Characterization and Identification of Variations in Types of Primary Care Visits Before and During the COVID-19 Pandemic in Catalonia: Big Data Analysis Study.
Francesc López SeguíGuillem Hernandez GuillametHéctor Pifarré ArolasFrancesc X Marin-GomezAnna Ruiz-ComellasAnna Maria Ramirez MorrosCristina Adroher MasJosep Vidal-AlaballPublished in: Journal of medical Internet research (2021)
The disruption in the primary care model in Catalonia has led to an explosive increase in the number of non-face-to-face visits. There has been a reduction in the number of visits for diagnoses related to chronic pathologies, respiratory infections, obesity, and bodily injuries. Instead, visits for diagnoses related to socioeconomic and housing problems have increased, which emphasizes the importance of social determinants of health in the context of this pandemic. Big data analytics with routine care data yield findings that are consistent with those derived from intuition in everyday clinical practice and can help inform decision making by health planners in order to use the next few years to focus on the least-treated diseases during the COVID-19 pandemic.
Keyphrases
- big data
- primary care
- data analysis
- artificial intelligence
- healthcare
- clinical practice
- machine learning
- mental health
- public health
- decision making
- sars cov
- health information
- metabolic syndrome
- coronavirus disease
- type diabetes
- palliative care
- weight loss
- insulin resistance
- health promotion
- quality improvement
- general practice
- mental illness
- electronic health record
- risk assessment
- body mass index
- skeletal muscle
- physical activity
- respiratory tract