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Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study BMJ Health Care Inform. Pub Date : 2024-04-01 Chih-Wei Sung, Joshua Ho, Cheng-Yi Fan, Ching-Yu Chen, Chi-Hsin Chen, Shao-Yung Lin, Jia-How Chang, Jiun-Wei Chen, Edward Pei-Chuan Huang
Background High-risk emergency department (ED) revisit is considered an important quality indicator that may reflect an increase in complications and medical burden. However, because of its multidimensional and highly complex nature, this factor has not been comprehensively investigated. This study aimed to predict high-risk ED revisit with a machine-learning (ML) approach. Methods This 3-year retrospective
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Development of a scoring system to quantify errors from semantic characteristics in incident reports BMJ Health Care Inform. Pub Date : 2024-04-01 Haruhiro Uematsu, Masakazu Uemura, Masaru Kurihara, Hiroo Yamamoto, Tomomi Umemura, Fumimasa Kitano, Mariko Hiramatsu, Yoshimasa Nagao
Objectives Incident reporting systems are widely used to identify risks and enable organisational learning. Free-text descriptions contain important information about factors associated with incidents. This study aimed to develop error scores by extracting information about the presence of error factors in incidents using an original decision-making model that partly relies on natural language processing
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Building a house without foundations? A 24-country qualitative interview study on artificial intelligence in intensive care medicine BMJ Health Care Inform. Pub Date : 2024-04-01 Stuart McLennan, Amelia Fiske, Leo Anthony Celi
Objectives To explore the views of intensive care professionals in high-income countries (HICs) and lower-to-middle-income countries (LMICs) regarding the use and implementation of artificial intelligence (AI) technologies in intensive care units (ICUs). Methods Individual semi-structured qualitative interviews were conducted between December 2021 and August 2022 with 59 intensive care professionals
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Impact of a pandemic shock on unmet medical needs of middle-aged and older adults in 10 countries BMJ Health Care Inform. Pub Date : 2024-04-01 Chao Guo, Dianqi Yuan, Huameng Tang, Xiyuan Hu, Yiyang Lei
Objective The objective is to explore the impact of the pandemic shock on the unmet medical needs of middle-aged and older adults worldwide. Methods The COVID-19 pandemic starting in 2020 was used as a quasiexperiment. Exposure to the pandemic was defined based on an individual’s context within the global pandemic. Data were obtained from the Integrated Values Surveys. A total of 11 932 middle-aged
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Definitions of digital biomarkers: a systematic mapping of the biomedical literature BMJ Health Care Inform. Pub Date : 2024-04-01 Ana Karen Macias Alonso, Julian Hirt, Tim Woelfle, Perrine Janiaud, Lars G Hemkens
Background Technological devices such as smartphones, wearables and virtual assistants enable health data collection, serving as digital alternatives to conventional biomarkers. We aimed to provide a systematic overview of emerging literature on ‘digital biomarkers,’ covering definitions, features and citations in biomedical research. Methods We analysed all articles in PubMed that used ‘digital biomarker(s)’
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Generative artificial intelligence and non-pharmacological bias: an experimental study on cancer patient sexual health communications BMJ Health Care Inform. Pub Date : 2024-04-01 Akiko Hanai, Tetsuo Ishikawa, Shoichiro Kawauchi, Yuta Iida, Eiryo Kawakami
Objectives The objective of this study was to explore the feature of generative artificial intelligence (AI) in asking sexual health among cancer survivors, which are often challenging for patients to discuss. Methods We employed the Generative Pre-trained Transformer-3.5 (GPT) as the generative AI platform and used DocsBot for citation retrieval (June 2023). A structured prompt was devised to generate
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Invitation to join the Healthcare AI Language Group: HeALgroup.AI Initiative BMJ Health Care Inform. Pub Date : 2024-03-01 Sebastian Manuel Staubli, Basel Jobeir, Michael Spiro, Dimitri Aristotle Raptis
The recent emergence of large language models (LLMs) has led to a revolution in information technology, with healthcare being at the forefront of this transformation. LLMs simulate and reproduce human language expression and understanding. When trained with appropriate data, they can accurately
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Codesign of health technology interventions to support best-practice perioperative care and surgical waitlist management BMJ Health Care Inform. Pub Date : 2024-03-01 Sarah Joy Aitken, Sophie James, Amy Lawrence, Anthony Glover, Henry Pleass, Janani Thillianadesan, Sue Monaro, Kerry Hitos, Vasi Naganathan
Objectives This project aimed to determine where health technology can support best-practice perioperative care for patients waiting for surgery. Methods An exploratory codesign process used personas and journey mapping in three interprofessional workshops to identify key challenges in perioperative care across four health districts in Sydney, Australia. Through participatory methodology, the research
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Performance of large language models on advocating the management of meningitis: a comparative qualitative stud BMJ Health Care Inform. Pub Date : 2024-02-01 Urs Fisch, Paulina Kliem, Pascale Grzonka, Raoul Sutter
Objectives We aimed to examine the adherence of large language models (LLMs) to bacterial meningitis guidelines using a hypothetical medical case, highlighting their utility and limitations in healthcare. Methods A simulated clinical scenario of a patient with bacterial meningitis secondary to mastoiditis was presented in three independent sessions to seven publicly accessible LLMs (Bard, Bing, Claude-2
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Explainable machine learning for breast cancer diagnosis from mammography and ultrasound images: a systematic review BMJ Health Care Inform. Pub Date : 2024-02-01 Daraje kaba Gurmessa, Worku Jimma
Background Breast cancer is the most common disease in women. Recently, explainable artificial intelligence (XAI) approaches have been dedicated to investigate breast cancer. An overwhelming study has been done on XAI for breast cancer. Therefore, this study aims to review an XAI for breast cancer diagnosis from mammography and ultrasound (US) images. We investigated how XAI methods for breast cancer
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Bibliometric analysis of the 3-year trends (2018–2021) in literature on artificial intelligence in ophthalmology and vision sciences BMJ Health Care Inform. Pub Date : 2024-02-01 Hayley Monson, Jeffrey Demaine, Adrianna Perryman, Tina Felfeli
Objectives The objective of this analysis is to present a current view of the field of ophthalmology and vision research and artificial intelligence (AI) from topical and geographical perspectives. This will clarify the direction of the field in the future and aid clinicians in adapting to new technological developments. Methods A comprehensive search of four different databases was conducted. Statistical
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Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM BMJ Health Care Inform. Pub Date : 2024-02-01 Christine Mary Hallinan, Roger Ward, Graeme K Hart, Clair Sullivan, Nicole Pratt, Ashley P Ng, Daniel Capurro, Anton Van Der Vegt, Siaw-Teng Liaw, Oliver Daly, Blanca Gallego Luxan, David Bunker, Douglas Boyle
Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers. Methods Through pseudonymisation
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Rapidly scalable and low-cost public health surveillance reporting system for COVID-19 BMJ Health Care Inform. Pub Date : 2024-01-01 Vivek Jason Jayaraj, Chiu-Wan Ng, Victor Chee-Wai Hoe, Diane Woei-Quan Chong, Sanjay Rampal
Objective Data-driven innovations are essential in strengthening disease control. We developed a low-cost, open-source system for robust epidemiological intelligence in response to the COVID-19 crisis, prioritising scalability, reproducibility and dynamic reporting. Methods A five-tiered workflow of data acquisition; processing; databasing, sharing, version control; visualisation; and monitoring was
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Call to digital health leaders: test and leverage this guideline to support health information technology implementation in practice BMJ Health Care Inform. Pub Date : 2023-12-01 Samantha Erin Harding, Karen Day, Peter Carswell
Background Health information technology (HIT) is increasingly used to enable health service/system transformation. Most HIT implementations fail to some degree; very few demonstrate sustainable success. No guidelines exist for health service leaders to leverage factors associated with success. The purpose of this paper is to present an evidence-based guideline for leaders to test and leverage in practice
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Electronic health record intervention to increase use of NSAIDs as analgesia for hospitalised patients: a cluster randomised controlled study BMJ Health Care Inform. Pub Date : 2023-12-01 Tasce Bongiovanni, Mark J Pletcher, Andrew Robinson, Elizabeth Lancaster, Li Zhang, Matthias Behrends, Elizabeth Wick, Andrew Auerbach
Background Prescribing non-opioid pain medications, such as non-steroidal anti-inflammatory (NSAIDs) medications, has been shown to reduce pain and decrease opioid use, but it is unclear how to effectively encourage multimodal pain medication prescribing for hospitalised patients. Therefore, the aim of this study is to evaluate the effect of prechecking non-opioid pain medication orders on clinician
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Regulating AI for health BMJ Health Care Inform. Pub Date : 2023-12-01 Ian Oppermann
Healthcare is a unique and complex mix of expert practitioners, small businesses, major providers, professional and semiprofessional contributors. It is highly regulated and procedural, but also an area where ethical issues are regularly tested. It encompasses cutting-edge research and pioneering
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Call for the responsible artificial intelligence in the healthcare BMJ Health Care Inform. Pub Date : 2023-12-01 Umashankar Upadhyay, Anton Gradisek, Usman Iqbal, Eshita Dhar, Yu-Chuan Li, Shabbir Syed-Abdul
The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models
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Exploring the reliability of inpatient EMR algorithms for diabetes identification BMJ Health Care Inform. Pub Date : 2023-12-01 Seungwon Lee, Elliot A Martin, Jie Pan, Cathy A Eastwood, Danielle A Southern, David J T Campbell, Abdel Aziz Shaheen, Hude Quan, Sonia Butalia
Introduction Accurate identification of medical conditions within a real-time inpatient setting is crucial for health systems. Current inpatient comorbidity algorithms rely on integrating various sources of administrative data, but at times, there is a considerable lag in obtaining and linking these data. Our study objective was to develop electronic medical records (EMR) data-based inpatient diabetes
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Electronic health card: a technological solution to promote the Chinese integrated healthcare system in the digital age BMJ Health Care Inform. Pub Date : 2023-12-01 Wenjuan Tao, Tao Gu, Yujue Li, Weimin Li
People-centred integrated care, with an emphasis on ensuring healthcare services are well coordinated around people’s needs,[1][1] is regarded as a global strategy towards universal health coverage.[2][2] Underutilisation of information technology and lack of interoperability are identified as the
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Cognitive science in the evaluation of medical AI systems BMJ Health Care Inform. Pub Date : 2023-12-01 Vimla Lodhia Patel
Clinical cognition is central to a clinician’s daily tasks, such as making diagnostic and therapeutic decisions. For example, doctors rely on their memory to recall relevant facts, concepts and experiences that can help them diagnose and treat their patients. Memory is needed for clinicians to
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Role of evaluation throughout the life cycle of biomedical and health AI applications BMJ Health Care Inform. Pub Date : 2023-12-01 Edward H Shortliffe
In the development and evaluation of medical artificial intelligence (AI) programmes, there is a tendency to focus the work on the system’s decision-making performance. This is natural, since the typical goal is to develop software that can assist physicians or other clinicians with decision tasks
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ChatGPT in Iranian medical licensing examination: evaluating the diagnostic accuracy and decision-making capabilities of an AI-based model BMJ Health Care Inform. Pub Date : 2023-12-01 Manoochehr Ebrahimian, Behdad Behnam, Negin Ghayebi, Elham Sobhrakhshankhah
Introduction Large language models such as ChatGPT have gained popularity for their ability to generate comprehensive responses to human queries. In the field of medicine, ChatGPT has shown promise in applications ranging from diagnostics to decision-making. However, its performance in medical examinations and its comparison to random guessing have not been extensively studied. Methods This study aimed
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Quantifying digital health inequality across a national healthcare system BMJ Health Care Inform. Pub Date : 2023-11-01 Joe Zhang, Jack Gallifant, Robin L Pierce, Aoife Fordham, James Teo, Leo Celi, Hutan Ashrafian
Objectives Digital health inequality, observed as differential utilisation of digital tools between population groups, has not previously been quantified in the National Health Service (NHS). Deployment of universal digital health interventions, including a national smartphone app and online primary care services, allows measurement of digital inequality across a nation. We aimed to measure population
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Proof-of-concept solution to create an interoperable timeline of healthcare data BMJ Health Care Inform. Pub Date : 2023-11-01 Sapna Trivedi, Stephen Hall, Fiona Inglis, Afzal Chaudhry
Objectives To overcome the barriers of interoperability by sharing simulated patient data from different electronic health records systems and presenting them in an intuitive timeline of events. Methods The ‘Patient Story’ software comprising database and blockchain, PS Timeline Windows interface, PS Timeline Web interface and network relays on Azure cloud was customised for Epic and Lorenzo electonic
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Signal processing and machine learning algorithm to classify anaesthesia depth BMJ Health Care Inform. Pub Date : 2023-10-01 Oscar Mosquera Dussan, Eduardo Tuta-Quintero, Daniel A. Botero-Rosas
Background Poor assessment of anaesthetic depth (AD) has led to overdosing or underdosing of the anaesthetic agent, which requires continuous monitoring to avoid complications. The evaluation of the central nervous system activity and autonomic nervous system could provide additional information on the monitoring of AD during surgical procedures. Methods Observational analytical single-centre study
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Time to treat the climate and nature crisis as one indivisible global health emergency BMJ Health Care Inform. Pub Date : 2023-10-01 Chris Zielinski
Over 200 health journals call on the United Nations (UN), political leaders and health professionals to recognise that climate change and biodiversity loss are one indivisible crisis and must be tackled together to preserve health and avoid catastrophe. This overall environmental crisis is now so
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Integrating digital health technologies into complex clinical systems BMJ Health Care Inform. Pub Date : 2023-10-01 Mark Sujan
Modern health systems must embrace digital technologies to address challenges like ongoing shortages in the global health and care workforce, significant diagnostic backlogs and the requirements of diverse and ageing populations. The COVID-19 pandemic and the exceptional advances in artificial
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Mapping loneliness through social intelligence analysis: a step towards creating global loneliness map BMJ Health Care Inform. Pub Date : 2023-10-01 Hurmat Ali Shah, Mowafa Househ
Objectives Loneliness is a prevalent global public health concern with complex dynamics requiring further exploration. This study aims to enhance understanding of loneliness dynamics through building towards a global loneliness map using social intelligence analysis. Settings and design This paper presents a proof of concept for the global loneliness map, using data collected in October 2022. Twitter
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Digital health and care: emerging from pandemic times BMJ Health Care Inform. Pub Date : 2023-10-01 Niels Peek, Mark Sujan, Philip Scott
In 2020, we published an editorial about the massive disruption of health and care services caused by the COVID-19 pandemic and the rapid changes in digital service delivery, artificial intelligence and data sharing that were taking place at the time. Now, 3 years later, we describe how these developments have progressed since, reflect on lessons learnt and consider key challenges and opportunities
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Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognised reporting standards BMJ Health Care Inform. Pub Date : 2023-10-01 Richard HR Roberts, Stephen R Ali, Hayley A Hutchings, Thomas D Dobbs, Iain S Whitaker
Introduction Amid clinicians’ challenges in staying updated with medical research, artificial intelligence (AI) tools like the large language model (LLM) ChatGPT could automate appraisal of research quality, saving time and reducing bias. This study compares the proficiency of ChatGPT3 against human evaluation in scoring abstracts to determine its potential as a tool for evidence synthesis. Methods
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Web-based eHealth Clinical Decision Support System as a tool for the treat-to-target management of patients with systemic lupus erythematosus: development and initial usability evaluation BMJ Health Care Inform. Pub Date : 2023-09-01 Agner Russo Parra Sanchez, Max G Grimberg, Myrthe Hanssen, Moon Aben, Elianne Jairth, Prishent Dhoeme, Michel W P Tsang-A-Sjoe, Alexandre Voskuyl, Hendrik Jan Jansen, Ronald van Vollenhoven
Background Treat-to-target (T2T) is a therapeutic strategy currently being studied for its application in systemic lupus erythematosus (SLE). Patients and rheumatologists have little support in making the best treatment decision in the context of a T2T strategy, thus, the use of information technology for systematically processing data and supporting information and knowledge may improve routine decision-making
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Implementer report: ICD-10 code F44.5 review for functional seizure disorder BMJ Health Care Inform. Pub Date : 2023-09-01 Sana F Ali, Yarden Bornovski, Margaret Gopaul, Daniela Galluzzo, Joseph Goulet, Stephanie Argraves, Ebony Jackson-Shaheed, Kei-Hoi Cheung, Cynthia A. Brandt, Hamada Hamid Altalib
Objective The study aimed to measure the validity of International Classification of Diseases, 10th Edition (ICD-10) code F44.5 for functional seizure disorder (FSD) in the Veterans Affairs Connecticut Healthcare System electronic health record (VA EHR). Methods The study used an informatics search tool, a natural language processing algorithm and a chart review to validate FSD coding. Results The
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How to organise a datathon for bridging between data science and healthcare? Insights from the Technion-Rambam machine learning in healthcare datathon event BMJ Health Care Inform. Pub Date : 2023-09-01 Jonathan Sobel, Ronit Almog, Leo Celi, Michal Yablowitz, Danny Eytan, Joachim Behar
A datathon is a time-constrained information-based competition involving data science applied to one or more challenges.[1–7][1] Datathons and hackathons differ in their focus, with datathons prioritising data analysis and modelling, while hackathons concentrate on building prototypes. Furthermore
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Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort BMJ Health Care Inform. Pub Date : 2023-09-01 Jen-Ting Chen, Rahil Mehrizi, Boudewijn Aasman, Michelle Ng Gong, Parsa Mirhaji
Objective To identify the risk of acute respiratory distress syndrome (ARDS) and in-hospital mortality using long short-term memory (LSTM) framework in a mechanically ventilated (MV) non-COVID-19 cohort and a COVID-19 cohort. Methods We included MV ICU patients between 2017 and 2018 and reviewed patient records for ARDS and death. Using active learning, we enriched this cohort with MV patients from
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Telehealth interventions during COVID-19 pandemic: a scoping review of applications, challenges, privacy and security issues BMJ Health Care Inform. Pub Date : 2023-08-01 Muhammad Tukur, Ghassan Saad, Fahad M AlShagathrh, Mowafa Househ, Marco Agus
Background The COVID-19, caused by the SARS-CoV-2 virus, proliferated worldwide, leading to a pandemic. Many governmental and non-governmental organisations and research institutes are contributing to the COVID-19 fight to control the pandemic. Motivation Numerous telehealth applications have been proposed and adopted during the pandemic to combat the spread of the disease. To this end, powerful tools
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Unleashing the potential of AI: a deeper dive into GPT prompts for medical research BMJ Health Care Inform. Pub Date : 2023-08-01 Dorian Garin
I read the article by Haemmerli et al on the performance of ChatGPT-3.5 in generating treatment recommendations for central nervous system (CNS) tumours, which were then evaluated by tumour board (TB) experts. While the study did illuminate promising aspects of the Artificial Intelligence (AI) model
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An online glaucoma educational course for patients to facilitate remote learning and patient empowerment BMJ Health Care Inform. Pub Date : 2023-08-01 Sana Hamid, Neda Minakaran, Chinedu Igwe, Alex Baneke, Marcus Pedersen, Rashmi G Mathew
In both face-to-face and teleophthalmology glaucoma clinics, there are significant time constraints and limited resources available to educate the patient and their carers regarding the glaucoma condition. Glaucoma patients are often not satisfied with the content and amount of information they receive and have demonstrated a substantial lack of knowledge regarding their condition. Innovative educational
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Assessing the efficient use of the lightwave health information management system for health service delivery in Ghana BMJ Health Care Inform. Pub Date : 2023-08-01 Edward Agyemang, Kobina Esia-Donkoh, Addae Boateng Adu-Gyamfi, Juabie Bennin Douri, Prince Owusu Adoma, Emmanuel Kusi Achampong
Background In achieving the WHO’s Universal Health Coverage and the Global Developmental Agenda: Sustainable Development Goal 3 and 9, the Ministry of Health launched a nationwide deployment of the lightwave health information management system (LHIMS) in the Central Region to facilitate health service delivery. This paper assessed the efficient use of the LHIMS among health professionals in the Central
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Experiences in aligning WHO SMART guidelines to classification and terminology standards BMJ Health Care Inform. Pub Date : 2023-08-01 Filippa Pretty, Tigest Tamrat, Natschja Ratanaprayul, Maria Barreix, Nenad Friedrich Ivan Kostanjsek, Mary-Lyn Gaffield, Jenny Thompson, Bryn Rhodes, Robert Jakob, Garrett Livingston Mehl, Özge Tunçalp
Objectives Digital adaptation kits (DAKs) distill WHO guidelines for digital use by representing them as workflows, data dictionaries and decision support tables. This paper aims to highlight key lessons learnt in coding data elements of the antenatal care (ANC) and family planning DAKs to standardised classifications and terminologies (CATs). Methods We encoded data elements within the ANC and family
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Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making BMJ Health Care Inform. Pub Date : 2023-08-01 Nehal Hassan, Robert Slight, Graham Morgan, David W Bates, Suzy Gallier, Elizabeth Sapey, Sarah Slight
Background Predictive models have been used in clinical care for decades. They can determine the risk of a patient developing a particular condition or complication and inform the shared decision-making process. Developing artificial intelligence (AI) predictive models for use in clinical practice is challenging; even if they have good predictive performance, this does not guarantee that they will
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Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models BMJ Health Care Inform. Pub Date : 2023-07-01 Tom Strating, Leila Shafiee Hanjani, Ida Tornvall, Ruth Hubbard, Ian A. Scott
Objectives Early identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature. Methods We searched ten databases up to June
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Social vulnerability and initial COVID-19 community spread in the US South: a machine learning approach BMJ Health Care Inform. Pub Date : 2023-07-01 Moosa Tatar, Mohammad Reza Faraji, Fernando A Wilson
Background and objectives More than 93 million COVID-19 cases and more than 1 million COVID-19 deaths have been reported in the USA by August 2022. The disproportionate effect of the pandemic and its severe impact on vulnerable communities raised concerns. This research aimed to identify and rank Social Vulnerability Index (SVI) factors highly predictive of the spread of COVID-19 in the US South at
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Adherence of randomised controlled trials using artificial intelligence in ophthalmology to CONSORT-AI guidelines: a systematic review and critical appraisal BMJ Health Care Inform. Pub Date : 2023-07-01 Niveditha Pattathil, Jonathan Z L Zhao, Olapeju Sam-Oyerinde, Tina Felfeli
Purpose Many efforts have been made to explore the potential of deep learning and artificial intelligence (AI) in disciplines such as medicine, including ophthalmology. This systematic review aims to evaluate the reporting quality of randomised controlled trials (RCTs) that evaluate AI technologies applied to ophthalmology. Methods A comprehensive search of three relevant databases (EMBASE, Medline
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Surgical pit crew: initiative to optimise measurement and accountability for operating room turnover time BMJ Health Care Inform. Pub Date : 2023-07-01 Nicole H Goldhaber, Robin L Schaefer, Roman Martinez, Andrew Graham, Elizabeth Malachowski, Lisa P Rhodes, Ruth S Waterman, Kristin L Mekeel, Brian J Clay, Michael McHale
Background and objectives Turnover time (TOT), defined as the time between surgical cases in the same operating room (OR), is often perceived to be lengthy without clear cause. With the aim of optimising and standardising OR turnover processes and decreasing TOT, we developed an innovative and staff-interactive TOT measurement method. Methods We divided TOT into task-based segments and created buttons
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Validation of US CDC National Death Index mortality data, focusing on differences in race and ethnicity BMJ Health Care Inform. Pub Date : 2023-07-01 Monica Ter-Minassian, Sundeep S Basra, Eric S Watson, Alphonse J Derus, Michael A Horberg
Objectives The US Center for Disease Control and Prevention’s National Death Index (NDI) is a gold standard for mortality data, yet matching patients to the database depends on accurate and available key identifiers. Our objective was to evaluate NDI data for future healthcare research studies with mortality outcomes. Methods We used a Kaiser Permanente Mid-Atlantic States’ Virtual Data Warehouse (KPMAS-VDW)
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Electronic consent in clinical care: an international scoping review BMJ Health Care Inform. Pub Date : 2023-07-01 Susan Chimonas, Allison Lipitz-Snyderman, Konstantina Matsoukas, Gilad Kuperman
Objective Digital technologies create opportunities for improvement of consenting processes in clinical care. Yet little is known about the prevalence, characteristics or outcomes of shifting from paper to electronic consenting, or e-consent, in clinical settings. Thus questions remain around e-consent’s impact on efficiency, data integrity, user experience, care access, equity and quality. Our objective
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Designing and implementing mHealth technology: the challenge of meeting the needs of diverse communities BMJ Health Care Inform. Pub Date : 2023-06-01 Vimla L. Patel, Edward H. Shortliffe
With the widespread adoption of mobile technologies, including in the developing world, there has been enthusiastic exploration of ways that such devices can support care delivery and management in a wide variety of settings. mHealth was accordingly introduced as a general term for the use of such
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ChatGPT in glioma adjuvant therapy decision making: ready to assume the role of a doctor in the tumour board? BMJ Health Care Inform. Pub Date : 2023-06-01 Julien Haemmerli, Lukas Sveikata, Aria Nouri, Adrien May, Kristof Egervari, Christian Freyschlag, Johannes A Lobrinus, Denis Migliorini, Shahan Momjian, Nicolae Sanda, Karl Schaller, Sebastien Tran, Jacky Yeung, Philippe Bijlenga
Objective To evaluate ChatGPT‘s performance in brain glioma adjuvant therapy decision-making. Methods We randomly selected 10 patients with brain gliomas discussed at our institution’s central nervous system tumour board (CNS TB). Patients’ clinical status, surgical outcome, textual imaging information and immuno-pathology results were provided to ChatGPT V.3.5 and seven CNS tumour experts. The chatbot
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From measures to action: can integrating quality measures provide system-wide insights for quality improvement decision making? BMJ Health Care Inform. Pub Date : 2023-06-01 Inas S Khayal, Jordan T. Sanz
Background Quality improvement decision makers are left to develop an understanding of quality within their healthcare system from a deluge of narrowly focused measures that reflect existing fragmentation in care and lack a clear method for triggering improvement. A one-to-one metric-to-improvement strategy is intractable and leads to unintended consequences. Although composite measures have been used
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Validation framework for the use of AI in healthcare: overview of the new British standard BS30440 BMJ Health Care Inform. Pub Date : 2023-06-01 Mark Sujan, Cassius Smith-Frazer, Christina Malamateniou, Joseph Connor, Allison Gardner, Harriet Unsworth, Haider Husain
The British standard ‘BS30440: Validation Framework for the Use of AI in Healthcare’ will be published in the second quarter of 2023.[1][1] It details the evidence required by technology developers to assess and validate products using artificial intelligence (AI) in healthcare settings.
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Pilot feasibility study of a digital technology approach to the systematic electronic capture of parent-reported data on cognitive and language development in children aged 2 years BMJ Health Care Inform. Pub Date : 2023-06-01 Neena Modi, Ricardo Ribas, Samantha Johnson, Elizabeth Lek, Sunit Godambe, Edit Fukari-Irvine, Enitan Ogundipe, Nora Tusor, Nayan Das, Abinithya Udayakumaran, Becky Moss, Victor Banda, Kayleigh Ougham, Victoria Cornelius, Anusha Arasu, Steve Wardle, Cheryl Battersby, Amanda Bravery
Background The assessment of language and cognition in children at risk of impaired neurodevelopment following neonatal care is a UK standard of care but there is no national, systematic approach for obtaining these data. To overcome these challenges, we developed and evaluated a digital version of a validated parent questionnaire to assess cognitive and language development at age 2 years, the Parent
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Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only BMJ Health Care Inform. Pub Date : 2023-06-01 Hope Watson, Jack Gallifant, Yuan Lai, Alexander P Radunsky, Cleva Villanueva, Nicole Martinez, Judy Gichoya, Uyen Kim Huynh, Leo Anthony Celi
Introduction In January, the National Institutes of Health (NIH) implemented a Data Management and Sharing Policy aiming to leverage data collected during NIH-funded research. The COVID-19 pandemic illustrated that this practice is equally vital for augmenting patient research. In addition, data sharing acts as a necessary safeguard against the introduction of analytical biases. While the pandemic
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Digital health in Tasmania – improving patient access and outcomes BMJ Health Care Inform. Pub Date : 2023-06-01 Usman Iqbal, Warren Prentice, Anthony Lawler
With digital health’s potential to transform healthcare delivery, Australia is investing significantly to improve healthcare quality and efficiency.[1][1] Investment is guided by national and global strategies, developed by the Australian Digital Health Agency[1][1] and the WHO,[2][2] respectively
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Twenty-year follow-up of promising clinical studies reported in highly circulated newspapers: a meta-epidemiological study BMJ Health Care Inform. Pub Date : 2023-06-01 Aran Tajika, Yasushi Tsujimoto, Akira Onishi, Yusuke Tsutsumi, Satoshi Funada, Yusuke Ogawa, Nozomi Takeshima, Yu Hayasaka, Naotsugu Iwakami, Toshi A Furukawa
Objectives Researchers have identified cases in which newspaper stories have exaggerated the results of medical studies reported in original articles. Moreover, the exaggeration sometimes begins with journal articles. We examined what proportion of the studies quoted in newspaper stories were confirmed. Methods We identified newspaper stories from 2000 that mentioned the effectiveness of certain treatments
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TransFAIR study: a European multicentre experimental comparison of EHR2EDC technology to the usual manual method for eCRF data collection BMJ Health Care Inform. Pub Date : 2023-06-01 Nadir Ammour, Nicolas Griffon, Juliette Djadi-Prat, Gilles Chatellier, Martine Lewi, Marija Todorovic, Augustín Gómez de la Cámara, Maria Teresa García Morales, Sara Testoni, Oriana Nanni, Christoph Schindler, Mats Sundgren, Almenia Garvey, Tomothy Victor, Manon Cariou, Christel Daniel
Purpose Regulatory authorities including the Food and Drug Administration and the European Medicines Agency are encouraging to conduct clinical trials using routinely collected data. The aim of the TransFAIR experimental comparison was to evaluate, within real-life conditions, the ability of the Electronic Health Records to Electronic Data Capture (EHR2EDC) module to accurately transfer from EHRs to
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Adoption of electronic health record systems to enhance the quality of healthcare in low-income countries: a systematic review BMJ Health Care Inform. Pub Date : 2023-06-01 Misganaw Tadesse Woldemariam, Worku Jimma
Background Electronic health record (EHR) systems are mentioned in several studies as tools for improving healthcare quality in developed and developing nations. However, there is a research gap in presenting the status of EHR adoption in low-income countries (LICs). Therefore, this study systematically reviews articles that discuss the adoption of EHR systems status, opportunities and challenges for
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Clinical decision support systems to improve drug prescription and therapy optimisation in clinical practice: a scoping review BMJ Health Care Inform. Pub Date : 2023-05-01 Lucrezia Greta Armando, Gianluca Miglio, Pierluigi de Cosmo, Clara Cena
Objective Clinical decision support systems (CDSSs) can reduce medical errors increasing drug prescription appropriateness. Deepening knowledge of existing CDSSs could increase their use by healthcare professionals in different settings (ie, hospitals, pharmacies, health research centres) of clinical practice. This review aims to identify the characteristics common to effective studies conducted with
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A natural language processing approach to categorise contributing factors from patient safety event reports BMJ Health Care Inform. Pub Date : 2023-05-01 Azade Tabaie, Srijan Sengupta, Zoe M Pruitt, Allan Fong
Objectives The objective of this study was to explore the use of natural language processing (NLP) algorithm to categorise contributing factors from patient safety event (PSE). Contributing factors are elements in the healthcare process (eg, communication failures) that instigate an event or allow an event to occur. Contributing factors can be used to further investigate why safety events occurred
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Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey BMJ Health Care Inform. Pub Date : 2023-05-01 Emma Kellie Frost, Pauline O’Shaughnessy, David Steel, Annette Braunack-Mayer, Yves Saint James Aquino, Stacy M Carter
Objectives: Applications of artificial intelligence (AI) have the potential to improve aspects of healthcare. However, studies have shown that healthcare AI algorithms also have the potential to perpetuate existing inequities in healthcare, performing less effectively for marginalised populations. Studies on public attitudes towards AI outside of the healthcare field have tended to show higher levels
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Willingness of diabetes mellitus patients to use mHealth applications and its associated factors for self-care management in a low-income country: an input for digital health implementation BMJ Health Care Inform. Pub Date : 2023-05-01 Agmasie Damtew Walle, Tigist Andargie Ferede, Adamu Ambachew Shibabaw, Sisay Maru Wubante, Habtamu Alganeh Guadie, Chalachew Msganaw Yehula, Addisalem Workie Demsash
Background Although mHealth applications are becoming more widely available and used, there is no evidence about why people are willing to use them. Therefore, this study aimed to assess the willingness of patients with diabetes to use mHealth applications and associated factors for self-care management in Ethiopia. Methods An institutional cross-sectional study was conducted among 422 patients with