Healthcare AI 30/60/90 Implementation Plan

Phase 1: Days 1-30 — Foundation and first pilot (Health Systems)

The first 30 days are about three things: establishing governance, achieving HIPAA-compliant tool access, and launching your first workflow pilot.

Week 1: Establish AI governance and choose your first workflow

Form an AI governance committee with representation from:

• Compliance/Privacy Officer (HIPAA oversight)
• CIO or designee (technical evaluation)
• CMO or clinical champion (clinical workflow oversight)
• Revenue cycle leadership (if the first workflow is documentation or coding-related)
• Legal (BAA review, liability considerations)

The governance committee's first action: define the administrative vs clinical AI boundary for your organisation (refer to Module 6). Document it in a one-page policy. This policy governs every subsequent AI decision.

Choose your first workflow. The recommended first workflow for health systems is prior authorisation letter drafting because:

• High volume — you will generate meaningful results within weeks
• Well-documented baseline cost ($31 per transaction)
• Clear success metrics (time per letter, approval rate, accuracy)
• Low clinical risk (letters are reviewed before submission)
• Limited HIPAA scope (specific clinical data, not full patient records)

If prior auth is not your highest-priority pain point, choose the workflow that meets these criteria: high volume, document-based, repeatable, measurable, and with a clear human review step before any output enters the patient record or revenue cycle.

Week 2: Select tools and establish HIPAA compliance

AI tool selection for healthcare is not just about capability — it is about compliance:

1. Identify vendors that offer BAAs. Not all do. Filter your vendor list immediately: no BAA, no consideration for PHI workflows.

2. Evaluate data handling. For each candidate vendor, determine:

   • Is data used for model training? (It should not be)
   • How long is data retained after processing? (Shorter is better)
   • Where is data processed geographically? (Matters for state privacy laws)
   • Is the deployment on shared infrastructure or dedicated? (Dedicated is more secure)
   • Does the platform provide audit logging? (Essential for compliance)

3. Execute the BAA. Your legal team reviews and executes the BAA with the selected vendor. Do not begin processing PHI until the BAA is fully executed.

4. Establish data handling protocols. Document the minimum necessary standard for your first workflow: what specific data fields will be included in AI prompts, and what will be excluded. Create prompt templates that enforce this standard (using the templates from Modules 3-5).

5. If the BAA process will take more than 2 weeks, start your pilot with a non-PHI workflow (literature review, policy drafting, communication templates) while the BAA is being finalised.

Weeks 3-4: Run the pilot

Deploy your first AI-assisted workflow in one department:

• Train the pilot team. Explain what AI does and does not do. Review the prompt templates. Establish the review protocol: who reviews AI outputs, what they check for, and how they document their review.

• Process the first 50-100 units (prior auth letters, discharge summaries, or whatever your chosen workflow is). Compare:

  • Time per unit: AI-assisted vs manual baseline
  • Quality: What percentage of AI drafts required no edits, minor edits, or substantive edits?
  • Accuracy: Did AI-assisted prior auth letters achieve the same or better approval rate?
  • Compliance: Any data handling issues? Any outputs that crossed the clinical AI boundary?
  • Staff experience: Qualitative feedback from the team using the tool

• Document everything. Every prompt used, every AI output, every human edit, every final product. This is your audit trail and your business case evidence.

Phase 1: Days 1-30 — Foundation and first pilot (Pharma/Life Sciences)

The pharma implementation track has different first-workflow priorities and different compliance considerations, but the methodology is the same.

Week 1: Establish governance and choose your first workflow

Form an AI governance committee with representation from:

• Regulatory Affairs (FDA and international regulatory implications)
• Quality Assurance (GxP compliance, 21 CFR Part 11 considerations)
• Medical Affairs (literature review and medical writing oversight)
• Pharmacovigilance (if PV workflows are in scope)
• IT/Information Security (data handling, vendor security)
• Legal (BAA review, intellectual property considerations)

Choose your first workflow. The recommended first workflow for pharma organisations is systematic literature review and evidence synthesis because:

• No PHI involved (published literature only) — no BAA required for the first pilot
• Clear baseline metrics (time per systematic review is well-documented)
• High strategic value (literature review is a bottleneck across medical affairs, regulatory, and clinical development)
• Quality is verifiable (AI screening and extraction can be compared against manual review)
• Low regulatory risk (AI is a research assistant, not a decision-maker)

If literature review is not your priority, consider: clinical trial protocol consistency review (Module 4), regulatory intelligence monitoring, or competitive pipeline analysis. All of these are non-PHI workflows that can be piloted without BAA infrastructure.

Week 2: Select tools and establish data governance

For pharma, tool selection considerations include:

• Intellectual property: Does the vendor's terms of service grant any rights to your data or outputs? For proprietary drug development data, this is a critical evaluation criterion
• Data residency: If your organisation has global operations, where is data processed? Some regulatory submissions are subject to data localisation requirements
• Audit trail: GxP-relevant workflows require documented audit trails per 21 CFR Part 11. Evaluate whether the AI platform provides the logging needed for regulatory inspection readiness
• Validation: If AI outputs will be incorporated into regulated documents, consider the vendor's approach to software validation and change control

Weeks 3-4: Run the pilot

For a literature review pilot:

• Select a systematic review that is about to begin (not one in progress)
• Process the first 500+ abstracts using AI-assisted screening alongside manual screening
• Compare: screening time per abstract, agreement rate between AI and human reviewer, extraction accuracy per data field, total review duration projection
• Document the complete methodology: how AI was used, what human validation was applied, and how discrepancies were resolved

For a protocol review pilot:

• Select a protocol currently under development or revision
• Run the AI protocol analysis prompt from Module 4
• Compare AI findings against the existing review committee's findings
• Measure: how many issues did AI identify that the committee missed? How many AI findings were false positives? What is the net value of the additional review layer?

Phase 2: Days 31-60 — Scale and expand

By day 30, you should have pilot results demonstrating that your first AI-assisted workflow saves significant time and maintains quality. Phase 2 scales the pilot to production and expands to a second workflow.

Weeks 5-6: Scale the pilot to full production

Move from your pilot department to all relevant departments:

For health systems (prior auth workflow):

1. Expand from one department to all departments with significant prior auth volume
2. Refine prompt templates based on pilot learnings — different specialties may need specialty-specific templates (orthopaedic prior auth letters differ from oncology prior auth letters in clinical evidence structure)
3. Train additional staff on the AI-assisted workflow and the review protocol
4. Connect the prior auth workflow to denial tracking — begin using AI-generated appeal letters for denials of AI-assisted prior auth submissions
5. Monitor the approval rate and compare to baseline — AI-assisted letters should achieve the same or better approval rate

For pharma (literature review workflow):

1. Apply the AI-assisted methodology to all new systematic reviews beginning during this period
2. Extend to targeted literature searches and evidence updates for existing reviews
3. Train additional medical writers and researchers on the AI-assisted methodology
4. Document the validated methodology for inclusion in your organisation's SOPs
5. Begin using AI for data extraction in addition to screening — the natural second step

Weeks 7-8: Add your second workflow

For health systems — recommended second workflow options:

If your first workflow was prior auth, your second should expand into the clinical documentation or revenue cycle:

• Clinical note summarisation / discharge summary drafting (Module 3): Directly addresses clinician burnout. Select one inpatient unit or outpatient clinic for the pilot. This workflow requires HIPAA-compliant infrastructure (BAA in place) and physician champion engagement.

• CDI query generation (Module 3): Directly impacts revenue cycle through improved coding accuracy. Work with your CDI programme director to pilot AI-assisted query identification on one service line.

• Claims scrubbing (Module 5): Directly impacts denial rates and revenue cycle performance. Pilot with one payer or one service line to compare denial rates.

For pharma — recommended second workflow options:

• Clinical trial protocol analysis (Module 4): Apply the protocol review workflow to your next protocol under development. Compare AI findings against the review committee's findings.

• ICSR processing for pharmacovigilance (Module 4): If your BAA and HIPAA infrastructure is ready, begin piloting AI-assisted case intake and triage. This is a high-volume, high-impact workflow.

• Regulatory intelligence monitoring (Module 4): Establish a systematic AI-powered monitoring process for FDA guidance documents, EMA updates, and ICH guidelines relevant to your therapeutic areas.

Phase 3: Days 61-90 — Governance maturation and enterprise planning

Phase 3 is where you transition from pilot success to organisational capability. The focus shifts from proving AI works to ensuring it works at scale with appropriate governance.

Weeks 9-10: Formalise governance and compliance documentation

Based on your Phase 1 and 2 experience, formalise the governance framework:

1. AI use case registry. Maintain a catalogue of all approved AI workflows: what data they use, what tool processes it, who reviews the output, and what compliance requirements apply. This becomes your single source of truth for AI governance.

2. Prompt library. Consolidate all tested and validated prompt templates into a managed library. Version-control the prompts — when you update a template, document what changed and why. This is especially important for regulated workflows (coding, pharmacovigilance, regulatory submissions).

3. Review protocols. Document the human review requirements for each workflow:

   • Who is qualified to review (physician for clinical notes, certified coder for ICD-10 suggestions, regulatory affairs professional for submission documents)
   • What they are checking for (clinical accuracy, coding guideline compliance, regulatory formatting)
   • How they document their review (EHR attestation, review checklist, sign-off log)
   • Escalation path for AI outputs that raise concerns

4. Quality monitoring. Establish ongoing quality metrics for each AI workflow:

   • Edit rate: What percentage of AI outputs require no edits, minor edits, substantive edits?
   • Error rate: What types of errors does AI make, and are they trending down over time?
   • User satisfaction: Are staff finding the tool helpful, or is it creating friction?
   • Compliance metrics: Any HIPAA incidents, any outputs that crossed the clinical AI boundary?

Weeks 11-12: Enterprise planning and budget request

With two workflows in production and quality data in hand, build the enterprise deployment plan:

1. Priority workflow roadmap. Based on your Module 7 business case analysis and your organisation's specific pain points, create a prioritised list of the next 3-5 workflows to deploy. For each, estimate the ROI, the HIPAA requirements, the governance needs, and the timeline.

2. Technology infrastructure assessment. Evaluate whether your current AI tool and deployment model can scale to enterprise use. Do you need a different pricing tier? A dedicated deployment? Integration with your EHR or clinical trial management system? API access for workflow automation?

3. Training programme design. As you scale from a pilot team to department-wide and then organisation-wide deployment, you need structured training: what AI does and does not do, how to use the prompt templates, how to review AI outputs, and what the compliance requirements are. Plan training for each wave of deployment.

4. Budget request. Present to leadership:

   • Pilot results (actual data from Phases 1 and 2)
   • Enterprise deployment plan (workflows, timeline, resource requirements)
   • Projected ROI (using the Module 7 framework with actual pilot data)
   • Risk analysis (compliance status, governance maturity, outstanding gaps)
   • Investment required (tool costs, training, governance overhead)

Your implementation checklist

Here is your complete implementation checklist, synthesised from all eight modules:

Phase 1 (Days 1-30):

• Form AI governance committee (compliance, IT, clinical, legal)
• Define and document the administrative vs clinical AI boundary
• Choose first workflow (recommended: prior auth for health systems, literature review for pharma)
• Evaluate and select AI vendor with BAA (for PHI workflows) or appropriate data handling (for non-PHI)
• Execute BAA if PHI is involved — do not process PHI until BAA is signed
• Create prompt templates with minimum necessary PHI standard enforced
• Establish human review protocol (who reviews, what they check, how they document)
• Train pilot team on AI tool, prompt templates, review protocol, and compliance requirements
• Run pilot: 50-100 units with parallel manual comparison
• Document pilot results: time savings, accuracy, quality, compliance, staff feedback
• Present pilot results to governance committee

Phase 2 (Days 31-60):

• Scale first workflow to all relevant departments
• Refine prompt templates based on pilot learnings
• Train additional staff on AI-assisted workflow
• Choose and launch second workflow
• Establish quality monitoring metrics for production workflows
• Connect related workflows (e.g., prior auth -> appeal letters, coding -> CDI queries)
• Document all workflows in the AI use case registry

Phase 3 (Days 61-90):

• Formalise governance documentation (use case registry, prompt library, review protocols)
• Establish ongoing quality monitoring and reporting
• Build the enterprise deployment plan (next 3-5 workflows, timeline, resources)
• Assess technology infrastructure needs for scale
• Design the organisation-wide training programme
• Present enterprise plan and budget request to leadership with actual pilot data
• Set the roadmap for the next 12 months of AI-assisted workflow deployment

This checklist is your roadmap. Every item connects back to a specific module in this course. When you need detail on any step, return to the relevant module for the prompts, frameworks, and compliance guidance.