Idea: Revolution in Healthcare using AI-Driven Transparency

Tired of the opaque world of medical insurance?

Objective: Build AI-powered solution to bring transparency, efficiency, and cost-savings to the healthcare industry.

Unveil Hidden Costs:

1. Real-world Example: A patient in New York City opts for knee replacement surgery. AI model ?analyzes historical data from various hospitals and insurance providers to reveal significant price disparities for the same procedure, even within the same insurance network

Empower Patients:

1. Personalized Insights: Patients can input their specific insurance plan, location, and medical condition to receive tailored recommendations on the most cost-effective and high-quality healthcare providers.
2. Data-Driven Decisions: The ?AI model vast amounts of data to identify hospitals with superior outcomes, shorter wait times, and lower complication rates.

Optimize Provider Relationships:

1. Fair Negotiations: Hospitals and insurance providers can use AI model to benchmark prices, identify cost-saving opportunities, and negotiate more equitable contracts.
2. Risk Mitigation: By analyzing historical claims data, AI model can predict potential cost overruns and help providers make informed decisions about resource allocation.?

Solution: Two AI Models for Healthcare Transparency

AI Model 1: Custom Hospital-Insurance Partnership Model

• Purpose: Accurately assess past transactions, deviations from standard processes, and calculate commissions and procedure costs using past medical transcription data.
• Data: Historical transactions, SOPs, deviation records, and external market data.
• Model: Regression, classification, and time series models to predict numerical and categorical values.
• Benefits: Fair financial settlements, efficient claims processing, and stronger partnerships.

AI Model 2: Generic Public Model for Healthcare Consumers

• Purpose: Provide transparent information on healthcare providers and insurers.
• Data: Publicly available data, patient reviews, insurance company data, and third-party sources.
• Model: NLP for processing patient reviews, recommendation systems, clustering, and classification models.
• Benefits: Informed decision-making, increased transparency, and improved healthcare outcomes.

Creating such models, we can improve healthcare transparency, efficiency, and patient outcomes. The benefits would be to provide i) transparency on costs involved ii) offer info on differentiation of hospitals and insurance provider and iii) mechanisms to understand best cost-quality combo on regular medical procedures leading standardisation of health market.