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ObjectiveResilience in healthcare has been defined as "the capacity to adapt to challenges and changes at different system levels, to maintain high quality care". This work aimed to investigate how the challenges posed by the presence of comorbidities impacted on the delivery of timely lung cancer/mesothelioma diagnosis in older patients.
MethodsPatients with incident lung cancer/mesothelioma aged at least 65y in 2019 were identified in the Clinical Practice Research Datalink and linked Cancer Registry data. Diagnostic interval (DI) was defined as time from first presentation with a symptom suggestive of lung cancer/mesothelioma to diagnosis date, including symptoms up to 12 months pre-diagnosis. Co-morbidities were grouped as four "alternative explanation" conditions, which might mimic lung cancer symptoms, and ten "competing demand" conditions, which might delay cancer referral by competing for the clinician's time. Other factors considered were usual consultation frequency, smoking and BMI. Associations with DI were investigated using multivariate linear regression.
ResultsData were available for 10424 lung cancer/mesothelioma patients. In adjusted analyses DI was longer in patients with "alternative explanation" conditions, increasing by 27.6 (95%CI 22.9 – 32.4 days) and 72.0 (65.6, 78.4) days in patients with one and two or more conditions respectively. Number of competing demand conditions was not associated with DI in adjusted analyses. However, both usual consultation frequency and increasing consultation frequency in the year before diagnosis were independently positively associated with diagnostic interval, which was 23.0 (17.8, 28.3) days higher in patients with an increased consultation rate. DI was also increased in ever-smokers and in underweight patients compared to those in the normal weight range.
ConclusionThe presence of conditions offering alternative explanations for lung cancer/mesothelioma symptom is associated with delayed diagnosis. Patients with higher consultation frequencies also had longer DIs, implying competing demand is also an issue. Strategies to increase the resilience of healthcare systems to these challenges to timely diagnosis should be considered.

Introduction and Objectives
The accuracy of conclusions based on Electronic Healthcare Record (EHR) research is highly dependent on the correct selection of descriptors (codes) by users. We aimed to evaluate the feasibility and acceptability of filmed vignette monologues as a resource-light method of assessing and comparing how different EHR users record the same clinical scenario.
Methods
Six short monologues of actors portraying patients presenting allergic conditions to their General Practitioners were filmed head-on then electronically distributed for the study; no researcher was present during data collection. The method was assessed by participant uptake, reported ease of completion by participants, compliance with instructions, the receipt of interpretable data by researchers, and participant perceptions of vignette quality, realism and information content.
Results
22 participants completed the study, reporting only minor difficulties. 132 screen prints were returned electronically, enabling analysis of codes, free text and EHR features. Participants assigned a quality rating of 7.7/10 (range 2-10) to the vignettes and rated the extent to which vignettes reflected real-life (86-100%). Between 1 and 2 hours were required to complete the task. Full compliance with instructions varied between participants but was largely successful.
Conclusions
Filmed monologues are a reproducible, standardized method which require few resources, yet allow clear assessment of clinicians' and EHRs systems' impact on documentation. The novel nature of this method necessitates clear instructions so participants can fully complete the study without face to face researcher oversight.

ABSTRACTObjectivesElectronic health records (EHRs) contain rich information for understanding health conditions and their treatment. A large proportion of clinical information in EHRs is stored in narrative free text. This text is currently under-utilised due to privacy concerns, as it is harder to remove patient identifiers from text than from structured data. Automated de-identification of clinical text is now possible using heuristic or machine-learning-based systems. We conducted a review of the literature on patient and public understanding and attitudes towards the use of patients' medical data for research, particularly seeking views on free text. The aim was to inform and develop a governance framework for the de-identification and use of medical free text for research, and to instigate a wider discussion on the topic. ApproachWe undertook a systematic search in Web of Science and ScienceDirect with terms such as "public attitudes" and "electronic health records". 3480 results were sifted by title, abstract and full text. Forty-two articles were retained for review, these reported on studies of patient and public perceptions, understanding and attitudes towards the use of patients' medical data in research. ResultsResearch participants were positively inclined towards information in records being used in research "for the greater good". However, no clear patterns by age, ethnicity, education level or SES emerged as to who was more favourable to data use. Participants generally trusted health care professionals and public sector researchers with de-identified medical data, whereas government health agencies and commercial entities were not trusted. No explicitly feared harms associated with data use were articulated. However the general objections appeared to be a dislike of personal data being exploited for commercial gain, and a dislike of personal data being moved around and used without personal knowledge or consent. Notably the use of EHR medical text for research did not emerge as a specific patient/public concern. De-identification was important to participants but text was not identified as a distinct privacy risk.ConclusionThis review demonstrates that transparency about data usage, and working "for the greater good" rather than financial gain, appear to be the most important public concerns to be addressed when using patients' medical data. Governance frameworks for using EHRs must now be enhanced to provide for the use of medical text. This will involve informing both regulators and the public about the current capabilities of automated de-identification, and developing other assurances to safeguard patients' privacy.

Objectives: Electronic health records (EHRs) contain rich information for understanding health conditions and their treatment. A large proportion of clinical information in EHRs is stored in narrative free text. This text is currently under-utilised due to privacy concerns, as it is harder to remove patient identifiers from text than from structured data. Automated de-identification of clinical text is now possible using heuristic or machine-learning-based systems. We conducted a review of the literature on patient and public understanding and attitudes towards the use of patients' medical data for research, particularly seeking views on free text. The aim was to inform and develop a governance framework for the de-identification and use of medical free text for research, and to instigate a wider discussion on the topic. Approach: We undertook a systematic search in Web of Science and ScienceDirect with terms such as "public attitudes" and "electronic health records". 3480 results were sifted by title, abstract and full text. Forty-two articles were retained for review, these reported on studies of patient and public perceptions, understanding and attitudes towards the use of patients' medical data in research. Results: Research participants were positively inclined towards information in records being used in research "for the greater good". However, no clear patterns by age, ethnicity, education level or SES emerged as to who was more favourable to data use. Participants generally trusted health care professionals and public sector researchers with de-identified medical data, whereas government health agencies and commercial entities were not trusted. No explicitly feared harms associated with data use were articulated. However the general objections appeared to be a dislike of personal data being exploited for commercial gain, and a dislike of personal data being moved around and used without personal knowledge or consent. Notably the use of EHR medical text for research did not emerge as a specific patient/public concern. De-identification was important to participants but text was not identified as a distinct privacy risk. Conclusion: This review demonstrates that transparency about data usage, and working "for the greater good" rather than financial gain, appear to be the most important public concerns to be addressed when using patients' medical data. Governance frameworks for using EHRs must now be enhanced to provide for the use of medical text. This will involve informing both regulators and the public about the current capabilities of automated de-identification, and developing other assurances to safeguard patients' privacy.

Objectives: Care.data was a 2013 UK government initiative to extract patient data from general practices in England to form a centralised whole-population database for service planning and health research. After a public outcry, the scheme was postponed and cancelled. Public views of care.data have previously been analysed; this study aimed to understand contemporary general practitioners' (GPs) views of the scheme, which may have been influential in its downfall. Design: Systematic search of media articles, followed by media content analysis. Setting: UK-based mainstream and GP-facing media in 2013 and 2014. Participants: Articles were eligible if they focused on care.data, and GPs were quoted, authored the article, or if articles were written for a majority GP audience. Interventions: N/A. Primary and secondary outcome measures: Themes which explained GPs' reactions to care.data and which could explain support for or opposition to the scheme. Results: 162 media articles met inclusion criteria and were drawn from newspapers, news websites and GP-facing websites. GPs recognised care.data's potential value for research and improving care, but had grave concerns about the scheme's implementation. These centred the lack of safeguards and purpose around the scheme which meant patients were not able to make informed decisions about opt-out. GPs perceived they were poorly resourced to meet competing demands to both share patients' data and protect confidentiality. They distrusted the government's likely uses of the data and perceived a risk of patient reidentification if the data were sold onto commercial entities. Conclusions: Findings show specific concerns which GPs had about care.data which led to the withdrawal of support. Future NHS patient data-sharing schemes should engage with GPs and other clinicians as key stakeholders from the earliest moments of planning, so that their views and needs are incorporated into the design of such schemes.

IntroductionIn the UK, primary care data are often used for cancer-related research, but the accuracy of cancer information is uncertain.
ObjectiveWe investigated socio-demographic variation based on the recording date of prostate cancer diagnosis between primary care and the National Cancer Registry (CR).
ApproachWe utilised a data extract of 1,600,000 patients over 65 years from Clinical Practice Research Datalink (CPRD). We extracted prostate cancer diagnoses using Read and SNOMED-CT codes from primary care, and ICD-10 from CR. Initial code entry determined diagnosis dates. We categorised recording timing differences as earlier, same-day or later in primary care than CR and used the chi-squared test and logistic regression (adjusted for recording year) to compare these discrepancies across age, deprivation, and ethnicity.
ResultsWe included 26,875 men with prostate cancer diagnoses commonly recorded in both sources during 2000-2016 (1,030 excluded with missing ethnicity). Compared to CR, 1,747 (7%) had diagnoses recorded on the same day in primary care, while 20,615 (77%) had later recordings with a median delay of 21 days (IQR: 13-38). Age at diagnosis was associated with recording discrepancies (p<0.001); older men were more likely to have earlier/same-day recordings. Adjusted ORs for age groups were 1.4 (95%CI: 1.3-1.5) for 60-69, 1.3 (1.2-1.5) for 70-79, and 0.9 (0.8-1.0) for ≥80 compared to <60. No associations were found between deprivation (p=0.096) or ethnicity (p=0.067) and recording differences.
Conclusion/ ImplicationsThe discrepancy in prostate cancer information between primary care and CR underscores potential biases in studies relying solely on one data source.

BACKGROUND: Use of routinely collected patient data for research and service planning is an explicit policy of the UK National Health Service and UK government. Much clinical information is recorded in free-text letters, reports and notes. These text data are generally lost to research, due to the increased privacy risk compared with structured data. We conducted a citizens' jury which asked members of the public whether their medical free-text data should be shared for research for public benefit, to inform an ethical policy. METHODS: Eighteen citizens took part over 3 days. Jurors heard a range of expert presentations as well as arguments for and against sharing free text, and then questioned presenters and deliberated together. They answered a questionnaire on whether and how free text should be shared for research, gave reasons for and against sharing and suggestions for alleviating their concerns. RESULTS: Jurors were in favour of sharing medical data and agreed this would benefit health research, but were more cautious about sharing free-text than structured data. They preferred processing of free text where a computer extracted information at scale. Their concerns were lack of transparency in uses of data, and privacy risks. They suggested keeping patients informed about uses of their data, and giving clear pathways to opt out of data sharing. CONCLUSIONS: Informed citizens suggested a transparent culture of research for the public benefit, and continuous improvement of technology to protect patient privacy, to mitigate their concerns regarding privacy risks of using patient text data.

Background Use of routinely collected patient data for research and service planning is an explicit policy of the UK National Health Service and UK government. Much clinical information is recorded in free-text letters, reports and notes. These text data are generally lost to research, due to the increased privacy risk compared with structured data. We conducted a citizens' jury which asked members of the public whether their medical free-text data should be shared for research for public benefit, to inform an ethical policy. Methods Eighteen citizens took part over 3 days. Jurors heard a range of expert presentations as well as arguments for and against sharing free text, and then questioned presenters and deliberated together. They answered a questionnaire on whether and how free text should be shared for research, gave reasons for and against sharing and suggestions for alleviating their concerns. Results Jurors were in favour of sharing medical data and agreed this would benefit health research, but were more cautious about sharing free-text than structured data. They preferred processing of free text where a computer extracted information at scale. Their concerns were lack of transparency in uses of data, and privacy risks. They suggested keeping patients informed about uses of their data, and giving clear pathways to opt out of data sharing. Conclusions Informed citizens suggested a transparent culture of research for the public benefit, and continuous improvement of technology to protect patient privacy, to mitigate their concerns regarding privacy risks of using patient text data.

BackgroundFree-text data represent a vast, untapped source of rich information to guide research and public service delivery. Free-text data contain a wealth of additional detail that, if more accessible, would clarify and supplement information coded in structured data fields. Personal data usually need to be de-identified or anonymised before they can be used for purposes such as audit and research, but there are major challenges in finding effective methods to de-identify free-text that do not damage data utility as a by-product. The main aim of the TexGov project is to work towards data governance standards to enable free-text data to be used safely for public benefit.
MethodsWe conducted: a rapid literature review to explore the data governance models used in working with free-text data, plus case studies of systems making de-identified free-text data available for research; we engaged with text mining researchers and the general public to explore barriers and solutions in working with free-text; and we outlined (UK) data protection legislation and regulations for context.
ResultsWe reviewed 50 articles and the models of 4 systems providing access to de-identified free-text. The main emerging themes were: i) patient involvement at identifiable and de-identified data stages; ii) questions of consent and notification for the reuse of free-text data; iii) working with identifiable data for Natural Language Processing algorithm development; and iv) de-identification methods and thresholds of reliability.
ConclusionWe have proposed a set of recommendations, including: ensuring public transparency in data flows and uses; adhering to the principles of minimal data extraction; treating de-identified blacklisted free-text as potentially identifiable with use limited to accredited data safe-havens; and, the need to commit to a culture of continuous improvement to understand the relationships between accuracy of de-identification and re-identification risk, so this can be communicated to all stakeholders.

ObjectivesIn Kent, Surrey and Sussex (KSS), linked health and social care datasets are in set-up phase in NHS integrated care systems (ICS), and governance models for using data for planning and research are under development. This represented an exceptional opportunity to consult with KSS citizens and work together to identify how ICSs in KSS can secure a social licence for data-linkage and data uses.
MethodsWe held online deliberative discussion focus groups asking KSS citizens to discuss the perceived benefits and risks of data-linkage for planning and research; to describe safeguards they expected around the data, and to describe how the public should be involved in, and communicated with, regarding governance and uses of datasets. We held one creative workshop in which participants artistically depicted their support or concerns around data.
Results79 KSS citizens took part in 5 focus groups, and 7 participants attended the creative workshop. There was widespread support for data-linkage to improve efficiency of services and information flows, with the expectation that this would improve patient experience. Proposed ICS governance models were acceptable, but participants identified four key values to ensure appropriate use: acknowledging experience of stigma and discrimination; public voices being heard; holding people to account; and keeping data trails and audits. Participants gave a range of suggestions for ensuring public involvement and communication would be accessible and reach a diverse audience, such as using community champions to ensure a range of contributors, using plain language, giving concise information, building trust through mutually respectful relationships, and valuing public contributions through appropriate payment.
ConclusionSocial licence theory describes expectations that organisations go beyond requirements of formal regulation and ensure transparent values of reciprocity, non-exploitation and service to the public good. Following findings from this project, ICSs in KSS are now in a good position to deliver social licence values, together with a strong public voice, to inform and determine governance arrangements for linked datasets in the region.

Objectives Care.data was a 2013 UK government initiative to extract patient data from general practices in England to form a centralised whole-population database for service planning and health research. After a public outcry, the scheme was postponed and cancelled. Public views of care.data have previously been analysed; this study aimed to understand contemporary general practitioners' (GPs) views of the scheme, which may have been influential in its downfall. Design Systematic search of media articles, followed by media content analysis. Setting UK-based mainstream and GP-facing media in 2013 and 2014. Participants Articles were eligible if they focused on care.data, and GPs were quoted, authored the article, or if articles were written for a majority GP audience. Interventions N/A. Primary and secondary outcome measures Themes which explained GPs' reactions to care.data and which could explain support for or opposition to the scheme. Results 162 media articles met inclusion criteria and were drawn from newspapers, news websites and GP-facing websites. GPs recognised care.data's potential value for research and improving care, but had grave concerns about the scheme's implementation. These centred the lack of safeguards and purpose around the scheme which meant patients were not able to make informed decisions about opt-out. GPs perceived they were poorly resourced to meet competing demands to both share patients' data and protect confidentiality. They distrusted the government's likely uses of the data and perceived a risk of patient reidentification if the data were sold onto commercial entities. Conclusions Findings show specific concerns which GPs had about care.data which led to the withdrawal of support. Future NHS patient data-sharing schemes should engage with GPs and other clinicians as key stakeholders from the earliest moments of planning, so that their views and needs are incorporated into the design of such schemes.

ObjectivesPublic health intelligence teams in Sussex wanted to use newly linked health and social care data, to gain insights into local patterns of multi-morbidity, service use, service provision and socio-demographic data. In this study we report initial exploration of this new linked dataset, in a partnership between university and local authority analysts.
ApproachThe Sussex Integrated Dataset (SID) comprises person-level health and social care data on residents and services users across Sussex. During a 6-month secondment, two analysts evaluated the number of data sources available for each individual, evaluated data quality for identifying long-term conditions, developed presentation methods to compare SID outputs on demographics and condition prevalence with open source or expected distributions, and identified the skills-mix and infrastructure required in local authorities for future work. They worked alongside the SID data processing team to inform and improve data quality; and with university data-scientists to learn prediction modelling.
ResultsAnalysts established an efficient querying system to investigate the breadth of data available, more thoroughly focusing on encounters and demographic data in all sources. Long-term conditions were identified through code lists in a range of NHS data sources, to enable consideration of multi-morbidity by demographic. A range of quality issues were identified, such as non-current patients being uploaded into the SID, distorting prevalence estimates, and GP practice populations that did not match expected figures published by NHS digital. Results were represented in multi-morbidity plots, maps, and theographs. Through this data exploration, we have been able to identify the skills-mix needed for local Public Health Intelligence teams to maximise the use of linked data to achieve Public Health objectives.
ConclusionWe have made many conceptual breakthroughs, particularly in understanding data quality, however still need a more complete understanding of quality issues in SID for public health outputs to have meaningful use. Further investigation into the patterns of service use, as well as modelling of multi-morbidity to make predictions and target interventions, will be key next steps.