Machine learning is a branch of data analytics that uses data to identify patterns and make predictions or decisions. Instead of manually creating every rule, we allow a computer model to learn from historical data and apply what it has learned to new situations.
In healthcare, machine learning can support many types of analysis, such as:
- Predicting patients at high risk for hospital readmission.
- Identifying members who may benefit from care management programs.
- Detecting unusual claim patterns that could indicate fraud, waste, or abuse.
- Estimating future healthcare costs and resource utilization.
- Supporting population health management and quality improvement initiatives.
Machine learning is often grouped into different categories. The most common type used in healthcare analytics is supervised learning, where the model learns from historical data with known outcomes. Two important supervised learning methods are classification and regression.
Healthcare Example
Imagine a health plan wants to identify members who may be readmitted to the hospital within 30 days after discharge. Historical patient data, including age, diagnoses, chronic conditions, and prior admissions, can be used to train a machine learning model. The model can then estimate which patients are at higher risk and may benefit from additional follow-up care.
As I continue learning Power BI and healthcare analytics, I am finding that machine learning is not about replacing human decision-making. Instead, it is another tool that helps us discover patterns, support better decisions, and answer important business questions using data.
🌱 Study Note: Machine learning begins with a business question. The model is simply one method of helping answer that question.