OBJECTIVE: This study shows the evolution of a biomedical observation dictionary within the Assistance Publique Hôpitaux Paris (AP-HP), the largest European university hospital group. The different steps are detailed as follows: the dictionary creation, the mapping to logical observation identifier names and codes (LOINC), the integration into a multiterminological management platform and, finally, the implementation in the health information system. METHODS: AP-HP decided to create a biomedical observation dictionary named AnaBio, to map it to LOINC and to maintain the mapping. A management platform based on methods used for knowledge engineering has been put in place. It aims at integrating AnaBio within the health information system and improving both the quality and stability of the dictionary. RESULTS: This new management platform is now active in AP-HP. The AnaBio dictionary is shared by 120 laboratories and currently includes 50 000 codes. The mapping implementation to LOINC reaches 40% of the AnaBio entries and uses 26% of LOINC records. The results of our work validate the choice made to develop a local dictionary aligned with LOINC. DISCUSSION AND CONCLUSIONS: This work constitutes a first step towards a wider use of the platform. The next step will support the entire biomedical production chain, from the clinician prescription, through laboratory tests tracking in the laboratory information system to the communication of results and the use for decision support and biomedical research. In addition, the increase in the mapping implementation to LOINC ensures the interoperability allowing communication with other international health institutions.
OBJECTIVE: This study shows the evolution of a biomedical observation dictionary within the Assistance Publique Hôpitaux Paris (AP-HP), the largest European university hospital group. The different steps are detailed as follows: the dictionary creation, the mapping to logical observation identifier names and codes (LOINC), the integration into a multiterminological management platform and, finally, the implementation in the health information system. METHODS:AP-HP decided to create a biomedical observation dictionary named AnaBio, to map it to LOINC and to maintain the mapping. A management platform based on methods used for knowledge engineering has been put in place. It aims at integrating AnaBio within the health information system and improving both the quality and stability of the dictionary. RESULTS: This new management platform is now active in AP-HP. The AnaBio dictionary is shared by 120 laboratories and currently includes 50 000 codes. The mapping implementation to LOINC reaches 40% of the AnaBio entries and uses 26% of LOINC records. The results of our work validate the choice made to develop a local dictionary aligned with LOINC. DISCUSSION AND CONCLUSIONS: This work constitutes a first step towards a wider use of the platform. The next step will support the entire biomedical production chain, from the clinician prescription, through laboratory tests tracking in the laboratory information system to the communication of results and the use for decision support and biomedical research. In addition, the increase in the mapping implementation to LOINC ensures the interoperability allowing communication with other international health institutions.
Authors: Clement J McDonald; Stanley M Huff; Jeffrey G Suico; Gilbert Hill; Dennis Leavelle; Raymond Aller; Arden Forrey; Kathy Mercer; Georges DeMoor; John Hook; Warren Williams; James Case; Pat Maloney Journal: Clin Chem Date: 2003-04 Impact factor: 8.327
Authors: S M Huff; R A Rocha; C J McDonald; G J De Moor; T Fiers; W D Bidgood; A W Forrey; W G Francis; W R Tracy; D Leavelle; F Stalling; B Griffin; P Maloney; D Leland; L Charles; K Hutchins; J Baenziger Journal: J Am Med Inform Assoc Date: 1998 May-Jun Impact factor: 4.497
Authors: A W Forrey; C J McDonald; G DeMoor; S M Huff; D Leavelle; D Leland; T Fiers; L Charles; B Griffin; F Stalling; A Tullis; K Hutchins; J Baenziger Journal: Clin Chem Date: 1996-01 Impact factor: 8.327