ACI open最新文献

{"title":"A Scoping Review of Artificial Intelligence Algorithms in Clinical Decision Support Systems for Internal Medicine Subspecialties","authors":"Ploypun Narindrarangkura, M. Kim, S. Boren","doi":"10.1055/s-0041-1735470","DOIUrl":"https://doi.org/10.1055/s-0041-1735470","url":null,"abstract":"Abstract Objectives Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed to solve medical problems and enhance health care management. We aimed to review the literature to identify trends and applications of AI algorithms in CDSS for internal medicine subspecialties. Methods A scoping review was conducted in PubMed, IEEE Xplore, and Scopus to determine articles related to CDSS using AI algorithms that use deep learning, machine learning, and pattern recognition. This review synthesized the main purposes of CDSS, types of AI algorithms, and overall accuracy of algorithms. We searched the original research published in English between 2009 and 2019. Results Given the volume of articles meeting inclusion criteria, the results of 218 of the 3,467 articles were analyzed and presented in this review. These 218 articles were related to AI-based CDSS for internal medicine subspecialties: neurocritical care (n = 89), cardiovascular disease (n = 79), and medical oncology (n = 50). We found that the main purposes of CDSS were prediction (48.4%) and diagnosis (47.1%). The five most common algorithms include: support vector machine (20.9%), neural network (14.6%), random forest (10.5%), deep learning (9.2%), and decision tree (8.8%). The accuracy ranges of algorithms were 61.8 to 100% in neurocritical care, 61.6 to 100% in cardiovascular disease, and 54 to 100% in medical oncology. Only 20.1% of those algorithms had an explainability of AI, which provides the results of the solution that humans can understand. Conclusion More AI algorithms are applied in CDSS and are important in improving clinical practice. Supervised learning still accounts for a majority of AI applications in internal medicine. This study identified four potential gaps: the need for AI explainability, the lack of ubiquity of CDSS, the narrow scope of target users of CDSS, and the need for AI in health care report standards.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"05 1","pages":"e67 - e79"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42192679","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":"Safe and Effective Digital Anticoagulation: A Continuous Iterative Improvement Approach","authors":"Jodie A Austin, M. Barras, Clair Sullivan","doi":"10.1055/s-0041-1736631","DOIUrl":"https://doi.org/10.1055/s-0041-1736631","url":null,"abstract":"Abstract Background Anticoagulant drugs are the leading cause of medication harm in hospitals and prescribing errors are common with traditional paper prescriptions. Electronic medicines management can reduce prescribing errors for many drugs; however, little is known about the impact of e-prescribing on anticoagulants. Our case study reports on the lessons learned during conversion from paper to e-prescribing and the ongoing optimization process. Methods The iterative implementation of an anticoagulant prescribing platform in an integrated electronic medical record (ieMR) and ongoing continuous enhancements was applied across five digital hospital sites utilizing a single domain. The collaborative management of each class of anticoagulant, optimization strategies, governance structures, and lessons learned is described. An analysis of the rate of errors and adverse events pre- and post-go live is presented. Results The transition to e-prescribing relied on a strong inter-disciplinary governance framework to promote the safe management of anticoagulants. There was no increase in overall prescribing errors, however unfamiliarity with the new system caused a transient increase in errors with unfractionated heparin (1.8/month pre-ieMR vs. 5.5/month post-ieMR). A dedicated real-time surveillance dashboard was introduced. The iterative nature of changes indicated the complexities involved with anticoagulants and the need for an interactive, optimization approach. This led to a significant decrease in anticoagulant related hospital acquired complications (12.1/month pre-ieMR vs. 7.8/month post-ieMR, p = 0.01). Conclusion Digitizing anticoagulant prescribing led to an overall reduction in errors, but a continuous iterative optimization approach was needed to achieve this outcome. The knowledge presented can help inform optimal therapeutic anticoagulation ieMR design strategies.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"05 1","pages":"e116 - e124"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43601652","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":"Can Utilizing Business Intelligence with Electronic Dental Record Data Improve Business Decisions for Dental Organizations: A Scoping Review","authors":"Jared S. Walters, Denise Higgins, M. Irving, J. Wallace","doi":"10.1055/s-0043-1764295","DOIUrl":"https://doi.org/10.1055/s-0043-1764295","url":null,"abstract":"Abstract Background  Business intelligence can give businesses the ability to understand their strengths, weaknesses, and opportunities for improvement and can help reduce uncertainty in the decision-making process. With the increasing use of electronic dental records creating more and more dental data each day, it is an opportune time to determine if the data can be coupled with business intelligence systems to improve the management decision-making process in dental organizations to result in service improvement. Methods  A scoping review was performed to map the research on the use of business intelligence in dental organizations and to identify any gaps in the existing literature. This scoping review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-Scr) framework. The following databases were searched: Medline, Embase, Emcare, Cinahl, Informit, Web of Science, and Scopus. Data extracted from the articles included the organization type, purpose/aims, the software utilized, data sources utilized, outcomes measured, decision-makers involved, service benefit type, and service improvements. Results  In all, 945 articles were found during the search strategy, with 25 articles selected for full-text review. Of these 25 articles, only 3 met the final inclusion in this review. All three included articles were centered around dental school organizations and all situated in the United States. All three articles demonstrated a benefit from management decision-makers utilizing business intelligence systems for improving service efficiency. Conclusion  There is limited evidence to show that managers utilizing business intelligence systems in dental school organizations can lead to improvements in the organization's services. There was no evidence to support the use of a business intelligence system in other types of dental organizations. More research is required in this area.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"07 1","pages":"e16 - e22"},"PeriodicalIF":0.0,"publicationDate":"2021-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42971210","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":"Rapid Implementation of Remote Online Patient Portal Activation in a Children's Hospital during the COVID-19 Pandemic","authors":"K. Vuong, Tiranun Rungvivatjarus, I. P. García, Cindy Lewis, Lisa Miller, Cynthia Kuelbs","doi":"10.1055/s-0041-1729983","DOIUrl":"https://doi.org/10.1055/s-0041-1729983","url":null,"abstract":"Abstract Background Patient portal access allows more patient engagement, yet portal usage is not universal. Telemedicine visits became critical when the coronavirus disease 2019 (COVID-19) pandemic limited in-office visits. Given physical distancing requirements and telemedicine platform integrated into the patient portal, our institution needed to rapidly enroll patients and their proxies remotely, including hospitalized patients, to minimize care disruptions. Objectives The study aimed to describe the rapid implementation of remote patient portal activation in a pediatric integrated delivery network during the COVID-19 pandemic. Additionally, to increase the percentage of patients with active portal status upon discharge by 15% (absolute change) across inpatient units within 3 months. Methods A multidisciplinary taskforce utilized quality improvement tools to identify barriers to successful inpatient patient portal activation followed by rapid intervention cycles to effect change. Weekly progress was monitored on a portal activation dashboard. Results We created clinical decision support tools and training within 2 weeks. Active patient portal status upon hospital discharge increased from 44 to 66% within 12 weeks. Online patient portal conversion within 7 days after hospital discharge increased from 6.0 to 24%. Offer rate increased from 73 to 85% across all inpatient units. Conclusion We rapidly and effectively implemented a remote proxy portal activation process in the inpatient setting. Remote activation, training, clinical decision support, multidisciplinary involvement, and emphasis on the portal as integral to ongoing care allowed us to dramatically increase portal conversion. This experience may be informative for pediatric hospitals aiming to increase proxy portal enrollment and adult hospitals with medical decision-making proxies.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"05 1","pages":"e17 - e26"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1729983","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44102649","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":"\"I didn't know you could do that\": A Pilot Assessment of EHR Optimization Training.","authors":"Rachel Gold,&nbsp;Arwen Bunce,&nbsp;James V Davis,&nbsp;Joan C Nelson,&nbsp;Stuart Cowburn,&nbsp;Jee Oakley,&nbsp;Stacie Carney,&nbsp;Michael A Horberg,&nbsp;James W Dearing,&nbsp;Gerardo Melgar,&nbsp;Joanna E Bulkley,&nbsp;Janet Seabrook,&nbsp;Heath Cloutier","doi":"10.1055/s-0041-1731005","DOIUrl":"https://doi.org/10.1055/s-0041-1731005","url":null,"abstract":"<p><strong>Background: </strong>Informatics tools within electronic health records (EHRs)-for example, data rosters and clinical reminders-can help disseminate care guidelines into clinical practice. Such tools' adoption varies widely, however, possibly because many primary care providers receive minimal training in even basic EHR functions.</p><p><strong>Objectives: </strong>This mixed-methods evaluation of a pilot training program sought to identify factors to consider when providing EHR use optimization training in community health centers (CHCs) as a step toward supporting CHC providers' adoption of EHR tools.</p><p><strong>Methods: </strong>In spring 2018, we offered 10 CHCs a 2-day, 16-hour training in EHR use optimization, provided by clinician trainers, and customized to each CHC's needs. We surveyed trainees pre- and immediately post-training and again 3 months later. We conducted post-training interviews with selected clinic staff, and conducted a focus group with the trainers, to assess satisfaction with the training, and perceptions of how it impacted subsequent EHR use.</p><p><strong>Results: </strong>Six CHCs accepted and received the training; 122 clinic staff members registered to attend, and most who completed the post-training survey reported high satisfaction. Three months post-training, 80% of survey respondents said the training had changed their daily EHR use somewhat or significantly.</p><p><strong>Conclusion: </strong>Factors to consider when planning EHR use optimization training in CHCs include: CHCs may face barriers to taking part in such training; it may be necessary to customize training to a given clinic's needs and to different trainees' clinic roles; identifying trainees' skill level a priori would help but is challenging; in-person training may be preferable; and inclusion of a practice coach may be helpful. Additional research is needed to identify how to provide such training most effectively.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"5 1","pages":"e27-e35"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1731005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39749327","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":"Effective and Collegial Governance of Digital Tools in Health Care","authors":"A. Auerbach, K. Burke, Raman R. Khanna","doi":"10.1055/s-0041-1729984","DOIUrl":"https://doi.org/10.1055/s-0041-1729984","url":null,"abstract":"Abstract Digital transformation of health care has provided an opportunity for broad-based innovation using tools and platforms implemented either through electronic health records (EHRs), adding software to EHRs or prescribing the software to patients directly (e.g., mobile apps). Effective use and management of digital health tools requires a coherent governance strategy that incorporates strategic thinking and clear management priorities. This manuscript reviews nine of the authors’ recommendations for achieving this challenging but critical overall goal for digital health.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"05 1","pages":"e13 - e16"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1729984","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48591794","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":"DeepSuggest: Using Neural Networks to Suggest Related Keywords for a Comprehensive Search of Clinical Notes","authors":"Soheil Moosavinasab, Emre Sezgin, Huan Sun, J. Hoffman, Yungui Huang, Simon M. Lin","doi":"10.1055/s-0041-1729982","DOIUrl":"https://doi.org/10.1055/s-0041-1729982","url":null,"abstract":"Abstract Objective A large amount of clinical data are stored in clinical notes that frequently contain spelling variations, typos, local practice-generated acronyms, synonyms, and informal words. Instead of relying on established but infrequently updated ontologies with keywords limited to formal language, we developed an artificial intelligence (AI) assistant (named “DeepSuggest”) that interactively offers suggestions to expand or pivot queries to help overcome these challenges. Methods We applied an unsupervised neural network (Word2Vec) to the clinical notes to build keyword contextual similarity matrix. With a user's input query, DeepSuggest generates a list of relevant keywords, including word variations (e.g., formal or informal forms, synonyms, abbreviations, and misspellings) and other relevant words (e.g., related diagnosis, medications, and procedures). Human intelligence is then used to further refine or pivot their query. Results DeepSuggest learns the semantic and linguistic relationships between the words from a large collection of local notes. Although DeepSuggest is only able to recall 0.54 of Systematized Nomenclature of Medicine (SNOMED) synonyms on average among the top 60 suggested terms, it covers the semantic relationship in our corpus for a larger number of raw concepts (6.3 million) than SNOMED ontology (24,921) and is able to retrieve terms that are not stored in existing ontologies. The precision for the top 60 suggested words averages at 0.72. Usability test resulted that DeepSuggest is able to achieve almost twice the recall on clinical notes compared with Epic (average of 5.6 notes retrieved by DeepSuggest compared with 2.6 by Epic). Conclusion DeepSuggest showed the ability to improve retrieval of relevant clinical notes when implemented on a local corpus by suggesting spelling variations, acronyms, and semantically related words. It is a promising tool in helping users to achieve a higher recall rate for clinical note searches and thus boosting productivity in clinical practice and research. DeepSuggest can supplement established ontologies for query expansion.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"05 1","pages":"e1 - e12"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1729982","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43009301","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":"Which Strategies Improve Physicians' Experience with Health Information Technology?","authors":"Jacqueline Haskell, Brittany Mandeville, Emily Cooper, Rebekah L. Gardner","doi":"10.1055/s-0041-1731338","DOIUrl":"https://doi.org/10.1055/s-0041-1731338","url":null,"abstract":"Abstract Objectives While electronic health records (EHRs) have improved billing efficiency and note legibility, they may also disrupt clinical workflows, affect patient interactions, and contribute to physician burnout. This study aimed to identify effective strategies, as reported by physicians, to mitigate these EHR shortcomings. Methods The Rhode Island Department of Health administers a health information technology (HIT) survey biennially to all physicians in active practice statewide. The 2019 survey asked physicians about strategies implemented personally or by their practice to improve their experience working with HIT. Physicians who identified at least one strategy were then asked if each implemented strategy was “actually useful.” Results The 2019 survey was administered to 4,266 physicians, with a response rate of 43%. Both office- and hospital-based physicians most commonly reported that their practices had implemented voice-recognition dictation software (48 and 68%, respectively). Office- and hospital-based physicians identified self-care as the most commonly implemented personal change (48 and 47%, respectively). However, 26% of office-based and 15% of hospital-based physicians reported reducing clinical hours or working part-time to improve their experience working with HIT. The strategies identified as “actually useful” varied by practice setting and were not always the most widely implemented approaches. Conclusion Most physicians reported that both they personally and their practices had implemented strategies to improve their experience with HIT. Physicians found some of these strategies more helpful than others, and the strategies identified as most useful differed between office- and hospital-based physicians. From a workforce and access perspective, prioritizing strategies that physicians find “actually useful” is critical, as many physicians in both settings reported reducing clinical hours to improve their experience.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"05 1","pages":"e47 - e53"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1731338","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47598250","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":"The Cosmos Collaborative: A Vendor-Facilitated Electronic Health Record Data Aggregation Platform.","authors":"Yasir Tarabichi,&nbsp;Adam Frees,&nbsp;Steven Honeywell,&nbsp;Courtney Huang,&nbsp;Andrew M Naidech,&nbsp;Jason H Moore,&nbsp;David C Kaelber","doi":"10.1055/s-0041-1731004","DOIUrl":"https://doi.org/10.1055/s-0041-1731004","url":null,"abstract":"<p><strong>Objective: </strong>Learning healthcare systems use routinely collected data to generate new evidence that informs future practice. While implementing an electronic health record (EHR) system can facilitate this goal for individual institutions, meaningfully aggregating data from multiple institutions can be more empowering. Cosmos is a cross-institution, single EHR vendor-facilitated data aggregation tool. This work aims to describe the initiative and illustrate its potential utility through several use cases.</p><p><strong>Methods: </strong>Cosmos is designed to scale rapidly by leveraging preexisting agreements, clinical health information exchange networks, and data standards. Data are stored centrally as a limited dataset, but the customer facing query tool limits results to prevent patient reidentification.</p><p><strong>Results: </strong>In 2 years, Cosmos grew to contain EHR data of more than 60 million patients. We present practical examples illustrating how Cosmos could further efforts in chronic disease surveillance (asthma and obesity), syndromic surveillance (seasonal influenza and the 2019 novel coronavirus), immunization adherence and adverse event reporting (human papilloma virus and measles, mumps, rubella, and varicella vaccination), and health services research (antibiotic usage for upper respiratory infection).</p><p><strong>Discussion: </strong>A low barrier of entry for Cosmos allows for the rapid accumulation of multi-institutional and mostly de-duplicated EHR data to power research and quality improvement queries characteristic of learning healthcare systems. Limitations are being vendor-specific, an \"all or none\" contribution model, and the lack of control over queries run on an institution's healthcare data.</p><p><strong>Conclusion: </strong>Cosmos provides a model for within-vendor data standardization and aggregation and a steppingstone for broader intervendor interoperability.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"5 1","pages":"e36-e46"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0041-1731004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39731135","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":"Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions.","authors":"Sally L Baxter,&nbsp;Jeremy S Bass,&nbsp;Amy M Sitapati","doi":"10.1055/s-0040-1716748","DOIUrl":"https://doi.org/10.1055/s-0040-1716748","url":null,"abstract":"<p><strong>Background: </strong>Electronic health record (EHR) vendors now offer \"off-the-shelf\" artificial intelligence (AI) models to client organizations. Our health system faced difficulties in promoting end-user utilization of a new AI model for predicting readmissions embedded in the EHR.</p><p><strong>Objectives: </strong>The aim is to conduct a case study centered on identifying barriers to uptake/utilization.</p><p><strong>Methods: </strong>A qualitative study was conducted using interviews with stakeholders. The interviews were used to identify relevant stakeholders, understand current workflows, identify implementation barriers, and formulate future strategies.</p><p><strong>Results: </strong>We discovered substantial variation in existing workflows around readmissions. Some stakeholders did not perform any formal readmissions risk assessment. Others accustomed to using existing risk scores such as LACE+ had concerns about transitioning to a new model. Some stakeholders had existing workflows in place that could accommodate the new model, but they were not previously aware that the new model was in production. Concerns expressed by end-users included: whether the model's predictors were relevant to their work, need for adoption of additional workflow processes, need for training and change management, and potential for unintended consequences (e.g., increased health care resource utilization due to potentially over-referring discharged patients to home health services).</p><p><strong>Conclusion: </strong>AI models for risk stratification, even if \"off-the-shelf\" by design, are unlikely to be \"plug-and-play\" in health care settings. Seeking out key stakeholders and defining clear use cases early in the implementation process can better facilitate utilization of these models.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":" ","pages":"e108-e113"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1716748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38672374","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}