Get AI-Powered Visibility into Your Healthcare Practice

Many practices rely on their EHR's built-in reporting modules, like Epic's Reporting Workbench. These tools are powerful for clinical queries but cannot join data from outside systems. You can report on referral volume from within the EHR, but you cannot connect that data to your marketing CRM to calculate patient acquisition cost by source. The reports are static snapshots, not live operational dashboards.

Business intelligence tools like Tableau or Power BI seem like a solution, but their standard connectors struggle with healthcare data formats like HL7 or FHIR. A practice manager trying to track referral leakage finds the default connector cannot link an outbound referral to an inbound patient record from another provider's system. The tools fail to parse the nested, non-relational data structures common in healthcare, requiring significant manual data prep.

Consider a 20-person orthopedic group trying to optimize referral management. They receive faxes, scan them, manually enter the data into their eClinicalWorks EHR, and then use a separate system for scheduling. To find the average time from referral receipt to first appointment, a manager spends 10 hours a week merging spreadsheets. The data is always a week old and riddled with copy-paste errors.

The structural issue is that EHRs are architected as clinical systems of record, not as open data platforms. Their priority is data integrity inside their own walled garden. They are not designed to provide the secure, real-time, queryable data access that AI visibility and operational analytics demand. The problem is not a lack of data; it is a lack of access.

The first step is a technical audit of your practice's systems. Syntora would map the data flow from your EHR, practice management software, and any digital fax services. We identify API availability, authentication methods, and data formats. This discovery process produces a clear integration plan and a data dictionary for key entities like patients, appointments, and referrals.

The technical approach uses a HIPAA-compliant Python service running on AWS Lambda to act as a secure data broker. This service connects to the EHR's API, pulls the necessary data, and uses the Pydantic library to validate and transform it into clean, standardized JSON. For unstructured referral notes, we would use the Claude API to perform named entity recognition, extracting details like the referring physician. This architecture processes data in-memory to minimize compliance risk, with typical response times under 500ms.

The final deliverable is a private, documented API your internal tools can consume. For the referral scenario, this API would provide a single endpoint to retrieve a patient's entire journey, updating every 15 minutes. This allows a practice manager to build a real-time dashboard in Google Sheets or Power BI. You receive the full source code, a deployment runbook, and control of the AWS account, which is capable of handling over 5,000 daily API calls for under $50 per month in hosting fees. A standard build takes 3-4 weeks.