Pub Date : 2024-10-24eCollection Date: 2024-12-01DOI: 10.1016/j.fhj.2024.100200
Liam Allan, Duncan Scott, Ed Paterson, Tony Golabek
Physician associates (PAs) are a developing profession in the UK. The Faculty of Physician Associates (FPA) recommends the establishment of local preceptorship programmes for PAs in the first year of practice who have successfully passed the PA national exam. Limited evidence currently exists around the evaluation of such local programmes and the experience of the intern PAs involved. Over the course of a 12-month period, four intern PAs completed a structured preceptorship comprising clinical supervision, teaching and mentoring. Upon completion of this programme, qualitative data were recorded and analysed to influence the development of the preceptorship in future. In conclusion, a structured preceptorship has been shown to be a useful method of improving clinician confidence and competence while offering a supported working environment for newly qualified PAs.
{"title":"Physician associate preceptorship: Experience of a novel programme in Inverness.","authors":"Liam Allan, Duncan Scott, Ed Paterson, Tony Golabek","doi":"10.1016/j.fhj.2024.100200","DOIUrl":"10.1016/j.fhj.2024.100200","url":null,"abstract":"<p><p>Physician associates (PAs) are a developing profession in the UK. The Faculty of Physician Associates (FPA) recommends the establishment of local preceptorship programmes for PAs in the first year of practice who have successfully passed the PA national exam. Limited evidence currently exists around the evaluation of such local programmes and the experience of the intern PAs involved. Over the course of a 12-month period, four intern PAs completed a structured preceptorship comprising clinical supervision, teaching and mentoring. Upon completion of this programme, qualitative data were recorded and analysed to influence the development of the preceptorship in future. In conclusion, a structured preceptorship has been shown to be a useful method of improving clinician confidence and competence while offering a supported working environment for newly qualified PAs.</p>","PeriodicalId":73125,"journal":{"name":"Future healthcare journal","volume":"11 4","pages":"100200"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11600754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-21eCollection Date: 2024-12-01DOI: 10.1016/j.fhj.2024.100194
Jonathan Guckian, Éabha Lynn, Sarah Edwards
{"title":"Sharpening the double-edged sword: Revisiting the evolving role of social media within medical education.","authors":"Jonathan Guckian, Éabha Lynn, Sarah Edwards","doi":"10.1016/j.fhj.2024.100194","DOIUrl":"10.1016/j.fhj.2024.100194","url":null,"abstract":"","PeriodicalId":73125,"journal":{"name":"Future healthcare journal","volume":"11 4","pages":"100194"},"PeriodicalIF":0.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19eCollection Date: 2024-09-01DOI: 10.1016/j.fhj.2024.100166
Barclay Burns, Bo Nemelka, Anmol Arora
{"title":"Practical implementation of generative artificial intelligence systems in healthcare: A United States perspective.","authors":"Barclay Burns, Bo Nemelka, Anmol Arora","doi":"10.1016/j.fhj.2024.100166","DOIUrl":"10.1016/j.fhj.2024.100166","url":null,"abstract":"","PeriodicalId":73125,"journal":{"name":"Future healthcare journal","volume":"11 3","pages":"100166"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19eCollection Date: 2024-09-01DOI: 10.1016/j.fhj.2024.100167
Marlene Winfield
{"title":"Artificial intelligence: The good, the bad and the beautifiable. A patient's view.","authors":"Marlene Winfield","doi":"10.1016/j.fhj.2024.100167","DOIUrl":"10.1016/j.fhj.2024.100167","url":null,"abstract":"","PeriodicalId":73125,"journal":{"name":"Future healthcare journal","volume":"11 3","pages":"100167"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19eCollection Date: 2024-09-01DOI: 10.1016/j.fhj.2024.100165
Ceilidh Welsh, Susana Román García, Gillian C Barnett, Raj Jena
The rapid advancement and widespread adoption of artificial intelligence (AI) has ushered in a new era of possibilities in healthcare, ranging from clinical task automation to disease detection. AI algorithms have the potential to analyse medical data, enhance diagnostic accuracy, personalise treatment plans and predict patient outcomes among other possibilities. With a surge in AI's popularity, its developments are outpacing policy and regulatory frameworks, leading to concerns about ethical considerations and collaborative development. Healthcare faces its own ethical challenges, including biased datasets, under-representation and inequitable access to resources, all contributing to mistrust in medical systems. To address these issues in the context of AI healthcare solutions and prevent perpetuating existing inequities, it is crucial to involve communities and stakeholders in the AI lifecycle. This article discusses four community-driven approaches for co-developing ethical AI healthcare solutions, including understanding and prioritising needs, defining a shared language, promoting mutual learning and co-creation, and democratising AI. These approaches emphasise bottom-up decision-making to reflect and centre impacted communities' needs and values. These collaborative approaches provide actionable considerations for creating equitable AI solutions in healthcare, fostering a more just and effective healthcare system that serves patient and community needs.
{"title":"Democratising artificial intelligence in healthcare: community-driven approaches for ethical solutions.","authors":"Ceilidh Welsh, Susana Román García, Gillian C Barnett, Raj Jena","doi":"10.1016/j.fhj.2024.100165","DOIUrl":"10.1016/j.fhj.2024.100165","url":null,"abstract":"<p><p>The rapid advancement and widespread adoption of artificial intelligence (AI) has ushered in a new era of possibilities in healthcare, ranging from clinical task automation to disease detection. AI algorithms have the potential to analyse medical data, enhance diagnostic accuracy, personalise treatment plans and predict patient outcomes among other possibilities. With a surge in AI's popularity, its developments are outpacing policy and regulatory frameworks, leading to concerns about ethical considerations and collaborative development. Healthcare faces its own ethical challenges, including biased datasets, under-representation and inequitable access to resources, all contributing to mistrust in medical systems. To address these issues in the context of AI healthcare solutions and prevent perpetuating existing inequities, it is crucial to involve communities and stakeholders in the AI lifecycle. This article discusses four community-driven approaches for co-developing ethical AI healthcare solutions, including understanding and prioritising needs, defining a shared language, promoting mutual learning and co-creation, and democratising AI. These approaches emphasise bottom-up decision-making to reflect and centre impacted communities' needs and values. These collaborative approaches provide actionable considerations for creating equitable AI solutions in healthcare, fostering a more just and effective healthcare system that serves patient and community needs.</p>","PeriodicalId":73125,"journal":{"name":"Future healthcare journal","volume":"11 3","pages":"100165"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19eCollection Date: 2024-09-01DOI: 10.1016/j.fhj.2024.100183
Anmol Arora, Tom Lawton
{"title":"Artificial intelligence in the NHS: Moving from ideation to implementation.","authors":"Anmol Arora, Tom Lawton","doi":"10.1016/j.fhj.2024.100183","DOIUrl":"10.1016/j.fhj.2024.100183","url":null,"abstract":"","PeriodicalId":73125,"journal":{"name":"Future healthcare journal","volume":"11 3","pages":"100183"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19eCollection Date: 2024-09-01DOI: 10.1016/j.fhj.2024.100177
Siân Carey, Allan Pang, Marc de Kamps
Artificial intelligence (AI) is a technology that enables computers to simulate human intelligence and has the potential to improve healthcare in a multitude of ways. However, there are also possibilities that it may continue, or exacerbate, current disparities. We discuss the problem of bias in healthcare and AI, and go on to highlight some of the ongoing and future solutions that are being researched in the area.
{"title":"Fairness in AI for healthcare.","authors":"Siân Carey, Allan Pang, Marc de Kamps","doi":"10.1016/j.fhj.2024.100177","DOIUrl":"10.1016/j.fhj.2024.100177","url":null,"abstract":"<p><p>Artificial intelligence (AI) is a technology that enables computers to simulate human intelligence and has the potential to improve healthcare in a multitude of ways. However, there are also possibilities that it may continue, or exacerbate, current disparities. We discuss the problem of bias in healthcare and AI, and go on to highlight some of the ongoing and future solutions that are being researched in the area.</p>","PeriodicalId":73125,"journal":{"name":"Future healthcare journal","volume":"11 3","pages":"100177"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19eCollection Date: 2024-09-01DOI: 10.1016/j.fhj.2024.100162
Rohan Misra, Pearse A Keane, Henry David Jeffry Hogg
{"title":"How should we train clinicians for artificial intelligence in healthcare?","authors":"Rohan Misra, Pearse A Keane, Henry David Jeffry Hogg","doi":"10.1016/j.fhj.2024.100162","DOIUrl":"10.1016/j.fhj.2024.100162","url":null,"abstract":"","PeriodicalId":73125,"journal":{"name":"Future healthcare journal","volume":"11 3","pages":"100162"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19eCollection Date: 2024-09-01DOI: 10.1016/j.fhj.2024.100171
Melissa D McCradden, Ian Stedman
Image, graphical abstract.
图像，图形摘要。
{"title":"Explaining decisions without explainability? Artificial intelligence and medicolegal accountability.","authors":"Melissa D McCradden, Ian Stedman","doi":"10.1016/j.fhj.2024.100171","DOIUrl":"10.1016/j.fhj.2024.100171","url":null,"abstract":"<p><p>Image, graphical abstract.</p>","PeriodicalId":73125,"journal":{"name":"Future healthcare journal","volume":"11 3","pages":"100171"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号