Predictive Healthcare Model for Soroka Hospital
GOAL
RESULT
DURATION
The goal was to develop a predictive model to identify which hospitalized patients would need a blood test the following day, enhancing patient care efficiency. Additionally, the model analyzed if there were statistical differences in test referral patterns across different departments to ensure fair and effective resource allocation.
The model successfully predicted the need for blood tests in over 80% of cases, significantly enhancing hospital operations and patient care. It proved an effective tool for supporting health professionals in making informed decisions, optimizing both resource allocation and patient management.
The development and testing of this predictive model spanned 3 months, culminating in a ready-to-deploy state that showcased its potential efficacy in improving hospital operations and patient outcomes.
WHAT CHARACTERISTICS ARE SPECIFIC TO PATIENTS NEEDING IMMEDIATE TESTS?
To analyze and predict patient needs, specific characteristics were identified:
Using these characteristics, the predictive model could accurately assess which patients were likely to require immediate tests.
WHAT CHARACTERISTICS ARE SPECIFIC TO PATIENTS NEEDING IMMEDIATE TESTS?
Calculating the Patient Care Value involved assessing the potential risk and consequences of delayed testing versus the resources required for immediate testing. This helped prioritize patients effectively, ensuring those in urgent need received care first.
HOW CAN WE PREDICT IF A PATIENT NEEDS IMMEDIATE TESTING?
The predictive model used machine learning algorithms, incorporating patient characteristics and historical data. By weighting each factor appropriately, the model provided a high accuracy rate in predicting the necessity for immediate blood tests, allowing hospital staff to prioritize patient care effectively.