What Types of Data Does Health Data Science Use?

Health Data Science utilizes a wide range of data to gain insights and improve healthcare outcomes. Some of the key data types include:

• Fitness and Sleep Tracker Data: Data collected from wearable devices provides valuable information about an individual's activity levels, sleep patterns, and overall health.
• Electronic Health Records (EHRs): EHRs contain comprehensive patient medical history, diagnoses, medications, and lab results, offering a rich source of data for analysis.
• Claims and Billing Data: Data related to healthcare claims and billing processes provides insights into healthcare utilization patterns, costs, and trends.
• Medical Imaging Data: Imaging scans like X-rays, CT scans, and MRIs offer detailed anatomical information, allowing for disease detection, diagnosis, and treatment monitoring.
• Genomic Data: Genetic information can be used to identify disease risks, predict drug responses, and personalize healthcare.
• Public Health Data: Data collected by public health agencies on disease outbreaks, vaccination rates, and other health indicators informs public health strategies and interventions.
• Social Determinants of Health Data: Data related to factors like socioeconomic status, education, and access to healthcare services helps understand how social factors influence health outcomes.
• Environmental Data: Data on air quality, water quality, and other environmental factors can help identify environmental health risks and develop mitigation strategies.