ACI open最新文献

{"title":"Feasibility of a Tertiary Hospital Antimicrobial Stewardship Ward Round Using an Electronic Prescribing System—A Pilot Study","authors":"Niall Hamilton., S. Gardiner, Q. Chuah, P. Chin, S. Dalton, S. Metcalf","doi":"10.1055/s-0040-1716749","DOIUrl":"https://doi.org/10.1055/s-0040-1716749","url":null,"abstract":"Abstract Objectives The aim was to conduct a pilot study to determine staff resource requirements for an antimicrobial stewardship (AMS) ward round informed by electronic prescriptions, and the number of restricted antimicrobial prescriptions that would prompt a ward round recommendation. Methods Prescription data on 26 restricted antimicrobial agents (which have specific prescribing criteria defined by the national drug funding agency) were extracted from the electronic prescribing and administration system (MedChart). A language query was used for specific antimicrobial names on Mondays, Wednesdays, and Fridays over 4 weeks. Prescriptions that had ceased or had an appropriate indication documented were excluded. The remaining prescriptions were evaluated in the ward round upon clinical record review with theoretical AMS recommendations made and time requirements recorded. The ward rounds were performed by two clinical staff, a doctor and pharmacist. Results In 12 days, 622 prescriptions were extracted. Of these, 66 were evaluated in ward rounds, with 67% (44/66) being for ciprofloxacin or piperacillin-tazobactam. Theoretical AMS recommendations were made in 61% (40/66) of cases, 45% (18/40) being to use a narrower spectrum agent, 30% (12/40) to consult the Infectious Diseases Service, and 23% (9/40) to stop antimicrobial therapy. Data extraction took an analyst approximately 15 minutes, screening by a doctor approximately 20 minutes, and ward rounds approximately 41 minutes per day. Conclusion Our approach required, in total, approximately 100 clinical staff minutes per day to screen approximately 50 prescriptions and identify and evaluate approximately four prescriptions and showed clinical value. Resource planning should also consider Infectious Diseases and/or Microbiology (physician and service) involvement, and audit capability.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"04 1","pages":"e119 - e125"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1716749","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43552020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solutions for Unexpected Challenges Encountered when Integrating Research Genomics Results into the EHR.","authors":"Luke V Rasmussen, Christin Hoell, Maureen E Smith, Rex Chisholm, Justin Starren, Sharon Aufox, Yuan Luo, Laura J Rasmussen-Torvik","doi":"10.1055/s-0040-1719059","DOIUrl":"10.1055/s-0040-1719059","url":null,"abstract":"<p><strong>Background: </strong>While there have been published reports detailing technical challenges of incorporating genetic test results into the electronic health record (EHR) with proposed solutions, less has been published about unanticipated sociotechnological or practical communication challenges involved in this process.</p><p><strong>Objectives: </strong>This study was aimed to describe unanticipated issues that arose returning genetic research results through the EHR as part of the National Human Genome Research Institute (NHGRI)-funded electronic Medical Records and Genomics (eMERGE) 3 consortium, and provide lessons learned for future implementations.</p><p><strong>Methods: </strong>We sequenced 3,000 participants on a 109-gene panel and returned genetic results initially in person and/or by letter, with a later release directly into the EHR and patient portal.</p><p><strong>Results: </strong>When results were returned through the EHR, multiple participants expressed confusion and contacted the health system, resulting in our institution temporarily freezing our return of research results.</p><p><strong>Discussion: </strong>We determined the likely causes of this issue to be (1) the delay between enrollment and results return, (2) inability to personalize mass e-mail messages announcing new research test results in the EHR, (3) limited space for description of test results in the EHR, and (4) the requirement to list an ordering physician for research results in the EHR. For future return of results, we propose sending preparatory e-mails to participants, including screenshots of how they can expect to see their results presented in the EHR portal.</p><p><strong>Conclusion: </strong>We hope our lessons learned can provide helpful guidance to other sites implementing research genetic results into the EHR and can encourage EHR developers to incorporate greater flexibility in the future.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"04 1","pages":"e132-e135"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13344334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45799435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of Comorbid Psychiatric Conditions among Youth Detainees in Juvenile Justice System using Clinical Data","authors":"Humayera Islam, A. Mosa, Hirsch K. Srivastava, Vasanthi Mandhadi, D. Rajendran, Laine Young-Walker","doi":"10.1055/s-0040-1718542","DOIUrl":"https://doi.org/10.1055/s-0040-1718542","url":null,"abstract":"Abstract Objective The main aim was to analyze the prevalence and patterns of comorbidity in 11 identified broad categories of psychiatric conditions and 48 specific psychiatric conditions among 613 youth from the Missouri Division of Youth Services (DYS) residential sites using advanced data mining techniques on clinical assessment data. Methods This study was based on youth detainee population at DYS residential placements receiving psychiatric care through the telemedicine network established between DYS and University of Missouri Department of Psychiatry. Association Rule Mining (ARM) algorithm was used to determine the associations and the co-occurrence pattern among the comorbid psychiatric conditions. Results About 88% of the DYS youth are diagnosed with two or more psychiatric disorders. From the ARM analysis, the most commonly co-occurred disorders are obtained as substance-related or -addicted disorders (SUD) and disruptive, impulse-control, and conduct disorders (CD) (n [%] = 258 [42.1%], followed by SUD, CD, and depressive disorder (DD) (145 [23.7%]), SUD, CD, and neurodevelopmental disorder (NDD) (133 [21.7%]), and DD, CD and NDD (120 [19.6%]). Discussion The study found high prevalence of comorbidity among the youth patients of the Missouri DYS facilities receiving care through the University of Missouri telemedicine network. The ideal scenario for assessment of any of these disorders in a patient should include substantial consideration in delineating the symptoms and history before eliminating any of them. Conclusion The comorbid patterns obtained can help in determining treatment regimens for DYS youth that can be effective in reducing recidivism and delinquency.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"04 1","pages":"e136 - e148"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1718542","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46074059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adapting Clinical Systems to Enable Adolescents' Genomic Choices.","authors":"Cynthia A Prows,&nbsp;Keith Marsolo,&nbsp;Melanie F Myers,&nbsp;Jeremy Nix,&nbsp;Eric S Hall","doi":"10.1055/s-0040-1718747","DOIUrl":"https://doi.org/10.1055/s-0040-1718747","url":null,"abstract":"<p><strong>Background: </strong>We offered adolescents personalized choices about the type of genetic results they wanted to learn during a research study and created a workflow to filter and transfer the results to the electronic health record (EHR).</p><p><strong>Methods: </strong>We describe adaptations needed to ensure that adolescents' results documented in the EHR and returned to adolescent/parent dyads matched their choices. A web application enabled manual modification of the underlying laboratory report data based on adolescents' choices. The final PDF format of the laboratory reports was not viewable through the EHR patient portal, so an EHR form was created to support the manual entry of discrete results that could be viewed in the portal.</p><p><strong>Results: </strong>Enabling adolescents' choices about genetic results was a labor-intensive process. More than 350 hours was required for development of the application and EHR form, as well as over 50 hours of a study professional's time to enter choices into the application and EHR. Adolescents and their parents who learned genetic results through the patient portal indicated that they were satisfied with the method of return and would make their choices again if given the option.</p><p><strong>Conclusion: </strong>Although future EHR upgrades are expected to enable patient portal access to PDFs, additional improvements are needed to allow the results to be partitioned and filtered based on patient preferences. Furthermore, separating these results into more discrete components will allow them to be stored separately in the EHR, supporting the use of these data in clinical decision support or artificial intelligence applications.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":" ","pages":"e126-e131"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1718747","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40385016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pilot Implementation of Clinical Genomic Data into the Native Electronic Health Record: Challenges of Scalability","authors":"Nephi A. Walton, Darren K. Johnson, T. Person, Jonathon C. Reynolds, Marc S. Williams","doi":"10.1055/s-0040-1721490","DOIUrl":"https://doi.org/10.1055/s-0040-1721490","url":null,"abstract":"Abstract Background Recently, electronic health record (EHR) vendors have allowed for the storage of discrete genomic data within the EHR. With this new capability there remain many challenges related to adoption and implementation. Geisinger has genomic data available for approximately 145,000 patients. With the goal of integrating genetic data into the EHR for the entire sequenced population, we performed a pilot study on a subpopulation to assess the plausibility of large-scale deployment. Objectives Import genetic results into the EHR for seven pharmacogenes, Center for Disease Control tier one conditions, and hereditary hemochromatosis. Build decision support and information resources for each condition. Identify barriers to scaling the deployment. Identify short-term and long-term solutions to overcome identified barriers. Methods Discrete genomic variants were imported into the EHR. Maintenance guidelines and information resources were developed for each genetic condition and best practice alerts were built for pharmacogenomic variants. Weekly calls were held to address barriers to implementation. A list of challenges was maintained throughout the process. Solutions were proposed and categorized into short-term and long-term solutions. Results Challenges were identified and solutions proposed for five discrete areas: Transmitting data from the laboratory to the EHR. Maintenance of information and decision support. Disparate implementation needs of geneticists versus primary care. Differing perceptions/uses of discrete genomic data versus a PDF report. Classification and reclassification of variants. Conclusion Scaling the use of genomic data in the EHR requires the engagement of hospitals, open standards communities, laboratories, EHR vendors, and genomic information resources.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"04 1","pages":"e162 - e166"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1721490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47329968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deploying Clinical Decision Support for Familial Hypercholesterolemia.","authors":"Hana Bangash,&nbsp;Joseph Sutton,&nbsp;Justin H Gundelach,&nbsp;Laurie Pencille,&nbsp;Ahmed Makkawy,&nbsp;Omar Elsekaily,&nbsp;Ozan Dikilitas,&nbsp;Ali Mir,&nbsp;Robert Freimuth,&nbsp;Pedro J Caraballo,&nbsp;Iftikhar J Kullo","doi":"10.1055/s-0040-1721489","DOIUrl":"https://doi.org/10.1055/s-0040-1721489","url":null,"abstract":"<p><strong>Objective: </strong>Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center.</p><p><strong>Methods: </strong>CDS development and implementation were conducted in four phases: (1) development and validation of an algorithm to identify \"possible FH\"; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS.</p><p><strong>Results: </strong>The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified: (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine.</p><p><strong>Conclusion: </strong>We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"4 2","pages":"e157-e161"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1721489","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10732916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indispensability of Clinical Bioinformatics for Effective Implementation of Genomic Medicine in Pathology Laboratories","authors":"S. Chamala, S. Majety, Sheshnath Mishra, Kimberly J. Newsom, Shaileshbhai R. Gothi, Nephi A. Walton, R. Dolin, P. Starostik","doi":"10.1055/s-0040-1721480","DOIUrl":"https://doi.org/10.1055/s-0040-1721480","url":null,"abstract":"Abstract Patient care is rapidly evolving toward the inclusion of precision genomic medicine when genomic tests are used by clinicians to determine disease predisposition, prognosis, diagnosis, and improve therapeutic decision-making. However, unlike other clinical pathology laboratory tests, the development, deployment, and delivery of genomic tests and results are an intricate process. Genomic technologies are diverse, fast changing, and generate massive data. Implementation of these technologies in a Clinical Laboratory Improvement Amendments-certified and College of American Pathologists-accredited pathology laboratory often require custom clinical grade computational data analysis and management workflows. Additionally, accurate classification and reporting of clinically actionable genetic mutation requires well-curated disease/application-specific knowledgebases and expertise. Moreover, lack of “out of the box” technical features in electronic health record systems necessitates custom solutions for communicating genetic information to clinicians and patients. Genomic data generated as part of clinical care easily adds great value for translational research. In this article, we discuss current and future innovative clinical bioinformatics solutions and workflows developed at our institution for effective implementation of precision genomic medicine across molecular pathology, patient care, and translational genomic research.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"04 1","pages":"e167 - e172"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1721480","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49564880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An HIT Ecosystem Capability Maturity Model for System-Wide Implementation, Management, and Governance","authors":"Jennifer E. Shivers, J. Amlung, T. Cullen","doi":"10.1055/s-0040-1715171","DOIUrl":"https://doi.org/10.1055/s-0040-1715171","url":null,"abstract":"Abstract Background An effective health system is dependent on the people, processes, and technology required to support its successful use. Technology, including essential health information technology (HIT) components, is not enough to successfully support quality patient care delivery. Without a strong support structure, HIT tools and data alone will fail to meet the desired goal of quality patient care that achieves health equity. Multiple factors, including governance processes, policies, and standards, are critical for the development, implementation, and consistent and effective use of clinical HIT systems and the resulting data. A comprehensive model that provided support for a holistic assessment of the HIT ecosystem could not be identified. The Facility HIT Ecosystem Capability Maturity Model (ECMM) was designed in response to this need. Objectives This project was designed to create and propose a comprehensive framework for self-assessment and support of a maturation pathway for a facility's comprehensive HIT ecosystem, including governance processes, policies, and standards. Methods Iterative methods based on agile and human-centered design practices were used to create and validate the framework and its contents. Results The ECMM and toolkit support a health care facility's clinical or technical leadership, including chief medical officers, chief information officers, and chief medical/health/nursing informatics officers, in identifying and assessing policies, processes, and capabilities surrounding HIT. This framework supports iterative evaluation and step-ladder style progression and goal setting to achieve desired capabilities, both at the local level and at the level of supporting organizations. Reviewer feedback was used to provide iterative tool improvement and refinement. Conclusion The proposed HIT maturity model toolkit showed potential as a tool to help empower health care facilities and their leadership to advance their HIT ecosystem.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"04 1","pages":"e102 - e107"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1715171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42910034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adult Learning Principles and Peer Delivery Improve Satisfaction of Electronic Medical Record Onboarding Education","authors":"J. Lawrence, S. Tanny, Victoria Heaton, L. Andrew","doi":"10.1055/s-0040-1716747","DOIUrl":"https://doi.org/10.1055/s-0040-1716747","url":null,"abstract":"Abstract Objectives Given the importance of onboarding education in ensuring the safety and efficiency of medical users in the electronic medical record (EMR), we re-designed our EMR curriculum to incorporate adult learning principles, informed and delivered by peers. We aimed to evaluate the impact of these changes based on their satisfaction with the training. Methods A single site pre- and post-observational study measured satisfaction scores (four questions) from junior doctors attending EMR onboarding education in 2018 (pre-implementation) compared with 2019 (post-implementation). An additional four questions were asked in the post-implementation survey. All questions employed a Likert scale (1–5) with an opportunity for free-text. Raw data were used to calculate averages, standard deviations and the student t-test was used to compare the two cohorts where applicable. Results There were a total of 98 respondents in 2018 (pre-implementation) and 119 in 2019 (post-implementation). Satisfaction increased from 3.8/5 to 4.5/5 (p < 0.0001) following implementation of a peer-delivered curriculum in line with adult learning practices. The highest-rated factors were being taught by other doctors (4.9/5) and doctors having the appropriate knowledge to deliver training (4.9/5). Ninety-two percent of junior doctors were motivated to engage in further EMR education and 90% felt classroom support was adequate. Conclusion EMR onboarding education for medical users is a critical ingredient to organizational safety and efficiency. An improvement in satisfaction ratings by junior doctors was demonstrated after significant re-design of the curriculum was informed and delivered by peers, in line with adult learning principles.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"04 1","pages":"e114 - e118"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1716747","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48952341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Usability Evaluation of GreyMatters: A Memory Clinic Information System","authors":"A. Tapuria, Matt Evans, V. Curcin, T. Austin, N. Lea, D. Kalra","doi":"10.1055/s-0040-1719060","DOIUrl":"https://doi.org/10.1055/s-0040-1719060","url":null,"abstract":"Abstract Objective This paper presents the development process of GreyMatters, a memory clinic system, outlining the conceptual, practical, technical, and ethical aspects, and focuses on the usability evaluation of the system. There was a need for a system to be developed for the memory clinics of Berkshire Healthcare National Health Service (NHS) Foundation Trust (BHFT) to aid the clinical and administrative processes of assessing, diagnosing, managing, and treating patients with cognitive disorders and mental health problems. Methods The methodology for development of the information system involved phases of requirements gathering, modeling, and prototype creation, and “bench testing” the prototype with experts. The standard Institute of Electrical and Electronics Engineers (IEEE) recommended approach for the specifications of software requirements was adopted. An electronic health record (EHR) standard (EN13606) was used, and clinical modeling was done through archetypes and the project complied with data protection and privacy legislation. Usability evaluation of GreyMatters was done using the IBM questionnaires. Results Though the initial development was complex, the requirements, methodology, and standards adopted made the construction, deployment, adoption, and population of a memory clinic and research database feasible. The electronic patient data including the assessment scales and scores provide a rich source of objective data for audits and research. In the usability evaluation of GreyMatters, overall responses to the Computer System Usability Questionnaire and After-Scenario Questionnaire demonstrated mild-to-moderate satisfaction with the overall system and with individual tasks. The results support that the system is an acceptable tool for clinical, administrative, business, and research use and forms a useful part of the wider information architecture. The implementation and sustainability issues and the lessons learnt were noted. Discussion The development of a system needs to take into account the existing data collection methods and other information systems that will be used alongside. Use of graphical development tools to communicate requirements, build interfaces, and prototype may improve the quality and efficiency of system development. Standardized data collection assists in the provision of reports for clinical, audit, and service development use to meet the requirements of commissioners and to allow the easier identification of potential research participants. It is possible that in the usability evaluation, the satisfaction scores are overall lower due to the extra complication of using this system in addition to the Trust's main EHR. The small number of users is a limitation. Conclusion The establishment of requirements and methodology, addressing issues of data security and confidentiality, future data compatibility, and interoperability and medicolegal aspects, such as access controls and audit trails, led to a rob","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"04 1","pages":"e149 - e156"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1719060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42272921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}