Name
Title | ||
|---|---|---|
Comprehensive mitigation framework for concurrent application of multiple clinical practice guidelines. |
Abstract | ||
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Display Omitted Using multiple CPGs to manage a multi-morbid patient is a major clinical challenge.Patient preferences are an important factor affecting treatment decisions.We propose a framework to manage multi-morbidities according to patient preferences.We use first-order logic to model and process CPGs and secondary medical knowledge.Clinical case study is used to illustrate how the proposed framework works. In this work we propose a comprehensive framework based on first-order logic (FOL) for mitigating (identifying and addressing) interactions between multiple clinical practice guidelines (CPGs) applied to a multi-morbid patient while also considering patient preferences related to the prescribed treatment. With this framework we respond to two fundamental challenges associated with clinical decision support: (1) concurrent application of multiple CPGs and (2) incorporation of patient preferences into the decision making process.We significantly expand our earlier research by (1) proposing a revised and improved mitigation-oriented representation of CPGs and secondary medical knowledge for addressing adverse interactions and incorporating patient preferences and (2) introducing a new mitigation algorithm. Specifically, actionable graphs representing CPGs allow for parallel and temporal activities (decisions and actions). Revision operators representing secondary medical knowledge support temporal interactions and complex revisions across multiple actionable graphs. The mitigation algorithm uses the actionable graphs, revision operators and available (and possibly incomplete) patient information represented in FOL. It relies on a depth-first search strategy to find a valid sequence of revisions and uses theorem proving and model finding techniques to identify applicable revision operators and to establish a management scenario for a given patient if one exists. The management scenario defines a safe (interaction-free) and preferred set of activities together with possible patient states. We illustrate the use of our framework with a clinical case study describing two patients who suffer from chronic kidney disease, hypertension, and atrial fibrillation, and who are managed according to CPGs for these diseases.While in this paper we are primarily concerned with the methodological aspects of mitigation, we also briefly discuss a high-level proof of concept implementation of the proposed framework in the form of a clinical decision support system (CDSS). The proposed mitigation CDSS insulates clinicians from the complexities of the FOL representations and provides semantically meaningful summaries of mitigation results. Ultimately we plan to implement the mitigation CDSS within our MET (Mobile Emergency Triage) decision support environment. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jbi.2016.12.002 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Adverse interaction mitigation,Clinical practice guidelines,First-order logic,Multi-morbidity,Patient preferences | Data mining,Graph,Computer science,Decision support system,Clinical Practice,Automated theorem proving,First-order logic,Triage,Operator (computer programming),Clinical decision support system | Journal |
Volume | Issue | ISSN |
66 | C | 1532-0464 |
Citations | PageRank | References |
4 | 0.48 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Szymon Wilk | 1 | 461 | 40.94 |
| Martin Michalowski | 2 | 4 | 3.18 |
| Wojtek Michalowski | 3 | 266 | 41.48 |
| Daniela Roşu | 4 | 248 | 35.85 |
| Marc Carrier | 5 | 12 | 2.68 |
| Mounira Kezadri-Hamiaz | 6 | 9 | 1.29 |