The selection rule is a clinical and operational decision
I would begin with a specific service population whose coding requirements the HIM and clinical owners understand. The review should establish why those accounts can use information already captured elsewhere and which scenarios require manual judgment. A department name alone is not a sufficient reason to automate every account associated with it. The initial selection would focus on accounts that do not require a DRG and can use the agreed source information without coder intervention.
I would ask for the current Simple Visit Coding selection logic, eligible account classes and service types, exclusion criteria, and manual coding assignment behavior. The team needs to understand when an account enters automated processing and what happens when a user has already started coding. Those details affect both the test population and the projected capacity benefit.
An expanded selection rule should have an approved business rationale. I would document which accounts are included, which remain excluded, and what evidence supports the distinction. The coding owner should approve those boundaries before application build is treated as ready for implementation.
The source hierarchy determines the coded result
I would inventory each item the workflow populates and where the source information comes from. Diagnoses, providers, procedures, and other account attributes may rely on different source workflows or interfaces. A successful source feed in one department does not establish that another department captures the same information consistently.
I would examine the order in which the system selects coding definitions separately from the source hierarchy inside each definition. Within a definition, the review should show whether information appends to an existing value, replaces it, or fills only an empty field. I would compare those settings with the intended treatment of prior user-entered data. The same source values can produce different results when the order or overwrite behavior differs.
For rule-based decisions, I would request the applicable CER records with their contexts, parameters, and references, plus the Simple Visit Coding settings that consume the results. Other account population or validation logic may use different configuration. I would keep the analysis tied to the local workflow rather than assume the extract can establish coding accuracy by itself.
The error queue should lead to a resolution
I would review accounts that qualified for automation but failed validation. The objective is to distinguish a legitimate exception requiring a coder from a recurring source-data problem or build defect. Repeatedly sending the same preventable error to manual review reduces the value of the automation and can hide a source problem at scale.
The error queue needs a responsible team, a clear resolution procedure, and visibility into reprocessing. I would verify when an account can be reevaluated automatically and when manual intervention changes that behavior. A user should understand what taking ownership of an account means for its next processing step.
I would also check for accounts that appear successful in the coding workflow but fail later claim validation. The review must follow a representative sample beyond completion status. Otherwise, a higher automated completion rate can coexist with more avoidable work for billing and claims staff.
The pilot should deliberately include difficult cases
My acceptance set would include a fully qualifying account, an account outside the approved selection criteria, a missing source value, conflicting source values, an account with prior user-entered data, and an account already assigned for manual coding. Each case needs an expected coded result, expected status, and expected routing outcome.
I would have qualified coding reviewers evaluate the resulting data against the approved requirements. The test should confirm both field values and the source hierarchy that produced them. Cases with incomplete documentation should remain visible as exceptions rather than become apparent automation successes through a permissive default.
A limited pilot should include a defined quality review, a route for unexpected results, and a rollback decision the owners understand. I would expand only after the selection boundary and error process behave predictably. A larger eligible volume is worthwhile when the organization can maintain the source quality on which it depends.
The outcome combines throughput, quality, and effort
I would separate the eligible population from the accounts that actually attempted automated processing. Then I would measure successful automated completion as a share of attempted eligible accounts, showing exclusions and manual takeovers separately. That prevents a changed selection denominator from creating an apparent improvement.
The scorecard should include failed validation by cause, repeat error volume, downstream claim corrections, reviewed coding accuracy, and manual minutes required per eligible account. Staff capacity released should be measured across the complete workflow, including the people who resolve source errors and validate results.
I would report faster coding and billing progression as operational and timing outcomes, and identify actual expense changes separately if they occur. The sustained benefit depends on periodic review when source interfaces, departments, clinical workflows, or coding requirements change. The selection boundary should remain an owned decision rather than become invisible infrastructure.