-on workshops (75.8%) and lectures and seminars (60.4%). 65.4% thought DH proficiency should be assessed in some capacity, of which 75.6% preferred formative assessment.ConclusionThis study represents the first national survey of UK medical students on DH education. Overwhelm","PeriodicalId":116980,"journal":{"name":"Part I: ePapers","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

9 Free text notes added to a patient’s allergy status in electronic prescribing systems digitally analysed for better usability 在电子处方系统中，为提高可用性，对患者的过敏状况进行了数字分析，并添加了免费文本注释

Part I: ePapers Pub Date : 2022-11-01 DOI: 10.1136/bmjhci-2022-fciasc.9

Ben Logan

引用次数: 0

1 Digital transformation of the acute medical take – improving standards of care 急症医疗数字化转型——提高护理水平

Part I: ePapers Pub Date : 2022-11-01 DOI: 10.1136/bmjhci-2022-fciasc.1

Helen Craggs

引用次数: 0

2 Evaluation of MaST (management and supervision tool) to support NHS community mental health teams in identifying risk of crisis and complexity across caseloads 2评估MaST(管理和监督工具)，以支持NHS社区精神卫生团队识别跨病例量的危机风险和复杂性

Part I: ePapers Pub Date : 2022-11-01 DOI: 10.1136/bmjhci-2022-fciasc.2

C. Gadd

引用次数: 0

4 Reduction of order alerts through filters: impact on pharmacists’ override rate and perceptions of alert fatigue 通过过滤器减少订单警报:对药剂师重写率和警报疲劳感知的影响

Part I: ePapers Pub Date : 2022-11-01 DOI: 10.1136/bmjhci-2022-fciasc.4

Daniel Chan

引用次数: 1

5 The limitations of using commercial wearable activity trackers, such as FitBits, for the clinical monitoring of patient activity levels 5 .使用商业可穿戴活动追踪器(如FitBits)用于临床监测患者活动水平的局限性

Part I: ePapers Pub Date : 2022-11-01 DOI: 10.1136/bmjhci-2022-fciasc.5

Olivia Curtis

{"title":"5 The limitations of using commercial wearable activity trackers, such as FitBits, for the clinical monitoring of patient activity levels","authors":"Olivia Curtis","doi":"10.1136/bmjhci-2022-fciasc.5","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-fciasc.5","url":null,"abstract":"ObjectiveThere is increasing interest in remote monitoring of patients within the comfort and safety of their homes or care homes and became more pertinent during the COVID-19 pandemic to reduce hospital footfall and staff risk. While specifically designed medical devices exist, commercial wearable activity trackers (WAT), such as FitBits, are cheap, easy to use, and patients may already use them for lifestyle advice so their value in clinical intervention is of interest.The feasibility of using commercial WAT for daily monitoring within a tertiary oncology centre was investigated, including limitations of non-medical devices, such as data collection and synchronisation errors.MethodsParticipants were recruited for a study that investigated if remote monitoring of step counts was feasible and acceptable. Patients with advanced lung, upper and lower gastrointestinal cancer, or mesothelioma who were starting a new line of systemic anti-cancer treatment were recruited between December 2020 and December 2021.Once recruited, participants were provided with a FitBit Inspire HR or Inspire 2 and asked to wear it every day for a 16-week monitoring period. Pseudo-anonymous accounts were created to register the FitBits without sharing patient identifiable data and the devices were set up to automatically synchronise data to the cloud-based platform, Fitabase, via their smartphone.Steps were monitored on every workday and the ability to record heart rate was used as a proxy marker for compliance as it confirmed that the device was being worn. A day was considered complaint if the device was worn for >70% of waking hours, assumed for purpose of trial to be 7am to 10pm.The manufacturer or age of the participant’s smartphone was not recorded. Previous discussions with FitBit regarding synchronisation issues had highlighted potential clashes with other Bluetooth devices preventing automatic synchronisation so use of other such devices was documented.ResultsForty-seven patients were recruited and 43 were eligible for ongoing monitoring. Average age was 66 (SD 9) and majority were men (72%). Twenty-nine patients completed the maximum 112 days of monitoring.Patients were eligible for monitoring on 3855 days. Of these, synchronisation errors occurred on 482 days (13%) and all data from the previous 24 hours was missing on 275 days (7%) due to synchronisation not occurring on the day on monitoring. Only 5 (11%) of participants did not have synchronisation errors during their monitoring period. The median number of synchronisation errors per patient was 8 and maximum of 49, which accounted for 64% of that participant’s monitored days. One participant was withdrawn due to 100% synchronisation error over the first seven monitored days.Twenty-two participants (47%) used other Bluetooth devices but there was no correlation between their use and synchronisation errors (r=-0.32), nor significant difference in synchronisation error rate (p=0.08).562 days (15%) were considere","PeriodicalId":116980,"journal":{"name":"Part I: ePapers","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114345988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0