ACI open最新文献

{"title":"Relationship between Diabetes Self-Management and the Use of Health Care Apps: A Cross-Sectional Study","authors":"Satoshi Inagaki, Kenji Kato, Kozue Abe, Hiroaki Takahashi, Tomokazu Matsuda","doi":"10.1055/s-0043-1766113","DOIUrl":"https://doi.org/10.1055/s-0043-1766113","url":null,"abstract":"Abstract Background  People with diabetes are increasingly using smartphone health care applications (apps) to manage their health. However, few studies have examined the percentage of people with diabetes using health care apps and their relationship to self-care. Objective  The purpose of this study is to determine the prevalence of health care apps among people with diabetes and the relationship between app use and self-management. Methods  A cross-sectional study was conducted using an online survey among people with type 2 diabetes. Multiple linear regression analysis was conducted using the scores of the Japanese version of Summary of Diabetes Self-Care Activities and exercise and general diet subscales as the objective variables. Results  Of 253 participants included in this study, 61 (24.1%) used health care apps. Approximately 20% of those aged ≥ 60 also used health care apps. Use of health care apps was a significant predictor of physical activity frequency along with autonomous motivation ( p  < 0.001). Participants who used health care apps showed a 0.91 point higher physical activity score than those who did not. Regarding the general diet score, the use of health care apps was not significantly associated with dietary habits ( p  = 0.29). Conclusion  Among people with type 2 diabetes, 24.1% used health care apps, and self-management scores of exercise were significantly higher in people with diabetes who used health care apps than in those who did not.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"07 1","pages":"e23 - e29"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49667733","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":"Variations among Electronic Health Record and Physiologic Streaming Vital Signs for Use in Predictive Algorithms in Pediatric Severe Sepsis","authors":"Adam C. Dziorny, R. Lindell, J. Fitzgerald, Christopher P. Bonafide","doi":"10.1055/s-0042-1755373","DOIUrl":"https://doi.org/10.1055/s-0042-1755373","url":null,"abstract":"\u0000 Objective This study sought to describe the similarities and differences among physiologic streaming vital signs (PSVSs) and electronic health record (EHR)-documented vital signs (EVSs) in pediatric sepsis.\u0000 Methods In this retrospective cohort study, we identified sepsis patients admitted to the pediatric intensive care unit. We compared PSVS and EVS measures of heart rate (HR), respiratory rate, oxyhemoglobin saturation, and blood pressure (BP) across domains of completeness, concordance, plausibility, and currency.\u0000 Results We report 1,095 epochs comprising vital sign data from 541 unique patients. While counts of PSVS measurements per epoch were substantially higher, increased missingness was observed compared with EVS. Concordance was highest among HR and lowest among BP measurements, with bias present in all measures. Percent of time above or below defined plausibility cutoffs significantly differed by measure. All EVS measures demonstrated a mean delay from time recorded at the patient to EHR entry.\u0000 Conclusion We measured differences between vital sign sources across all data domains. Bias direction differed by measure, possibly related to bedside monitor measurement artifact. Plausibility differences may reflect the more granular nature of PSVS which can be critical in illness detection. Delays in EVS measure currency may impact real-time decision support systems. Technical limitations increased missingness in PSVS measures and reflect the importance of systems monitoring for data continuity. Both PSVS and EVS have advantages and disadvantages that must be weighed when making use of vital signs in decision support systems or as covariates in retrospective analyses.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44026385","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":"Physicians' Perceptions and Expectations of an Artificial Intelligence-Based Clinical Decision Support System in Cancer Care in an Underserved Setting","authors":"Rubina F. Rizvi, S. Emani, H. L. Rocha, Camila M. de Aquino, Pamela M. Garabedian, A. Rui, Carlos André Moura Arruda, Megan Sands-Lincoln, R. Rozenblum, W. Felix, G. Jackson, S. Juaçaba, D. Bates","doi":"10.1055/s-0042-1751088","DOIUrl":"https://doi.org/10.1055/s-0042-1751088","url":null,"abstract":"\u0000 Objectives Artificial intelligence (AI) tools are being increasingly incorporated into health care. However, few studies have evaluated users' expectations of such tools, prior to implementation, specifically in an underserved setting.\u0000 Methods We conducted a qualitative research study employing semistructured interviews of physicians at The Instituto do Câncer do Ceará, Fortaleza, Brazil. The interview guide focused on anticipated, perceived benefits and challenges of using an AI-based clinical decision support system tool, Watson for Oncology. We recruited physician oncologists, working full or part-time, without prior experience with any AI-based tool. The interviews were taped and transcribed in Portuguese and then translated into English. Thematic analysis using the constant comparative approach was performed.\u0000 Results Eleven oncologists participated in the study. The following overarching themes and subthemes emerged from the analysis of interview transcripts: theme-1, “general context” including (1) current setting, workload, and patient population and (2) existing challenges in cancer treatment, and theme-2, “perceptions around the potential use of an AI-based tool,” including (1) perceived benefits and (2) perceived challenges. Physicians expected that the implementation of an AI-based tool would result in easy access to the latest clinical recommendations, facilitate standardized cancer care, and allow it to be delivered with greater confidence and efficiency. Participants had several concerns such as availability of innovative treatments in resource-poor settings, treatment acceptance, trust, physician autonomy, and workflow disruptions.\u0000 Conclusion This study provides physicians' anticipated perspectives, both benefits and challenges, about the use of an AI-based tool in cancer treatment in a resource-limited setting.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44230227","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":"Using a Sociotechnical Model to Understand Challenges with Sepsis Recognition among Critically Ill Infants","authors":"D. Karavite, M. Harris, R. Grundmeier, L. Srinivasan, Gerald P. Shaeffer, Naveen Muthu","doi":"10.1055/s-0042-1749318","DOIUrl":"https://doi.org/10.1055/s-0042-1749318","url":null,"abstract":"\u0000 Objective The aim of the study is to apply a sociotechnical model to the requirements phase of implementing a machine learning algorithm-based system to support sepsis recognition in the neonatal intensive care unit.\u0000 Methods We incorporated components from the sociotechnical model, Safety in Engineering for Patient Safety 2.0, in three requirements phase activities: (1) semi-structured interviews, (2) user profiles, and (3) system use cases.\u0000 Results Thirty-one neonatal intensive care unit clinicians participated in semi-structured interviews (11 nurses, 10 front line ordering clinician, five fellows, and five attending physician). Interview transcripts were coded and then compiled into themes deductively based on components from the sociotechnical model (persons, environment, organization, tasks, tools and technology, collaboration, and outcomes). The interview analysis was used to create four user profiles defining responsibilities in sepsis recognition, team collaboration, and attributes relevant to sepsis recognition. Two user profiles (nurse, front line ordering clinician) included variants based on experience relevant to sepsis recognition. The interview analysis was used to develop three system use cases representing clinical sepsis scenarios. Each use case defines the precondition, actors, and high-level sequence of actions, and includes variants based on sociotechnical works system factors that can complicate sepsis recognition. The interview analysis, user profiles, and use cases serve as the foundation for supporting sociotechnical design to all subsequent human-centered design methods including subject recruitment, formative design, summative user testing, and simulation testing.\u0000 Conclusion Integration of the sociotechnical model-guided requirements gathering activities, analysis, and deliverables by framing a range of sociotechnical components and the interconnectedness of these components in the broader work system. Applying the sociotechnical model resulted in discovering work system, process, and outcome requirements that would otherwise be difficult to capture, or missed entirely, using traditional requirements gathering methods or approaches to clinical decision support design.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46231023","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 Impact of an Organization-Wide Electronic Health Record (EHR) System Upgrade on Physicians' Daily EHR Activity Time: An EHR Log Data Study","authors":"Lori Wong, K. Sexton, Joseph A. Sanford","doi":"10.1055/s-0042-1757156","DOIUrl":"https://doi.org/10.1055/s-0042-1757156","url":null,"abstract":"\u0000 Objective This article assesses the impact of a health care organization's electronic health record (EHR) upgrade on providers' daily EHR activity time.\u0000 Methods Daily EHR activity time (minutes/day) was acquired through EHR log data that automatically tracks user activity. Subjects were attending and resident physicians in the departments of family medicine, hospitalist medicine, and the neonatal intensive care unit working in the inpatient setting. The EHR upgrade occurred in August 2020, and the comparison groups were pre-upgrade (May 31, 2020–July 25, 2020) and post-upgrade (August 30, 2020–October 31, 2020). A two-tailed, two-sample t-test was used to assess statistical significance.\u0000 Results The pre-upgrade group had 146 users, and the post-upgrade group had 140 users. There was no statistically significant difference between the pre-upgrade group (mean (M): 104.74 minutes/day, standard deviation [SD]: 70.64) and post-upgrade group (M: 103.38 minutes/day, SD: 64.77), even after splitting the data by user type and user type and department.\u0000 Conclusion This study showed no significant difference in daily EHR activity time post-upgrade. More research is needed to truly understand the impact of EHR upgrades on user efficiency. Understanding the content of each upgrade might be key in understanding their effect on users, and we hope to explore that in the future.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47641424","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":"COVID-19 and the Electronic Health Record: Tool Design and Evolution at the U.S. Pandemic Epicenter","authors":"Surafel Tsega, M. Vijayaraghavan, Marianne Chronister, S. Srinivas, A. Bassily-Marcus, J. Gumprecht, Avniel Shetreat-Klein, Bruce Darrow, C. Craven","doi":"10.1055/s-0042-1756435","DOIUrl":"https://doi.org/10.1055/s-0042-1756435","url":null,"abstract":"\u0000 Objective We detail inpatient electronic health record (EHR) system tools created at Mount Sinai Health System for the clinical management of patients with coronavirus disease 2019 (COVID-19) during the early pandemic months in the U.S. epicenter, New York City. We discuss how we revised these tools to create a robust Care pathway, unlike other tools reported, that helped providers care for these patients as guidelines evolved.\u0000 Methods Mount Sinai Health System launched a Command Center on March 8, 2020. The Chief Medical Information Officer launched a workgroup of clinical informaticists and Epic analysts tasked with rapidly creating COVID-19-related EHR tools for the inpatient setting.\u0000 Results Initial EHR tools focused on inpatient order sets for care standardization and resource utilization. In preparation for a fall 2020-winter 2021 surge, we created a clinician-facing, integrated Care pathway incorporating additional Epic System-specific tools: a Care Path, a dedicated Navigator, Summary and Timeline Reports, and SmartTexts.\u0000 Discussion Initial tools offered standard functionality but included complex decision-making support to account for the lack of COVID-19 clinical knowledge, operational challenges during a dramatic patient surge, and resource limitations. We revised content and built a more comprehensive Care pathway that provided real-time clinical data along with treatment recommendations as knowledge evolved, e.g., convalescent plasma.\u0000 Conclusion We have provided a framework that can inform future informaticists in developing EHR tools during an evolving pandemic.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47112180","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":"Power of Heuristics to Improve Health Information Technology System Design.","authors":"Don Roosan, Justin Clutter, Brian Kendall, Charlene Weir","doi":"10.1055/s-0042-1758462","DOIUrl":"10.1055/s-0042-1758462","url":null,"abstract":"<p><strong>Background: </strong>Clinical decision-making can be prone to error if health system design does not match expert clinicians' higher cognitive skills. There is a gap in understanding the need for the importance of heuristics in clinical decision-making. The heuristic approach can provide cognitive support in designing intuitive health information systems for complex cases.</p><p><strong>Objective: </strong>We explored complex decision-making by infectious diseases (ID) clinicians focusing on fast and frugal heuristics. We hypothesized that ID clinicians use simple heuristics to understand complex cases using their experience.</p><p><strong>Methods: </strong>The study utilized cognitive task analysis and heuristics-based decision modeling. We conducted cognitive interviews and provided clinicians with a fast-and-frugal tree algorithm to convert complex information into simple decision algorithms. We conducted a critical decision method-based analysis to generate if-then logic sentences from the transcript. We conducted a thematic analysis of heuristics and calculated the average time to complete and the number of crucial information in the decision nodes.</p><p><strong>Results: </strong>A total of 27 if-then logic heuristics sentences were generated from analyzing the data. The average time to construct the fast-and-frugal trees was 1.65 ± 0.37 minutes, and the average number of crucial pieces of information clinicians focused on was 5.4 ± 3.1.</p><p><strong>Conclusion: </strong>Clinicians use shortcut mental models to reduce complex cases into simple mental model algorithms. The innovative use of artificial intelligence could allow clinical decision support systems to focus on creative and intuitive interface design matching the higher cognitive skills of expert clinicians.</p>","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"06 1","pages":"e114-e122"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13556268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44068512","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":"Surgeon-Perceived Requirements for a Platform to Integrate Patient-Reported Outcome Measures into Clinical Practice","authors":"L. Leonard, Brittni Driscoll, Sudheer Vemuru, Alexandra Kovar, Joshua Billings, Simon P. Kim, Chen-Tan Lin, S. Tevis, E. Cumbler","doi":"10.1055/s-0043-1771238","DOIUrl":"https://doi.org/10.1055/s-0043-1771238","url":null,"abstract":"Abstract Background  Patient-reported outcome measures (PROMs) are standardized, validated tools that translate subjective patient-reported concerns about their health status into quantitative data. PROMs were initially developed as research instruments; however, they have more recently been recognized as important clinical tools. PROMs have not been widely adopted into surgical practices and this study sought to uncover the system requirements of a platform to integrate PROMs into surgical practice, as perceived by surgeons. Methods  Semi-structured interviews were performed from November 2019 until August of 2020. Interviews continued until thematic saturation was achieved. All interviews were recorded and transcribed verbatim. Qualitative interview data were thematically analyzed using an inductive approach. Results  Analysis revealed 12 system features desired by surgeons for a platform to integrate PROMs into clinical use. These were further grouped into four unique overarching themes. Surgeons asserted that the platform must (1) be user-friendly, (2) promote information transparency, (3) incorporate validated questionnaires while still allowing for some degree of customizability, and (4) support the collection and display of longitudinal data. Conclusions  Health care systems planning to develop a platform to integrate PROMs into their clinical practices should investigate the feasibility of the system features identified as essential by this study. While surgeons represent an important stakeholder group when designing a new platform for use in surgical practice, it will also be crucial to explore the features desired by patients before designing or adopting a platform for clinical use.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"07 1","pages":"e41 - e48"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43308653","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":"Implementation of Eye-Tracking Technology to Monitor Clinician Fatigue in Routine Clinical Care: A Feasibility Study","authors":"Bashar Kadhim, Saif S. Khairat, Fangyong Li, I. Gross, Bidisha Nath, R. Hauser, E. Melnick","doi":"10.1055/s-0042-1760267","DOIUrl":"https://doi.org/10.1055/s-0042-1760267","url":null,"abstract":"Abstract Background  Physician fatigue increases the likelihood of medical errors. Eye-tracking technology offers an unobtrusive and objective way to measure fatigue but has only been implemented in controlled settings. Objective  Our objective was to determine the feasibility of capturing physiological indicators of fatigue using eye-tracking technology in a real-world clinical setting. Methods  A mixed-methods feasibility study was performed in a convenience sample of clinicians practicing in an urban, academic emergency department from November 11 to December 15, 2021. Outcomes included fatigue assessed at the beginning and end of each shift via eye-tracking (with low scores indicating greater fatigue) and self-report. Results  Among 15 participants, self-reported fatigue and task load increased from the beginning to the end of their shift (fatigue visual analog scale [FVAS] 3.7–4.6, p  = 0.04; physician task load [PTL] 97.7–154.3, p  = 0.01). It was feasible to collect eye-tracking data at a fixed computer workstation with twice daily calibration and 61% capture of reliable data when the clinician was working at the study computer. Eye-tracking metrics did not change significantly from the beginning to the end of the shift. Eye metric fatigue score was associated with the change in PTL score (r 0.59, p  = 0.02) but not FVAS. This association persisted after adjusting for age, gender, and role, with every 10-point increase in PTL, there was a 0.02-point increase in fatigue score ( p  = 0.04). Conclusion  It is unclear whether the inability to detect fatigue via eye-tracking in routine clinical care was due to confounding factors, the technology, study design, sample size, or an absence of physiological fatigue. Further research and advances in functionality are needed to determine the eye-tracking technology's role in measuring clinician fatigue in routine care.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"07 1","pages":"e1 - e7"},"PeriodicalIF":0.0,"publicationDate":"2022-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57975870","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 Prototyping and Implementation of Electronic Order Sets for Critically Ill Adults with COVID-19 Admitted to a Children's Hospital","authors":"N. McKinnon, S. Silver, S. Pong, W. Seto, Phoebe Chan, E. Gilfoyle, K. Jessa, Seth Gray","doi":"10.1055/s-0042-1758074","DOIUrl":"https://doi.org/10.1055/s-0042-1758074","url":null,"abstract":"Abstract Objectives  An eight-bed adult coronavirus (COVID-19) critical care (CC) unit was established within our pediatric CC unit (PCCU) when SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) variants increased the CC bed demand. Our objective was to rapidly implement electronic order sets (OSs) to facilitate computerized provider order entry (CPOE) for adult patients admitted within a children's hospital. Methods  OS development began from the assessment of OSs from seven adult CC units. Using a pre-existing PCCU admission template, we created two OSs: adult COVID-19 admission and ongoing care. We tested the prototypes in a multidisciplinary onsite–virtual hybrid tabletop simulation to evaluate usability within established workflows. Participants utilized role-specific profiles within the electronic health record (EHR) training environment which paralleled their computer interface, permitting charting and documentation. EHR analysts were present to gather change requests. Following implementation, we performed twice-daily huddles with end users to identify issues. Results  A total of 13 multidisciplinary bedside providers participated in simulation testing of the prototypes. Two safety issues were addressed before implementation. The electronic OSs were developed, tested, and implemented within 8 days. The postimplementation huddles identified one medication addition, and no deletions were necessary. Conclusion  Caring for adult COVID-19 patients within a freestanding children's hospital presents challenges and has the potential to introduce latent safety threats. Rapid development and implementation of electronic OSs within 8 days to facilitate CPOE and reduce health care provider cognitive burden relied on leveraging functionality within the EMR system, performing iterative testing with a tabletop simulation, integration into previously established workflows, and gathering post-implementation feedback for continuous improvement.","PeriodicalId":72041,"journal":{"name":"ACI open","volume":"06 1","pages":"e98 - e102"},"PeriodicalIF":0.0,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46434498","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}