Build a Custom Analytics Dashboard from Disparate EHR Data

Small hospitals often rely on the built-in reporting modules of their primary EHR, such as Epic's Reporting Workbench or Cerner's PowerInsight. These tools are adequate for analyzing data within that single system. They cannot, however, join patient admission data from the EHR with billing data from a separate system like athenahealth or claims data from a third-party clearinghouse. This siloing makes it impossible to track a patient's full journey or calculate key metrics like the true cost per case.

To overcome this, staff often turn to general-purpose BI tools like Tableau or Power BI. The problem is these tools are only a visualization layer; they expect a clean, unified data warehouse that small hospitals do not have. This forces a staff member to spend 20 to 30 hours every month manually exporting CSV files from each system, attempting to clean and join them in Excel, and then loading the result into Tableau. The entire workflow is fragile, error-prone, and the data is weeks old by the time anyone sees it.

Consider a 50-person hospital trying to reduce emergency department wait times. The patient check-in times are in their EHR, but patient satisfaction scores are in a separate survey tool, and staffing levels are in a scheduling system. The clinical director cannot get a single, up-to-date view to see how staffing affects wait times and satisfaction. Any change in one system's CSV export format breaks the entire manual reporting process, and the team is blind for another month.

The structural issue is that EHRs are transactional systems optimized for data entry, not analytics. Their architecture is designed to capture one patient record at a time, not to facilitate aggregate queries across multiple domains. BI tools assume the complex data engineering work of extraction, transformation, and loading (ETL) is already done. For a small hospital without a dedicated data engineering team, this gap makes achieving a unified view nearly impossible with off-the-shelf software.

The first step is a comprehensive audit of your current data systems. Syntora would work with your team to map every source: the primary EHR, billing system, lab information system (LIS), and any patient survey tools. We'd investigate their specific data access methods, whether they offer REST APIs, SFTP access, or require HL7 message stream processing. This audit results in a data dictionary and a detailed integration plan, which you approve before any build work begins.

The core of the solution would be an automated data pipeline built with Python, running on AWS Lambda for serverless execution. This pipeline would connect to each data source on a schedule, typically every 60 minutes. For systems with modern APIs, it would use `httpx` for efficient data retrieval. For systems exporting files, it would process them from a secure S3 bucket. The extracted data is then normalized into a consistent schema and loaded into a central Supabase Postgres database, creating a single source of truth.

The delivered system is a secure web dashboard your staff can access from any browser with role-based permissions. The dashboard displays the key metrics you defined, such as bed turnover rates, average patient wait times, and claims denial rates, updated hourly. You receive the full source code in your GitHub, a runbook for maintenance, and complete ownership of the system running in your cloud accounts.