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Biomedical subjects

Martin J O'Connor

Publications and source records attributed to Martin J O'Connor.

4 recordsLinked to original sources

Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers.

Information technology can support the implementation of clinical research findings in practice settings. Technology can address the quality gap in health care by providing automated decision support to clinicians that integrates guideline knowledge with electronic patient data to present real-time, patient-specific recommendations. However, technical success in implementing decision support systems may not translate directly into system use by clinicians. Successful technology integration into clinical work settings requires explicit attention to the organizational context. We describe the application of a "sociotechnical" approach to integration of ATHENA DSS, a decision support system for the treatment of hypertension, into geographically dispersed primary care clinics. We applied an iterative technical design in response to organizational input and obtained ongoing endorsements of the project by the organization's administrative and clinical leadership. Conscious attention to organizational context at the time of development, deployment, and maintenance of the system was associated with extensive clinician use of the system.

Academic Medical Centers↗

BioSTORM: a system for automated surveillance of diverse data sources.

Heightened concerns about bioterrorism are forcing changes to the traditional biosurveillance-model. Public health departments are under pressure to follow multiple, non-specific, pre-diagnostic indicators, often drawn from many data sources. As a result, there is a need for biosurveillance systems that can use a variety of analysis techniques to rapidly integrate and process multiple diverse data feeds using a variety of problem solving techniques to give timely analysis. To meet these requirements, we are developing a new system called BioSTORM (Biological Spatio-Temporal Outbreak Reasoning Module).

Bioterrorism↗

Knowledge-based bioterrorism surveillance.

An epidemic resulting from an act of bioterrorism could be catastrophic. However, if an epidemic can be detected and characterized early on, prompt public health intervention may mitigate its impact. Current surveillance approaches do not perform well in terms of rapid epidemic detection or epidemic monitoring. One reason for this shortcoming is their failure to bring existing knowledge and data to bear on the problem in a coherent manner. Knowledge-based methods can integrate surveillance data and knowledge, and allow for careful evaluation of problem-solving methods. This paper presents an argument for knowledge-based surveillance, describes a prototype of BioSTORM, a system for real-time epidemic surveillance, and shows an initial evaluation of this system applied to a simulated epidemic from a bioterrorism attack.

Artificial Intelligence↗

The Chronus II temporal database mediator.

Clinical databases typically contain a significant amount of temporal information. This information is often crucial in medical decision-support systems. Although temporal queries are common in clinical systems, the medical informatics field has no standard means for representing or querying temporal data. Over the past decade, the temporal database community has made a significant amount of progress in temporal systems. Much of this research can be applied to clinical database systems. This paper outlines a temporal database mediator called Chronus II. Chronus II extends the standard relational model and the SQL query language to support temporal queries. It provides an expressive general-purpose temporal query language that is tuned to the querying requirements of clinical decision support systems. This paper describes how we have used Chronus II to tackle a variety of clinical problems in decision support systems developed by our group.

Database Management Systems↗