AugMend Health: VRAI-Based Behavioral Health Platform For Evaluation and Exercises For Patients with Chronic Health Issues

Introduction

This document provides a detailed specification for AugMend Health's VR-based behavioral health platform, integrating clinical requirements, technical specifications, and implementation guidance. The platform aims to address critical gaps in behavioral healthcare delivery for oncology and chronic pain patients while supporting clinical workflows and improving patient outcomes.

1. EHR VR Bi-Directional System Integration

The first stage of the AugMend Health VR-based behavioral health platform implementation focuses on establishing the necessary data infrastructure and processes to support personalized patient care. This includes integrating with existing electronic health record (EHR) systems, particularly EPIC, to enable secure and efficient data sharing. The goal is to collect and process all clinically relevant data to inform the AI system and provide healthcare teams with actionable insights, while minimizing additional workload on providers.

EHR Integration

User Story:

• As a clinician, I want the system to integrate seamlessly with EPIC so I can access updated patient data in real-time without manual input.

Features:

Data Extraction from EHR to VR:

The AugMend system will create a bi-directional integration with the EHR that maintains full HIPAA compliance and data security. This integration will automatically extract structured patient data on a regular basis without requiring manual input from clinicians. Relevant data will be categorized and processed to inform personalized care, with a focus on elements such as:

• Automated retrieval of structured data:
  • Previous diagnoses (this may be related to what they are currently going through)
  • Any existing pain scores and symptom tracking data (related to what they are going through)
  • Previous treatments (may influence the structure of questions if it is relevant for what they are currently going through)
  • Socio-Demographic and cultural background information (influences how the AI should approach the conversation and structure the report as these factors influence treatment decisions)
  • Patient communication preferences (should influence how the AI communicates)
  • Health literacy indicators

To paint a comprehensive picture of each patient's health status and needs, the AugMend system requires access to detailed medical history data from the EHR. This includes:

• Medical history: diagnosis, staging, treatments, medications
• Pain history: location, intensity, patterns
• Objective and patient-reported outcome measures
• Data to inform AI interaction and 5-min clinician reviews

By synthesizing data from multiple sources, the AI system can develop a nuanced understanding of each patient's unique bio-psycho-social context. This data serves the dual purpose of guiding the AI's interaction style and content, while also providing valuable insights for the clinical team. Importantly, the data processing algorithms will distill this wealth of information into concise, actionable snippets that providers can review in under 5 minutes, as requested by clinicians interviewed during development.

By systematically collecting and organizing this data, the AugMend system can provide a holistic view of each patient's unique needs and tailor subsequent interventions accordingly. Cultural and linguistic data will be leveraged to deliver assessments and educational content in the patient's preferred language and format. Health literacy data will inform the level of complexity in patient-facing communications, with a target of a 4th-5th grade reading level, which has been shown to improve understanding in populations with limited health literacy.

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Key points:

• Establish secure, bidirectional EHR integration
  • Maintain HIPAA compliance and data security
  • Minimize clinician workload
• Categorize data based on clinical relevance
  • Focus on pain scores, symptom tracking, treatment adherence
• Capture cultural/demographic data for responsive care
  • Preferred language, cultural background, health beliefs
• Track literacy levels to tailor communications (4th-5th grade level)
• Capture patient communication/therapeutic approach preferences </aside>