PMID- 37697839
OWN - NLM
STAT- MEDLINE
DCOM- 20230913
LR  - 20230913
IS  - 1879-8365 (Electronic)
IS  - 0926-9630 (Linking)
VI  - 307
DP  - 2023 Sep 12
TI  - Modelling Adverse Events with the TOP Phenotyping Framework.
PG  - 69-77
LID - 10.3233/SHTI230695 [doi]
AB  - The detection and prevention of medication-related health risks, such as 
      medication-associated adverse events (AEs), is a major challenge in patient care. 
      A systematic review on the incidence and nature of in-hospital AEs found that 
      9.2% of hospitalised patients suffer an AE, and approximately 43% of these AEs 
      are considered to be preventable. Adverse events can be identified using 
      algorithms that operate on electronic medical records (EMRs) and research 
      databases. Such algorithms normally consist of structured filter criteria and 
      rules to identify individuals with certain phenotypic traits, thus are referred 
      to as phenotype algorithms. Many attempts have been made to create tools that 
      support the development of algorithms and their application to EMRs. However, 
      there are still gaps in terms of functionalities of such tools, such as 
      standardised representation of algorithms and complex Boolean and temporal logic. 
      In this work, we focus on the AE delirium, an acute brain disorder affecting 
      mental status and attention, thus not trivial to operationalise in EMR data. We 
      use this AE as an example to demonstrate the modelling process in our 
      ontology-based framework (TOP Framework) for modelling and executing phenotype 
      algorithms. The resulting semantically modelled delirium phenotype algorithm is 
      independent of data structure, query languages and other technical aspects, and 
      can be run on a variety of source systems in different institutions.
FAU - Beger, Christoph
AU  - Beger C
AD  - Institute for Medical Informatics, Statistics and Epidemiology, Leipzig 
      University.
FAU - Boehmer, Anna Maria
AU  - Boehmer AM
AD  - Institute of Pharmacy, Department of Clinical Pharmacy, University of Bonn.
FAU - Mussawy, Beate
AU  - Mussawy B
AD  - Hospital Pharmacy, University Medical Center Hamburg-Eppendorf.
FAU - Redeker, Louisa
AU  - Redeker L
AD  - Department of Clinical Pharmacology, School of Medicine, Faculty of Health, 
      Witten/Herdecke University.
FAU - Matthies, Franz
AU  - Matthies F
AD  - Institute for Medical Informatics, Statistics and Epidemiology, Leipzig 
      University.
FAU - Schafermeier, Ralph
AU  - Schafermeier R
AD  - Institute for Medical Informatics, Statistics and Epidemiology, Leipzig 
      University.
FAU - Hardtlein, Annette
AU  - Hardtlein A
AD  - Institute of General Practice and Family Medicine, University Hospital, LMU 
      Munich.
FAU - Dreischulte, Tobias
AU  - Dreischulte T
AD  - Institute of General Practice and Family Medicine, University Hospital, LMU 
      Munich.
FAU - Neumann, Daniel
AU  - Neumann D
AD  - Institute for Medical Informatics, Statistics and Epidemiology, Leipzig 
      University.
FAU - Uciteli, Alexandr
AU  - Uciteli A
AD  - Institute for Medical Informatics, Statistics and Epidemiology, Leipzig 
      University.
LA  - eng
PT  - Journal Article
PT  - Systematic Review
PL  - Netherlands
TA  - Stud Health Technol Inform
JT  - Studies in health technology and informatics
JID - 9214582
MH  - Humans
MH  - *Algorithms
MH  - Brain
MH  - Databases, Factual
MH  - Electronic Health Records
MH  - *Delirium
OTO - NOTNLM
OT  - adverse events
OT  - algorithms
OT  - computable phenotypes
OT  - electronic health records
EDAT- 2023/09/12 06:42
MHDA- 2023/09/13 06:42
CRDT- 2023/09/12 03:32
PHST- 2023/09/13 06:42 [medline]
PHST- 2023/09/12 06:42 [pubmed]
PHST- 2023/09/12 03:32 [entrez]
AID - SHTI230695 [pii]
AID - 10.3233/SHTI230695 [doi]
PST - ppublish
SO  - Stud Health Technol Inform. 2023 Sep 12;307:69-77. doi: 10.3233/SHTI230695.