Hire a RAG Developer · Phoenix, AZ

Hire a RAG developer in Phoenix.

Phoenix health systems handle patient inquiries through contact centers at $18-25 per call. Routine clinical questions account for 60-70% of call volume: what is my procedure prep, what does this test result mean, is this medication in the formulary. Those calls do not require clinical judgment. They require accurate answers from existing documentation.

At 1,000 routine clinical calls per day, that is $18,000-25,000 in daily contact center costs. A RAG system on Banner Health's clinical guidelines, HonorHealth's procedure preparation documents, and formulary data handles the majority of those queries through the patient portal without a call agent involved.

Arizona's retirement population drives high-volume Medicare and insurance documentation queries that add to the same cost center. We scope each engagement around your call volume and document sources, with a fixed price quoted after the scoping call.

How we work

Describe your patient-facing or internal document search need.

The $18-25 per call problem.

Industry benchmark for healthcare contact center cost per call sits between $18 and $25 for routine inquiries. For a Phoenix health system at 1,000 routine clinical calls per day, that is $6.5M-$9M per year on calls that are fundamentally information retrieval tasks. The agent picks up the phone, finds the answer in a clinical protocol or formulary document, and reads it to the patient. The clinical content already exists in a document.

Banner Health operates 33 hospitals across Arizona, with a large presence in Metro Phoenix. HonorHealth operates 6 hospitals in the Scottsdale and Phoenix area. Dignity Health's Arizona division and Valleywise Health add further volume. The aggregate contact center infrastructure supporting these systems represents a substantial portion of Phoenix's healthcare administrative cost.

The cost per call is only part of the problem. Patient satisfaction with phone hold times and callback delays is a measurable component of HCAHPS scores. A patient who waits 22 minutes to ask about colonoscopy prep and then receives the correct answer is less satisfied than a patient who gets the same correct answer from a patient portal tool in 15 seconds.

Arizona's demographic profile amplifies the volume. The state's large retirement-age population generates above-average per-capita Medicare and Medicaid documentation questions: formulary coverage, prior authorization requirements, and Medicare Advantage plan benefit explanations. These are documentation-retrieval questions at high volume.

What patient-facing RAG changes.

Self-service for 60-70% of routine inquiry types

Procedure preparation, formulary lookups, test result explanation (what does this value mean), appointment preparation instructions, and discharge follow-up instructions are all answerable from existing clinical documents. A patient portal RAG tool handles these without agent involvement.

Symptom escalation routing

A query classifier runs before retrieval. Any query with symptom language routes to the nurse advice line or urgent care, not to the RAG retrieval pipeline. The system answers administrative and educational questions. Clinical judgment stays with clinicians.

Protocol currency maintained automatically

Banner Health maintains thousands of clinical protocols across multiple hospitals. The ingestion pipeline monitors the protocol management system for updates and ingests new versions automatically. Patients never receive an answer from a superseded protocol.

HIPAA-compliant query logging

Query logs are stored in the health system's existing HIPAA-compliant infrastructure. Query text is anonymized using a named entity recognition model before logging if patient-identifiable content may be present. The audit trail supports operational review without creating a PHI liability.

What we build.

Clinical protocol ingestion pipeline with version tracking

The pipeline connects to PolicyStat, HealthStream, or a SharePoint-based protocol library and monitors for document updates on a nightly schedule. New versions are ingested automatically. Prior versions are retained with effective dates. The active retrieval path returns only current protocols.

Intent classifier for safe query routing

A lightweight classifier evaluates each patient query before retrieval runs. Symptom queries, clinical advice requests, and medication change requests route to escalation responses. Administrative, logistical, and educational queries route to the RAG pipeline. The classifier is evaluated against 200+ test queries before deployment.

Patient-facing retrieval with source attribution

Every answer includes the source document name, section, and publication date. Patients see 'From Banner Health Colonoscopy Preparation Guide, updated January 2024' rather than an unattributed answer. This attribution is required for both patient trust and liability documentation.

Medicare and insurance documentation layer

Arizona's Medicare Advantage market requires separate document handling for plan formularies, prior authorization criteria, and benefit summaries. These documents have a different update cadence than clinical protocols and require a separate ingestion track with plan-year metadata.

Integration with patient portal

The RAG interface deploys as an embeddable component that integrates with Epic MyChart or your existing patient portal. Patients interact through the portal they already use. The authentication and session handling use your existing portal infrastructure.

How we work.

Scoping call

One hour. You describe the inquiry types that generate the most call volume, the document sources you have available, your patient portal platform, and any compliance requirements.

Pilot on a document subset

We build the retrieval and classification system on a representative set of your protocols, typically one service line. You run real patient queries through it. We measure accuracy and false escalation rate before committing to the full build.

Production build and handoff

Fixed scope, 6-10 weeks. You get the running system, the protocol ingestion pipeline, the query log infrastructure, and documentation for your IT and compliance teams.

Full process overview →

Questions about patient-facing clinical RAG systems.

Can a RAG system handle clinical guidelines safely for patient-facing use? What guardrails are required?: Patient-facing clinical RAG requires three guardrails that differ from internal-facing systems. First, scope restriction: the system answers questions about the health system's own clinical protocols, procedure preparation, and formulary. It does not attempt to diagnose or recommend treatment changes. Every system prompt explicitly defines this boundary and the model is tested against out-of-scope queries. Second, escalation routing: any query that contains symptom language, medication change requests, or expressions of urgency triggers a response that routes to the nurse line or urgent care, regardless of whether the RAG retrieval was successful. Third, disclaimer attribution: every answer includes the source document name and a statement that the information is from the health system's published patient education materials. Patients seeing 'from Banner Health Procedure Preparation Guide, updated March 2024' builds trust. Patients seeing an unattributed answer do not know whether to rely on it.
How do you handle the 'I have a symptom' type query that might need clinical escalation rather than a document answer?: Symptom queries are classified before retrieval runs. We train a lightweight intent classifier (or use a prompt-based classifier with the language model) that detects symptom language: 'I feel', 'I have been experiencing', 'my pain', 'I noticed', and similar patterns. When the classifier flags a symptom query, the system does not attempt RAG retrieval. It responds with a templated escalation message that routes the patient to the nurse advice line, the patient portal message center, or urgent care depending on symptom severity signals in the query. This classification step runs in under 100ms and is evaluated against a test set of 200+ real symptom queries before deployment. The retrieval pipeline only runs on queries the classifier has confirmed are administrative, logistical, or educational in nature.
How do you keep the system current when clinical guidelines update? Banner Health uses thousands of clinical protocols.: Clinical protocol libraries at Banner Health scale require a structured update pipeline, not manual maintenance. We build an ingestion pipeline that monitors the health system's policy management system (typically PolicyStat, HealthStream, or a SharePoint-based library) for document version changes. When a protocol is updated, the new version enters the ingestion queue automatically, the prior version is marked superseded with its effective-through date, and the active retrieval path returns only current versions. For Banner's scale, where protocols update continuously across multiple hospitals and service lines, the monitoring pipeline runs on a nightly schedule with an alert for any document that fails ingestion. The goal is that the patient-facing system never returns a response based on a superseded clinical protocol.
What HIPAA considerations apply to a patient-facing RAG system that queries clinical content?: A patient-facing RAG system that queries clinical guidelines and procedure preparation documents does not itself contain PHI. The document library is generic clinical content, not patient records. The HIPAA considerations attach when patient queries are logged. Query logs containing patient-submitted text (which may include symptoms, diagnoses, or medication names) constitute PHI if they can be linked to an identified patient. We address this in two ways: first, the query log is stored in your existing HIPAA-compliant infrastructure (your EHR vendor's environment or a BAA-covered cloud account) rather than a third-party logging service. Second, we implement query anonymization as an option: the logged query strips personally identifying information using a named entity recognition model before writing to the audit table. This retains the query pattern for system improvement purposes without storing identifiable content.

Scope your Phoenix patient-facing RAG project.

Describe your contact center inquiry breakdown, the clinical document sources you have available, and your patient portal platform. We reply within one business day.

Hire a RAG developer in Phoenix.

The $18-25 per call problem.

What patient-facing RAG changes.

Self-service for 60-70% of routine inquiry types

Symptom escalation routing

Protocol currency maintained automatically

HIPAA-compliant query logging

What we build.

Clinical protocol ingestion pipeline with version tracking

Intent classifier for safe query routing

Patient-facing retrieval with source attribution

Medicare and insurance documentation layer

Integration with patient portal

How we work.

Scoping call

One hour. You describe the inquiry types that generate the most call volume, the document sources you have available, your patient portal platform, and any compliance requirements.

Pilot on a document subset

Production build and handoff

Fixed scope, 6-10 weeks. You get the running system, the protocol ingestion pipeline, the query log infrastructure, and documentation for your IT and compliance teams.

Questions about patient-facing clinical RAG systems.

Can a RAG system handle clinical guidelines safely for patient-facing use? What guardrails are required?

Patient-facing clinical RAG requires three guardrails that differ from internal-facing systems. First, scope restriction: the system answers questions about the health system's own clinical protocols, procedure preparation, and formulary. It does not attempt to diagnose or recommend treatment changes. Every system prompt explicitly defines this boundary and the model is tested against out-of-scope queries. Second, escalation routing: any query that contains symptom language, medication change requests, or expressions of urgency triggers a response that routes to the nurse line or urgent care, regardless of whether the RAG retrieval was successful. Third, disclaimer attribution: every answer includes the source document name and a statement that the information is from the health system's published patient education materials. Patients seeing 'from Banner Health Procedure Preparation Guide, updated March 2024' builds trust. Patients seeing an unattributed answer do not know whether to rely on it.

How do you handle the 'I have a symptom' type query that might need clinical escalation rather than a document answer?

Symptom queries are classified before retrieval runs. We train a lightweight intent classifier (or use a prompt-based classifier with the language model) that detects symptom language: 'I feel', 'I have been experiencing', 'my pain', 'I noticed', and similar patterns. When the classifier flags a symptom query, the system does not attempt RAG retrieval. It responds with a templated escalation message that routes the patient to the nurse advice line, the patient portal message center, or urgent care depending on symptom severity signals in the query. This classification step runs in under 100ms and is evaluated against a test set of 200+ real symptom queries before deployment. The retrieval pipeline only runs on queries the classifier has confirmed are administrative, logistical, or educational in nature.

How do you keep the system current when clinical guidelines update? Banner Health uses thousands of clinical protocols.

Clinical protocol libraries at Banner Health scale require a structured update pipeline, not manual maintenance. We build an ingestion pipeline that monitors the health system's policy management system (typically PolicyStat, HealthStream, or a SharePoint-based library) for document version changes. When a protocol is updated, the new version enters the ingestion queue automatically, the prior version is marked superseded with its effective-through date, and the active retrieval path returns only current versions. For Banner's scale, where protocols update continuously across multiple hospitals and service lines, the monitoring pipeline runs on a nightly schedule with an alert for any document that fails ingestion. The goal is that the patient-facing system never returns a response based on a superseded clinical protocol.

What HIPAA considerations apply to a patient-facing RAG system that queries clinical content?

A patient-facing RAG system that queries clinical guidelines and procedure preparation documents does not itself contain PHI. The document library is generic clinical content, not patient records. The HIPAA considerations attach when patient queries are logged. Query logs containing patient-submitted text (which may include symptoms, diagnoses, or medication names) constitute PHI if they can be linked to an identified patient. We address this in two ways: first, the query log is stored in your existing HIPAA-compliant infrastructure (your EHR vendor's environment or a BAA-covered cloud account) rather than a third-party logging service. Second, we implement query anonymization as an option: the logged query strips personally identifying information using a named entity recognition model before writing to the audit table. This retains the query pattern for system improvement purposes without storing identifiable content.

Scope your Phoenix patient-facing RAG project.

Describe your contact center inquiry breakdown, the clinical document sources you have available, and your patient portal platform. We reply within one business day.