The skåne emergency medicine (SEM) cohort
(2024) In Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 32. p.1-8- Abstract
BACKGROUND: In the European Union alone, more than 100 million people present to the emergency department (ED) each year, and this has increased steadily year-on-year by 2-3%. Better patient management decisions have the potential to reduce ED crowding, the number of diagnostic tests, the use of inpatient beds, and healthcare costs.
METHODS: We have established the Skåne Emergency Medicine (SEM) cohort for developing clinical decision support systems (CDSS) based on artificial intelligence or machine learning as well as traditional statistical methods. The SEM cohort consists of 325 539 unselected unique patients with 630 275 visits from January 1st, 2017 to December 31st, 2018 at eight EDs in the region Skåne in southern Sweden.... (More)
BACKGROUND: In the European Union alone, more than 100 million people present to the emergency department (ED) each year, and this has increased steadily year-on-year by 2-3%. Better patient management decisions have the potential to reduce ED crowding, the number of diagnostic tests, the use of inpatient beds, and healthcare costs.
METHODS: We have established the Skåne Emergency Medicine (SEM) cohort for developing clinical decision support systems (CDSS) based on artificial intelligence or machine learning as well as traditional statistical methods. The SEM cohort consists of 325 539 unselected unique patients with 630 275 visits from January 1st, 2017 to December 31st, 2018 at eight EDs in the region Skåne in southern Sweden. Data on sociodemographics, previous diseases and current medication are available for each ED patient visit, as well as their chief complaint, test results, disposition and the outcome in the form of subsequent diagnoses, treatments, healthcare costs and mortality within a follow-up period of at least 30 days, and up to 3 years.
DISCUSSION: The SEM cohort provides a platform for CDSS research, and we welcome collaboration. In addition, SEM's large amount of real-world patient data with almost complete short-term follow-up will allow research in epidemiology, patient management, diagnostics, prognostics, ED crowding, resource allocation, and social medicine.
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- author
- Ekelund, Ulf LU ; Ohlsson, Bodil LU ; Melander, Olle LU ; Björk, Jonas LU ; Ohlsson, Mattias LU ; Forberg, Jakob Lundager LU ; de Capretz, Pontus Olsson LU ; Nyström, Axel LU and Björkelund, Anders LU
- organization
-
- EpiHealth: Epidemiology for Health
- Internal Medicine - Epidemiology (research group)
- MultiPark: Multidisciplinary research focused on Parkinson´s disease
- EXODIAB: Excellence of Diabetes Research in Sweden
- Cardiovascular Research - Hypertension (research group)
- LU Profile Area: Nature-based future solutions
- EPI@LUND (research group)
- Surgery and public health (research group)
- eSSENCE: The e-Science Collaboration
- Computational Science for Health and Environment (research group)
- LU Profile Area: Natural and Artificial Cognition
- Artificial Intelligence in CardioThoracic Sciences (AICTS) (research group)
- Emergency medicine (research group)
- Division of Occupational and Environmental Medicine, Lund University
- Centre for Environmental and Climate Science (CEC)
- Electrocardiology Research Group - CIEL (research group)
- publishing date
- 2024-04-26
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Humans, Sweden, Emergency Service, Hospital/statistics & numerical data, Emergency Medicine, Female, Male, Decision Support Systems, Clinical, Cohort Studies, Artificial Intelligence, Adult
- in
- Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
- volume
- 32
- article number
- 37
- pages
- 1 - 8
- publisher
- BioMed Central (BMC)
- external identifiers
-
- scopus:85191630503
- pmid:38671511
- ISSN
- 1757-7241
- DOI
- 10.1186/s13049-024-01206-0
- project
- AIR Lund - Artificially Intelligent use of Registers
- language
- English
- LU publication?
- yes
- additional info
- © 2024. The Author(s).
- id
- 633da053-654e-49d9-a188-dde545ede196
- date added to LUP
- 2024-04-29 23:01:26
- date last changed
- 2024-05-16 14:08:58
@article{633da053-654e-49d9-a188-dde545ede196, abstract = {{<p>BACKGROUND: In the European Union alone, more than 100 million people present to the emergency department (ED) each year, and this has increased steadily year-on-year by 2-3%. Better patient management decisions have the potential to reduce ED crowding, the number of diagnostic tests, the use of inpatient beds, and healthcare costs.</p><p>METHODS: We have established the Skåne Emergency Medicine (SEM) cohort for developing clinical decision support systems (CDSS) based on artificial intelligence or machine learning as well as traditional statistical methods. The SEM cohort consists of 325 539 unselected unique patients with 630 275 visits from January 1st, 2017 to December 31st, 2018 at eight EDs in the region Skåne in southern Sweden. Data on sociodemographics, previous diseases and current medication are available for each ED patient visit, as well as their chief complaint, test results, disposition and the outcome in the form of subsequent diagnoses, treatments, healthcare costs and mortality within a follow-up period of at least 30 days, and up to 3 years.</p><p>DISCUSSION: The SEM cohort provides a platform for CDSS research, and we welcome collaboration. In addition, SEM's large amount of real-world patient data with almost complete short-term follow-up will allow research in epidemiology, patient management, diagnostics, prognostics, ED crowding, resource allocation, and social medicine.</p>}}, author = {{Ekelund, Ulf and Ohlsson, Bodil and Melander, Olle and Björk, Jonas and Ohlsson, Mattias and Forberg, Jakob Lundager and de Capretz, Pontus Olsson and Nyström, Axel and Björkelund, Anders}}, issn = {{1757-7241}}, keywords = {{Humans; Sweden; Emergency Service, Hospital/statistics & numerical data; Emergency Medicine; Female; Male; Decision Support Systems, Clinical; Cohort Studies; Artificial Intelligence; Adult}}, language = {{eng}}, month = {{04}}, pages = {{1--8}}, publisher = {{BioMed Central (BMC)}}, series = {{Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine}}, title = {{The skåne emergency medicine (SEM) cohort}}, url = {{http://dx.doi.org/10.1186/s13049-024-01206-0}}, doi = {{10.1186/s13049-024-01206-0}}, volume = {{32}}, year = {{2024}}, }