BMJ Health & Care Informatics最新文献

{"title":"An online glaucoma educational course for patients to facilitate remote learning and patient empowerment.","authors":"Sana Hamid,&nbsp;Neda Minakaran,&nbsp;Chinedu Igwe,&nbsp;Alex Baneke,&nbsp;Marcus Pedersen,&nbsp;Rashmi G Mathew","doi":"10.1136/bmjhci-2023-100748","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100748","url":null,"abstract":"<p><p>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 tools that facilitate accessible digital remote patient education can be a powerful adjunct to empower patients in becoming healthcare partners.We describe the development of a free, comprehensive, multimodal online glaucoma patient education course for adults with glaucoma, their family and friends and carers, with the aim of providing a readable resource to aid remote learning and understanding of the condition.The working group for the development of the course comprised of consultants, medical practitioners and education specialists and expert patients. Given the specialised nature of ophthalmology and glaucoma, certain aspects can be difficult to conceptualise, and, therefore, clear and adequate explanations of concepts are provided in the course using diagrams, flow charts, medical illustrations, images, videos, written text, analogies and quizzes.The course is available in a short and long version to suit different learning needs which take approximately 2 hours and 10 hours to complete respectively. The contents list allows course takers to find sections relevant to them and it can be taken anywhere, as long as there is Internet access.We invite you to share this resource with your patients and their families, friends and carers.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/35/4e/bmjhci-2023-100748.PMC10450125.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10455120","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}

{"title":"Unleashing the potential of AI: a deeper dive into GPT prompts for medical research.","authors":"Dorian Garin","doi":"10.1136/bmjhci-2023-100857","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100857","url":null,"abstract":"© Author(s) (or their employer(s)) 2023. Reuse permitted under CC BYNC. No commercial reuse. See rights and permissions. Published by BMJ. I read the article by Haemmerli et al on the performance of ChatGPT3.5 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, the design of the prompt used to interact with ChatGPT warrants further consideration. In the study, the prompt employed was a brief patient history, followed by two questions, which appears to have limited the model’s performance. As a sophisticated large language model (LLM), GPT3.5 relies heavily on the context and specificity of the provided prompt. 2 Based on cited literature, an alternative prompt structure could have included context, specific intent, a question and an expected response format. Moreover, pretraining the LLM with examples of the expected answer significantly improves the quality of the answer. 3 Finally, the introduction of GPT4 in early March 2023 has shown considerable improvement in understanding and generating responses when compared with ChatGPT3.5. 5","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/47/ca/bmjhci-2023-100857.PMC10462930.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10124451","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}

{"title":"Experiences in aligning WHO SMART guidelines to classification and terminology standards.","authors":"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","doi":"10.1136/bmjhci-2022-100691","DOIUrl":"10.1136/bmjhci-2022-100691","url":null,"abstract":"<p><strong>Objectives: </strong>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).</p><p><strong>Methods: </strong>We encoded data elements within the ANC and family planning DAKs to standardised CATs from the WHO CATs and other freely available CATs.</p><p><strong>Results: </strong>The coding process demonstrated approaches to refine the data dictionaries and enhance alignment between data elements and CATs.</p><p><strong>Discussion: </strong>Applying CATs to WHO clinical and public health guidelines can ensure that recommendations are operationalised in a digital system with appropriate consistency and clarity. This requires a multidisciplinary team and careful review to achieve conceptual equivalence between data elements and standardised terminologies.</p><p><strong>Conclusion: </strong>The systematic translation of guidelines into digital systems provides an opportunity for leveraging CATs; however, this approach needs further exploration into its implementation in country contexts and transition into machine-readable components.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10381616","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}

{"title":"Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making.","authors":"Nehal Hassan, Robert Slight, Graham Morgan, David W Bates, Suzy Gallier, Elizabeth Sapey, Sarah Slight","doi":"10.1136/bmjhci-2023-100784","DOIUrl":"10.1136/bmjhci-2023-100784","url":null,"abstract":"<p><strong>Background: </strong>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 be used or enhance decision-making. We describe nine stages of developing and evaluating a predictive AI model, recognising the challenges that clinicians might face at each stage and providing practical tips to help manage them.</p><p><strong>Findings: </strong>The nine stages included clarifying the clinical question or outcome(s) of interest (output), identifying appropriate predictors (features selection), choosing relevant datasets, developing the AI predictive model, validating and testing the developed model, presenting and interpreting the model prediction(s), licensing and maintaining the AI predictive model and evaluating the impact of the AI predictive model. The introduction of an AI prediction model into clinical practice usually consists of multiple interacting components, including the accuracy of the model predictions, physician and patient understanding and use of these probabilities, expected effectiveness of subsequent actions or interventions and adherence to these. Much of the difference in whether benefits are realised relates to whether the predictions are given to clinicians in a timely way that enables them to take an appropriate action.</p><p><strong>Conclusion: </strong>The downstream effects on processes and outcomes of AI prediction models vary widely, and it is essential to evaluate the use in clinical practice using an appropriate study design.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/4a/36/bmjhci-2023-100784.PMC10414079.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10349461","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}

{"title":"Telehealth interventions during COVID-19 pandemic: a scoping review of applications, challenges, privacy and security issues.","authors":"Muhammad Tukur, Ghassan Saad, Fahad M AlShagathrh, Mowafa Househ, Marco Agus","doi":"10.1136/bmjhci-2022-100676","DOIUrl":"10.1136/bmjhci-2022-100676","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Motivation: &lt;/strong&gt;Numerous telehealth applications have been proposed and adopted during the pandemic to combat the spread of the disease. To this end, powerful tools such as artificial intelligence (AI)/robotic technologies, tracking, monitoring, consultation apps and other telehealth interventions have been extensively used. However, there are several issues and challenges that are currently facing this technology.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The purpose of this scoping review is to analyse the primary goal of these techniques; document their contribution to tackling COVID-19; identify and categorise their main challenges and future direction in fighting against the COVID-19 or future pandemic outbreaks.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Four digital libraries (ACM, IEEE, Scopus and Google Scholar) were searched to identify relevant sources. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was used as a guideline procedure to develop a comprehensive scoping review. General telehealth features were extracted from the studies reviewed and analysed in the context of the intervention type, technology used, contributions, challenges, issues and limitations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A collection of 27 studies were analysed. The reported telehealth interventions were classified into two main categories: AI-based and non-AI-based interventions; their main contributions to tackling COVID-19 are in the aspects of disease detection and diagnosis, pathogenesis and virology, vaccine and drug development, transmission and epidemic predictions, online patient consultation, tracing, and observation; 28 telehealth intervention challenges/issues have been reported and categorised into technical (14), non-technical (10), and privacy, and policy issues (4). The most critical technical challenges are: network issues, system reliability issues, performance, accuracy and compatibility issues. Moreover, the most critical non-technical issues are: the skills required, hardware/software cost, inability to entirely replace physical treatment and people's uncertainty about using the technology. Stringent laws/regulations, ethical issues are some of the policy and privacy issues affecting the development of the telehealth interventions reported in the literature.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;This study provides medical and scientific scholars with a comprehensive overview of telehealth technologies' current and future applications in the fight against COVID-19 to motivate researchers to continue to maximise the benefits of these techniques in the fight against pandemics. Lastly, we recommend that the identified challen","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/fb/69/bmjhci-2022-100676.PMC10407386.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10025879","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}

{"title":"Adherence of randomised controlled trials using artificial intelligence in ophthalmology to CONSORT-AI guidelines: a systematic review and critical appraisal.","authors":"Niveditha Pattathil,&nbsp;Jonathan Z L Zhao,&nbsp;Olapeju Sam-Oyerinde,&nbsp;Tina Felfeli","doi":"10.1136/bmjhci-2023-100757","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100757","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>A comprehensive search of three relevant databases (EMBASE, Medline, Cochrane) from 1 January 2010 to 5 February 2022 was conducted. The reporting quality of these papers was scored using the Consolidated Standards of Reporting Trials-Artificial Intelligence (CONSORT-AI) checklist and further risk of bias was assessed using the RoB-2 tool.</p><p><strong>Results: </strong>The initial search yielded 2973 citations from which 5 articles satisfied the inclusion/exclusion criteria. These articles featured AI technologies applied to diabetic retinopathy screening, ophthalmologic education, fungal keratitis detection and paediatric cataract diagnosis. None of the articles reported all items in the CONSORT-AI checklist. The overall mean CONSORT-AI score of the included RCTs was 53% (range 37%-78%). The individual scores of the articles were 37% (19/51), 39% (20), 49% (25), 61% (31) and 78% (40). All articles were scored as being moderate risk, or 'some concerns present', regarding potential risk of bias according to the RoB-2 tool.</p><p><strong>Conclusion: </strong>A small number of RCTs have been published to date on the applications of AI in ophthalmology and vision science. Adherence to the 2020 CONSORT-AI reporting guidelines is suboptimal with notable reporting items often missed. Greater adherence will help facilitate reproducibility of AI research which can be a stimulus for more AI-based RCTs and clinical applications in ophthalmology.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1c/4b/bmjhci-2023-100757.PMC10357814.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9850084","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}

{"title":"Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models.","authors":"Tom Strating, Leila Shafiee Hanjani, Ida Tornvall, Ruth Hubbard, Ian A Scott","doi":"10.1136/bmjhci-2023-100767","DOIUrl":"10.1136/bmjhci-2023-100767","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>We searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reporting at least one performance measure (area under receiver-operator curve (AUROC), sensitivity, specificity, positive or negative predictive value). Included models were categorised by their stage of maturation and assessed for performance, utility and user acceptance in clinical practice.</p><p><strong>Results: </strong>Among 921 screened studies, 39 met eligibility criteria. In-silico performance was consistently high (median AUROC: 0.85); however, only six articles (15.4%) reported external validation, revealing degraded performance (median AUROC: 0.75). Three studies (7.7%) of models deployed within clinical workflows reported high accuracy (median AUROC: 0.92) and high user acceptance.</p><p><strong>Discussion: </strong>ML models have potential to identify inpatients at risk of developing delirium before symptom onset. However, few models were externally validated and even fewer underwent prospective evaluation in clinical settings.</p><p><strong>Conclusion: </strong>This review confirms a rapidly growing body of research into using ML for predicting delirium risk in hospital settings. Our findings offer insights for both developers and clinicians into strengths and limitations of current ML delirium prediction applications aiming to support but not usurp clinician decision-making.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ec/23/bmjhci-2023-100767.PMC10335592.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9802826","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}

{"title":"Validation of US CDC National Death Index mortality data, focusing on differences in race and ethnicity.","authors":"Monica Ter-Minassian,&nbsp;Sundeep S Basra,&nbsp;Eric S Watson,&nbsp;Alphonse J Derus,&nbsp;Michael A Horberg","doi":"10.1136/bmjhci-2023-100737","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100737","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>We used a Kaiser Permanente Mid-Atlantic States' Virtual Data Warehouse (KPMAS-VDW) sourced from the Social Security Administration and electronic health records on members enrolled between 1 January 2005 to 31 December 2017. We submitted data to NDI on 1 036 449 members. We compared results from the NDI best match algorithm to the KPMAS-VDW for vital status and death date. We compared probabilistic scores by sex and race and ethnicity.</p><p><strong>Results: </strong>NDI returned 372 865 (36%) unique possible matches, 663 061 (64%) records not matched to the NDI database and 522 (<1%) rejected records. The NDI algorithm resulted in 38 862 records, presumed dead, with a lower percentage of women, and Asian/Pacific Islander and Hispanic people than presumed alive. There were 27 306 presumed dead members whose death dates matched exactly between the NDI results and VDW, but 1539 did not have an exact match. There were 10 017 additional deaths from NDI results that were not present in the VDW death data.</p><p><strong>Conclusions: </strong>NDI data can substantially improve the overall capture of deaths. However, further quality control measures were needed to ensure the accuracy of the NDI best match algorithm.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/41/38/bmjhci-2023-100737.PMC10335466.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9812271","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}

{"title":"Social vulnerability and initial COVID-19 community spread in the US South: a machine learning approach.","authors":"Moosa Tatar,&nbsp;Mohammad Reza Faraji,&nbsp;Fernando A Wilson","doi":"10.1136/bmjhci-2022-100703","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100703","url":null,"abstract":"<p><strong>Background and objectives: </strong>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 the beginning of the pandemic.</p><p><strong>Methods: </strong>We used Extreme Gradient Boosting (XGBoost) machine learning methodology and SVI data, and the number of COVID-19 cases across all counties in the US South to predict the number of positive cases within 30 days of a county's first case.</p><p><strong>Results: </strong>Our results showed that the percentage of mobile homes is the most important feature in predicting the increase in COVID-19. Also, population density per square mile, per capita income, percentage of housing in structures with 10+ units, percentage of people below poverty and percentage of people with no high school diploma are important predictors of COVID-19 community spread, respectively.</p><p><strong>Conclusions: </strong>SVI can help assess the vulnerability or resilience of communities to the spread of COVID-19 and can help identify communities at high risk of COVID-19 spread.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/19/59/bmjhci-2022-100703.PMC10373713.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9940440","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}

{"title":"Surgical pit crew: initiative to optimise measurement and accountability for operating room turnover time.","authors":"Nicole H Goldhaber,&nbsp;Robin L Schaefer,&nbsp;Roman Martinez,&nbsp;Andrew Graham,&nbsp;Elizabeth Malachowski,&nbsp;Lisa P Rhodes,&nbsp;Ruth S Waterman,&nbsp;Kristin L Mekeel,&nbsp;Brian J Clay,&nbsp;Michael McHale","doi":"10.1136/bmjhci-2023-100741","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100741","url":null,"abstract":"<p><strong>Background and objectives: </strong>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.</p><p><strong>Methods: </strong>We divided TOT into task-based segments and created buttons on the electronic health record (EHR) default prelogin screen for appropriate staff workflows to collect more granular data. We created submeasures, including 'clean-up start', 'clean-up complete', 'set-up start' and 'room ready for patient', to calculate environmental services (EVS) response time, EVS cleaning time, room set-up response time, room set-up time and time to room accordingly.</p><p><strong>Results: </strong>Since developing and implementing these workflows, measures have demonstrated excellent staff adoption. Median times of EVS response and cleaning have decreased significantly at our main hospital ORs and ambulatory surgery centre.</p><p><strong>Conclusion: </strong>OR delays are costly to hospital systems. TOT, in particular, has been recognised as a potential dissatisfier and cause of delay in the perioperative environment. Viewing TOT as one finite entity and not a series of necessary tasks by a variety of team members limits the possibility of critical assessment and improvement. By dividing the measurement of TOT into respective segments necessary to transition the room at the completion of one case to the onset of another, valuable insight was gained into the causes associated with turnover delays, which increased awareness and improved accountability of staff members to complete assigned tasks efficiently.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a1/22/bmjhci-2023-100741.PMC10351225.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9831138","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}