For the CMS Rural Health Transformation Program

The curated data layer for rural health

A $50B program. A 5-year window. One missing layer.

Predoc connects rural health medical records between HIE networks, EHRs, pharmacy data, imaging networks, direct provider outreach, fax, and non-connected facilities. We create a standardized, longitudinal record that solves the rural health interoperability gap and improves downstream analytics, AI, and care models.

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HIE Networks

Pharmacy Records

EHR Connections

Imaging Networks

Direct Provider Requests

Fax-Based Providers

Non-Connected Facilities

HIE Networks

Pharmacy Records

EHR Connections

Imaging Networks

Direct Provider Requests

Fax-Based Providers

Non-Connected Facilities

HIE Networks

Pharmacy Records

EHR Connections

Imaging Networks

Direct Provider Requests

Fax-Based Providers

Non-Connected Facilities

HIE Networks

Pharmacy Records

EHR Connections

Imaging Networks

Direct Provider Requests

Fax-Based Providers

Non-Connected Facilities

INGESTING SOURCESCURATED DATA LAYERSYNCING…

Patient cohort: rural health

Lab Results

Medications

Problems

Provider Facility Notes

Pathology

The rural data problem

Rural health data is fragmented by design.

Three structural problems undermine every downstream initiative, from RHT planning to AI-assisted care.

Fragmented rural data

Rural patients move across HIE-connected and non-connected facilities, solo practitioners, and regional hospitals. No single network sees the full record.

Fax-driven workflows

Records are still connected by fax, paper, and direct provider outreach. The data exists. It's just not in a system anyone can use.

Downstream AI starvation

Population analysis, risk adjustment, and AI-assisted care depend on a curated longitudinal record. Without one, every model underperforms.

The curated data layer

Where rural health data becomes infrastructure.

Seven source types (including the fax-based, direct-outreach, and non-connected facility flows that define rural ingestion) converge into one queryable longitudinal record.

01Clinical Data Sources

Multi-format, unstructured

02AI / ML Parsing Engine

Extraction, normalization, curation

03Curated Data Layer

Structured, aggregated, indexed

Digital sources

HIE Networks

Pharmacy Records

EHR Connections

Imaging Networks

Direct outreach (PDFs)

Direct Provider Requests

Fax-Based Providers

Non-Connected Facilities

Extraction

OCR and clinical parsing across PDFs, imaging, exchange feeds, and handwritten notes.

Normalization

Codes, units, and concepts mapped to standard medical vocabularies.

Curation

Resolved, deduplicated, clinically organized into the Predoc patient model.

Lab Results

lab-results.json

{

"loinc": "718-7"

}

12345678

{

"loinc": "718-7",

"name": "Hemoglobin",

"value": "13.2 g/dL",

"ref_range": "12.0-15.5",

"effective": "2025-08-14",

"source": "EHR / Epic"

}

Medications

medications.json

{

"rxnorm": "861009"

}

12345678

{

"rxnorm": "861009",

"name": "Lisinopril 10 MG",

"status": "active",

"started": "2024-11-02",

"dose": "10 mg PO daily",

"source": "Pharmacy / NCPDP"

}

Problems

problems.json

{

"icd10": "I10"

}

12345678

{

"icd10": "I10",

"snomed": "59621000",

"name": "Essential hypertension",

"status": "active",

"onset": "2023-04",

"source": "EHR Problem List"

}

Provider Facility Notes

provider-facility-notes.json

{

"type": "progress_note"

}

12345678

{

"type": "progress_note",

"author": "Dr. M. Chen",

"facility": "Rural Health Clinic",

"date": "2025-09-12",

"pages": 4,

"source": "Direct provider outreach"

}

Pathology

pathology.json

{

"snomed": "M-87200"

}

12345678

{

"specimen": "Skin, left forearm",

"diagnosis": "Benign nevus",

"snomed": "M-87200",

"date": "2025-06-30",

"pathologist": "Dr. R. Patel",

"source": "Non-connected facility"

}

Imaging Notes

imaging-notes.json

{

"cpt": "71250"

}

12345678

{

"modality": "CT",

"study": "Chest w/o contrast",

"cpt": "71250",

"impression": "No acute findings",

"date": "2025-07-21",

"source": "Imaging Network"

}

Procedures

procedures.json

{

"cpt": "45378"

}

12345678

{

"cpt": "45378",

"snomed": "73761001",

"name": "Colonoscopy, diagnostic",

"date": "2024-12-08",

"facility": "Regional Hospital",

"source": "Fax-Based Providers"

}

Family History

family-history.json

{

"icd10": "E11"

}

12345678

{

"relation": "mother",

"condition": "Type 2 Diabetes",

"icd10": "E11",

"age_onset": 52,

"verified": "patient-reported",

"source": "Direct provider outreach"

}

01Clinical Data Sources

Multi-format, unstructured

Digital sources

HIE Networks

Pharmacy Records

EHR Connections

Imaging Networks

Direct outreach (PDFs)

Direct Provider Requests

Fax-Based Providers

Non-Connected Facilities

02AI / ML Parsing Engine

Extraction, normalization, curation

Extraction

OCR and clinical parsing across PDFs, imaging, exchange feeds, and handwritten notes.

Normalization

Codes, units, and concepts mapped to standard medical vocabularies.

Curation

Resolved, deduplicated, clinically organized into the Predoc patient model.

03Curated Data Layer

Structured, aggregated, indexed

Lab Results

lab-results.json

{

"loinc": "718-7"

}

12345678

{

"loinc": "718-7",

"name": "Hemoglobin",

"value": "13.2 g/dL",

"ref_range": "12.0-15.5",

"effective": "2025-08-14",

"source": "EHR / Epic"

}

Medications

medications.json

{

"rxnorm": "861009"

}

12345678

{

"rxnorm": "861009",

"name": "Lisinopril 10 MG",

"status": "active",

"started": "2024-11-02",

"dose": "10 mg PO daily",

"source": "Pharmacy / NCPDP"

}

Problems

problems.json

{

"icd10": "I10"

}

12345678

{

"icd10": "I10",

"snomed": "59621000",

"name": "Essential hypertension",

"status": "active",

"onset": "2023-04",

"source": "EHR Problem List"

}

Provider Facility Notes

provider-facility-notes.json

{

"type": "progress_note"

}

12345678

{

"type": "progress_note",

"author": "Dr. M. Chen",

"facility": "Rural Health Clinic",

"date": "2025-09-12",

"pages": 4,

"source": "Direct provider outreach"

}

Pathology

pathology.json

{

"snomed": "M-87200"

}

12345678

{

"specimen": "Skin, left forearm",

"diagnosis": "Benign nevus",

"snomed": "M-87200",

"date": "2025-06-30",

"pathologist": "Dr. R. Patel",

"source": "Non-connected facility"

}

Imaging Notes

imaging-notes.json

{

"cpt": "71250"

}

12345678

{

"modality": "CT",

"study": "Chest w/o contrast",

"cpt": "71250",

"impression": "No acute findings",

"date": "2025-07-21",

"source": "Imaging Network"

}

Procedures

procedures.json

{

"cpt": "45378"

}

12345678

{

"cpt": "45378",

"snomed": "73761001",

"name": "Colonoscopy, diagnostic",

"date": "2024-12-08",

"facility": "Regional Hospital",

"source": "Fax-Based Providers"

}

Family History

family-history.json

{

"icd10": "E11"

}

12345678

{

"relation": "mother",

"condition": "Type 2 Diabetes",

"icd10": "E11",

"age_onset": 52,

"verified": "patient-reported",

"source": "Direct provider outreach"

}

The Predoc Data Layer works with the rest of your stack

EHRsHIEsTelehealth platformsAnalytics warehousesImaging networksPatient portals

Four capabilities make the layer possible

Master Patient Index

Resolve a single patient identity across HIEs, EHRs, and unconnected facilities.

Learn more

Terminology Mapping

Normalize codes and free-text concepts to standard ontologies (RxNorm, LOINC, SNOMED).

Learn more

Unstructured Data Extraction

Parse PDFs, faxes, and scanned documents into structured clinical data.

Learn more

Data Standardization

Normalize fragmented sources to FHIR, HL7, and DICOM canonical formats.

Learn more

CMS RHTP fit

Three of the five CMS pillars depend on a curated data layer.

The Rural Health Transformation Program targets five outcomes: Make Rural America Healthy Again, Sustainable Access, Workforce Development, Innovative Care, and Tech Innovation. Predoc maps directly to three of them.

primary fit

Tech Innovation

Predoc is core technology infrastructure for the program: a curated medical-record data layer underneath every AI and analytics initiative.

Telehealth enablement

Virtual specialty clinics depend on the specialist seeing the full record before the visit begins. The curated data layer brings rural providers into connected, modern data and telehealth systems, so telehealth, remote monitoring, and virtual consults run on the same evidence base as an in-person visit.

Predoc’s role

Tech Innovation directly funds, in CMS’s own framing, “improving data sharing” and “investing in emerging technologies.” This is the wedge Predoc fills. The Curated Data Layer modernizes health IT infrastructure by unifying patient data across HIEs, EHRs, fax-based provisions, and non-connected facilities into a clean, structured record. Lab results, medications, imaging, provider notes, and discharge summaries all flow through the same ingestion pipeline.

Every funded innovation benefits from the same curated longitudinal record, so funding allocated to Predoc compounds across every downstream tool rather than each vendor rebuilding rural-health ingestion from scratch.

secondary fit

Sustainable Access

Shared data infrastructure across rural facilities reduces duplicated investment and keeps services viable as facilities consolidate or partner.

Predoc’s role

Rural facility consolidation is a one-way trend. Solo practices close, regional hospitals absorb their patients, telehealth fills gaps. Each transition fragments records further.

A shared data layer maintains patient continuity through those transitions. When a closing rural clinic transfers patients to a regional hospital, the new provider's first encounter starts with the full record, not a paper binder. Infrastructure decoupled from facility-level volatility is what makes “sustainable” sustainable.

tertiary fit

Innovative Care

Virtual specialty clinics, telehealth-enabled chronic care, and AI-assisted screening all share the same dependency: a complete, unified patient record. Predoc is that dependency.

Predoc’s role

Innovative Care funds new care models that move services closer to patients. Virtual specialty clinics, telehealth-enabled chronic disease management, and remote monitoring all depend on a clean, unified patient record. Without that shared record, each new care model rebuilds its own ingestion layer, and the patient experience fragments across the very services meant to integrate it.

The curated data layer is the precondition that makes specialty care at distance clinically real, not just logistically possible.

Predoc is listed by CMS as a Friend of the Health Tech Ecosystem, pledged to support connected, secure health data access: the same infrastructure backbone the RHT Program depends on.

How this fits depends on where you sit.

Proof at scale

The architecture transfers.

The same source mix that defines rural ingestion (Non-Connected Facilities, Fax-Based Providers) is what the architecture already handles end-to-end at a major specialty practice.

Care Provider

The Oncology Institute Saves ~$2M Annually with Predoc

~$2M

Annual savings

Faster record retrieval

0%+

Record retrieval rate

“I've never seen anything that matches the quality of Predoc. One of the cofounders comes from the medical field; they really understand what we're up against.”
— Kiran Annavarapu, Regional President, The Oncology Institute

Read the full case study

Let's talk about your RHT plan.

Predoc partners with state teams, technology integrators, and rural health systems to build the curated data layer underneath every RHT initiative.

Get in touch