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CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

ABSTRACT: Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.

SUBMITTER: Kotecha D

PROVIDER: S-EPMC9452067 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

Kotecha Dipak D Asselbergs Folkert W FW Achenbach Stephan S Anker Stefan D SD Atar Dan D Baigent Colin C Banerjee Amitava A Beger Birgit B Brobert Gunnar G Casadei Barbara B Ceccarelli Cinzia C Cowie Martin R MR Crea Filippo F Cronin Maureen M Denaxas Spiros S Derix Andrea A Fitzsimons Donna D Fredriksson Martin M Gale Chris P CP Gkoutos Georgios V GV Goettsch Wim W Hemingway Harry H Ingvar Martin M Jonas Adrian A Kazmierski Robert R Løgstrup Susanne S Thomas Lumbers R R Lüscher Thomas F TF McGreavy Paul P Piña Ileana L IL Roessig Lothar L Steinbeisser Carl C Sundgren Mats M Tyl Benoît B van Thiel Ghislaine G van Bochove Kees K Vardas Panos E PE Villanueva Tiago T Vrana Marilena M Weber Wim W Weidinger Franz F Windecker Stephan S Wood Angela A Grobbee Diederick E DE

European heart journal 20221001 37

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The ...[more]

PMID: 36208161

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Project description:IntroductionEffective electronic health record (EHR)-based training interventions facilitate improved EHR use for healthcare providers. One such training intervention is simulation-based training that emphasises learning actual tasks through experimentation in a risk-free environment without negative patient outcomes. EHR-specific simulation-based training can be employed to improve EHR use, thereby enhancing healthcare providers' skills and behaviours. Despite the potential advantages of this type of training, no study has identified and mapped the available evidence. To fill that gap, this scoping review will synthesise the current state of literature on EHR simulation-based training.Methods and analysisThe Arksey and O'Malley methodological framework will be employed. Three databases (PubMed, Embase and Cumulative Index to Nursing and Allied Health Literature) will be searched for published articles. ProQuest and Google Scholar will be searched to identify unpublished articles. Databases will be searched from inception to 29 January 2020. Only articles written in English, randomised control trials, cohort studies, cross-sectional studies and case-control studies will be considered for inclusion. Two reviewers will independently screen titles and abstracts against inclusion and exclusion criteria. Then, they will review full texts to determine articles for final inclusion. Citation chaining will be conducted to manually screen references of all included studies to identify additional studies not found by the search. A data abstraction form with relevant characteristics will be developed to help address the research question. Descriptive numerical analysis will be used to describe characteristics of included studies. Based on the extracted data, research evidence of EHR simulation-based training will be synthesised.Ethics and disseminationSince no primary data will be collected, there will be no formal ethical review. Research findings will be disseminated through publications, presentations and meetings with relevant stakeholders.

Project description:BackgroundParticipant recruitment, especially for frail, elderly, hospitalized patients, remains one of the greatest challenges for many research groups. Traditional recruitment methods such as chart reviews are often inefficient, low-yielding, time consuming, and expensive. Best Practice Alert (BPA) systems have previously been used to improve clinical care and inform provider decision making, but the system has not been widely used in the setting of clinical research.ObjectiveThe primary objective of this quality-improvement initiative was to develop, implement, and refine a silent Best Practice Alert (sBPA) system that could maximize recruitment efficiency.MethodsThe captured duration of the screening sessions for both methods combined with the allotted research coordinator hours in the Emerald-COPD (chronic obstructive pulmonary disease) study budget enabled research coordinators to estimate the cost-efficiency.ResultsPrior to implementation, the sBPA system underwent three primary stages of development. Ultimately, the final iteration produced a system that provided similar results as the manual Epic Reporting Workbench method of screening. A total of 559 potential participants who met the basic prescreen criteria were identified through the two screening methods. Of those, 418 potential participants were identified by both methods simultaneously, 99 were identified only by the Epic Reporting Workbench Method, and 42 were identified only by the sBPA method. Of those identified by the Epic Reporting Workbench, only 12 (of 99, 12.12%) were considered eligible. Of those identified by the sBPA method, 30 (of 42, 71.43%) were considered eligible. Using a side-by-side comparison of the sBPA and the traditional Epic Reporting Workbench method of screening, the sBPA screening method was shown to be approximately four times faster than our previous screening method and estimated a projected 442.5 hours saved over the duration of the study. Additionally, since implementation, the sBPA system identified the equivalent of three additional potential participants per week.ConclusionsAutomation of the recruitment process allowed us to identify potential participants in real time and find more potential participants who meet basic eligibility criteria. sBPA screening is a considerably faster method that allows for more efficient use of resources. This innovative and instrumental functionality can be modified to the needs of other research studies aiming to use the electronic medical records system for participant recruitment.

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Project description:IntroductionPhysiotherapy assistants/support workers are an important part of the physiotherapy workforce in the UK. Many of them work in National Health Service (NHS) physiotherapy outpatient services treating patients with musculoskeletal (MSK) conditions. In many services, they take responsibility, under professional supervision, for types of clinical work traditionally undertaken by physiotherapists such as leading exercise classes and treating individual patients. Nevertheless, their role(s) are relatively undefined and as such, there is considerable variation in the duties and tasks they undertake. This study aims to design a framework of 'best practice' in delegation to guide the work of clinicians in NHS physiotherapy MSK services and facilitate standardisation of practice to ensure that patients receive safe and effective treatment by the most appropriate person.Methods and analysisThis mixed-methods study will be conducted in four stages. In stage 1, a focused ethnography in two MSK outpatient physiotherapy services will explore how the current use of delegation is informed by the culture within the clinical setting as well as views, attitudes about, and experiences of, delegation among clinicians, managers and patients. In stage 2a, nominal group technique will be used with three separate groups (physiotherapists/physiotherapy assistants/support workers, managers) to reach a consensus about what components should be included in a best practice framework of delegation. In stage 2b, a discrete choice experiment will elicit patients' preferences between care from physiotherapists and physiotherapy assistants/support workers within MSK physiotherapy services. In the final stage, the results of all previous stages will be triangulated to inform the development of a best practice delegation framework for future testing and use within NHS MSK outpatient physiotherapy services.Ethics and disseminationEthical approval has been granted by the South West-Frenchay Research Ethics Committee. The findings will be disseminated in peer-reviewed journals, conference presentations, the lay press and social media.

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CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

Publications

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

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