This is a set of deidentified synthetic data in 2023 related to patient healthcare journey demonstrating patient's visits, admission, readmission, comorbidities, and treatment plans. Use cases: Descriptive Analysis: Descriptive analysis involves summarizing and presenting healthcare patient data in a meaningful way. It includes basic statistics, such as counts, percentages, averages, and distributions. Descriptive analysis helps to understand the characteristics of the patient population, such as age, gender, and demographic information. Diagnostic Analysis: Diagnostic analysis aims to identify patterns, trends, and relationships within healthcare patient data. It involves exploring correlations, associations, and dependencies between variables. Diagnostic analysis helps to identify risk factors, understand disease progression, and identify potential causes of specific conditions or outcomes. Predictive Analysis: Predictive analysis uses historical healthcare patient data to create models and make predictions about future events or outcomes. It involves applying statistical and machine learning techniques to identify patterns and build predictive models. Predictive analysis can be used to forecast patient readmissions, disease progression, treatment response, or other relevant healthcare outcomes. Prescriptive Analysis: Prescriptive analysis focuses on determining the best course of action based on healthcare patient data. It involves using optimization algorithms, simulation models, and decision-making tools. Prescriptive analysis helps healthcare providers in making informed decisions about treatment plans, resource allocation, and interventions to optimize patient outcomes. Cohort Analysis: Cohort analysis involves grouping patients based on specific criteria, such as demographics, disease type, or treatment received. It allows for comparing outcomes, treatment responses, or other variables between different patient cohorts. Cohort analysis helps identify differences in patient populations and can inform personalized treatment strategies. Survival Analysis: Survival analysis examines the time until the occurrence of a specific event, such as patient mortality or disease recurrence. It helps estimate survival probabilities, assess treatment effectiveness, and identify factors that impact patient survival. Survival analysis is particularly useful in oncology, chronic diseases, and other areas where time-to-event analysis is relevant.