Data Analytics in Healthcare
In the modern day we live in, people working with various organizations worldwide are generating and processing a massive amount of data. The healthcare industry successfully uses big data to collect and store critical information. The digitization of all clinical exams and medical records in healthcare has recently become a standard and widely adopted practice.
Data analytics in healthcare can be applied to every aspect of patient care and operations management. There are four categories of analytics (based on maturity level) for analyzing a wide variety of health data in the healthcare field:
- Descriptive analytics;
- Diagnostic analytics;
- Predictive analytics;
- Prescriptive analytics.
Benefits of Big Data Analytics for Healthcare:
- Individual patient care;
- Preventing unnecessary emergency room visits;
- Predicting the cost and risks of treatment;
- Fewer medical errors and more accurate treatment;
- Preventing mass diseases and preventive care;
- Modeling the spread of disease;
- New therapy and drug discovery;
- Improved HR management;
- Early detection of diseases;
- Identifying and managing high-risk patients;
- Real-time alert;
- Preventing suicide and self-harm;
- Optimized hospital operation;
- Better customer service;
- Reducing the cost of medical care.
Analytics Implementation Stages:
Stage 1. Developing a big data strategy.
Stage 2. Identify big data sources.
Stage 3. Developing methods for accessing, managing, and storing your big data.
Stage 4. Analyzing big data.
Stage 5. Make well-informed decisions.
With the ever-growing amount of data available, the healthcare industry will continue to move down a path where processes, care and outcomes are informed by the predictive power of data analytics. As this potential continues to evolve, healthcare organizations should be deploying practical uses of big data now while considering how to advance their data strategies in ways that transform every aspect of the health system.
