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AI in Case Management: ACMA Position Statement

AI and Virtual/Telephonic Use in Case Management and Transitions of Care

Artificial Intelligence (AI), including predictive analytics, machine learning, and large language models, has the potential to extend the reach of case management (CM, inclusive of all case management roles) and transitions of care (TOC) by focusing clinicians’ attention, streamlining administrative work, and improving coordination across settings. National accreditation and quality bodies are converging in a clear direction: adopt AI and virtual modalities where they add value, but anchor adoption in governance, patient safety, equity, privacy, and measurable outcomes. The Joint Commission and Coalition for Health AI (CHAI)’s Responsible Use of AI in Healthcare[i] guidance sets out a practical, high-level framework, policies, local validation, ongoing monitoring, bias assessment, event reporting, and staff training, to help health systems deploy AI responsibly at scale. Our position aligns with those pillars and interprets them for CM/TOC practice.

Virtual and telephonic CM/TOC should be held to the same quality standards as in-person care, with adaptations only when the modality truly requires it. This mirrors the National Committee for Quality Assurance (NCQA) Taskforce on Telehealth Policy[ii] stance that telehealth is a site/setting of care, not a different type of care, and that established measures should apply wherever appropriate. NCQA’s recent AI activities, public comment on AI use across programs and a cross-sector AI working group, signal momentum toward common, evidence-based guardrails for quality and safety that complement our governance approach.

Operationally, Utilization Review Accreditation Commission (URAC) Telehealth[iii] standards reinforce critical protections our statement embraces: privacy and security of personal health information, patient consent, credentialing and oversight, defined clinical/technical leadership roles, personnel education and training, quality-management programs, equipment safety, and routine performance monitoring (with annual quality reporting). These expectations map directly to our guardrails for virtual CM/TOC, including consented video interactions, role clarity, safe escalation, EMR-integrated documentation, and measurable value.

From a systems and assurance perspective, DNV Healthcare[iv] highlights two complementary threads: (1) hospital accreditation programs that embed best practices for clinical operations and (2) growing emphasis on AI management systems to institutionalize responsible AI governance, auditing, and continual improvement. These trends strengthen our call for pre-deployment validation, risk-based monitoring, rollback plans, vendor accountability, and board-level oversight for AI impacting CM/TOC.

It is important to note that audit plans, validation processes, and Responsible Use Committees should be specific to the hospitals, health systems, and health plans as well as the AI platforms utilized, but when case management processes and workflows are incorporated into the functions, case management Subject Matter Experts (SMEs) must be included. A formally designated oversight lead with case management expertise should be responsible for the review, approval, and ongoing monitoring of all case management content utilized in AI systems and the AI product.

Oversight should outline clear roles and responsibilities, including content accuracy review, documentation standards, escalation pathways, and coordination with technology partners. We recommend that SMEs validating case management content hold ACMA certification, maintain active practice competency, and demonstrate expertise related to the validated material. Documentation of SME involvement must be tracked and retained as part of the validation record.

Implementation of AI validation and audit processes will require dedicated time and resources, particularly for logic modeling and judgment-intensive review activities. These must be incorporated into operational planning. Multidisciplinary resources, not just case management, need to be considered such as Information Technology (IT), data science, digital strategy leader, etc.

Validation activities shall involve certified case management professionals responsible for content expertise in partnership with IT or technical teams. IT may support technical aspects but shall not serve as content validators. The audit process should include pre-deployment validations, post-deployment monitoring, scheduled re-validation intervals, and clearly documented procedures for addressing errors, drift, or emerging practice standard changes. A standardized documentation protocol should be created to ensure transparency, traceability, and regulatory compliance for all validation and audit activities.

Our association’s stance advances the sector’s shared direction, deploy AI and virtual CM/TOC to improve outcomes, equity, and efficiency; preserve human accountability; protect privacy and security; and prove value with transparent measures, while aligning with the emergent national playbooks and accreditation expectations shaping responsible adoption. It is imperative for the human subject matter expert as well as the AI (traditional and agentic) to adhere to ACMA’s Standards of Practice and Scope of Service[v] and ACMA’s Transitions of Care[vi].

AI Use in Case Management

Essential Parameters

For AI to be responsibly deployed in CM/TOC, six essential parameters must guide practice:
  1. Bias mitigation and representation: When applicable, it must include diverse training and testing of datasets, equity monitoring across populations, and corrective action when disparities emerge.
  2. Security, privacy, and compliance: Appropriate safeguarding of the data with proper de-identification of data sets and attention to efforts to prevent re-identification HIPAA-compliant design, vendor accountability, and auditability.
  3. Human oversight and accountability: Clear decision rights, escalation pathways, and clinician sign-off whenever patient safety, education, or care transition needs further scrutiny. The human oversight should be done by professionals who conform to the ACMA certification standard to ensure the highest level of accountability.
  4. Validation and monitoring: Pre-go-live testing, ongoing drift/bias tracking, and sampling audits of AI-generated outputs (especially summaries).
  5. Workflow fit and measurable value: Seamless Electronic Medical Record (EMR) integration, minimal double entry, allowance for clinical nuance, and proof of impact (Length of Stay [LOS], avoidable days, readmissions, authorization turnaround).
  6. Implementation of Change Management: Thoughtful implementation of change management for incorporating AI to ensure successful adoption and ongoing use that includes initial and ongoing lifecycle trainings. This includes proactive, human-centered approaches to addressing employee concerns, building trust, and ensuring technology delivers value and confidence in automated processes.

In-Scope AI Functions

AI is best positioned to relieve case managers of low-risk administrative tasks while enhancing visibility into complex patients. It is important to recognize and allocate the significant investment in resources to utilize AI for these (and other) functions. It is not a “one and done” process while continuing to ensure that the highest level of standards is upheld and risks to patients are minimized This may include estimating and allocating the time and resources needed for logic programming and nuanced judgment work. The in-scope AI function examples include:

  • Risk stratification and screening for discharge-planning intensity, with safety nets (nurse/MD consult triggers, Social Determinants of Health [SDOH] auto-flags, Interdisciplinary Team Rounds) to capture exceptions.
  • Dynamic worklists and prioritization of CM caseloads.
  • Progression-of-care orchestration through automated flags and clinician-approved prompts.
  • Predictive LOS and discharge readiness guidance for Interdisciplinary Team Rounds.
  • Referral and authorization automation; in-network matching; bidirectional status tracking, clinical need, and geographic proximity matching.
  • Drafting support for appeals and chart summaries, always subject to human validation.
  • Compilation of comprehensive medical record data (demographics, therapies, orders, historic encounters, and referral coordination) into assessments, reducing administrative burden, and freeing clinicians to focus on patient interaction, including the usage of agentic agents to automate documentation.

Guardrails for Clinical Domains

Certain domains demand human leadership and cannot be delegated:

  • Goals-of-care conversations and psychosocial assessments must remain clinician-led but could have support through AI.
  • Medication reconciliation may be supported by AI for data compilation and contraindication checking, but a pharmacist or clinician must finalize and educate patients/families.
  • Dynamic care planning can be enhanced through AI prompts in Interdisciplinary Team Rounds, but patient and family voices must remain central, transparent, and editable, with patient autonomy at the forefront of all planning.
  • Human oversight, validation, and monitoring are necessary for agentic medical record documentation for validation and oversight to ensure accuracy and safety.
  • Referral and authorization automation; in-network matching; bidirectional status tracking, clinical need, and geographic proximity matching.
  • Utilization management can benefit from automation of routine cases, but human advocacy is essential at the margins, such as peer-to-peer discussions for atypical presentations.
  • Information transfer is safe when structured EMR packets are used, but AI-generated summaries must undergo quality sampling and human oversight to prevent inaccuracy or “hallucination.”

Virtual and Telephonic Use in Case Management

Essential Parameters

    Five parameters define safe and effective VCM/TCM practice:

    1. Patient selection and equity: Clear eligibility criteria and safeguards for equitable access across technology, language, and disability barriers.
    2. Clinical safety and escalation: Defined triggers for bedside evaluation, explicit Interdisciplinary Team Round participation expectations, and protections to preserve patient/family voice in care planning.
    3. Privacy and security: HIPAA-compliant platforms, secure Protected Health Information (PHI) routing, and consent processes for video interactions.
    4. Workflow integration: Real-time documentation in the EMR, clearly defined roles, and seamless handoffs with bedside teams.
    5. Interoperability and partnerships: Effective coordination with payers, post-acute providers, and community resources to maintain continuity.

    In-Scope VCM/TCM Functions

      VCM/TCM is well-suited to:

      • Payor and provider utilization review, physician-advisor input, and observation review.
      • Transition calls, referral management, and discharge education with language-concordant care (patient and health care professional speak a shared language) and teach-back validation.
      • Virtual care conferences that include family, payers, and post-acute partners to expedite discharges.
      • Tele-behavioral health assessments.
      • Hospital-at-home, post-acute, and chronic care management, as part of care coordination.
      • Crisis or rural coverage where onsite presence is limited.

      Guardrails for VCM/TCM

        • In-person participation is preferred for Interdisciplinary Team Rounds, but hybrid or virtual attendance may be acceptable to maintain cadence, access, and equity.
        • Internal remote teams (case managers employed by the organization but working offsite) must be distinguished from outsourced or offshore models, with clear oversight and accountability for both.
        • Virtual should augment, not replace, in-person presence for high-risk or complex encounters.
        • Appropriate assessment of patient’s ability to engage using the technology of VCM/TCM versus in-person care coordination and adequate support is provided, when necessary and appropriate, when utilizing VCM/TCM.
        [i] Joint Commission/CHAI Guidance. (2025, September 17). Joint Commission and Coalition for Health AI (CHAI) release initial Guidance to Support Responsible AI Adoption Across U.S. Health Systems. https://www.jointcommisison.org/en-us/knowledge-library/news/2025-09-jc-and-chai-release-initial-guidance-to-support-responsible-ai-adoption.
        [ii] NCQA Telehealth, Telehealth Taskforce National Committee for Quality Assurance (2020, September). Taskforce on Telehealth Policy-Executive Summary: Findings & Recommendations (Final Report). https://wpcdn.ncqa.org/www-prod/wp-content/uploads/2020/09/20200914_Taskforce_on_Telehealth_Policy_Final_Report.pdf.
        [iii] URAC Telehealth Accreditation. URAC. (n.d.) Telehealth accreditation. https://www.urac.org/accrediation-cert/telehealth-accrediation/.
        [iv] DNV/AI management systems & healthcare accreditation. (n.d.). DNV Healthcare. https://www.dnv.com/healthcare/.
        [v] American Case Management Association. (2023). Case Management Standards of Practice and Scope of Services. https://www.acmaweb.org/section.aspx?mn=&sn=&wpg=&sid=22
        [vi] American Case Management Association. (2024). Transitions of Care. https://transitionsofcare.org/standards/
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