BMJ Health & Care Informatics最新文献

{"title":"From measures to action: can integrating quality measures provide system-wide insights for quality improvement decision making?","authors":"Inas S Khayal,&nbsp;Jordan T Sanz","doi":"10.1136/bmjhci-2023-100792","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100792","url":null,"abstract":"<p><strong>Background: </strong>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 and their limitations noted in the literature, what remains unknown is 'Can integrating multiple quality measures provide a systemic understanding of care quality across a healthcare system?'</p><p><strong>Methods: </strong>We devised a four-part data-driven analytic strategy to determine if consistent insights exist about the differential utilisation of end-of-life care using up to eight publicly available end-of-life cancer care quality measures across National Cancer Institute and National Comprehensive Cancer Network-designated cancer hospitals/centres. We performed 92 experiments that included 28 correlation analyses, 4 principal component analyses, 6 parallel coordinate analyses with agglomerative hierarchical clustering across hospitals and 54 parallel coordinate analyses with agglomerative hierarchical clustering within each hospital.</p><p><strong>Results: </strong>Across 54 centres, integrating quality measures provided no consistent insights across different integration analyses. In other words, we could not integrate quality measures to describe how the underlying quality constructs of interest-intensive care unit (ICU) visits, emergency department (ED) visits, palliative care use, lack of hospice, recent hospice, use of life-sustaining therapy, chemotherapy and advance care planning-are used relative to each other across patients. Quality measure calculations lack interconnection information to construct a story that provides insights about where, when or what care is provided to which patients. And yet, we posit and discuss why administrative claims data-used to calculate quality measures-do contain such interconnection information.</p><p><strong>Conclusion: </strong>While integrating quality measures does not provide systemic information, new systemic mathematical constructs designed to convey interconnection information can be developed from the same administrative claims data to support quality improvement decision making.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9f/8c/bmjhci-2023-100792.PMC10314486.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9753679","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":"Twenty-year follow-up of promising clinical studies reported in highly circulated newspapers: a meta-epidemiological study.","authors":"Aran Tajika,&nbsp;Yasushi Tsujimoto,&nbsp;Akira Onishi,&nbsp;Yusuke Tsutsumi,&nbsp;Satoshi Funada,&nbsp;Yusuke Ogawa,&nbsp;Nozomi Takeshima,&nbsp;Yu Hayasaka,&nbsp;Naotsugu Iwakami,&nbsp;Toshi A Furukawa","doi":"10.1136/bmjhci-2023-100768","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100768","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>We identified newspaper stories from 2000 that mentioned the effectiveness of certain treatments or preventions based on original studies from 40 main medical journals. We searched for subsequent studies until June 2022 with the same topic and stronger research design than each original study. The results of the original studies were verified by comparison with those of subsequent studies.</p><p><strong>Results: </strong>We identified 164 original articles from 1298 newspaper stories and randomly selected 100 of them. Four studies were not found to be effective in terms of the primary outcome, and 18 had no subsequent studies. Of the remaining studies, the proportion of confirmed studies was 68.6% (95% CI 58.1% to 77.5%). Among the 59 confirmed studies, 13 of 16 studies were considered to have been replicated in terms of effect size. However, the results of the remaining 43 studies were not comparable.</p><p><strong>Discussion: </strong>In the dichotomous judgement of effectiveness, about two-thirds of the results were nominally confirmed by subsequent studies. However, for most confirmed results, it was impossible to determine whether the effect sizes were stable.</p><p><strong>Conclusions: </strong>Newspaper readers should be aware that some claims made by high-quality newspapers based on high-profile journal articles may be overturned by subsequent studies within the next 20 years.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/4e/3f/bmjhci-2023-100768.PMC10277065.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9715225","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":"TransFAIR study: a European multicentre experimental comparison of EHR2EDC technology to the usual manual method for eCRF data collection.","authors":"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","doi":"10.1136/bmjhci-2022-100602","DOIUrl":"10.1136/bmjhci-2022-100602","url":null,"abstract":"<p><strong>Purpose: </strong>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 EDC systems patients' data of clinical studies in various therapeutic areas.</p><p><strong>Methods: </strong>A prospective study including six clinical trials from three different sponsors running in three hospitals across Europe has been conducted. The same data from the six studies were collected using both traditional manual data entry and the EHR2EDC module. The outcome variable was the percentage of data accurately transferred using the EHR2EDC technology. This percentage was calculated considering all collected data and the data in four domains: demographics (DM), vital signs (VS), laboratories (LB) and concomitant medications (CM).</p><p><strong>Results: </strong>Overall, 6143 data points (39.6% of the data in the scope of the TransFAIR study and 16.9% when considering all data) were accurately transferred using the platform. LB data represented 65.4% of the data transferred; VS data, 30.8%; DM data, 0.7% and CM data, 3.1%.</p><p><strong>Conclusions: </strong>The objective of accurately transferring at least 15% of the manually entered trial datapoints using the EHR2EDC module was achieved. Collaboration and codesign by hospitals, industry, technology company, supported by the Institute of Innovation through Health Data was a success factor in accomplishing these results. Further work should focus on the harmonisation of data standards and improved interoperability to extend the scope of transferable EHR data.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9661657","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":"Adoption of electronic health record systems to enhance the quality of healthcare in low-income countries: a systematic review.","authors":"Misganaw Tadesse Woldemariam,&nbsp;Worku Jimma","doi":"10.1136/bmjhci-2022-100704","DOIUrl":"https://doi.org/10.1136/bmjhci-2022-100704","url":null,"abstract":"<p><strong>Background: </strong>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 improving healthcare quality in LICs.</p><p><strong>Methods: </strong>We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses in articles selected from PubMed, Science Direct, IEEE Xplore, citations and manual searches. We focused on peer-reviewed articles published from January 2017 to 30 September 2022, and those focusing on the status, challenges or opportunities of EHR adoption in LICs. However, we excluded articles that did not consider EHR in LICs, reviews or secondary representations of existing knowledge. Joanna Briggs Institute checklists were used to appraise the articles to minimise the risk of bias.</p><p><strong>Results: </strong>We identified 12 studies for the review. The finding indicated EHR systems are not well implemented and are at a pilot stage in various LICs. The barriers to EHR adoption were poor infrastructure, lack of management commitment, standards, interoperability, support, experience and poor EHR systems. However, healthcare providers' perception, their goodwill to use EMR and the immaturity of health information exchange infrastructure are key facilitators for EHR adoption in LICs.</p><p><strong>Conclusion: </strong>Most LICs are adopting EHR systems, although it is at an early stage of implementation. EHR systems adoption is facilitated or influenced by people, environment, tools, tasks and the interaction among these factors.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/4c/b3/bmjhci-2022-100704.PMC10277040.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9660362","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":"Digital health in Tasmania - improving patient access and outcomes.","authors":"Usman Iqbal,&nbsp;Warren Prentice,&nbsp;Anthony Lawler","doi":"10.1136/bmjhci-2023-100802","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100802","url":null,"abstract":"","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2a/71/bmjhci-2023-100802.PMC10277071.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9715226","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":"ChatGPT in glioma adjuvant therapy decision making: ready to assume the role of a doctor in the tumour board?","authors":"Julien Haemmerli,&nbsp;Lukas Sveikata,&nbsp;Aria Nouri,&nbsp;Adrien May,&nbsp;Kristof Egervari,&nbsp;Christian Freyschlag,&nbsp;Johannes A Lobrinus,&nbsp;Denis Migliorini,&nbsp;Shahan Momjian,&nbsp;Nicolae Sanda,&nbsp;Karl Schaller,&nbsp;Sebastien Tran,&nbsp;Jacky Yeung,&nbsp;Philippe Bijlenga","doi":"10.1136/bmjhci-2023-100775","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100775","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate ChatGPT's performance in brain glioma adjuvant therapy decision-making.</p><p><strong>Methods: </strong>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 was asked to give the adjuvant treatment choice, and the regimen while considering the patient's functional status. The experts rated the artificial intelligence-based recommendations from 0 (complete disagreement) to 10 (complete agreement). An intraclass correlation coefficient agreement (ICC) was used to measure the inter-rater agreement.</p><p><strong>Results: </strong>Eight patients (80%) met the criteria for glioblastoma and two (20%) were low-grade gliomas. The experts rated the quality of ChatGPT recommendations as poor for diagnosis (median 3, IQR 1-7.8, ICC 0.9, 95% CI 0.7 to 1.0), good for treatment recommendation (7, IQR 6-8, ICC 0.8, 95% CI 0.4 to 0.9), good for therapy regimen (7, IQR 4-8, ICC 0.8, 95% CI 0.5 to 0.9), moderate for functional status consideration (6, IQR 1-7, ICC 0.7, 95% CI 0.3 to 0.9) and moderate for overall agreement with the recommendations (5, IQR 3-7, ICC 0.7, 95% CI 0.3 to 0.9). No differences were observed between the glioblastomas and low-grade glioma ratings.</p><p><strong>Conclusions: </strong>ChatGPT performed poorly in classifying glioma types but was good for adjuvant treatment recommendations as evaluated by CNS TB experts. Even though the ChatGPT lacks the precision to replace expert opinion, it may serve as a promising supplemental tool within a human-in-the-loop approach.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8e/7e/bmjhci-2023-100775.PMC10314415.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9747924","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":"Designing and implementing mHealth technology: the challenge of meeting the needs of diverse communities.","authors":"Vimla L Patel,&nbsp;Edward H Shortliffe","doi":"10.1136/bmjhci-2023-100813","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100813","url":null,"abstract":"","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8d/50/bmjhci-2023-100813.PMC10314586.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9816967","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":"Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only.","authors":"Hope Watson,&nbsp;Jack Gallifant,&nbsp;Yuan Lai,&nbsp;Alexander P Radunsky,&nbsp;Cleva Villanueva,&nbsp;Nicole Martinez,&nbsp;Judy Gichoya,&nbsp;Uyen Kim Huynh,&nbsp;Leo Anthony Celi","doi":"10.1136/bmjhci-2023-100771","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100771","url":null,"abstract":"<p><p><b>Introduction</b> 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 provided an opportunity to curtail critical research issues such as reproducibility and validity through data sharing, this did not materialise in practice and became an example of 'Open Data in Appearance Only' (ODIAO). Here, we define ODIAO as the intent of data sharing without the occurrence of actual data sharing (eg, material or digital data transfers).<b>Objective</b> Propose a framework that states the main risks associated with data sharing, systematically present risk mitigation strategies and provide examples through a healthcare lens.<b>Methods</b> This framework was informed by critical aspects of both the Open Data Institute and the NIH's 2023 Data Management and Sharing Policy plan guidelines.<b>Results</b> Through our examination of legal, technical, reputational and commercial categories, we find barriers to data sharing ranging from misinterpretation of General Data Privacy Rule to lack of technical personnel able to execute large data transfers. From this, we deduce that at numerous touchpoints, data sharing is presently too disincentivised to become the norm.<b>Conclusion</b> In order to move towards Open Data, we propose the creation of mechanisms for incentivisation, beginning with recentring data sharing on patient benefits, additional clauses in grant requirements and committees to encourage adherence to data reporting practices.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7c/4a/bmjhci-2023-100771.PMC10314418.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9740894","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 framework for the use of AI in healthcare: overview of the new British standard BS30440.","authors":"Mark Sujan,&nbsp;Cassius Smith-Frazer,&nbsp;Christina Malamateniou,&nbsp;Joseph Connor,&nbsp;Allison Gardner,&nbsp;Harriet Unsworth,&nbsp;Haider Husain","doi":"10.1136/bmjhci-2023-100749","DOIUrl":"https://doi.org/10.1136/bmjhci-2023-100749","url":null,"abstract":"","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/fc/d2/bmjhci-2023-100749.PMC10410839.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9973511","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":"Prevalence of electronic screening for sepsis in National Health Service acute hospitals in England.","authors":"Kate Honeyford, Amen-Patrick Nwosu, Runa Lazzarino, Anne Kinderlerer, John Welch, Andrew J Brent, Graham Cooke, Peter Ghazal, Shashank Patil, Ceire E Costelloe","doi":"10.1136/bmjhci-2023-100743","DOIUrl":"10.1136/bmjhci-2023-100743","url":null,"abstract":"<p><p>Sepsis is a worldwide public health problem. Rapid identification is associated with improved patient outcomes-if followed by timely appropriate treatment.</p><p><strong>Objectives: </strong>Describe digital sepsis alerts (DSAs) in use in English National Health Service (NHS) acute hospitals.</p><p><strong>Methods: </strong>A Freedom of Information request surveyed acute NHS Trusts on their adoption of electronic patient records (EPRs) and DSAs.</p><p><strong>Results: </strong>Of the 99 Trusts that responded, 84 had an EPR. Over 20 different EPR system providers were identified as operational in England. The most common providers were Cerner (21%). System C, Dedalus and Allscripts Sunrise were also relatively common (13%, 10% and 7%, respectively). 70% of NHS Trusts with an EPR responded that they had a DSA; most of these use the National Early Warning Score (NEWS2). There was evidence that the EPR provider was related to the DSA algorithm. We found no evidence that Trusts were using EPRs to introduce data driven algorithms or DSAs able to include, for example, pre-existing conditions that may be known to increase risk.Not all Trusts were willing or able to provide details of their EPR or the underlying algorithm.</p><p><strong>Discussion: </strong>The majority of NHS Trusts use an EPR of some kind; many use a NEWS2-based DSA in keeping with national guidelines.</p><p><strong>Conclusion: </strong>Many English NHS Trusts use DSAs; even those using similar triggers vary and many recreate paper systems. Despite the proliferation of machine learning algorithms being developed to support early detection of sepsis, there is little evidence that these are being used to improve personalised sepsis detection.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"30 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10186434/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9838939","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}