AI Apex Health, the healthcare industry vertical of AI Apex, is committed to delivering cutting-edge AI solutions tailored specifically for the healthcare sector. We leverage the latest advancements in AI to address today's challenges and provide innovative solutions.
Within AI Apex Health, we have Apex Healthcare Consulting, a management consultancy that offers expert guidance on AI strategies, adoption, and integration within the healthcare domain. Our consulting services encompass transformative AI solutions by combining the expertise of two cross-industry groups: the AI Apex Solution Engineering Group and our GRC (Governance, Risk & Compliance) Practice, which includes our specialists in HiTrust and HIPAA compliance.
Operating under the AI Apex Enterprise Transformation Framework, our team of experienced professionals provides distinct advantages to our clients, including:
• Access to industry experts with deep knowledge of the healthcare domain and experience in driving business and clinical transformation.
• A fusion of strategy consulting, transformation expertise, systems integration, and the capabilities of an experienced AI engineering team. We manage the entire lifecycle of your AI solutions, from strategizing and managing to developing, implementing, and providing ongoing support, offering a comprehensive approach.
• A unique ability to reimagine existing business and clinical processes by placing AI at the core of our redesign efforts, fostering creativity and innovation.
• Development of value-driven business cases for healthcare AI projects, estimating tangible improvements in performance metrics and quantifiable benefits.
• Establishment of a robust AI technical foundation and effective management/governance of AI within your healthcare organization, ensuring long-term success.
Most importantly, we conduct an initial project which we call “Alignment & Discovery” for your organization as the first step in our collaboration, ensuring that we embark on the AI journey together with a collective understanding, a shared definition of AI, its benefits, and costs, all considered in a planned approach with defined results and outcomes at every step.
To reimagine the Credentialing process augmented by artificial intelligence, the approach begins with rethinking the traditional and time-consuming credentialing process. The envisioned future state centers around the development of an AI-driven process that includes automated document validation, prioritizes document requests, and intelligently ranks case files for priority completion based on various criteria. A streamlined process redesigned to take advantage of AI offers benefits such as time and cost savings, avoiding backlogs, enhancing provider experience, improving compliance, and supporting expedited collection and verification tasks. By further integrating AI technology with downstream processes to on-board newly credentialed providers, (e.g., greatly reducing the time lag to be listed in on-line provider directories), the time from credentialing to enrollment to patient scheduling can be significantly reduced, enabling new providers to start seeing patients. Embracing AI-driven methods in credentialing transforms the workflow, making it more efficient and effective while also laying the foundation for sharing new provider data with downstream processes and related core administrative systems.
1. Utilize the Credentialing Copilot to anticipate issues and delays continuously analyzing process activity, resource productivity, status of overdue information or verification requests, and other sources of data that assist in managing credentialing performance
2. Utilize the Copilot Console for transmission status in sending/receiving data feeds, knowledge of new member/patient complaints, coordination with internal credentialing committees, expected volume changes in new clinicians being on-boarded
3. Apply the Copilot’s experience with the credentialing function’s actual performance over time to plan for resources to be allocated to re-credentialing workload volume
4. Review AI driven analysis of network growth plans and service area expansion to plan for peak credentialing periods with additional staff or a third party organization to handle credentialing capacity overflow
5. Add learned knowledge/on-going operations experience to the AI Knowledge Base for future reference by the AI to assist in decision-making for provider network expansion, workforce planning, operational excellence and unexpected events such as a merger or acquisition
6. Produce AI learned best practices when determining how to plan for and implement changes in contracted provider networks that may require re-credentialing
7. Apply the knowledge of the AI to other core systems and workflows that create their own provider data files from the provider files created during the credentialing process to ensure provider data accuracy and timeliness - especially after re-credentialing events
8. Allow the Credentialing AI to access more information by expanding its knowledge base continuously teaching the AI any unexpected results of its predictions, filling gaps in its data models to improve the reliability of its predictions to enhance its recommendations when estimating time duration, level of effort and probable outcome for peaks in credentialing volume
9. Enhance the Credentialing AI’s knowledge to become an enterprise resource which can access strategic growth plans for the year, and then predict the required provider network expansion offering guidance, insights, reminders and relevant recommendations to prepare in advance for increased credentialing volume
10. Grow the AI Credentialing Copilot to be a “partner” - not a replacement - for the experienced users in your provider credentialing function
This use case leverages AI technology to address the challenge of maintaining accurate On-Line Provider Directories. The proposed solution involves implementing AI-driven processes to ensure the accuracy of provider data, helping healthcare providers and health plans avoid member/patient complaints, comply with regulatory requirements, and avoiding penalties. The solution includes enhancing internal health plan business processes through AI, including proactively identifying changes in provider data, and accelerating updates to the directories while creating a “curated” and tightly controlled provider data master. By incorporating AI technology, health plans can streamline provider data management, proactively flag provider data changes for review, expedite the update process and then automatically send updates to downstream applications and third parties who also depend upon accurate provider information. The benefits of staying in compliance, improving efficiency, eliminating complaints through timely updates, and increasing patient and member satisfaction make this AI-driven solution crucial for maintaining reliable provider data in On-Line Provider Directories.
1. Utilize the AI to anticipate provider data changes by continuously analyzing activity in other systems, processing of rosters from provider systems, portal provider demographic change requests, provider contracts, member complaints, and other sources of provider data changes like re-credentialing and provider relations
2. Review AI driven analysis of current inaccurate provider data to identify sources and reasons of inaccuracy and determine remediation activities through root case analysis methods to “cleanse” provider directory data once and ensure on-going accuracy. Add learned knowledge to the AI Knowledge Base for future reference by the AI
3. Produce AI learned best practices when determining how to remediate inaccurate data and sources
4. Apply the knowledge of the AI to other health plan business systems and workflows that create their own provider data files that are impacting provider data accuracy in on-line directories
5. Utilize the real-time AI Copilot monitoring changes to provider data feeds, new complaints, timing of updates from Health System Rosters, volume of new clinicians being on-boarded after credentialing, provider portal demographic updates and others, apply AI to continuously get “smarter” over time to offer insights for continuous Improvement of data accuracy
6. Integrate new knowledge into the AI by continuously teaching the AI the results of remediation, its time duration, level of effort and outcome
7. Enhance the AI’s known experience into an enterprise resource which can access strategic growth planned for provider network expansion offering guidance, insights, reminders and relevant recommendations to expedite the entry of accurate and verified provider data to the on-line directory updates
8. Grow the AI Copilot to be a “partner” - not a replacement - for the experienced users in your provider data management function
The objective of this solution is to leverage AI technology to enhance the configuration process of provider contracts in Health Plan administrative systems. The proposed solution involves utilizing AI to analyze new provider contracts, modifications and extensions, identifying changes in written terms and conditions, rate tables, reimbursement terms and conditions, payment and banking information, and the steps to complete required updates to ensure accurate and timely provider payments. The AI system would analyze contract details, generate configuration recommendations, identify the correct sequence of completing configuration in the system and automate the completion of input for final review and approval of the necessary updates before being released for testing. Once testing is completed and errors are corrected (which is a related AI Solution), the configuration input would be generated, reviewed and then released to update the production system. The benefits include improved claims payment accuracy, efficiency, contract compliance, enhanced provider experience and easier on-going contract administration. By implementing this AI-enhanced solution, healthcare organizations can streamline contract configuration processes and ensure a level of accuracy, completeness and timeliness in configuring new contracts or contract renewals to ensure seamless provider payment processes at Health Plans.
1. Utilize the AI to “read” and analyze provider contracts end to end, identify and document those contractual items that must be configured: Written terms and conditions, formulas, rates/rate tables, procedures and modifiers including provider/contract specific modifiers, bundled procedure rates, and any value-based care reimbursement arrangements
2. Review AI driven analysis of the provider contract and summarize for users the key changes from the new contract or the prior contract (if an extension or new contract with an existing provider)
3. Use the AI Configuration Copilot to prioritize configuration activities, estimate timing for configuration go-live, determine resource level of effort based upon complexity and amount of configuration, and make recommendations for sequence of activities and steps
4. Incorporate a AI-Driven configuration pre-production testing capability (can be a separate AI in Healthcare Solution) to ensure that a large volume of claims are tested against the new vs. old provider contract claims adjudication configuration. AI can greatly reduce the manual effort for identifying discrepancies, and determining configuration changes
5. Produce AI learned best practices when determining how to streamline business processes and subsequent pre-production configuration testing
6. Apply the knowledge of the AI to related claims adjudication health plan business systems and workflows such as pre-payment review, claims audits for overpayments/underpayments, special audits/reviews, and compliance.
7. Utilize the real-time AI Copilot to monitor the progress and performance against the AI estimated completion timeframe to assess staff performance, identify process issues, and bottlenecks that could impact the estimated completion timeframe against the effective date of the provider contract
8. Integrate new knowledge into the AI by continuously teaching the AI the results of audit results from over/underpayments, provider complaints/appeals, better ways to sequence configuration tasks, under/over utilization of staff resources, prevention of bottlenecks and other “learnings” to be added to the AI’s best practices knowledge
9. Grow the AI to be the Provider Contract Configuration Copilot that is a “partner” - not a replacement - for the experienced users in your health plan systems configuration and testing business function and processes