Jeffery Painter - Academia.edu (original) (raw)

Papers by Jeffery Painter

Research paper thumbnail of Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases

Journal of The American Medical Informatics Association, 2010

Objective Active drug safety surveillance may be enhanced by analysis of multiple observational h... more Objective Active drug safety surveillance may be enhanced by analysis of multiple observational healthcare databases, including administrative claims and electronic health records. The objective of this study was to develop and evaluate a common data model (CDM) enabling rapid, comparable, systematic analyses across disparate observational data sources to identify and evaluate the effects of medicines. Design The CDM uses a person-centric design, with attributes for demographics, drug exposures, and condition occurrence. Drug eras, constructed to represent periods of persistent drug use, are derived from available elements from pharmacy dispensings, prescriptions written, and other medication history. Condition eras aggregate diagnoses that occur within a single episode of care. Drugs and conditions from source data are mapped to biomedical ontologies to standardize terminologies and enable analyses of higher-order effects. Measurements The CDM was applied to two source types: an administrative claims and an electronic medical record database. Descriptive statistics were used to evaluate transformation rules. Two case studies demonstrate the ability of the CDM to enable standard analyses across disparate sources: analyses of persons exposed to rofecoxib and persons with an acute myocardial infarction. Results Over 43 million persons, with nearly 1 billion drug exposures and 3.7 billion condition occurrences from both databases were successfully transformed into the CDM. An analysis routine applied to transformed data from each database produced consistent, comparable results. Conclusion A CDM can normalize the structure and content of disparate observational data, enabling standardized analyses that are meaningfully comparable when assessing the effects of medicines.

Research paper thumbnail of Containing the Cloud: Security Issues in a Large Scale Observational Pharmacovigilance Research Project

The Observational Medical Outcomes Partnership (OMOP) is a public-private partnership designed to... more The Observational Medical Outcomes Partnership (OMOP) is a public-private partnership designed to help improve the monitoring of drugs for safety. A software model for analysis of disparate data sources must take into account security, repeatability and efficiency in the transmission and communication of results. Each data provider has an individual stake in the ownership of their data and care must be taken to minimize the possibility of data compromise in the use of this data for regulatory purposes. An evaluation of system security must also be taken into account. Since this system will be comprised of several data providers and data consumers, care must be taken to evaluate the critical points of data access privilege and while maintaining the overall goals of sharing knowledge with the community.

Research paper thumbnail of Construction and Annotation of a UMLS/SNOMED-based Drug Ontology for Observational Pharmacovigilance

The primary goal of the SafetyWorks project has been the development of an integrated set of meth... more The primary goal of the SafetyWorks project has been the development of an integrated set of methodologies enabling the use of large observational data sources in monitoring and assessing drug safety concerns. To support its analytical and exploratory capabilities, SafetyWorks makes use of two large hierarchically structured ontologies -one for medical conditions, and one for drugs. In this paper we focus on the drug ontology employed in SafetyWorks and on its construction and annotation based on the SNOMED CT and RxNorm subsets of the Unified Medical Language System Metathesaurus. The result is a case study illustrating the value of SNOMED and its integration with UMLS and RxNorm in a critical and innovative drug safety application. We expose sufficient details of our methods to enable others to make use of these methods and to encourage the expanded use of both SNOMED and the UMLS in data exploration and analysis applications, particularly in the area of improving approaches to drug safety. 1

Research paper thumbnail of Toward Automating an Inference Model on Unstructured Terminologies: OXMIS Case Study

Most modern biomedical vocabularies employ some hierarchical representation that provides a “broa... more Most modern biomedical vocabularies employ some hierarchical representation that provides a “broader/narrower” meaning relationship among the “codes” or “concepts” found within them. Often, however, we may find within the clinical setting the creation and curation of unstructured custom vocabularies used in the everyday practice of classifying and categorizing clinical data and findings. A significant and widely used example of this lies in the General Practice Research Database which makes use of the Oxford Medical Information Systems (OXMIS) coding scheme to represent drugs and medical conditions. This scheme is intrinsically unstructured, is generally regarded as disorganized, and is not amenable to comparison with other hierarchically structured medical coding schemes. To improve processes of data analysis and extraction, we define a semantically meaningful representation of the OXMIS codes by way of the Unified Medical Language System (UMLS) Metathesaurus. A structure-imposing ontology mapping is created, and this process provides a complete illustration of a general semantic mapping technique applicable to unstructured biomedical terminologies.

Research paper thumbnail of Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases

Journal of The American Medical Informatics Association, 2010

Objective Active drug safety surveillance may be enhanced by analysis of multiple observational h... more Objective Active drug safety surveillance may be enhanced by analysis of multiple observational healthcare databases, including administrative claims and electronic health records. The objective of this study was to develop and evaluate a common data model (CDM) enabling rapid, comparable, systematic analyses across disparate observational data sources to identify and evaluate the effects of medicines. Design The CDM uses a person-centric design, with attributes for demographics, drug exposures, and condition occurrence. Drug eras, constructed to represent periods of persistent drug use, are derived from available elements from pharmacy dispensings, prescriptions written, and other medication history. Condition eras aggregate diagnoses that occur within a single episode of care. Drugs and conditions from source data are mapped to biomedical ontologies to standardize terminologies and enable analyses of higher-order effects. Measurements The CDM was applied to two source types: an administrative claims and an electronic medical record database. Descriptive statistics were used to evaluate transformation rules. Two case studies demonstrate the ability of the CDM to enable standard analyses across disparate sources: analyses of persons exposed to rofecoxib and persons with an acute myocardial infarction. Results Over 43 million persons, with nearly 1 billion drug exposures and 3.7 billion condition occurrences from both databases were successfully transformed into the CDM. An analysis routine applied to transformed data from each database produced consistent, comparable results. Conclusion A CDM can normalize the structure and content of disparate observational data, enabling standardized analyses that are meaningfully comparable when assessing the effects of medicines.

Research paper thumbnail of Containing the Cloud: Security Issues in a Large Scale Observational Pharmacovigilance Research Project

The Observational Medical Outcomes Partnership (OMOP) is a public-private partnership designed to... more The Observational Medical Outcomes Partnership (OMOP) is a public-private partnership designed to help improve the monitoring of drugs for safety. A software model for analysis of disparate data sources must take into account security, repeatability and efficiency in the transmission and communication of results. Each data provider has an individual stake in the ownership of their data and care must be taken to minimize the possibility of data compromise in the use of this data for regulatory purposes. An evaluation of system security must also be taken into account. Since this system will be comprised of several data providers and data consumers, care must be taken to evaluate the critical points of data access privilege and while maintaining the overall goals of sharing knowledge with the community.

Research paper thumbnail of Construction and Annotation of a UMLS/SNOMED-based Drug Ontology for Observational Pharmacovigilance

The primary goal of the SafetyWorks project has been the development of an integrated set of meth... more The primary goal of the SafetyWorks project has been the development of an integrated set of methodologies enabling the use of large observational data sources in monitoring and assessing drug safety concerns. To support its analytical and exploratory capabilities, SafetyWorks makes use of two large hierarchically structured ontologies -one for medical conditions, and one for drugs. In this paper we focus on the drug ontology employed in SafetyWorks and on its construction and annotation based on the SNOMED CT and RxNorm subsets of the Unified Medical Language System Metathesaurus. The result is a case study illustrating the value of SNOMED and its integration with UMLS and RxNorm in a critical and innovative drug safety application. We expose sufficient details of our methods to enable others to make use of these methods and to encourage the expanded use of both SNOMED and the UMLS in data exploration and analysis applications, particularly in the area of improving approaches to drug safety. 1

Research paper thumbnail of Toward Automating an Inference Model on Unstructured Terminologies: OXMIS Case Study

Most modern biomedical vocabularies employ some hierarchical representation that provides a “broa... more Most modern biomedical vocabularies employ some hierarchical representation that provides a “broader/narrower” meaning relationship among the “codes” or “concepts” found within them. Often, however, we may find within the clinical setting the creation and curation of unstructured custom vocabularies used in the everyday practice of classifying and categorizing clinical data and findings. A significant and widely used example of this lies in the General Practice Research Database which makes use of the Oxford Medical Information Systems (OXMIS) coding scheme to represent drugs and medical conditions. This scheme is intrinsically unstructured, is generally regarded as disorganized, and is not amenable to comparison with other hierarchically structured medical coding schemes. To improve processes of data analysis and extraction, we define a semantically meaningful representation of the OXMIS codes by way of the Unified Medical Language System (UMLS) Metathesaurus. A structure-imposing ontology mapping is created, and this process provides a complete illustration of a general semantic mapping technique applicable to unstructured biomedical terminologies.