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

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6. Reactive alerts and reminders
 
6. Reactive alerts and reminders
  
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== Temporal Abstraction ==
  
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Temporal abstraction is an integral component within intelligent data analysis (IDA), which is defined as “encompassing statistical, pattern recognition, machine learning, data abstraction and visualization tools to support the analysis of data and discovery of principles that are encoded within the data” [4].  This was described early on by Stacey et al., in the paper titled “Temporal abstraction in intelligent clinical data analysis: A survey”.  Temporal abstraction provides the means to achieve precise, high level qualitative descriptions from low level quantitative patient data, which can then be used as input to a reasoning engine where they are evaluated against a knowledge base to arrive at possible clinical hypothesis [4]. 
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== Temporal Reasoning ==
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Temporal reasoning was also described by J.C. Augusto in his paper, “Temporal Reasoning for Decision Support in Medicine” [1].  “Clinicians in general need to know that some symptoms were recurrent with some particular temporal pattern in some specific context in order to diagnose correctly instead of having the rough data that, after possibly long consideration, would lead to the discovery of a certain condition” [1].
  
  

Revision as of 21:03, 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” [3]. 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:

Types of CDS tools [3]

1. Documentation forms/templates

2. Relevant data presentation

3. Order creation facilitators

4. Time based checking and protocol/pathway support

5. Reference information and guidance

6. Reactive alerts and reminders

Temporal Abstraction

Temporal abstraction is an integral component within intelligent data analysis (IDA), which is defined as “encompassing statistical, pattern recognition, machine learning, data abstraction and visualization tools to support the analysis of data and discovery of principles that are encoded within the data” [4]. This was described early on by Stacey et al., in the paper titled “Temporal abstraction in intelligent clinical data analysis: A survey”. Temporal abstraction provides the means to achieve precise, high level qualitative descriptions from low level quantitative patient data, which can then be used as input to a reasoning engine where they are evaluated against a knowledge base to arrive at possible clinical hypothesis [4].

Temporal Reasoning

Temporal reasoning was also described by J.C. Augusto in his paper, “Temporal Reasoning for Decision Support in Medicine” [1]. “Clinicians in general need to know that some symptoms were recurrent with some particular temporal pattern in some specific context in order to diagnose correctly instead of having the rough data that, after possibly long consideration, would lead to the discovery of a certain condition” [1].


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.