<aside> 💡 Overview

CareAlign AI is an inventive healthcare platform designed to streamline patient care through advanced empanelment strategies, AI-driven insights for enhanced healthcare management. This project takes aim at primary health care systems by facilitating effective universal health coverage through structured patient-provider alignment.

The concept of empanelment revolves around the creation of patient panels, which are essentially lists of patients assigned to each provider or care team in a practice. These teams, which might include a physician, a medical assistant, and a health educator, are then responsible for a wide range of care needs for their assigned patients. This includes preventive care, disease management, and acute care. Empanelment aims to shift healthcare from a reactive model, where patients see any available clinician, to a proactive model that fosters continuous patient-provider relationships.

Key aspects of CareAlign.AI include:

• Patient-Provider Matching: CareAlign.AI will match patients with the most suitable primary care providers or teams, ensuring personalized and effective care.
• Population Health Management: The platform emphasizes the management of entire patient populations, identifying health risks and gaps in care through predictive AI.
• Continuity of Care: CareAlign AI fosters long-term patient-provider and provider-team relationships, enhancing continuity and consistency in care.
• Panel Management: Advanced tools for managing patient panels are embedded, facilitating targeted outreach and improved follow-up care.
• Proactive Healthcare Delivery: The platform encourages a shift from reactive to proactive healthcare, with providers actively responsible for a defined group of patients.
• Data-Driven Decision Making: CareAlign.AI leverages data to inform healthcare decisions, tracking health trends and outcomes within patient panels.
• Enhanced Patient Engagement: Designed to increase patient engagement by offering consistent care teams, leading to higher satisfaction and better healthcare results.
• Resource Optimization: Efficiently allocates healthcare resources by balancing patient demand with provider capacity, optimizing the healthcare delivery process. </aside>

Problem

<aside> ⚠️ The current healthcare system frequently faces challenges in ensuring effective and efficient care coordination. Patients often encounter issues such as inconsistent care, long wait times, and a lack of personalized attention. These problems primarily stem from a reactive and fragmented approach to healthcare delivery. Consequently, patient satisfaction and health outcomes are compromised, and healthcare resources are inefficiently utilized.

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Problem Statement

<aside> 🤔 Many healthcare systems do not have a structured approach for assigning patients to specific providers or care teams. This results in a lack of continuity in patient care, underutilization of healthcare resources, and missed opportunities for preventive care and chronic disease management. Patients often do not have a consistent care provider, which can negatively affect the quality of care and patient-provider relationships. Furthermore, healthcare providers face challenges in effectively managing their patient load, which can lead to burnout and a decrease in the quality of care.

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Solution

<aside> ✅ CareAlign.AI offers a comprehensive solution to these challenges by leveraging Predictive AI and Reinforcement Learning to implement effective empanelment strategies in healthcare.

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Design

Ideation

During the ideation phase of CareAlign AI, the main objective was to identify and address the core challenges within the current healthcare delivery system. This phase entailed conducting thorough research, organizing brainstorming sessions, and consulting with healthcare professionals and patients. The aim was to obtain a comprehensive understanding of the existing gaps and needs in primary healthcare.

Building an MVP

<aside> ✍🏻 We will release an initial version to a small, potentially independent provider network. This approach will facilitate integration with existing EMR (Electronic Medical Record) solutions and allow for a faster feedback loop with providers and care teams.

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MVP

• Objective: To create a basic, functional version of CareAlign AI that includes core features to address the most critical pain points.
• Features:
  • Manual Matching Matching: Basic version to match patients with suitable providers.
  • Basic Patient Panel Management: Simple tools for managing patient panels.
  • User-Friendly Interface: Initial interface for providers and patients.
  • Data Security: Basic security protocols to protect patient data in line with HIPPA requirements.

Phase 1 - Enhancement and Integration: