title: Model-based data integration in clinical environments.
abstract: As a result of improved hospital information-technology infrastructure and declining costs of storage media, vast amounts of physiological waveform and trend data can now be continuously collected and archived from bedside monitors in operating rooms, intensive care units, or even regular hospital rooms. The real-time or off-line processing of such volumes of high-resolution data, in attempts to turn raw data into clinically actionable information, poses significant challenges. However, it also presents researchers - and eventually clinicians - with unprecedented opportunities to move beyond the traditional individual-channel analysis of waveform data, and towards an integrative patient-monitoring framework, with likely improvements in patient care and safety. We outline some of the challenges and opportunities, and propose strategies for model-based integration of physiological data to improve patient monitoring.
authors.affiliation: Computational Physiology and Clinical Inference Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. thomas@mit.edu
authors.forename: Thomas
authors.initials: T
authors.lastname: Heldt
authors.forename: George C
authors.initials: GC
authors.lastname: Verghese
mesh_terms: D003422:Critical Care; D016208:Databases, Factual; D004562:Electrocardiography; D006801:Humans; D008962:Models, Theoretical; D008991:Monitoring, Physiologic