MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...最新文献
The efficient management and usage of information within integrated care delivery systems will have substantial impacts on patients' care outcomes. Electronic health information management systems need to guarantee the integrity of clinical data capture and the quality of information processing, in order to deliver actionable knowledge to health professionals at the point of care. Generic tools and evaluation frameworks are needed to assess the quality of eHealth information systems for a wide range of stakeholders: end-users, including health professionals and patients, healthcare organisations and policymakers. We present an overview of data and information quality assessment in electronic health systems. We use the model of the patient / clinician encounter of Brown and Warmington (2002) to describe how issues of poor data quality and information mismanagement impact on the clinical encounter. We then use the 6 dimensions model of quality in information systems first proposed by DeLone & McLean (1992) to propose a comprehensive description of data quality issues in eHealth.
{"title":"An overview of electronic health information management systems quality assessment","authors":"M. Bouamrane, F. Mair, C. Tao","doi":"10.1145/2389672.2389680","DOIUrl":"https://doi.org/10.1145/2389672.2389680","url":null,"abstract":"The efficient management and usage of information within integrated care delivery systems will have substantial impacts on patients' care outcomes. Electronic health information management systems need to guarantee the integrity of clinical data capture and the quality of information processing, in order to deliver actionable knowledge to health professionals at the point of care. Generic tools and evaluation frameworks are needed to assess the quality of eHealth information systems for a wide range of stakeholders: end-users, including health professionals and patients, healthcare organisations and policymakers.\u0000 We present an overview of data and information quality assessment in electronic health systems. We use the model of the patient / clinician encounter of Brown and Warmington (2002) to describe how issues of poor data quality and information mismanagement impact on the clinical encounter. We then use the 6 dimensions model of quality in information systems first proposed by DeLone & McLean (1992) to propose a comprehensive description of data quality issues in eHealth.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"2 1","pages":"37-46"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73943383","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":"Session details: Bio-medical knowledge representation & engineering","authors":"Hua Min","doi":"10.1145/3251599","DOIUrl":"https://doi.org/10.1145/3251599","url":null,"abstract":"","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85150763","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}
The prevalence of medical report standards and structured reporting systems reflects the increasing demand for representing and preserving medical and clinical data with controlled vocabularies in well structured format. Strictly formatted medical reports offer high human readability and facilitate further data processing, such as querying, statistical analysis, and reasoning to support decision making. However, many medical reports, such as pathology reports, nursing notes and physician's notes, are written in free-text narration. Manually extracting free text reports by filling predefined data fields is cumbersome and error-prone. Meanwhile, information extraction tools try to automate such process, for example, through machine learning based methods. Such methods often require large volumes of training datasets annotated manually by humans, which is expensive to obtain. Furthermore, they are also limited by their accuracy (both precision and recall). To facilitate the process of extracting information from narrative medical reports and transforming extracted data into standardized structured forms, we present in this paper a semi-automatic system, ASLForm, that interacts with users, analyzes free text input and generates normalized answers to populate forms in real-time. This system learns from users' feedback transparently and establishes decision models incrementally. It requires no additional configurations and training datasets. ASLForm is not constrained to any domain, and is adaptable to free text input in any format. These features of the system offer high usability and portability. Its design also enables easy integration with existing reporting systems.
{"title":"Bridging the unstructured and structured worlds: an adaptive self learning medical form generating system","authors":"Shuai Zheng, Fusheng Wang, James J. Lu","doi":"10.1145/2389672.2389684","DOIUrl":"https://doi.org/10.1145/2389672.2389684","url":null,"abstract":"The prevalence of medical report standards and structured reporting systems reflects the increasing demand for representing and preserving medical and clinical data with controlled vocabularies in well structured format. Strictly formatted medical reports offer high human readability and facilitate further data processing, such as querying, statistical analysis, and reasoning to support decision making. However, many medical reports, such as pathology reports, nursing notes and physician's notes, are written in free-text narration. Manually extracting free text reports by filling predefined data fields is cumbersome and error-prone. Meanwhile, information extraction tools try to automate such process, for example, through machine learning based methods. Such methods often require large volumes of training datasets annotated manually by humans, which is expensive to obtain. Furthermore, they are also limited by their accuracy (both precision and recall).\u0000 To facilitate the process of extracting information from narrative medical reports and transforming extracted data into standardized structured forms, we present in this paper a semi-automatic system, ASLForm, that interacts with users, analyzes free text input and generates normalized answers to populate forms in real-time. This system learns from users' feedback transparently and establishes decision models incrementally. It requires no additional configurations and training datasets. ASLForm is not constrained to any domain, and is adaptable to free text input in any format. These features of the system offer high usability and portability. Its design also enables easy integration with existing reporting systems.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"8 1","pages":"59-66"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82352523","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}
With the recent development and adoption of NLP framework architectures, NLP modules/tools developed independently in the research community can be adopted as integrated applications. Development of wrappers and interfaces required to adopt NLP modules/tools, however, still requires huge amount of efforts. In this paper, we focus on one NLP framework architecture, UIMA (Unstructured Information Management Architecture), which defines annotations as types described in a type system and can achieve direct interoperability if a common type system is used. We explore the use of ontology to model UIMA types and argue existing ontology development or reasoning tools can be utilized to understand types (we use types and annotations interchangeably) from existing NLP systems developed under UIMA, define equivalent annotations in different NLP systems, and apply the practice in the ontology community to draw agreements on the definition of common NLP types, thereby achieving better interoperability among NLP modules/tools.
{"title":"Modeling UIMA type system using web ontology language: towards interoperability among UIMA-based NLP tools","authors":"Hongfang Liu, Stephen T Wu, C. Tao, C. Chute","doi":"10.1145/2389672.2389679","DOIUrl":"https://doi.org/10.1145/2389672.2389679","url":null,"abstract":"With the recent development and adoption of NLP framework architectures, NLP modules/tools developed independently in the research community can be adopted as integrated applications. Development of wrappers and interfaces required to adopt NLP modules/tools, however, still requires huge amount of efforts. In this paper, we focus on one NLP framework architecture, UIMA (Unstructured Information Management Architecture), which defines annotations as types described in a type system and can achieve direct interoperability if a common type system is used. We explore the use of ontology to model UIMA types and argue existing ontology development or reasoning tools can be utilized to understand types (we use types and annotations interchangeably) from existing NLP systems developed under UIMA, define equivalent annotations in different NLP systems, and apply the practice in the ontology community to draw agreements on the definition of common NLP types, thereby achieving better interoperability among NLP modules/tools.","PeriodicalId":91363,"journal":{"name":"MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...","volume":"12 1","pages":"31-36"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88326558","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}
MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...
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