Markov modeling for cost-effectiveness using federated health data network.
Markus HaugMarek OjaMaarja PajusaluKerli MoosesSulev ReisbergJaak ViloAntonio Fernández GiménezThomas FalconerAna DanilovićFilip MaljkovicDalia DawoudRaivo KoldePublished in: Journal of the American Medical Informatics Association : JAMIA (2024)
We created 2 R-packages to pioneer cost-effectiveness analysis in OMOP CDM data networks. The first manages state definitions and database interaction, while the second focuses on Markov model learning and profile synthesis. We demonstrated their utility in a multisite heart failure study, comparing telemonitoring and standard care, finding telemonitoring not cost-effective.
Keyphrases
- heart failure
- healthcare
- electronic health record
- big data
- public health
- palliative care
- mental health
- left ventricular
- quality improvement
- data analysis
- health information
- atrial fibrillation
- machine learning
- artificial intelligence
- chronic pain
- acute heart failure
- pain management
- climate change
- health promotion
- deep learning