Electronic Healthcare Record Research Articles

Clinical risk factors for pancreatic cancer: protocol for an umbrella review

IntroductionIdentifying cancer earlier can help save lives. An increasingly popular approach to diagnosing cancer earlier is in the development of risk prediction models to be applied to the electronic healthcare...

Improving Family-Centered Rounds With a Nursing Checklist in the Electronic Health Care Record.

Family-centered rounds (FCR) is the standard for pediatric communication, but community pediatric hospital medicine services may face barriers in implementation, including offering FCR to families with a language preference other than English (LOE) versus those with an English preference (EP). The goal of our quality improvement project was to increase FCR from 33% to 80% over 1 year. Interventions included an FCR checklist integrated into the electronic healthcare record (EHR-FCR checklist), staff education, visual prompts, and interpreters. Our primary outcome measure was weekly % FCR. Our main process measure was weekly % nursing documentation. To address language inclusion, we compared FCR encounters for families with LOE versus EP. The use of the checklist without interventions was assessed during the sustain period. Control charts were used to analyze measures. Fisher's exact test was used to compare FCR for families with LOE versus EP. FCR increased to 81% during the intervention period and then decreased to 73% during the sustain period. Nursing documentation increased to 93% with the EHR-FCR checklist implemented as a flowsheet. There was no statistical difference in the proportion of families with LOE versus EP who participated in FCR (84% versus 81%, P = .38) during the intervention period; a statistically higher proportion of families with LOE participated in FCR during the sustain period (87% vs 72%, P <.01). We increased documented FCR on our community pediatric hospital medicine service, including FCR for families with LOE, by implementing a nursing-completed EHR-FCR checklist supported by staff education, visual prompts, and interpreters.

Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare

Robust data privacy regulations hinder the exchange of healthcare data among institutions, crucial for global insights and developing generalised clinical models. Federated learning (FL) is ideal for training global models using datasets from different institutions without compromising privacy. However, disparities in electronic healthcare records (EHRs) lead to inconsistencies in ML-ready data views, making FL challenging without extensive preprocessing and information loss. These differences arise from variations in services, care standards, and record-keeping practices. This paper addresses data view heterogeneity by introducing a knowledge abstraction and filtering-based FL framework that allows FL over heterogeneous data views without manual alignment or information loss. The knowledge abstraction and filtering mechanism maps raw input representations to a unified, semantically rich shared space for effective global model training. Experiments on three healthcare datasets demonstrate the framework’s effectiveness in overcoming data view heterogeneity and facilitating information sharing in a federated setup.

Genetic architecture of routinely acquired blood tests in a British South Asian cohort

Understanding the genetic basis of routinely-acquired blood tests can provide insights into several aspects of human physiology. We report a genome-wide association study of 42 quantitative blood test traits defined using Electronic Healthcare Records (EHRs) of ~50,000 British Bangladeshi and British Pakistani adults. We demonstrate a causal variant within the PIEZO1 locus which was associated with alterations in red cell traits and glycated haemoglobin. Conditional analysis and within-ancestry fine mapping confirmed that this signal is driven by a missense variant - chr16-88716656-G-TT - which is common in South Asian ancestries (MAF 3.9%) but ultra-rare in other ancestries. Carriers of the T allele had lower mean HbA1c values, lower HbA1c values for a given level of random or fasting glucose, and delayed diagnosis of Type 2 Diabetes Mellitus. Our results shed light on the genetic basis of clinically-relevant traits in an under-represented population, and emphasise the importance of ancestral diversity in genetic studies.

Opportunities and challenges for identifying undiagnosed Rare Disease patients through analysis of primary care records: long QT syndrome as a test case.

Patients with rare genetic diseases frequently experience significant diagnostic delays. Routinely collected data in the electronic health record (EHR) may be used to help identify patients at risk of undiagnosed conditions. Long QT syndrome (LQTS) is a rare inherited cardiac condition associated with significant morbidity and premature mortality. In this study, we examine LQTS as an exemplar disease to assess if clinical features recorded in the primary care EHR can be used to develop and validate a predictive model to aid earlier detection. 1495 patients with an LQTS diagnostic code and 7475 propensity-score matched controls were identified from 10.5million patients' electronic primary care records in the UK's Clinical Practice Research Datalink (CPRD). Associated clinical features recorded before diagnosis (with p < 0.05) were incorporated into a multivariable logistic regression model, the final model was determined by backwards regression and validated by bootstrapping to determine model optimism. The mean age at LQTS diagnosis was 58.4 (SD 19.41). 18 features were included in the final model. Discriminative accuracy, assessed by area under the curve (AUC), was 0.74, (95% CI 0.73, 0.75) (optimism 6%). Features occurring at significantly greater frequency before diagnosis included: epilepsy, palpitations, syncope, collapse, mitral valve disease and irritable bowel syndrome. This study demonstrates the potential to develop primary care prediction models for rare conditions, like LQTS, in routine primary care records and highlights key considerations including disease suitability, finding an appropriate linked dataset, the need for accurate case ascertainment and utilising an approach to modelling suitable for rare events.

Blockchain technology for secure electronic healthcare record in cloud based cryptographic hash generator

Blockchain technology for secure electronic healthcare record in cloud based cryptographic hash generator

Global use of angiotensin receptor neprilysin inhibitor in heart failure and reduced, below normal and supranormal ejection fraction.

The global use of angiotensin receptor neprilysin inhibitor (ARNI) in clinical practice, especially in patients with heart failure and below-normal ejection fraction (HFbnEF), has not been thoroughly evaluated. We aimed to investigate the characteristics, outcomes, and adverse events in patients treated with ARNI for HF with reduced (HFrEF), below-normal (HFbnEF), and supranormal left ventricular EF (HFsnEF). This observational study analyzed data from the electronic healthcare records (EHR) of patients with HF treated with ARNI between 2015 and 2022 in North and South America, Europe, the Middle East, Africa, and Asia-Pacific. Based on the left ventricular EF, patients were categorized as HFrEF (< 40%), HFbnEF (40-60%), and HFsnEF (> 60%). Mortality and the incidence of adverse events were investigated. Of the 11,141 patients analyzed, HFrEF, HFbnEF and HFsnEF accounted for 74%, 22%, and 4%, respectively. Patients with a higher EF were more likely to be older, female, and obese. Hypertension and atrial fibrillation were the most common in HFsnEF. Systolic blood pressure was lower and natriuretic peptide levels were higher in the lower EF groups. Mortality was lowest in HFbnEF (7.7 per 100 patient-years follow-up in HFrEF, 5.8 in HFmrEF, and 6.0 in HFsnEF). Similarly, hypotension and acute kidney injury were the least frequent in HFbnEF. Incidence of elevated serum potassium levels was similar between the groups. In this analysis of large-scale EHR, ARNI was mainly used in HFrEF and HFbnEF, consistent with previous randomized trials and pooled analyses. Adverse events were less common in HFbnEF.

Facilitators and Barriers to Integrating Patient-Generated Blood Pressure Data into Primary Care EHR Workflows.

Evidence supports using patient-generated blood pressure data for better outcomes in hypertension management. However, obstacles like dealing with home-generated paper data sets and questions of validity slowed the meaningful incorporation of home blood pressure into clinical care. As clinicians value patient data more, reliance on digital health solutions for data collection and shared decision-making grows. The purpose of this study is to evaluate the design and early implementation of an electronic health record (EHR)-based data visualization tool and explore the barriers or facilitators to integrating) patients' home blood pressure data into the electronic workflow in the clinical setting. Findings can inform potential next steps for implementation and provide recommendations for leveraging patient-generated health data (PGHD) in hypertension management. We qualitatively explored pre- and early-implementation factors for integrating PGHD into clinicians' EHR interfaces intended to support shared decision-making using the Consolidated Framework for Implementation Research (CFIR). We collected data in the form of notes and transcripts from clinician focus groups, administrative leadership feedback sessions, research team observations, and recurring team meetings. This study took place at a midwestern academic health center. We identify implementation facilitating factors, adoption considerations, and next steps across CFIR domains focusing on large-scale implementation. Key recommendations include aligning internal and external priorities, empowering champions to facilitate uptake, using intuitive design, and anticipating and planning for unintended consequences. These findings can guide future efforts to include PGHD in workflows, thus enhancing shared decision-making and laying the groundwork for larger implementations. Understanding the implementation barriers and facilitators to connect PGHD to clinician apps in the EHR workspace can promote their adoption and maintenance.

EBH-IoT: Energy-efficient secured data collection and distribution of electronics health record for cloud assisted blockchain enabled IoT based healthcare system

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation, distribution and storage for EHR data. Therefore, the integration of H-IoT and Blockchain technologies can become a reasonable choice for the design of decentralized H-IoT-based e-healthcare systems. But the H-IoT network has some intrinsic challenges like low computation, energy constraint, security, energy optimization, data storage, and real-time data analytic. Also, conventional EHR-based systems suffer from issues such as the potential loss of data, inadequate security and consensus on the unchangeable nature of health records, fragmented connections between different institutions, and ineffective clinical data retrieval methods, among other challenges. In this article, first, we study the performance of blockchain technology in the healthcare system. Second, we propose an improved Harris Hawk Optimization algorithm (HHO) based clustering mechanism for the collection and sharing of EHR. The proposed system was tested and validated using the Hyperledger-fabric based electronic healthcare record (EHR) sharing system along with Matlab. The proposed system achieves 12%, and 7% incremental improvement in terms of latency, throughput for the Blockchain networks. While the proposed clustering technique achieves 10%, 12%, 14%, and 16% improvements in alive node, energy consumption, throughput and average transmission delay compared to existing state of the art.

Synergy of AI and Blockchain to Secure Electronic Healthcare Records

ABSTRACTIn this article, we delve into the intersection of blockchain technology and artificial intelligence (AI) to fortify the security of electronic health records (EHRs). Existing EHR systems face challenges like interoperability issues, user interface complexities, and data security concerns, hindering seamless patient care and healthcare delivery. This article thoroughly explores the hurdles associated with EHR security and investigates the potential of employing blockchain and AI solutions to mitigate these challenges. The review underscores the necessity for resilient, interoperable systems to protect sensitive health information by pinpointing opportunities for collaborative strategies. In essence, this article contributes valuable perspectives on the dynamic landscape of EHR security, guiding future research and development at the crossroads of blockchain; we presented a case study on EHR security, customized care, predictive analytics, and more efficient healthcare delivery. Using case studies as illustrative examples, the article scrutinizes practical applications, shedding light on successful implementations and areas requiring refinement. Several outcomes for the predictive analytics of patients' surgeries are shown in this case study. Next, we compare blockchain‐based EHR systems with existing, non‐blockchain‐based EHR systems. Finally, we present the concluding remarks with future directions for integrating blockchain, AI, and EHR systems.

Antipsychotic Prescribing and Falls Risk in Nursing Home Residents with Dementia

Abstract Background Falls are a common syndrome in older adults leading to adverse outcomes including functional decline, activity restriction, injury and death. They can still occur in residential care. We examined prescribing of medications included in STOPPFall (Screening Tool of Older Persons Prescriptions in older adults with high fall risk) (1) in nursing home residents with dementia. Methods A point prevalence audit was conducted in a dementia-specific nursing home. Data were collected from the electronic healthcare record on demographics, dementia severity, and number of falls in the preceding 12 months. The number and class of regular medications were recorded. Non-cognitive symptoms of dementia were quantified using the Cohen Mansfield Agitation Inventory (CMAI). Results Seventy-four residents were included with mean age 82 years, 59(70%) female. The average Mini Mental State Examination (MMSE) was 5/30 and 80% had severe dementia. The average CMAI score was 50. The mean number of falls was 3 (range 0-14). Regular prescribing of specific STOPPFall medications was as follows: antipsychotics 42/74 (57%), benzodiazepines 8/74 (11%), opioids 13/74 (18%), antidepressants 44/74 (59%), antiepileptics 11.74 (15%), diuretics 12/74 (16%). Prescription of a regular antipsychotic was associated with one or more falls (OR 3.19, p = 0.029). There was no significant difference in falls with the other medications studied. The number of falls increased with higher agitation scores. Conclusion Antipsychotics increased the risk of falls in a nursing home setting. Fallers had higher rates of agitation, perhaps explaining the increased usage of antipsychotics. Medications should be reviewed on a regular basis and deprescribing considered where appropriate. Reference 1. Seppala LJ, Petrovic M, Ryg J, Bahat G, Topinkova E, Szczerbińska K, et al. STOPPFall (Screening Tool of Older Persons Prescriptions in older adults with high fall risk): a Delphi study by the EuGMS Task and Finish Group on Fall-Risk-Increasing Drgs. Age and Ageing. 2020;50(4):1189-99.

“Conversations with Friends”: A review of communication between two services

Abstract Background EDITH (Emergency Department in the home) is a rapid response team consisting of a doctor/ANP and OT who assess patients in their home to avoid an ED attendance. The Integrated Care Team for Older People (ICPOP) is a community-based specialist multidisciplinary team that provides assessment and intervention for frail older adults in order to facilitate them to remain living at home. These two teams work collaboratively to keep older adults out of hospital and refer a high volume of patients between the two services. It is imperative to reflect on current communication practices in order to future proof these services and ensure optimum patient care. Methods An electronic survey was designed to evaluate the service and was circulated to members of both teams. Quantitative data was captured via likert scales and qualitative feedback was analysed via thematic analysis. There was a total of 13 respondents. Results One hundred percentage of participants rated confidentiality of communication “average” and “above average”. 92% rated accuracy and accessibility of communication as “average” and “above average” while 15% rated speed of communication as “below average”. Key themes such as “prompt communication”, “friendly working relationships” and “using therapy specific language” emerged as areas that are working well. Area for improvement include “close loop communication/referral process” and “lack of a shared electronic healthcare record”. Conclusion Overall, the feedback from both teams appears to be positive and it is beneficial to reflect on communication processes. It is important to build on good working relations between acute and community teams as it likely results in better patient outcomes. Key areas for improvement have been highlighted and an action plan to address same can be formulated.

Text mining method to unravel long COVID’s clinical condition in hospitalized patients

Long COVID is characterized by persistent that extends symptoms beyond established timeframes. Its varied presentation across different populations and healthcare systems poses significant challenges in understanding its clinical manifestations and implications. In this study, we present a novel application of text mining technique to automatically extract unstructured data from a long COVID survey conducted at a prominent university hospital in São Paulo, Brazil. Our phonetic text clustering (PTC) method enables the exploration of unstructured Electronic Healthcare Records (EHR) data to unify different written forms of similar terms into a single phonemic representation. We used n-gram text analysis to detect compound words and negated terms in Portuguese-BR, focusing on medical conditions and symptoms related to long COVID. By leveraging text mining, we aim to contribute to a deeper understanding of this chronic condition and its implications for healthcare systems globally. The model developed in this study has the potential for scalability and applicability in other healthcare settings, thereby supporting broader research efforts and informing clinical decision-making for long COVID patients.

Adverse childhood experiences and multimorbidity of internalising and cardiometabolic conditions in an older-age population.

BackgroundTackling multimorbidity (ICM-MM) of internalising conditions (IC) and cardiometabolic conditions (CM) is key to improving the future of healthcare. Early life factors such as adverse childhood experiences (ACEs) have been linked to long term, poor mental and physical health conditions. AimsThis study investigated whether: 1) ACEs increase the risk of IC, CM and ICM-MM; 2) the effect of ACEs on risk of developing CM depends on the presence of IC and vice versa; 3) how risk differs by gender, socioeconomic status and ethnicity. MethodsIn UK Biobank, the Childhood Trauma Screener was used to identify 157,256 participants with ACE data. Electronic healthcare records (EHR) and interview data identified those with anxiety or depression (IC) and those with hypertension, obesity, type 2 diabetes, dyslipidaemia or chronic kidney disease (CM). Multivariate logistic regression models were employed to find associations between ACEs and IC, CM and ICM-MM. Covariates included age, gender, ethnicity, socioeconomic status, diet, alcohol intake, smoking behaviour and physical activity. ResultsAll ACEs significantly increase risk for IC (all ACEs, p = &lt;2e-16). Emotional neglect, physical neglect and emotional abuse increase risk of CM (e.g. physical neglect, OR = 1.43, CF = 1.30-1.58, p = 1.88e-13). ACEs appear to increase IC and CM risk independently. ConclusionsThe effects of ACEs last well into adulthood and confer risk for IC and, sometimes, CM. ACE history should be included in routine primary care and the development of therapeutic interventions tailored to older adults with ACEs is key to reducing poor health.

Incidence and prevalence of asthma, COPD, and ILD across England, Wales, and South-East Scotland – a harmonised approach to data analysis

ObjectiveThis study aimed to create harmonised datasets for asthma, chronic obstructive pulmonary disease (COPD), and Interstitial Lung Disease (ILD), and subsequently use them to calculate the incidence and prevalence of these diseases across three UK nations (England, Wales, and South-East Scotland). ApproachHarmonised research-ready datasets were created using three electronic healthcare record databases. Records were selected between 01/01/2004 and 31/12/2019, with yearly age and sex standardised incidence and prevalence rates calculated. ResultsIn 2019, the incidence rates for asthma were: England, 0.89 (95%CI: 0.88–0.90); Wales, 0.66 (0.65–0.68); and South-East Scotland, 0.67 (0.64–0.71), which were all lower than in 2004. Incidence rates of COPD in England were 0.83 (0.82–0.85) and 0.67 (0.65–0.69) in Wales, which were both lower than in 2004, but Scotland remained similar at 1.06 (0.99–1.13). Incidence rates of ILD were: England, 3.27 (3.05–3.50); Wales, 1.39 (1.27–1.53); and Scotland, 1.63 (1.36–1.95), which were all higher than in 2004. Between 2004 and 2019, the prevalence of all three diseases increased across all three nations. ConclusionsSolving differences in coding between CPRD, SAIL Databank, and DataLoch databases enabled comparable cohorts to be created and the first harmonised UK estimates of incidence and prevalence for asthma, ILD, and COPD. ImplicationsHarmonised studies of asthma, COPD, and ILD across the UK can now be performed. The code produced is publicly available to facilitate future research, which can be adapted for other conditions and databases.

Mapping OMOP-CDM to RDF: Bringing Real-World-Data to the Semantic Web Realm.

Real-world data (RWD) (i.e., data from Electronic Healthcare Records - EHRs, ePrescription systems, patient registries, etc.) gain increasing attention as they could support observational studies on a large scale. OHDSI is one of the most prominent initiatives regarding the harmonization of RWD and the development of relevant tools via the use of a common data model, OMOP-CDM. OMOP-CDM is a crucial step towards syntactic and semantic data interoperability. Still, OMOP-CDM is based on a typical relational database format, and thus, the vision of a fully connected semantically enriched model is not fully realized. This work presents an open-source effort to map the OMOP-CDM model and the data it hosts, to an ontological model using RDF to support the FAIRness of RWD and their interlinking with Linked Open Data (LOD) towards the vision of the Semantic Web.

Real-world evaluation of early remdesivir in high-risk COVID-19 outpatients during Omicron including BQ.1/BQ.1.1/XBB.1.5

BackgroundA trial performed among unvaccinated, high-risk outpatients with COVID-19 during the delta period showed remdesivir reduced hospitalization. We used our real-world data platform to determine the effectiveness of remdesivir on reducing 28-day hospitalization among outpatients with mild-moderate COVID-19 during an Omicron period including BQ.1/BQ.1.1/XBB.1.5.MethodsWe did a propensity-matched, retrospective cohort study of non-hospitalized adults with SARS-CoV-2 infection between April 7, 2022, and February 7, 2023. Electronic healthcare record data from a large health system in Colorado were linked to statewide vaccination and mortality data. We included patients with a positive SARS-CoV-2 test or outpatient remdesivir administration. Exclusion criteria were other SARS-CoV-2 treatments or positive SARS-CoV-2 test more than seven days before remdesivir. The primary outcome was all-cause hospitalization up to day 28. Secondary outcomes included 28-day COVID-related hospitalization and 28-day all-cause mortality.ResultsAmong 29,270 patients with SARS-CoV-2 infection, 1,252 remdesivir-treated patients were matched to 2,499 untreated patients. Remdesivir was associated with lower 28-day all-cause hospitalization (1.3% vs. 3.3%, adjusted hazard ratio (aHR) 0.39 [95% CI 0.23–0.67], p < 0.001) than no treatment. All-cause mortality at 28 days was numerically lower among remdesivir-treated patients (0.1% vs. 0.4%; aOR 0.32 [95% CI 0.03–1.40]). Similar benefit of RDV treatment on 28-day all-cause hospitalization was observed across Omicron periods, aOR (95% CI): BA.2/BA2.12.1 (0.77[0.19–2.41]), BA.4/5 (0.50[95% CI 0.50–1.01]), BQ.1/BQ.1.1/XBB.1.5 (0.21[95% CI 0.08–0.57].ConclusionAmong outpatients with SARS-CoV-2 during recent Omicron surges, remdesivir was associated with lower hospitalization than no treatment, supporting current National Institutes of Health Guidelines.

Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease

BackgroundDespite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hospital workflows...

P-384 The risk of early pregnancy loss following medicated frozen embryo transfer

Abstract Study question Do exogenous gonadocorticoids protect against early pregnancy loss (EPL) following frozen embryo transfer (FET)? Summary answer Exogenous gonadocorticoids conferred a 59.5% increased risk of EPL (p&amp;lt;.05) without any advantage to biochemical pregnancy rates. This corresponded to a significantly thinner pre-FET endometrium. What is known already During a medicated FET cycle, exogenous progestogens and/ or oestrogens are administered with the intention of inducing endometrial maturation prior to implantation. However, the concomitant administration of both these gonadocorticoids is known to produce secretory changes alongside endometrial proliferation. It is unclear whether this non-physiological morphology is truly favourable for implantation, with some authors reporting similar EPL rates between medicated and “natural” FET cohorts while others have found exogenous gonadocorticoids to reduce the risk of EPL compared to alternative protocols. Study design, size, duration Retrospective, single-centre cohort study assessing the rates of biochemical pregnancy and EPL following 819 FETs. All FETs were conducted between January 2019 and November 2022 within a single British centre. Participants were followed up at 6-8 weeks post-FET to assess for biochemical pregnancy and EPL. The former was defined as a positive urinary human chorionic gonadotropin; the latter was defined as the absence of pregnancy on transvaginal ultrasound despite previous confirmation of biochemical pregnancy. Participants/materials, setting, methods Participants comprised 586 women undergoing medicated FET cycles and 233 women undergoing “natural” cycles. Electronic healthcare records (EHRs) were accessed to record pregnancy outcomes, hormonal therapies and endometrial thickness at 48hrs prior to FET. Binomial variables were compared using Pearson’s chi-squared test; continuous variables were analysed via T-tests and logistic regressions. Cut-offs of 8mm, 9mm and 10mm were applied to endometrial thickness in sub-group analyses. Statistical test were conducted using STATA v18.0 (StataCorp LLC, Texas). Main results and the role of chance Women undergoing medicated FET cycles achieved 16.9% higher rates of biochemical pregnancy compared to women undergoing “natural” cycles (57.7% versus 49.4%, p&amp;lt;.05). However, they also suffered a 59.5% higher rate of EPL (28.8% versus 18.0%, p&amp;lt;.05), which corresponded to significantly thinner endometrium at pre-FET ultrasound in comparison to women on “natural” cycles (9.13mm versus 9.48mm, p&amp;lt;.05). Logistic regressions revealed that, within the medical FET cohort, each 1mm increase in thickness conferred a 13.8% increased risk of EPL (95%CI: 3.1%-25.5% p&amp;lt;.01) but no advantage in biochemical pregnancy rates. “Natural” cycles demonstrated no such association. Endometrial thickness was then dichotomised using three thresholds. Women undergoing medicated FET cycles whose endometrium exceeded 9mm or 10mm had 62.1% and 71.4% greater risk of EPL, respectively, compared to those below either threshold. Biochemical pregnancy rates did not differ. Women undergoing “natural” FET cycles did not exhibit any significant difference using either threshold, suggesting that the disparate EPL rate in medicated cycles may result from non-physiological endometrial stimulation by exogenous gonadocorticoids. The 8mm cut-off was insignificant in both cohorts. Although these results cannot confirm a causal relationship between exogenous gonadocorticoids, endometrial thickness and EPL, they are sufficiently striking to warrant further investigation into their pathophysiological basis. Limitations, reasons for caution EHRs failed to report endometrial thickness for 125 participants (15.3%), limiting statistical power in logistic regressions. Our results cannot be confidently applied to other centres until further studies are conducted. Additionally, our design did not allow for the disparity in EPL rates to be attributed to a particular pathophysiological mechanism. Wider implications of the findings This data generates further uncertainty within the literature with regards to the relative risk of EPL in medicated versus “natural” FET cycles. The empirical evidence to support one protocol over the other is insufficient at present, and thus clinical recommendations should be based on the individual preferences of each patient. Trial registration number not applicable

Using Data to Improve Healthcare: A Case Study of Pancreatic Enzyme Replacement in Pancreatic Cancer

ObjectivesIn the UK, guidelines recommend pancreatic enzyme replacement therapy (PERT) to all people with unresectable pancreatic cancer. In 2023, we published a national audit of PERT which showed suboptimal prescribing and wide regional variation in England. The aim of this manuscript was to describe how we used the PERT audit to drive improvements in healthcare. MethodsBuilding on the PERT audit, we deployed an online dashboard which will deliver ongoing updates of the PERT audit. We developed a collaborative intervention with cancer nurse specialists (CNS) to improve care delivered to people with pancreatic cancer. The intervention called Creating a natiOnAL CNS pancrEatic cancer network to Standardise and improve CarE (COALESCE) will use the dashboard to evaluate improvements in prescribing of PERT. ResultsWe demonstrated how large databases of electronic healthcare records (EHRs) can be used to improve cancer care. The PERT audit was implemented into a dashboard for tracking the progress of COALESCE. We will measure improvements in PERT prescribing as the intervention with CNS progresses. ConclusionsImproving healthcare is an ongoing and iterative process. By implementing the PERT dashboard, we created a resource-efficient, automated evaluation method enabling COALESCE to deliver a sustainable change. National-scale databases of EHRs enable rapid cycles of audits, providing regular feedback to interventions, working systematically to deliver change. Here, the focus is on pancreatic cancer. However, this methodology is transferable to other areas of healthcare. Implications for Nursing PracticeNurses play a key role in collecting good quality data which are needed in clinical audits to identify shortcomings in healthcare. Nurse-driven interventions can be designed to improve healthcare. In this study, we capitalize on the unique role of CNS coordinating care for every patient with cancer. COALESCE is the first national collaborative study which uses CNS as researchers and change agents.