S B Johnson1. 1. Department of Medical Informatics, Columbia University, New York, NY 10032, USA. stephen.johnson@columbia.edu
Abstract
OBJECTIVE: To construct a large-scale clinical repository that accurately captures a detailed understanding of the data vital to the process of health care and that provides highly efficient access to patient information for the users of a clinical information system. DESIGN: Conventional approaches to data modeling encourage the development of a highly specific data schema in order to capture as much information as possible about a given domain. In contrast, current database technology functions most effectively for clinical databases when a generic data schema is used. The technique of "generic data modeling" is presented as a method of reconciling these opposing views of clinical data, using formal operations to transform a detailed schema into a generic one. RESULTS: A complex schema consisting of hundreds of entities and representing a rich set of constraints about the patient care domain is transformed into a generic schema consisting of roughly two dozen tables. The resulting database design is efficient for patient-oriented queries and is highly flexible in adapting to the changing information needs of a health care institution, particularly changes involving the collection of new data elements. CONCLUSION: Conventional approaches to data modeling can be used to develop rich, complex models of clinical data that are useful for understanding and managing the process of patient care. Generic data modeling techniques can successfully transform a detailed design into an efficient generic design that is flexible enough to meet the needs of an operational clinical information system.
OBJECTIVE: To construct a large-scale clinical repository that accurately captures a detailed understanding of the data vital to the process of health care and that provides highly efficient access to patient information for the users of a clinical information system. DESIGN: Conventional approaches to data modeling encourage the development of a highly specific data schema in order to capture as much information as possible about a given domain. In contrast, current database technology functions most effectively for clinical databases when a generic data schema is used. The technique of "generic data modeling" is presented as a method of reconciling these opposing views of clinical data, using formal operations to transform a detailed schema into a generic one. RESULTS: A complex schema consisting of hundreds of entities and representing a rich set of constraints about the patient care domain is transformed into a generic schema consisting of roughly two dozen tables. The resulting database design is efficient for patient-oriented queries and is highly flexible in adapting to the changing information needs of a health care institution, particularly changes involving the collection of new data elements. CONCLUSION: Conventional approaches to data modeling can be used to develop rich, complex models of clinical data that are useful for understanding and managing the process of patient care. Generic data modeling techniques can successfully transform a detailed design into an efficient generic design that is flexible enough to meet the needs of an operational clinical information system.