20+ Hours of Manual Imports, Gone: What the EHR-to-GL Workflow Looks Like After Cloud Migration
Every month, finance teams of hospitals and clinics across the country run the same routine. Data gets pulled out of the EHR. It gets reformatted in Excel. It gets manually loaded into the financial system. Then someone checks it, because the last time nobody checked it, the numbers were wrong for two weeks before anyone noticed.
Add payroll allocations, AP invoice data, and operational statistics to that cycle, and you are looking at 20 or more hours of staff time per month dedicated to moving data between systems that should be talking to each other.
This is not a technology problem in the traditional sense. The systems work. The data exists. The issue is that the connection between them was built with flat files, batch jobs, and manual workarounds instead of a modern integration architecture. And every month, your team pays for that gap in hours.
Here is what those 20+ hours actually consist of, where the time goes, and what each workflow looks like when the manual layer is replaced with automated, API-driven data flows on a cloud financial platform like Sage Intacct.
Workflow 1: EHR Charge and Payment Data
This is typically the largest single source of manual import time.
The manual process. The EHR (Epic, Cerner, athenahealth, eClinicalWorks, or similar) generates a nightly or weekly export of charges, payments, and adjustments. The file lands on an SFTP server or shared directory. Someone on the finance team picks it up, opens it, and reformats it to match the GL account structure. Charge codes get mapped to revenue accounts. Payer categories get assigned. Adjustments get classified. The reformatted data gets imported into the financial system, usually through a CSV upload or a custom import utility.
When the file format changes (and it does, often after EHR updates), the import breaks. The team discovers the break the next morning when numbers do not tie. Someone troubleshoots, rebuilds the mapping, and re-imports. That cycle can consume half a day or more, and it happens several times a year.
What this looks like after migration. On Sage Intacct, EHR data flows through an API-based connector. Charges, payments, and adjustments post to the dimensional GL as they occur, mapped to the correct revenue accounts, payer dimensions, and service-line tags through a configuration layer that does not depend on static file formats. When the EHR releases an update, the API connection handles the change through versioning. No broken files. No manual remapping.
According to KLAS (2023), API-level EHR integration with a cloud financial platform shrinks close time by approximately 40%. That is not a theoretical number. It reflects the elimination of the manual extraction, reformatting, and verification steps that consume real hours every cycle.
Time Recovered:
6 to 10 hours per month at a mid-market healthcare organization running 5 or more entities.
Workflow 2: Payroll Allocations and Labor Cost Distribution
Healthcare labor costs typically represent 50% to 60% of total expenses. Getting payroll data into the financial system accurately and on time is not optional. But at most organizations, the process is anything but automatic.
The manual process. Payroll runs on its own platform (ADP, Criterion, WFG, or similar). After each pay period, someone exports the payroll journal. That journal needs to be allocated across entities, departments, and cost centers based on where employees worked, not just where they are administratively assigned. For organizations with per-provider labor costing, the allocation is even more granular: by physician, by clinic, by service line.
This allocation is almost always done in Excel. Someone builds a workbook that takes the raw payroll data, applies allocation percentages, and produces journal entries for each entity. The journal entries get manually keyed or uploaded into the financial system. If the allocation percentages change (new hire, departmental reorganization, clinic closure), the workbook needs to be updated before the next cycle.
What this looks like after migration. Sage Intacct integrates with major payroll platforms through connectors that pull payroll data directly into the GL with dimensional tagging. The allocation logic lives in the system, not in a spreadsheet. When an employee’s cost center changes, the allocation updates in the configuration layer. Journal entries are generated automatically. The finance team reviews and approves instead of building from scratch.
The MGMA 2024 data on administrative vacancy rates (exceeding 20% in many healthcare organizations) makes this particularly relevant. When your team is already short-staffed, spending hours on manual payroll allocation is capacity you cannot afford to lose.
Time Recovered:
4 to 6 hours per month, more at organizations with complex per-provider or multi-entity allocation models.
Workflow 3: Accounts Payable and Invoice Processing
AP is a volume game. Mid-market healthcare organizations process hundreds or thousands of invoices per month across supplies, pharmaceuticals, contracted services, and capital items. The manual import burden is proportional to that volume.
The manual process. Invoices arrive by mail, email, and vendor portal. Someone opens each one, keys the data into the financial system (vendor, amount, GL coding, entity, approval routing), and routes it for approval. Paper invoices get scanned and filed. Duplicates get caught (sometimes) through manual checking. Coding errors get discovered during close reconciliation, not at the point of entry.
At many organizations, the AP team maintains a separate tracking spreadsheet alongside the financial system because the system’s native workflow tools are insufficient or too cumbersome to configure. That spreadsheet becomes the real system of record, which creates its own reconciliation burden.
What this looks like after migration: Sage Intacct’s AP automation (and integrated third-party tools) uses OCR capture to extract invoice data, applies coding rules based on vendor and GL history, routes for approval through system-native workflows, and posts the entry automatically upon approval. Duplicate detection is automatic. Coding suggestions are based on historical patterns.
Intacct Cloud data (2024) shows that AP automation reduces the AP cycle from an average of 14 days to under 3, while unlocking early-payment discounts that flow directly to the bottom line. For a healthcare organization operating on 1% margins, those discounts are meaningful.
Time Recovered:
4 to 6 hours per month in direct import and coding time, with additional time savings from reduced reconciliation effort during close.
Workflow 4: Operational Statistics and Non-Financial Data
This is the workflow most organizations forget to count when they estimate their manual import burden. But it adds up.
The manual process. Finance teams need non-financial data to produce meaningful reports: patient volumes, bed occupancy, procedure counts, referral patterns, payer mix by service line. This data lives in the EHR, in scheduling systems, in departmental databases. Getting it into the financial reporting environment requires manual extraction, reformatting, and loading. At some organizations, this is a monthly ritual. At others, it happens ad hoc whenever leadership asks a question that requires operational context alongside financial data.
The problem is not just the time. It is the lag. By the time operational statistics are manually compiled and matched to financial results, the data is weeks old. Service-line profitability reports that combine financial and operational data become stale before they reach the people who need them.
What this looks like after migration. A cloud-native financial platform with an open API layer can ingest non-financial data as dimensions or statistical accounts. Patient volume data, bed occupancy rates, and procedure counts can feed directly into the GL reporting engine alongside financial transactions. Service-line profitability reports that combine revenue, cost, and volume data are produced from a single system instead of assembled from three.
Time Recovered:
2 to 4 hours per month, with the larger impact being report timeliness and relevance.
The Compound Effect: Where 20+ Hours Becomes Something Bigger
Add those workflows together:
| Workflow | Monthly Hours (Manual) | Monthly Hours (Automated) |
|---|---|---|
|
EHR charge and payment imports
|
6 to 10
|
< 1
|
|
Payroll allocations
|
4 to 6
|
< 1
|
|
AP invoice processing
|
4 to 6
|
< 1 |
|
Operational statistics
|
2 to 4
|
< 1
|
|
Total
|
16 to 26
|
< 4
|
The 20+ hour figure is conservative for a multi-entity healthcare organization. Some organizations we work with were spending closer to 30 before migration, depending on entity count and integration complexity.
But the hours are only part of the story. Each manual import introduces error risk. Each error requires investigation and correction during close. Each correction delays the close timeline. Each delayed close means leadership is making decisions on older data. At 1% margins, the cost of those delayed decisions is harder to quantify but no less real.
The IT side compounds similarly. Every flat file integration, every batch job, every custom import utility requires ongoing maintenance from IT staff. When the source system updates, the integration needs attention. When a new entity is added, the import process needs to be extended. That maintenance consumes 2 to 4 FTEs worth of IT capacity at most mid-market hospitals. Capacity that could be directed toward cybersecurity, clinical systems, or the digital transformation initiatives the board keeps asking about.
What DSD Sees in the First Conversation
Across dozens of healthcare implementations, the pattern is remarkably consistent. Organizations know the manual import burden exists. They feel it every month. But they have rarely quantified it.
The first step in any engagement with DSD is a diagnostic conversation that maps these workflows in your specific environment. How many data sources feed your GL? How does data move between them? Where are the manual steps, and how many hours do they consume? What breaks most often, and what does it cost when it does?
The answers are always more than people expect. Not because the team is inefficient. Because the system forces them to be the integration layer that the technology should be providing.
DSD’s consulting team includes professionals who held Controller, Director of Finance, and VP of Accounting roles at healthcare organizations before joining DSD. They have personally sat through these import cycles. They know what the manual process feels like, not just what the automated alternative looks like. That is a different kind of implementation conversation.
Start the Conversation
If your finance team is spending 20+ hours a month on manual data imports, the math on eliminating that burden is straightforward. The question is not whether it is worth fixing. It is how quickly you can get from where you are to where the data flows without your team hand-carrying it between systems.
DSD can walk you through what that transition looks like for your specific environment: your EHR, your payroll platform, your entity structure, your current import workflows. Not a demo. A diagnostic.
See what your team could do with 20 extra hours per month.
Douglas Luchansky
Director, Client Transformation
