Closing the gap between the EHR and the ledger
Key takeaways
Every month, your billing team exports data from your electronic health record (EHR) system—Epic, Athenahealth, eClinicalWorks, MEDITECH or another system—and someone manually works that data into NetSuite. Charges are rekeyed. Payments are matched by hand. Contractual adjustments are calculated using spreadsheet lookups. Accounts receivable (AR) aging is summarized in Excel and reviewed before anyone can post a journal entry.
This process works. But it is slow, expensive and fragile—and in a health care organization where revenue recognition, payer mix reporting and AR management are mission-critical, those three limitations carry real financial consequences.
At first glance, the obvious answer is straightforward: "build an extract, transform, load (ETL) pipeline." Connect the EHR to NetSuite, map the fields and schedule a nightly sync. Unfortunately, health care revenue cycle data does not cooperate with standard ETL approaches.
Traditional ETL tools are excellent at structured, predictable data movement. However, they often fail in the health care revenue cycle due to variable export formats, complex business logic and frequent schema changes. Artificial intelligence handles these scenarios differently, with greater speed, agility, scalability and insight.
Ultimately, the benefits of automating the EHR-to-NetSuite workflow generally fall into three categories: operational efficiency (you do less manual work), financial performance (you collect more of what you're owed) and strategic visibility (you know things you didn't know before).
Read our white paper to learn how AI enables a more efficient transition of EHR data into NetSuite, including:
- Why the problem is harder than it looks
- How AI can solve it
- What AI actually does
- What changes after automation
- How to quantify real results and performance benchmarks