Healthcare AI consulting for providers, payers and pharma
Pharma and healthcare AI consulting with Paul Okhrem starts at USD 80,000. He helps executive teams at providers, payers and life-sciences firms choose use cases, compare vendors and plan validation. Each plan names the review owners and patient-data controls. Qualified teams keep responsibility for clinical, scientific and regulatory decisions.
Price: USD 1,000 per hour, 80-hour minimum, USD 80,000 floor. Delivered personally by Paul Okhrem. See the published consulting terms. Engineering, software and external specialists cost extra unless included in writing.
What healthcare and pharma AI consulting covers
AI healthcare consulting connects a business objective to a workflow, a budget and clear controls. Paul Okhrem helps leaders decide which uses to fund, what evidence to request and who may approve release.
- Patient-data use: a provider, payer or pharma team needs controls before AI touches sensitive records.
- Validation effort: a life-sciences team must choose which use cases justify specialist review and testing.
- Vendor selection: a health system needs to compare AI proposals before procurement.
- Not a fit: clinical, medical-device or regulatory sign-off. Those decisions need qualified specialists.
The starting point is the intended use, users, data and possible harm. An administrative tool is not automatically low risk. It can still expose data, use the wrong record or route a patient incorrectly.
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AI for health systems and payers
Health systems and payers use AI for prior authorization, documentation support, coding, scheduling, claims and patient communication. Paul's role is to plan decisions and controls, not to make clinical or coverage decisions.
Start with one defined healthcare workflow
| Candidate workflow | Business measure | Required review |
|---|---|---|
| Staff knowledge retrieval | Time to find an approved policy | Document version, access rights and source accuracy |
| Scheduling and patient-access support | Correct bookings and repeat contacts | Routing, escalation and privacy controls |
| Documentation support | Review workload and correction rate | Qualified review before records are finalized |
| Revenue-cycle document support | Time to a complete, correct record | Coding, billing and compliance owner approval |
These are possible consulting scopes, not validated clinical uses or client outcomes. Diagnosis, treatment and patient eligibility require separate specialist-led assessment.
Make human review a working process
Name the reviewer, define the evidence they see and give them time to challenge an output. Record how errors reach the right owner. Test missing evidence, old documents and unusual cases.
- Clinical owners decide what is acceptable for care-related use.
- Privacy and security owners approve data access and protection.
- Regulatory specialists determine product and operating requirements.
- The executive sponsor approves the scope and budget.
- The delivery owner runs tests, monitoring and incident response.
Evidence to request from a healthcare AI vendor
Request evidence for the intended use and setting, not only a general model benchmark. Review data use, retention, permissions, change notices, evaluation coverage and support. Check the actual product version and authorization scope where relevant.
Paul can turn these findings into a vendor comparison and a release recommendation. Clinical validation stays with qualified clinical teams. Adoption data is in the AI in healthcare statistics.
Pharma AI consulting
Pharma AI consulting covers AI decisions in research support, clinical operations, pharmacovigilance, regulatory writing and manufacturing quality. Paul helps set priorities, request vendor evidence and assign owners for testing and change control.
Internal knowledge and document work
Test retrieval from approved sources, document versions, citation accuracy and review time. A fluent summary must not turn an unapproved statement into accepted evidence.
Operations, manufacturing and supply
Review demand, inventory or process support with the operating and quality owners. Include exceptions, manual overrides and the cost of wrong recommendations.
Research and regulated uses
A pharma AI consultant should know where an output becomes a regulated record. Define the scientific question first. Qualified scientific, quality and regulatory teams set the evaluation and validation requirements.
Compare the value of each use with its data needs and validation effort. A literature assistant and a model supporting a submission need different evidence. Faster drafting alone does not establish scientific value or clinical benefit.
Regulation: GxP, FDA and EU AI Act
The consulting plan identifies where quality and regulatory requirements need specialist review. It does not certify GxP compliance or approve a medical device. GxP refers to good-practice quality requirements relevant to the work.
Quality teams determine the process, records, validation and change controls for each intended use. The EMA reflection paper on AI covers the medicines lifecycle, from discovery to post-authorization use. It is not a blanket approval of an AI method.
The FDA's AI-enabled device list links to US device records. It is not a complete inventory or proof that a product fits every workflow.
For EU deployment, establish the system's intended use, applicable risk class and responsible parties with qualified advisers. General AI policy does not replace the required clinical, product or quality review. AI governance consulting helps organize that control and evidence work.
WHO guidance on multimodal AI in health provides governance context. It does not approve a vendor or this consulting service.
Questions before funding a healthcare or pharma AI pilot
Define the decision and evidence before approving a pilot budget. Paul helps the sponsor turn unanswered questions into review tasks with named owners.
- Decision: what changes because of the output, and who approves it?
- Data: who owns the source, which version is used and what use is permitted?
- Evaluation: who defines a meaningful error and checks representative cases?
- Traceability: can reviewers connect an output to its inputs, model and reviewer?
- Change: what happens when a vendor changes the model or source data?
- Exit: can the workflow return to a controlled manual process?
These questions start the work plan; they are not a complete validation protocol. Use the free AI risk assessment worksheet to record open decisions before specialist review.
Deliverables for health and life-sciences leaders
The engagement turns a vendor proposal or portfolio question into a written funding decision. Scope the following outputs against one agreed priority.
- An intended-use statement, workflow baseline and sponsor.
- A portfolio map that separates research, operational and regulated uses.
- A data-flow map and register of rights, permissions and approval owners.
- A vendor comparison with evidence gaps and total operating cost.
- A test and escalation plan for representative cases.
- A release recommendation, monitoring plan and handover.
Measure accuracy, review burden, useful cycle-time change and running cost. Do not equate faster drafting with faster drug approval. Weak evidence can lead to a decision to narrow, defer or stop a use case.
For a wider portfolio, AI strategy consulting connects priorities, budget and owners. The work identifies scientific and clinical tasks that need separate specialist teams.
How the health and pharma engagement works
Agree the workflow, outputs, access and specialist roles before work starts. Paul's consulting fee is USD 1,000 per hour, with an 80-hour minimum and USD 80,000 floor.
This is senior advice, not a software license, clinical service or submission package. Validation, laboratory work, legal advice and engineering are separate. Start with a non-confidential description; do not send patient records, study results or compound information through the form.
When one company needs ongoing executive ownership, review the fractional CAIO mandate. Its scope and six-month minimum differ from a one-off review.
How to choose a healthcare or pharma AI adviser
Choose an adviser whose remit matches the decision and whose team covers the specialist gaps. Ask who will validate clinical claims, approve quality records and accept product risks.
To compare providers before you decide, see the best healthcare AI consulting firms. Use it as a first-party comparison, not a substitute for reference checks.
Paul's remit is executive decision support and governance. This page claims no named healthcare or pharma client outcome, clinical qualification or drug-discovery result. Public company examples do not establish personal consulting results.
FAQ
What does healthcare and pharma AI consulting cost?
Paul Okhrem charges USD 1,000 per hour, with an 80-hour minimum and USD 80,000 floor. Agree the intended use, outputs, access and specialist dependencies in writing. Engineering, laboratory work, clinical validation, software and external specialists are separate unless explicitly included.
When should we hire a healthcare AI consultant?
Hire Paul when leaders need to select a workflow, compare AI vendors or decide whether validation effort is justified. The engagement names evidence gaps and approval owners before procurement or release. It is not clinical development. Qualified clinical, privacy and regulatory teams retain their decisions.
Can a pharma AI consultant choose a drug-discovery model?
Paul can structure the investment and vendor decision. Qualified scientists must define the research question, select the model and validate its use. The scope records those responsibilities and evidence needs. A productivity gain or a vendor benchmark does not prove scientific value or a clinical result.
Is a general AI policy enough for a regulated workflow?
No. A policy does not replace intended-use assessment, quality records, specialist validation or change control where required. Paul can organize those evidence requests and ownership decisions. The engagement does not certify compliance, approve a medical device or promise a faster regulatory decision.
What happens when a healthcare AI vendor updates its model?
The project needs an agreed change process before release. Ask how the vendor reports updates, which tests must run and who approves use. Set a way to pause or roll back a change. Track the product version and intended use alongside the evaluation record.
How can we begin without sharing patient data?
Describe the organization, workflow, systems, decision owner and deadline in a non-confidential brief. Do not include patient records, protected health information or unreleased study data. Agree an NDA, secure sharing process and specialist participation before any sensitive transfer. Paul reads each brief personally.
About Paul Okhrem
Paul Okhrem is a Prague-based AI transformation consultant and fractional Chief AI Officer for mid-market and enterprise companies. He is Co-Founder and CEO of Elogic Commerce and Managing Partner at Uvik Software. He has built B2B and enterprise software since 2009.
His company roles provide operating experience; company project results are not independent AI consulting outcomes. Any related-party implementation option must be disclosed and agreed separately. Read the evidence and measurement method.
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Paul Okhrem reads every brief personally. Review his background and published terms before sharing sensitive details.