Improve on this plan """**Product Name:** AI-Powered Case Management Decision Support System for Admission Optimization (CM-DSS)

**Date:** 2025-05-16

**Version:** 0.1

**Purpose:** This document outlines the product requirements for the CM-DSS, an application designed to assist hospital Case Managers in optimizing patient admissions by providing real-time, AI-driven guidance based on payer contracts, clinical guidelines, Core Measures, patient history, and other quality/revenue-linked KPIs. The system will leverage a series of specialized Agno Memory Modules (AMMs) deployed as Model Control Protocol (MCP) servers.

**Intended Audience:** Product Management, Engineering, Quality Assurance, Project Stakeholders.

## 2. Goals and Objectives

* **Primary Goal:** To empower Hospital Case Managers with intelligent, actionable, and timely recommendations to ensure patient admissions are appropriately documented, meet all necessary clinical and payer criteria from the outset, and align with quality/revenue KPIs.
* **Strategic Objectives:**
    * Reduce claim denials related to medical necessity, documentation gaps, and non-compliance with payer/clinical/quality guidelines.
    * Improve hospital performance on targeted Core Measures and other quality/revenue-linked KPIs.
    * Enhance Case Manager efficiency and job satisfaction by streamlining information synthesis and decision-making.
    * Improve patient flow and resource utilization through proactive discharge planning and appropriate status assignment.

## 3. Target Users & Audience

* **Primary Users:**
    * Hospital Case Managers: Responsible for utilization review, discharge planning, and care coordination. They will interact directly with the CM-DSS to receive guidance.
* **Secondary Users/Beneficiaries:**
    * Physicians and Clinical Care Teams: Will receive clarified documentation and order recommendations via the Case Manager.
    * Hospital Revenue Cycle Management: Will benefit from reduced denials and optimized reimbursement.
    * Quality Improvement Department: Will see improved compliance with Core Measures and KPIs.
    * Hospital Administration: Will benefit from improved financial performance and quality ratings.
    * Patients: Will benefit from more efficient care progression and appropriately justified hospital stays.

## 4. Problem Statement

Hospital Case Managers face a significant challenge in concurrently navigating and ensuring compliance with a multitude of complex, dynamic requirements. These include payer-specific contract terms, clinical appropriateness guidelines (e.g., InterQual/MCG), national Core Measures, and various other revenue and quality-linked Key Performance Indicators (KPIs), all while managing daily patient care progression.

This complexity often leads to:
* Suboptimal or incomplete clinical documentation.
* Misaligned patient admission statuses.
* Missed opportunities for achieving quality metric compliance.
* Inefficient patient flow and discharge planning.
* Increased risk of claim denials, impacting hospital revenue.
* High cognitive load and potential burnout for Case Managers.

The CM-DSS aims to address these issues by providing an AI-powered system that synthesizes these diverse requirements and offers real-time, actionable guidance.

## 5. Proposed Solution Overview

The CM-DSS will be an application that:
1.  Allows Case Managers to input collated preliminary patient information (e.g., from ED notes, initial tests, history).
2.  Orchestrates queries to a suite of specialized MCP servers, each an Agno Memory Module (AMM) instance with its own expert knowledge base:
    * **Payer Contract Server (AMM-Payer):** Expertise in various payer rules, authorization requirements, and contract specifics.
    * **Clinical Guidelines Server (AMM-Clinical):** Expertise in InterQual, MCG, and other medical necessity criteria.
    * **Patient Information Server (AMM-Patient):** Securely manages and retrieves relevant longitudinal and current patient clinical data (leveraging AMM's adaptive memory and fixed knowledge capabilities).
    * **Core Measures Server (AMM-CoreMeasures):** Expertise in documentation and care elements required for Core Measure compliance.
    * **KPI Optimization Server (AMM-KPI):** Expertise in other specific revenue/quality linked KPIs (e.g., HACs, readmissions, LOS).
3.  Synthesizes the responses from these MCP servers.
4.  Presents the Case Manager with daily, actionable recommendations regarding:
    * Specific clinical documentation needed from the care team.
    * Appropriate medical orders to consider.
    * Proactive discharge planning opportunities and barriers.
    * Guidance for meeting Core Measures and other KPIs.
5.  The system will be built leveraging the Agno Memory Module framework for creating the specialized MCP servers, ensuring secure and standardized context exchange.

## 6. Key Features (Initial - To be detailed in Phase 2)

* **Patient Data Input:** Interface for CMs to input initial patient context.
* **MCP Orchestration Engine:** Core logic to query multiple specialized AMM/MCP servers.
* **Guidance Synthesis Module:** Aggregates and prioritizes recommendations from MCP servers.
* **Recommendation Display:** Clear and actionable display of daily guidance for CMs.
* **User Authentication & Authorization:** Secure access for Case Managers.
* **Audit Logging:** Track system usage and recommendations provided.
* **(Future) EHR Integration Hooks:** Define points for potential future integration for data input/output.

## 7. Assumptions and Dependencies

* **AMM Framework:** The Agno Memory Module system (as described in `digest.txt`) is available and capable of being deployed as secure, reliable MCP servers with the necessary knowledge loading and adaptive memory features.
* **MCP Server Development:** Specialized AMM instances for Payer Contracts, Clinical Guidelines, Patient Information, Core Measures, and KPIs can be successfully developed, populated, and maintained.
* **External Data Input:** Case Managers will have an existing process/application to gather initial patient information before inputting it into the CM-DSS.
* **Availability of Knowledge:** Accurate and up-to-date knowledge for payer contracts, clinical guidelines, Core Measures, etc., can be acquired and structured for ingestion into the respective AMM/MCP servers.
* **User Adoption:** Case Managers will be willing to adopt and integrate the CM-DSS into their daily workflow.
* **Compliance:** The system must be designed to be HIPAA compliant and handle PHI securely.

## 8. Constraints

* **Regulatory Compliance:** Must adhere to HIPAA, HITECH, and other relevant healthcare data privacy and security regulations.
* **Interoperability (Initial):** Initially, the system relies on manual text input. Deeper EHR integration is a future consideration and a current constraint.
* **Real-time Performance:** Recommendations should be available in a timely manner to be useful within the CM workflow.
* **Accuracy of AI Models:** The effectiveness of the specialized AMM/MCP servers depends on the accuracy of their underlying models and knowledge.
* **Data Security:** Robust security measures for data at rest and in transit, especially PHI.
* **Maintainability:** The knowledge bases of the MCP servers will require ongoing updates. The AMM system should support this.
* **User Interface (UI) Complexity:** The UI for the CM must be intuitive and not add to their cognitive load.

## 9. Success Metrics (To be refined)

* **Reduction in claim denial rate** attributed to medical necessity or documentation (Target: TBD%).
* **Improvement in compliance rates** for 3-5 targeted Core Measures (Target: TBD%).
* **Reduction in average Case Manager time** spent on synthesizing information for admission justification (Target: TBD minutes/case).
* **User satisfaction score** (e.g., NPS or survey-based) from Case Managers (Target: TBD).
* **Adoption rate** by Case Managers within the pilot department/hospital.
* **(Future) Impact on Length of Stay** (LOS) against GMLOS.
* **(Future) Impact on readmission rates** for targeted conditions.

## 10. Future Considerations / Roadmap Highlights (High-Level)

* Deeper EHR integration for automated data retrieval and recommendation delivery.
* Expansion to support a wider range of KPIs and clinical conditions.
* Advanced analytics and reporting on CM-DSS impact.
* Integration with downstream systems (e.g., discharge planning tools).
* Mobile access for Case Managers.
* Machine learning models to predict high-risk cases for proactive intervention.
"""