What type of healthcare data is commonly used for predictive analytics?

Predictive analytics in healthcare relies heavily on data that can provide insights into patient outcomes, treatment effectiveness, and potential health risks. The correct choice reflects a combination of patient demographics and clinical data, which are critical for developing predictive models.

Patient demographics, such as age, gender, and socioeconomic status, assist in forming a profile that can indicate risk factors for certain conditions. Clinical data, including medical history, lab results, vital signs, and information on past and current treatments, provide a deeper understanding of individual patient health and trends over time.

These combined data sets allow healthcare professionals to use statistical techniques and algorithms to identify patterns and predict future health events, such as the likelihood of hospital readmission or the onset of chronic diseases. This predictive capability is essential for preventative care initiatives, personalized treatment plans, and resource allocation.

In contrast, while hospital administrative data can provide insights into operational aspects, they may not contain the necessary depth regarding individual patient health to be predictive. Patient self-reported outcomes give valuable information about a patient's experience but lack the comprehensive clinical context needed. Financial expenditure records focus primarily on costs and do not provide the clinical or demographic context required for predictive modeling.