Difference between revisions of "Automated Clinical Decision Support (CDS) using Pattern Recognition/Temporal Relationships"

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== Overview ==
 
== Overview ==
  
Clinical decision support (CDS) has come a long way, most notably when one thinks of decision support, we think of alerts, reminders, drug-drug interaction checking, order sets, and note templates.  As “big data” only gets bigger on a daily basis, data warehouses fill with unstructured and structured data which provide a means for developing CDS.  “One of the “grand challenges” in CDS is thus the automatic production of CDS from the bottom-up by data-mining clinical data sources” [1].
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CDS has come a long way, most notably when one thinks of decision support, we think of alerts, reminders, drug-drug interaction checking, order sets, and note templates.  As “big data” only gets bigger on a daily basis, data warehouses fill with unstructured and structured data which provide a means for developing CDS.  “One of the “grand challenges” in CDS is thus the automatic production of CDS from the bottom-up by data-mining clinical data sources” [2].
  
 
== Definition ==
 
== Definition ==

Revision as of 20:58, 22 October 2015

Overview

CDS has come a long way, most notably when one thinks of decision support, we think of alerts, reminders, drug-drug interaction checking, order sets, and note templates. As “big data” only gets bigger on a daily basis, data warehouses fill with unstructured and structured data which provide a means for developing CDS. “One of the “grand challenges” in CDS is thus the automatic production of CDS from the bottom-up by data-mining clinical data sources” [2].

Definition

Clinical Decision Support has been defined by many authors, though simply put, its “clinical knowledge or patient-related information, filtered or presented at appropriate times to enhance patient care” [2]. Many argue that our current CDS tools with the EHR are quite primitive, traditionally it has been broken down into the following 6 categories, none of which are automated:

Related Projects

Veterans Like Mine – Support for therapeutic decision making. A novel idea, VLMine is planned to serve as a CDS tool when resources like PubMed or UpToDate are not adequately detailed or specific enough to answer questions in patients with clinical uncertainty. On average, clinicians accrue questions about patient care every two to three outpatient encounters, and yet more than half of these questions go unanswered [7]. VLMine will retrieve data from data warehouses about other patients similar to the individual patient at hand and present information to clinicians to facilitate management. This will provide VA clinicians access to information from the collective experience of fellow clinicians using data processing from all facilities. VLMine will constitute a new kind of clinical decision support, a type that does not currently exist.