Pub Date : 2001-03-01DOI: 10.1136/jamia.2001.0080117
B. Rocha, J. Christenson, R. Evans, R. Gardner
Objective: To analyze whether computer-generated reminders about infections could influence clinicians' practice patterns and consequently improve the detection and manage- ment of nosocomial infections. Design: The conclusions produced by an expert system developed to detect and manage infections were presented to the attending clinicians in a pediatric hospital to determine whether this infor- mation could improve detection and management. Clinician interventions were compared before and after the implementation of the system. Measurements: The responses of the clinicians (staff physicians, physician assistants, and nurse practitioners) to the reminders were determined by review of paper medical charts. Main outcome measures were the number of suggestions to treat and manage infections that were followed before and after the implementation of COMPISS (Computerized Pediatric Infection Surveillance System). The clinicians' opinions about the system were assessed by means of a paper questionnaire distrib- uted following the experiment. Results: The results failed to show a statistical difference between the clinicians' treatment strategies before and after implementation of the system (P > 0.33 for clinicians working in the emergency room and P > 0.45 for clinicians working in the pediatric intensive care unit). The questionnaire results showed that the respondents appreciated the information presented by the system. Conclusion: The computer-generated reminders about infections were unable to influence the practice patterns of clinicians. The methodologic problems that may have contributed to this negative result are discussed. � J Am Med Inform Assoc. 2001;8:117-125.
{"title":"Research Paper: Clinicians' Response to Computerized Detection of Infections","authors":"B. Rocha, J. Christenson, R. Evans, R. Gardner","doi":"10.1136/jamia.2001.0080117","DOIUrl":"https://doi.org/10.1136/jamia.2001.0080117","url":null,"abstract":"Objective: To analyze whether computer-generated reminders about infections could influence clinicians' practice patterns and consequently improve the detection and manage- ment of nosocomial infections. Design: The conclusions produced by an expert system developed to detect and manage infections were presented to the attending clinicians in a pediatric hospital to determine whether this infor- mation could improve detection and management. Clinician interventions were compared before and after the implementation of the system. Measurements: The responses of the clinicians (staff physicians, physician assistants, and nurse practitioners) to the reminders were determined by review of paper medical charts. Main outcome measures were the number of suggestions to treat and manage infections that were followed before and after the implementation of COMPISS (Computerized Pediatric Infection Surveillance System). The clinicians' opinions about the system were assessed by means of a paper questionnaire distrib- uted following the experiment. Results: The results failed to show a statistical difference between the clinicians' treatment strategies before and after implementation of the system (P > 0.33 for clinicians working in the emergency room and P > 0.45 for clinicians working in the pediatric intensive care unit). The questionnaire results showed that the respondents appreciated the information presented by the system. Conclusion: The computer-generated reminders about infections were unable to influence the practice patterns of clinicians. The methodologic problems that may have contributed to this negative result are discussed. � J Am Med Inform Assoc. 2001;8:117-125.","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115357959","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}
Pub Date : 2001-03-01DOI: 10.1136/jamia.2001.0080163
J. Cimino, V. Patel, A. Kushniruk
Objective: To explore the use of an observational, cognitive-based approach for differentiating between successful, suboptimal, and failed entry of coded data by clinicians in actual practice, and to detect whether causes for unsuccessful attempts to capture true intended meaning were due to terminology content, terminology representation, or user interface problems. Design: Observational study with videotaping and subsequent coding of data entry events in an outpatient clinic at New York Presbyterian Hospital. Participants: Eight attending physicians, 18 resident physicians, and 1 nurse practitioner, using the Medical Entities Dictionary (MED) to record patient problems, medications, and adverse reactions in an outpatient medical record system. Measurements: Classification of data entry events as successful, suboptimal, or failed, and estimation of cause; recording of system response time and total event time. Results: Two hundred thirty-eight data entry events were analyzed; 71.0 percent were successful, 6.3 percent suboptimal, and 22.7 percent failed; unsuccessful entries were due to problems with content in 13.0 percent of events, representation problems in 10.1 percent of events, and usability problems in 5.9 percent of events. Response time averaged 0.74 sec, and total event time averaged 40.4 sec. Of an additional 209 tasks related to drug dose and frequency terms, 94 percent were successful, 0.5 percent were suboptimal, and 6 percent failed, for an overall success rate of 82 percent. Conclusions: Data entry by clinicians using the outpatient system and the MED was generally successful and efficient. The cognitive-based observational approach permitted detection of false- positive (suboptimal) and false-negative (failed due to user interface) data entry. � J Am Med Inform Assoc. 2001;8:163-173.
{"title":"Research Paper: Studying the Human- Computer-Terminology Interface","authors":"J. Cimino, V. Patel, A. Kushniruk","doi":"10.1136/jamia.2001.0080163","DOIUrl":"https://doi.org/10.1136/jamia.2001.0080163","url":null,"abstract":"Objective: To explore the use of an observational, cognitive-based approach for differentiating between successful, suboptimal, and failed entry of coded data by clinicians in actual practice, and to detect whether causes for unsuccessful attempts to capture true intended meaning were due to terminology content, terminology representation, or user interface problems. Design: Observational study with videotaping and subsequent coding of data entry events in an outpatient clinic at New York Presbyterian Hospital. Participants: Eight attending physicians, 18 resident physicians, and 1 nurse practitioner, using the Medical Entities Dictionary (MED) to record patient problems, medications, and adverse reactions in an outpatient medical record system. Measurements: Classification of data entry events as successful, suboptimal, or failed, and estimation of cause; recording of system response time and total event time. Results: Two hundred thirty-eight data entry events were analyzed; 71.0 percent were successful, 6.3 percent suboptimal, and 22.7 percent failed; unsuccessful entries were due to problems with content in 13.0 percent of events, representation problems in 10.1 percent of events, and usability problems in 5.9 percent of events. Response time averaged 0.74 sec, and total event time averaged 40.4 sec. Of an additional 209 tasks related to drug dose and frequency terms, 94 percent were successful, 0.5 percent were suboptimal, and 6 percent failed, for an overall success rate of 82 percent. Conclusions: Data entry by clinicians using the outpatient system and the MED was generally successful and efficient. The cognitive-based observational approach permitted detection of false- positive (suboptimal) and false-negative (failed due to user interface) data entry. � J Am Med Inform Assoc. 2001;8:163-173.","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116491078","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}
Pub Date : 2001-03-01DOI: 10.1136/jamia.2001.0080126
A. Maas, A. J. T. Hoopen, A. Hofstede
The prevailing view of medical informatics as a primarily subservient discipline in health care is challenged. Developments in both general informatics and medical informatics are described to identify desirable properties of modeling languages and tools needed to solve key problems in the application field. For progress in medical informatics, it is considered essential to develop far more formal modeling languages, modeling techniques, and tools. A major aim of this development should be to expel ambiguity from concepts essential to medicine, positioning medical informatics "at the heart of health care."
{"title":"Viewpoint: Progress with Formalization in Medical Informatics?","authors":"A. Maas, A. J. T. Hoopen, A. Hofstede","doi":"10.1136/jamia.2001.0080126","DOIUrl":"https://doi.org/10.1136/jamia.2001.0080126","url":null,"abstract":"The prevailing view of medical informatics as a primarily subservient discipline in health care is challenged. Developments in both general informatics and medical informatics are described to identify desirable properties of modeling languages and tools needed to solve key problems in the application field. For progress in medical informatics, it is considered essential to develop far more formal modeling languages, modeling techniques, and tools. A major aim of this development should be to expel ambiguity from concepts essential to medicine, positioning medical informatics \"at the heart of health care.\"","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133628251","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}
Pub Date : 2001-03-01DOI: 10.1136/jamia.2001.0080194
W. Stead
David Bates is Chief, Division of General Medicine at Brigham and Women's Hospital, and Medical Director, Clinical and Quality Analysis at Partners HealthCare System, Inc. He is also an Associate Professor in Medicine at Harvard Medical School and has a joint appointment at Harvard School of Public Health in the Department of Health Policy and Management. Dr. Bates received his BS degree in Chemistry from Stanford University, his MD from Johns Hopkins School of Medicine, and his MSc from Harvard School of Public Health (Department of Health Policy and Management). He completed residency training in internal medicine at the Oregon Health Sciences University and a postdoctoral research fellowship in medicine at the Harvard Medical School. Dr. Bates' primary informatics interest has been the use of computerized decision support to improve safety and thereby reduce the costs of care. A particular focus has been on improving the systems by which drugs are given, to reduce the frequency of medication errors and adverse drug events. One study he led demonstrated that implementation of computerized physician order entry reduced the rate of serious medication errors by 55 percent. Other research interests include affecting physicians' decision making, particularly using computerized interventions; quality of care and cost-effectiveness in medical practice; and outcomes assessment. Dr. Bates has been the recipient of numerous awards, including a National Research Service Award; the Henry Christian Award for Excellence in Research; the Culpeper Award; the Young Investigator of the Year from the Society of General Internal Medicine, Northeast Region; Clinical Investigator of the Year, Center for Healthcare Information Management; the Partners in Excellence Award Quality Treatment and Service and Leadership and Innovation; and the Cheers Award for Outstanding Contribution to Medication Error Prevention from the Institute for Safe Medication Practices. Dr. Bates is a Scientific Advisor, SCRIPT Project, Health Care …
David Bates是Brigham and Women's Hospital的综合医学部主任，Partners HealthCare System, Inc.的临床和质量分析医学主任。他也是哈佛大学医学院的医学副教授，并在哈佛大学公共卫生学院的卫生政策和管理系担任联合任命。他持有Stanford University的化学学士学位，Johns Hopkins School of Medicine的医学博士学位，以及Harvard School of Public Health (Health Policy and Management Department)的理学硕士学位。他在俄勒冈健康科学大学(Oregon Health Sciences University)完成了内科住院医师培训，并在哈佛医学院(Harvard Medical school)获得医学博士后研究奖学金。贝茨的主要信息学兴趣是使用计算机决策支持来提高安全性，从而降低护理成本。一个特别的重点是改善给药系统，以减少药物错误和药物不良事件的频率。他领导的一项研究表明，计算机化医嘱输入的实施将严重用药错误率降低了55%。其他研究兴趣包括影响医生的决策，特别是使用计算机干预;医疗服务的质量和成本效益;和结果评估。贝茨获得了许多奖项，包括国家研究服务奖;亨利·克里斯蒂安卓越研究奖;卡尔佩珀奖;东北地区全科内科学会年度优秀青年研究员;医疗信息管理中心年度最佳临床研究者;卓越合作伙伴奖、优质待遇及服务奖、领导及创新奖;以及安全用药实践研究所颁发的“预防用药错误杰出贡献奖”。贝茨是科学顾问，SCRIPT项目，医疗保健…
{"title":"American College of Medical Informatics FELLOWS, 2000","authors":"W. Stead","doi":"10.1136/jamia.2001.0080194","DOIUrl":"https://doi.org/10.1136/jamia.2001.0080194","url":null,"abstract":"David Bates is Chief, Division of General Medicine at Brigham and Women's Hospital, and Medical Director, Clinical and Quality Analysis at Partners HealthCare System, Inc. He is also an Associate Professor in Medicine at Harvard Medical School and has a joint appointment at Harvard School of Public Health in the Department of Health Policy and Management. Dr. Bates received his BS degree in Chemistry from Stanford University, his MD from Johns Hopkins School of Medicine, and his MSc from Harvard School of Public Health (Department of Health Policy and Management). He completed residency training in internal medicine at the Oregon Health Sciences University and a postdoctoral research fellowship in medicine at the Harvard Medical School.\u0000\u0000Dr. Bates' primary informatics interest has been the use of computerized decision support to improve safety and thereby reduce the costs of care. A particular focus has been on improving the systems by which drugs are given, to reduce the frequency of medication errors and adverse drug events. One study he led demonstrated that implementation of computerized physician order entry reduced the rate of serious medication errors by 55 percent. Other research interests include affecting physicians' decision making, particularly using computerized interventions; quality of care and cost-effectiveness in medical practice; and outcomes assessment.\u0000\u0000Dr. Bates has been the recipient of numerous awards, including a National Research Service Award; the Henry Christian Award for Excellence in Research; the Culpeper Award; the Young Investigator of the Year from the Society of General Internal Medicine, Northeast Region; Clinical Investigator of the Year, Center for Healthcare Information Management; the Partners in Excellence Award Quality Treatment and Service and Leadership and Innovation; and the Cheers Award for Outstanding Contribution to Medication Error Prevention from the Institute for Safe Medication Practices.\u0000\u0000Dr. Bates is a Scientific Advisor, SCRIPT Project, Health Care …","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130822122","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}
Pub Date : 1997-11-01DOI: 10.1136/jamia.1997.0040483
Committees
AMIA working groups and committees are encouraged to discuss, document, circulate, and publish (in paper or electronic media) articles, views, issues and recommendations for positions and policy of pertinence to health care informatics. However, documents from AMIA working groups and committees cannot be circulated or distributed as working group or committee documents without the approval of the appropriate AMIA administrative body. Thus, a document that is to represent the work or views of a working group must have the approval of the working group and the Working Group Steering Committee, and a document that is to represent the work of a committee of the AMIA board must have the approval of the Board. Further, …
{"title":"Policy of the American Medical Informatics Association (AMIA) on Documents for Circulation, Position Papers and Policy Statements","authors":"Committees","doi":"10.1136/jamia.1997.0040483","DOIUrl":"https://doi.org/10.1136/jamia.1997.0040483","url":null,"abstract":"AMIA working groups and committees are encouraged to discuss, document, circulate, and publish (in paper or electronic media) articles, views, issues and recommendations for positions and policy of pertinence to health care informatics. However, documents from AMIA working groups and committees cannot be circulated or distributed as working group or committee documents without the approval of the appropriate AMIA administrative body. Thus, a document that is to represent the work or views of a working group must have the approval of the working group and the Working Group Steering Committee, and a document that is to represent the work of a committee of the AMIA board must have the approval of the Board. Further, …","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117166771","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}
Pub Date : 1997-03-01DOI: 10.1136/jamia.1997.0040136
D. Masys
{"title":"Book Review: Telemedicine: A Guide to Assessing Telecommunications in Health Care","authors":"D. Masys","doi":"10.1136/jamia.1997.0040136","DOIUrl":"https://doi.org/10.1136/jamia.1997.0040136","url":null,"abstract":"","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129313164","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}
Stephen T. C. Wong, K. S. Hoo, R. Knowlton, K. Laxer, Xinhua Cao, R. Hawkins, W. Dillon, R. Arenson
{"title":"Application of Information Technology: Design and Applications of a Multimodality Image Data Warehouse Framework","authors":"Stephen T. C. Wong, K. S. Hoo, R. Knowlton, K. Laxer, Xinhua Cao, R. Hawkins, W. Dillon, R. Arenson","doi":"10.1197/jamia.M0988","DOIUrl":"https://doi.org/10.1197/jamia.M0988","url":null,"abstract":"","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114545705","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}
JAMIA has documented the evolution of biomedical informatics through its dissemination of original research and applications, brief communications and case studies, thought-provoking perspectives, and insightful reviews. The number and diversity of data-driven models have increased substantially in the past few years. From the first developments in machine and statistical learning that were applied to health sciences decades ago, our field has flourished to include biomedical data science as one of its important components, which is possible only because of other informatics work that allows data to be standardized, integrated, and used in various learning models. This issue is focused on biomedical data science and illustrates a broad range of techniques and application areas in this field; articles submitted in response to a specific request for papers are featured in an editorial by Brennan et al. (p. 2). In addition to the articles covered in the editorial, this issue highlights tools and applications of data science in a variety of domains, all of which use clinical text as a source of data: Trivedi (p. 81) presents an interactive tool for processing clinical text, Luo (p. 93) uses convolutional neural networks to classify relations in clinical notes, and Bejan (p. 61) introduces an approach to find homelessness and adverse childhood experiences described in clinical narratives. Additionally, nonclinical text is increasing in importance for health care and public health. Xie (p. 72) uses recurrent neural networks to find e-cigarette adverse events in social media posts, while Vigo (p. 88) describes a method to collect seasonal allergy symptoms for the British population. New types of structured data and new ways to integrate them are also continuously being produced: Doostparasti (p. 99) describes a novel approach for integrating -omics data to enhance phenotype classification performance, and Yu (p. 54) introduces a phenotyping algorithm that does not depend on expert-labeled observations. The articles listed above are only a few examples of the scope of informatics activities covered in JAMIA. Starting with this January issue, readers will be able to easily group articles into themes based on technologies used or application areas. This grouping is made possible by JAMIA’s change in frequency and format (to monthly online), which will allow for more frequent indexing. Readers will be able to compare approaches and discover solutions that are best suited to their problems. Stay tuned for additional data science articles in future monthly issues, as well as articles focused on clinical informatics systems (including clinical decision support), clinical research systems, translational bioinformatics, global public health informatics, and many other subfields of informatics that help us, through information technology, understand and address human health and disease.
{"title":"Special Focus on Biomedical Data Science","authors":"L. Ohno-Machado","doi":"10.1093/jamia/ocx151","DOIUrl":"https://doi.org/10.1093/jamia/ocx151","url":null,"abstract":"JAMIA has documented the evolution of biomedical informatics through its dissemination of original research and applications, brief communications and case studies, thought-provoking perspectives, and insightful reviews. The number and diversity of data-driven models have increased substantially in the past few years. From the first developments in machine and statistical learning that were applied to health sciences decades ago, our field has flourished to include biomedical data science as one of its important components, which is possible only because of other informatics work that allows data to be standardized, integrated, and used in various learning models. This issue is focused on biomedical data science and illustrates a broad range of techniques and application areas in this field; articles submitted in response to a specific request for papers are featured in an editorial by Brennan et al. (p. 2). In addition to the articles covered in the editorial, this issue highlights tools and applications of data science in a variety of domains, all of which use clinical text as a source of data: Trivedi (p. 81) presents an interactive tool for processing clinical text, Luo (p. 93) uses convolutional neural networks to classify relations in clinical notes, and Bejan (p. 61) introduces an approach to find homelessness and adverse childhood experiences described in clinical narratives. Additionally, nonclinical text is increasing in importance for health care and public health. Xie (p. 72) uses recurrent neural networks to find e-cigarette adverse events in social media posts, while Vigo (p. 88) describes a method to collect seasonal allergy symptoms for the British population. New types of structured data and new ways to integrate them are also continuously being produced: Doostparasti (p. 99) describes a novel approach for integrating -omics data to enhance phenotype classification performance, and Yu (p. 54) introduces a phenotyping algorithm that does not depend on expert-labeled observations. The articles listed above are only a few examples of the scope of informatics activities covered in JAMIA. Starting with this January issue, readers will be able to easily group articles into themes based on technologies used or application areas. This grouping is made possible by JAMIA’s change in frequency and format (to monthly online), which will allow for more frequent indexing. Readers will be able to compare approaches and discover solutions that are best suited to their problems. Stay tuned for additional data science articles in future monthly issues, as well as articles focused on clinical informatics systems (including clinical decision support), clinical research systems, translational bioinformatics, global public health informatics, and many other subfields of informatics that help us, through information technology, understand and address human health and disease.","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124061804","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}
“We have two ears and one mouth so that we can listen twice as much as we speak.” Epictetus (AD c. 55 – 135) As the new President and CEO of AMIA, I’m excited to begin a journey with the organization that has been my professional home for over 20 years. AMIA is the professional home to informatics researchers, clinicians, implementers, practitioners and health IT professionals who recognize the value of applying informatics knowledge and expertise to the problems of health and health care. I’m proud to be able to serve my colleagues and the field, gently stepping in the shoes of predecessors, Drs. Don E. Detmer, Edward H. Shortliffe, and Kevin M. Fickenscher. Professionally, this is the culmination of my informatics work in academic research, clinical practice, and government service. I come to AMIA after five years with the Office of the National Coordinator for Health Information Technology …
“我们有两只耳朵和一张嘴，所以我们听的是说的两倍。”爱彼泰德(公元55 - 135年)作为AMIA的新任总裁兼首席执行官，我很高兴能与这个20多年来一直是我职业家园的组织开始新的旅程。AMIA是信息学研究人员、临床医生、实施者、从业者和卫生IT专业人员的专业之家，他们认识到将信息学知识和专业知识应用于卫生和卫生保健问题的价值。我很自豪能够为我的同事和这个领域服务，轻轻地踏着前任的脚步。Don E. Detmer, Edward H. Shortliffe和Kevin M. Fickenscher。在专业方面，这是我在学术研究、临床实践和政府服务方面信息学工作的高潮。我在国家卫生信息技术协调办公室工作了五年之后来到AMIA…
{"title":"A familiar home, a new beginning, a bright future","authors":"D. Fridsma","doi":"10.1093/jamia/ocu032","DOIUrl":"https://doi.org/10.1093/jamia/ocu032","url":null,"abstract":"“We have two ears and one mouth so that we can listen twice as much as we speak.” Epictetus (AD c. 55 – 135) \u0000\u0000As the new President and CEO of AMIA, I’m excited to begin a journey with the organization that has been my professional home for over 20 years. AMIA is the professional home to informatics researchers, clinicians, implementers, practitioners and health IT professionals who recognize the value of applying informatics knowledge and expertise to the problems of health and health care. I’m proud to be able to serve my colleagues and the field, gently stepping in the shoes of predecessors, Drs. Don E. Detmer, Edward H. Shortliffe, and Kevin M. Fickenscher.\u0000\u0000Professionally, this is the culmination of my informatics work in academic research, clinical practice, and government service. I come to AMIA after five years with the Office of the National Coordinator for Health Information Technology …","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123810827","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}
Pub Date : 1900-01-01DOI: 10.1136/jamia.2001.0080146
Arnoud Van Der Maas, A. Hofstede, A. J. T. Hoopen
{"title":"Model Formulation: Requirements for Medical Modeling Languages","authors":"Arnoud Van Der Maas, A. Hofstede, A. J. T. Hoopen","doi":"10.1136/jamia.2001.0080146","DOIUrl":"https://doi.org/10.1136/jamia.2001.0080146","url":null,"abstract":"","PeriodicalId":344533,"journal":{"name":"J. Am. Medical Informatics Assoc.","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124647169","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}
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