Background Identifying clinical deterioration is a global health priority. Sepsis is a leading cause of deterioration, responsible for around 46,000 deaths annually in the United Kingdom. Early warning scores based on patients’ vital signs can be embedded into electronic patient records to digitally alert clinicians to those at risk. Rapid identification and treatment – particularly with targeted intravenous antibiotics – are critical to improving outcomes in sepsis patients. Research question This study aimed to evaluate the effectiveness of digital alerts in improving outcomes for patients with sepsis. Using routine electronic patient record data from four United Kingdom National Health Service acute trusts, we investigated how digital alert systems influence patient outcomes and explored mechanisms and mediators of their effectiveness. Objectives Map the types of digital alerts currently in use across United Kingdom hospitals for identifying patients at risk of sepsis (Workstream 1). Evaluate the impact of digital alerts on patient outcomes (Workstream 2). Examine how the implementation process affects alert performance, guided by the consolidated framework for implementation research (Workstream 3). Provide recommendations on alert effectiveness and implementation strategies using systems modelling and mediation analysis (Workstream 4). Methods A mixed-methods approach was employed. A national survey assessed the use of digital sepsis alerts in English National Health Survey hospitals (Workstream 1). Qualitative interviews and focus groups explored the implementation process and its influence on alert performance (Workstream 3). A natural experiment with multilevel interrupted time series analysis examined the impact of sepsis screening tools and digital alerts on outcomes, primarily in-hospital mortality (Workstream 2). Routinely collected clinical data were processed following National Institute for Health Research-Health Information Collaborative standards. Combining quantitative and qualitative data enabled us to link implementation processes with patient outcomes. Results All four trusts experienced reduced mortality rates among patients with serious infections following the introduction of digital sepsis screening tools. After adjustment for patient case-mix, admission patterns and pre-existing trends, one trust showed a statistically significant decrease in mortality linked to digital alert implementation. In two trusts, older patients experienced greater mortality reduction than younger ones following alert introduction. Qualitative findings highlighted factors contributing to more effective use of digital alerts: deployment in general wards rather than intensive care units; use by clinicians familiar with similar technologies; availability of 24/7 emergency outreach teams; robust technological infrastructure and alerts that were user-friendly, non-intrusive and not part of multiple competing alert systems. Conclusions The effectiveness of digital sepsis screening tools varies and may depend on patient’s age and care setting. Our findings suggest that digital alerts should leverage a wider range of electronic patient record data and be tailored to specific patient groups. Different trusts and patient populations may require distinct indicators, thresholds and treatment protocols. These findings align with healthcare practitioners’ calls for more sophisticated, patient-centred sepsis screening tools targeted at relevant clinical teams. Future work and limitations The study involved four National Health Service Trusts with strong data collaboration, but noted limitations include reliance on simple algorithms and varied case-mix and implementation processes. Future research should focus on robust evaluation methods, leveraging granular electronic patient record data and establishing a public registry of digital alert tools. Funding This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR129082. Plain language summary Identifying clinical deterioration is a major focus for health systems across the world. Sepsis is a specific cause of clinical deterioration and death, with an estimated 123,000 cases and 46,000 deaths in the United Kingdom annually. Early warning screening systems are used to identify clinical deterioration and prevent avoidable mortality. Many of these systems use early warning scores based on patients’ vital signs: heart rate, blood pressure, temperature and oxygen saturation. Predetermined levels in each vital sign are associated with scores; these are then added up. The Digital Alerts for Sepsis study aimed to investigate the impact of digital sepsis alerts on patient outcomes and staff activity in National Health Service hospital trusts across England and Wales. As United Kingdom hospitals move from paper-based to electronic health records, the integration of digital alerts to identify patients at risk of deterioration has also become common. The implementation of digital alerts in hospitals is a complex health intervention. Therefore, we used a mixed-methods approach to ensure understanding of the relationship between inherent aspects of the alerts, such as the underlying algorithm and the method of clinician notification. Using appropriate qualitative and quantitative methods, we evaluated the implementation of digital screening tools across four National Health Service Trusts. We examined the impact of these digital tools on mortality due to sepsis. We found that, in some hospitals, there was an important reduction in mortality following introduction of sepsis screening tools. We also showed that these tools may have a bigger impact on older patient groups. Overall, we found that none of the tools made the best use of the rich information contained in the electronic patient record. We recommend that, in future, digital screening tools and alerts should use more of the patients data and that tools should be designed specifically for different patient groups.

Falls risk is multifactorial, involving a combination of clinical and sociodemographic factors. Although guidelines acknowledge this complexity, most research has focused on individual risk factors, leaving the combined impact of comorbidities relatively understudied. This population-wide study used electronic health records (EHR) linked across primary and secondary care to identify falls risk profiles in the North West London (NWL) population and to stratify patients by their likelihood of requiring falls-related hospital care using unsupervised clustering. We conducted cluster analysis on patients from NWL General Practice records using coded falls risk factors. Cluster membership was compared against the risk of falls-related hospital encounters. Among four identified clusters, two groups of older, multimorbid patients were 11 times more likely to have a fall-related hospital encounter (RR 11.45, 95% CI 10.14-12.92 and RR 11.63, 95% CI 10.30-13.13) and had significantly longer mean length of stay compared with younger, fitter patients. Between two younger clusters, patients with higher deprivation levels were 29% more likely to have a fall-related hospital encounter (RR 1.29, 95% CI 1.12-1.49). These findings demonstrate that clustering routinely collected EHR data can identify population segments at highest risk of falls-related hospital use, supporting more targeted, multifactorial risk assessment and prevention strategies.

Abstract Venous thromboembolism (VTE) is a leading cause of preventable inpatient mortality, while the real-world performance of mandated risk assessment and the potential for automating using electronic health record (EHR) data remain unclear. We analysed 577,904 admissions and 726,896 VTE assessment forms across five NHS hospitals between 2015 and 2025 to evaluate assessment completion, concordance with structured EHR data, clinical validity, and feasibility of EHR-based automation assisted by machine learning. Overall completion was high (96.7%), and timely completion improved from 47.4% in 2015 to 90.5% in 2024. Agreement between forms and EHR data was good for common risk factors, but low-prevalence variables were often under-documented in the forms. Despite these discrepancies, form-derived thrombosis risk was associated with increased VTE incidence (OR 3.31, 95% CI 2.81–3.90). Machine learning models using first-14-hour EHR data achieved discrimination comparable to clinician-recorded variables (AUROC 0.709 vs 0.704), supporting real-time EHR-integrated assessment pre-population and decision support. Author summary We studied whether information already stored in hospital electronic health records could make required venous thromboembolism (VTE) risk assessments quicker and more reliable. VTE refers to potentially serious blood clots in the deep veins or lungs. We examined more than half a million admissions across five NHS hospitals over ten years. Most assessments were eventually completed, but many were not finished within the recommended 14-hour window. Information entered manually by clinicians often agreed with existing electronic records for common risk factors, but uncommon factors and bleeding risks were missed more often. Even with these documentation differences, the assessments identified patients who were more likely to develop VTE. We also tested machine-learning models using information available during the first 14 hours of admission. These models performed about as well as models based on clinician-completed forms. Our findings suggest that hospital systems could pre-fill parts of the assessment using data already recorded, while leaving clinicians to verify the information and make the final decision. This approach could reduce repetitive manual data entry, improve timely completion, and help clinicians identify patients who may benefit from preventive treatment.

BACKGROUND:Serum troponin measurement forms a cornerstone of acute myocardial infarction (AMI) diagnosis. A major challenge is interpretation of an elevated first troponin in patients with impaired renal function. We aimed to (1) evaluate the relationship between estimated glomerular filtration rate (eGFR) and first troponin, (2) characterise the performance of different troponin assays for diagnosing AMI and (3) derive eGFR-specific thresholds for potential clinical use. METHODS:We analysed the distribution of troponin values stratified by eGFR and AMI. Diagnostic performance was analysed using the C-statistic. Test detection rate, false positive rate and positive predictive value were calculated for different cut-offs. RESULTS:We included 221 175 patients between 2010 and 2017 from four acute tertiary care hospitals in London, UK, with a median age of 65 years (IQR 49-79). eGFR was<60 mL/min/1.73 m2 in 20.6% of patients and 6.4% of patients had a diagnosis of AMI. In patients without AMI, we observed an inverse log-linear relationship between eGFR and troponin. Diagnostic performance for AMI was best in patients with eGFR>90 (C-statistic 0.93) and worst in eGFR<15 (C-statistic 0.81). For high-sensitive troponin T, using the conventional cut-off of 14 ng/L, false positive rates ranged from 68-93% for eGFRs between 15 and 60 mL/min/1.73 m2. Restricting the false positive rate to 15% yields eGFR specific cut-offs of 73, 112 and 184 ng/L, with detection rates of 73%, 70% and 68% in patients with an eGFR of 45-60, 30-45 or 15-30 mL/min/1.73 m2. CONCLUSIONS:The diagnostic performance of an unadjusted troponin cut-off for AMI falls with worsening renal function. We propose consideration of eGFR specific cut-offs to support more effective triage and early management of suspected AMI in patients with renal impairment. TRIAL REGISTRATION NUMBER:NCT03507309.

Objectives Rising demand for emergency care in England is a continuing challenge driven by population ageing and increasing multimorbidity. Ambulatory emergency care (AEC) refers to the provision of same-day acute care for patients who might otherwise require admission. However, the contribution of AEC conditions to demand remains unclear. This study aimed to examine the proportion and nature of patients attending emergency departments (ED) with AEC-related conditions and to describe variation between hospitals in attendances and emergency admissions for AEC conditions. Design and setting A retrospective study of routine data from 21 acute hospitals in England, including adult ED attendances and emergency admissions between 1 November 2021 and 31 October 2022. We used a federated approach to ensure data security, applying established AEC definitions to explore variation by age, socioeconomic status and length of stay. Outcome measures Primary: Proportion of (i) ED attendances and (ii) emergency admissions for AEC conditions. Secondary: (i) Proportion of patients presenting at ED with an AEC condition who were admitted; (ii) proportion of emergency admissions with an AEC condition with a length of stay <2 days. Results We analysed 1 513 480 attendances (median per hospital: 73 125) and 660 105 admissions (median per hospital: 30 425). AEC accounted for 29.6% of attendances and 40.8% of admissions, with substantial inter-hospital variability. Patients aged ≥65 were more likely to present with an AEC, while patients from deprived areas had lower rates. Among AEC-related admissions, 49.3% had a stay of less than 2 days. Conclusions Nearly one-third of attendances and two-fifths of admissions were for conditions potentially manageable in AEC or community settings. Variation between hospitals suggests local factors, including service configuration and primary care access, may influence avoidable acute care use. These findings suggest a need for a more nuanced understanding of the drivers behind AEC, or SDEC Services, to better understand their impact on reducing hospital admissions. Analysing these patterns may inform interventions to reduce avoidable hospital utilisation. Further research is needed to identify drivers of variation and to develop scalable strategies for prevention.

Nucleos/tide analogue (NA) drugs are used for long-term treatment of chronic hepatitis B virus (HBV) infection, with treatment eligibility criteria changing rapidly amidst globally evolving clinical guidelines. We aimed to quantify the prescription of NA drugs to date, and to undertake a preliminary assessment of the impact of relaxing treatment eligibility thresholds, leveraging a unique large real-world secondary care dataset. We assimilated longitudinal clinical data, collected between February 1997 and April 2023 from adults with chronic HBV infection from six centres in England through the UK NIHR Health Informatics Collaborative (HIC) Viral Hepatitis and Liver Disease theme. We describe factors currently associated with the receipt of NA treatment and determine the proportion of the population who would become treatment eligible as thresholds change. Across 7558 adults with a mean follow-up of 4.0 years (SD 3.9), NA treatment was prescribed in 2014/7558 (26.6%), and as expected according to guidelines at the time, was associated with HBV e-antigen (HBeAg) positivity and alanine transferase (ALT) above the upper limit of normal (> ULN). Treatment was more likely in males, older adults, in Asian and Other ethnicities (compared to White), and less likely in socioeconomically deprived individuals. The proportion of treatment-eligible individuals was 32.3% based on 2 records of ALT > ULN over 6-12 months, 41.7% based on ALT > ULN and viral load (VL) > 2000 IU/mL, and 95.1% based on detectable VL and either ALT > ULN or age > 30 years. Evolving clinical guidelines will lead to substantial increases in the proportion of individuals living with HBV who are eligible for treatment, underlining the need for services to adapt rapidly to the changing clinical environment.

Objectives The ‘tumour, node, metastasis’ (TNM) classification of colorectal cancer (CRC) predicts prognosis and so is vital to consider in analyses of patterns and outcomes of care when using electronic health records. Unfortunately, it is often only available in free-text reports. This study aimed to develop regex-based text-processing algorithms that identify the reports describing CRC and extract the TNM staging at a low computational cost.Methods The CRC and TNM extraction algorithms were iteratively developed using 58 634 imaging and pathology reports of patients with CRC from the Oxford University Hospitals (OUH) and Royal Marsden (RMH) NHS Foundation Trusts (FT), with additional input from Imperial College Healthcare and Christie NHS FTs. The algorithms were evaluated on a stratified random sample of 400 OUH development data reports and 400 newer ‘unseen’ OUH reports. The reports were annotated with the help of two clinicians.Results The CRC algorithm achieved at least 93.0% positive predictive value (PPV), 72.1% sensitivity, 64.0% negative predictive value (NPV) and 90.1% specificity for primary CRC on pathology reports. On imaging reports, it demonstrated at least 78.0% PPV, 91.8% sensitivity, 93.0% NPV and 80.9% specificity. For the main T/N/M categories, the TNM algorithm achieved PPVs of at least 93.9% (T), 97.7% (N) and 97.2% (M), and sensitivities of 63.6% (T), 89.6% (N) and 64.8% (M). NPVs were at least 45.0% (T), 91.1% (N), 88.4% (M), and specificities 95.7% (T), 98.1% (N), 99.3% (M). Reductions in performance were mostly due to implicit staging. For extracting explicit TNM stages, current or historical, the algorithm made no errors on 400 pathology reports and six errors on 400 imaging reports.Conclusion The TNM algorithm accurately extracts explicit TNM staging, but other methods are needed for retrieving implicit stages. The CRC algorithm is accurate on non-supplementary reports, but outputs need additional review if higher precision is required.

The mechanisms by which vaginal microbiota shape spontaneous preterm birth (sPTB) risk remain poorly defined. Using electronic clinical records data from 74,913 maternities in conjunction with metaxanomic (n = 596) and immune profiling (n = 314) data, we show that the B blood group phenotype associates with increased risk of sPTB and adverse vaginal microbiota composition. The O blood group associates with sPTB in women who have a combination of a previous history of sPTB, an adverse vaginal microbial composition and pro-inflammatory cervicovaginal milieu. In contrast, women of blood group A have a higher prevalence of vaginal Lactobacillus crispatus, a lower risk of sPTB, with sPTB cases showing no association with vaginal microbiota composition or inflammation. We found that cervicovaginal fluid contains ABH(O) glycans and shows variable binding to key vaginal bacteria. This indicates that cervicovaginal ABH(O) glycans influence microbiota-host interactions implicated in sPTB risk, suggesting a novel target for sPTB prediction and prevention.

IntroductionThe fight against sepsis is an ongoing healthcare challenge, where digital tools are increasingly used with some promising results. The experience of survivors and their family members can help optimize digital alerts for sepsis/deterioration. This study pairs the experiences of survivors of their sepsis journey and family members with their knowledge and views on the role of digital alerts.MethodsA qualitative study with online, semi-structured interviews and focus groups with sepsis survivors and family members in England. Data were analyzed inductively using thematic analysis.ResultsWe included 11 survivors, and 5 family members recruited via sepsis charities and other social media, for a total of 15 sepsis cases. Identified categories correspond to the three stages of the sepsis journey: 1. Pre-hospital, onset symptoms and help-seeking; 2. Hospital admission and stay; 3. Post-sepsis syndrome. The role of digital alerts at each stage of the sepsis journey is discussed. Participants’ experiences were varied, previous sepsis awareness scant, and knowledge of digital alerts minimal. However, participants were confident in the potential of alerts contributing along the sepsis journey. They perceived digital alerts as important in healthcare professionals’ decision-making to expedite identification and treatment of sepsis and suggested their expansion across healthcare services. Participants expressed that awareness should be increased among the general public about digital alerts for sepsis/deterioration.DiscussionIn light of sepsis’ insidious and variable manifestation, the involvement of patients and family members in the development of digital alerts is crucial to optimize their design and deployment towards improving outcomes. Digital alerts should enhance the connection across healthcare services as well as the care quality. They should also enhance the communication between patients and healthcare professionals.Clinical trial registrationThe ClinicalTrials.gov registration identifier for this study is NCT05741801; the protocol ID is 16347.

Introduction Manual investigation of falls incidents for quality improvement is time-consuming for clinical staff. Routine care delivery generates a large volume of relevant data in disparate systems, yet these data are seldom integrated and transformed into real-time, actionable insights for frontline staff. This protocol describes the co-design and testing of a safe mobility and falls informatics platform for automated, real-time insights to support the learning response to inpatient falls.Methods Underpinned by the learning health system model and human-centred design principles, this mixed-methods study will involve (1) collaboration between healthcare professionals, patients, data scientists and researchers to co-design a safe mobility and falls informatics platform; (2) co-production of natural language processing pipelines and integration with a user interface for automated, near-real-time insights and (3) platform usability testing. Platform features (data taxonomy and insights display) will be co-designed during workshops with lay partners and clinical staff. The data to be included in the informatics platform will be curated from electronic health records and incident reports within an existing secure data environment, with appropriate data access approvals and controls. Exploratory analysis of a preliminary static dataset will examine the variety (structured/unstructured), veracity (accuracy/completeness) and value (clinical utility) of the data. Based on these initial insights and further consultation with lay partners and clinical staff, a final data extraction template will be agreed. Natural language processing pipelines will be co-produced, clinically validated and integrated with QlikView. Prototype testing will be underpinned by the Technology Acceptance Model, comprising a validated survey and think-aloud interviews to inform platform optimisation.Ethics and dissemination This study protocol was approved by the National Institute for Health Research Imperial Biomedical Research Centre Data Access and Prioritisation Committee (Database: iCARE—Research Data Environment; REC reference: 21/SW/0120). Our dissemination plan includes presenting our findings to the National Falls Prevention Coordination Group, publication in peer-reviewed journals, conference presentations and sharing findings with patient groups most affected by falls in hospital.

Background & Aims: The dynamics of HBV viral load (VL) in patients with chronic hepatitis B (CHB) on nucleos(t)ide analogue (NA) treatment and its relationship with liver disease are poorly understood. We aimed to study longitudinal VL patterns and their associations with CHB clinical outcomes. Methods: Utilising large scale, routinely collected electronic health records from six centres in England, collated by the National Institute for Health and Care Research Health Informatics Collaborative (NIHR HIC), we applied latent class mixed models to investigate VL trajectory patterns in adults receiving NA treatment. We assessed associations of VL trajectory with alanine transaminase, and with liver fibrosis/cirrhosis. Results: We retrieved data from 1,885 adults on NA treatment (median follow-up 6.2 years, IQR 3.7-9.3 years), with 21,691 VL measurements (median 10 per patient, IQR 5-17). Five VL classes were identified from the derivation cohort (n = 1,367, discrimination: 0.93, entropy: 0.90): class 1 'long term suppression' (n = 827, 60.5%), class 2 'timely virological suppression' (n = 254, 18.6%), class 3 'persistent moderate viraemia' (n = 140, 10.2%), class 4 'persistent high-level viraemia' (n = 44, 3.2%), and class 5 'slow virological suppression' (n =102, 7.5%). The model demonstrated a discrimination of 0.93 and entropy of 0.88 for the validation cohort (n = 518). Alanine transaminase decreased variably over time in VL-suppressed groups (classes 1, 2, 5; all p <0.001), but did not significantly improve in those with persistent viraemia (classes 3, 4). Patients in class 5 had twofold increased hazards of fibrosis/cirrhosis compared with class 1 (adjusted hazard ratio, 2.00; 95% CI, 1.33-3.02). Conclusions: Heterogeneity exists in virological response to NA therapy in CHB patients, with over 20% showing potentially suboptimal responses. Slow virological suppression is associated with liver disease progression. (c) 2024 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Characterisation of people with hepatitis B (PwHB) remains limited, particularly regarding treatment status, disease severity and biomarker profiles. Quantitative hepatitis B surface antigen (qHBsAg) is a key predictor of response to emerging therapies, but its distribution is poorly described. Using a large, ethnically diverse UK cohort, we assessed demographics, clinical features and HBsAg levels to guide treatment strategies. This cross-sectional analysis of PwHB (N = 2000 [prespecified]) used data from four English hospitals, collected via the National Institute for Health and Care Research Health Informatics Collaborative framework. Individual characteristics were assessed overall, and post hoc by qHBsAg levels (≤ 3000/> 3000 IU/mL; < 100/≥ 100-≤ 1000/> 1000 IU/mL) available from one centre (N = 457). The cohort had a slight male predominance (54%) and a mean age of 44.9 years. White and Asian ethnicity each accounted for 25%, and 23% were on nucleos(t)ide analogue therapy. Centres collecting HBsAg data had more individuals with undetectable HBV DNA or on treatment. Among individuals with non-missing qHBsAg data (263/457), 167/263 (63.5%) had qHBsAg ≤ 3000 IU/mL. These were older (49.6 vs. 43.5 years), more likely to be male (53.9% vs. 35.4%), Asian (40.7% vs. 20.8%) or have undetectable HBV DNA (35.9% vs. 17.7%), and less likely to be Black (13.2% vs. 34.4%) versus those with qHBsAg > 3000 IU/mL. Fifty-six (21.3%) people had qHBsAg < 100 IU/mL, 60 (22.8%) between ≥ 100 and ≤ 1000 IU/mL, and 147 (55.9%) > 1000 IU/mL. This cohort of PwHB highlights qHBsAg distribution in clinical settings and could identify people more likely to achieve functional cure with emerging therapies.

Introduction Transparency in the use of data for research benefits the public and researchers by fostering trust and enabling efficient data sharing. Public support for access to their data for research depends on robust data security, the absence of conflicting interests, and a clear demonstration of public benefit, all of which must be evident through transparent practices. A lack of clarity in data access processes can delay research, highlighting the need for clear and streamlined approval procedures. To maintain what is often referred to as a `social license to operate', organisations must meet and uphold societal expectations, with transparency being a key dimension of that responsibility. Objective To develop and foster adoption of a set of transparency standards for the data science community, supporting trustworthy and streamlined data use for health and socio-economic research and planning. Methods A multi-stakeholder deliberation was undertaken, informed by two reviews of existing data access procedures across participating organisations. Stakeholders included healthcare and research organisations, data custodians, regulators, industry representatives, academic experts, and members of the public. Results The review and deliberation identified missed opportunities to inform and involve the public in data access procedures, along with inconsistencies in data access processes and supporting materials across the organisations. In response, we developed the Transparency Standards, comprising 28 recommended actions grouped into four themes: provision of clear data access guidance; clear website navigation designed to meet the needs of public and research users; regular review and iterative improvement of processes; and reporting of data access outcomes and information security findings. A targeted funding call facilitated the adoption of standards in 19 organisations, resulting in reusable transparency materials and transferable knowledge to support wider implementation. Conclusion The Transparency Standards support data custodians in strengthening openness and accountability in data access processes, helping to build public trust while simplifying procedures for researchers. Their broad adoption demonstrates a shared commitment to the ethical use of data. However, varying levels of implementation point to the need for continued investment to sustain progress and respond to public and researcher expectations.

Background:Electronic health records (EHRs) are a cornerstone of modern health care delivery, but their current configuration often fragments information across systems, impeding timely and effective clinical decision-making. In gynecological oncology, where care involves complex, multidisciplinary coordination, these limitations can significantly impact the quality and efficiency of patient management. Few studies have examined how EHR systems support clinical decision-making from the perspective of end users. This study aimed to explore multiprofessional experiences of EHR use in gynecological oncology and to develop a co-designed informatics platform to improve decision-making for ovarian cancer care. Objective:This study aims to evaluate the perspectives of health care professionals on retrieving routine clinical data from EHRs in the management of ovarian cancer and to design an integrated informatics platform that supports clinical decision-making. Methods:We conducted a national cross-sectional survey of 92 UK-based professionals working in gynecological oncology, including oncologists, nurses, radiologists, and other specialists in ovarian cancer. The web-based questionnaire, combining quantitative and free-text responses, assessed their experiences with EHR use, focusing on information retrieval, usability challenges, perceived risks, and benefits. In parallel, a human-centered design approach involving health care professionals, data engineers, and informatics experts codeveloped a digital informatics platform that integrates structured and unstructured data from multiple clinical systems into a unified patient summary view for clinical decision-making. Natural language processing was applied to extract genomic and surgical information from free-text records, with data pipelines validated by clinicians against original clinical system sources. Results:Among 92 respondents, 84 out of 91 (92%) routinely accessed multiple EHR systems, with 26 out of 91 (29%) using 5 or more. Notably, 16 out of 92 respondents (17%) reported spending more than 50% of their clinical time searching for patient information. Key challenges included lack of interoperability (35/141 reported challenges, 24.8%), difficulty locating critical data such as genetic results (57/85 respondents, 67%), and poor organization of information. Only 10 out of 92 professionals (11%) strongly agreed that their systems provided well-organized data for clinical use. While ease of access to patient data was a key benefit, 54 out of 90 respondents (60%) reported lacking access to comprehensive patient summaries. To address these issues, our co-designed informatics platform consolidates disparate patients' data from different EHR systems into a single visual display to support clinical decision-making and audit. Conclusions:Current EHR systems are suboptimal for supporting complex gynecological oncology care. Our findings highlight the urgent need for integrated, user-centered clinical decision tools. Fragmentation and lack of interoperability hinder information retrieval and may compromise patient care. Our co-designed ovarian cancer informatics platform is a potential real-world solution to improve data visibility, clinical efficiency, and ultimately the quality of ovarian cancer care.

BackgroundThe iCARE (Imperial Clinical Analytics, Research and Evaluation) Secure Data Environment (SDE) is committed to further enhancing its website to improve awareness of processes and procedures for appropriately accessing the healthcare data it hosts for research. Our iCARE SDE is a cloud-based, big data analytics platform that offers clinicians, allied healthcare professionals, researchers, and data scientists access to large-scale, curated project-defined data for research via a HRA Research Ethics Committee compliant application and approval process, with lay partners embedded at each step to ensure that each project is of patient and public benefit. By improving our website for researchers, patients, and the public, our ongoing work aims to further increase the awareness, accessibility, and transparency of the iCARE processes and use of healthcare data for research. IntroductionBy improving the iCARE website for researchers, patients and the public, our work aims to further increase the awareness, accessibility, and transparency of the iCARE processes and use of healthcare data for research. The primary objective was to review and enhance iCARE website, aligning with The Alliance Transparency Standards 2-6: Improve transparency of the application process and criteria, aligning with five safes framework (Standard 2); clearer and accessible website navigation (Standard 3); work with lay partners and researchers to ensure content is clear and transparent (Standard 4); comprehensive review of website content and FAQs, with ongoing review (Standard 5); transparency of data use and auditing, with accessible and user-friendly project lay summaries (Standard 6). MethodsWe began by reviewing existing materials and meeting with web developers to outline project requirements. We also co-developed with community partners a public guide on how data and artificial intelligence is used for research. Our website designer then built the website, integrating feedback from the iCARE team, researchers, and community partners. To gather further feedback on the new website and co-develop the public-facing website content, including the FAQs, we held two workshops with a diverse panel of community partners in Northwest London. ResultsThe updated iCARE website enhances accessibility, clarity, and transparency through feedback from community partners and researchers. The website now contains detailed steps streamlining the application process, reducing communication delays, and providing applicants with clearer expectations. Additionally, the website clarifies the analysis tools available in the iCARE SDE. For the public, the website more clearly describes the rigorous data access requirements and processes that researchers follow, and the measures taken to ensure the ethical and secure access and use of healthcare data for research in the iCARE SDE. We also integrated an educational guide on how data and artificial intelligence is used in healthcare and research, which was co-developed by lay partners for researchers and the public. ConclusionsWe anticipate that the redesigned iCARE website will improve awareness, clarity, and access to healthcare data for research by meeting The Alliance Transparency Standards and involving community partners and researchers in its redesign.