Clinical coding is one of the few places in a hospital where a software error has a directly calculable dollar value. Under activity based funding, the 2025-26 National Efficient Price is series, and it covers the architecture we would put in front of a coding team, what the evidence supports, and what it costs.
The problem is scale, not competence
Separations grew about 7%, from 11.3 million in 2018 to 12.1 million in 2022, while the coder workforce ages and universities close their health information management programs. Half the previously offered undergraduate HIM courses were discontinued because enrolments were too low, which the AIHW flagged in its as separations rise, with real-time feedback to coders supporting accurate DRG and NWAU reporting.
So the demand is real. The question is what you can safely hand to a model.
Why fully automatic coding is not defensible today
The strongest single citation here is the NEJM AI benchmark by Soroush et al., which tested GPT-3.5, GPT-4, Gemini Pro and Llama2-70b on generating ICD-9-CM, ICD-10-CM and CPT codes. GPT-4 was the best of them and still managed only 33.9% exact match on ICD-10-CM. The models also produced codes that were valid-looking but non-billable, or simply , in force for separations from 1 July 2025, with AR-DRG V12.0 sitting on top. AR-DRG V12.0 assigns Diagnosis Complexity Levels across . Satisfaction varied with coder experience and certification, which matters more than it sounds: a tool your senior coders distrust will be worked around.
HIMAA's national guideline for AI-generated clinically coded data lands in the same place, listing human expertise and human-in-the-loop among its for.
The audit record is the part people skip and later regret. It should look roughly like this:
{
"episode_id": "",
"separation_date": "2026-03-14",
"classification_edition": "ICD-10-AM 13th",
"grouper_version": "AR-DRG V12.0",
"model_id": "coder-assist-2026-02",
"index_snapshot": "sha256:",
"suggested": [{"code": "J45.0", "confidence": 0.82, "evidence_doc": "DS-4471", "span": [1180, 1246]}],
"coder_id": "",
"action": "rejected",
"final_code": null,
"reviewed_at": "2026-03-19T09:41:12+11:00"
}
That log gives you two things. Externally, it demonstrates that a qualified human assigned every code and that the tool suggested rather than decided. Internally, it is your quality feedback loop. Coding errors are systematic, not random: external audits of 55 hospitals across two states, covering 6,300 records, found a , which covers software for the administration or management of health processes including financial records, claims and billing. That exclusion holds only if every function meets the criteria and the tool does not influence clinical decision-making. The moment someone asks for the suggestions to be surfaced to treating clinicians during the episode, you have changed the intended purpose and you need to re-examine the classification.
Governance can follow the ACSQHC's (225 of 1,205). Decide early whether inference runs in Australian-hosted infrastructure, who can query the index, and how long request payloads are retained. Those answers are harder to change later.
What it costs, honestly
Budget for licensed IHACPA classification products, a re-index and re-validation every three years when the edition changes, and inference costs that scale with document volume rather than with episodes. The largest cost is not the model. It is assembling a gold-coded evaluation set from your own separations, ideally auditor-reviewed, plus the coder hours to run a proper before-and-after measurement. Without that set you cannot tell whether the tool is helping or quietly teaching your team to accept plausible wrong codes, which is the failure mode that worries us most.
Start on a single high-volume specialty, measure minutes per episode and post-audit mismatch rate against a matched baseline, and keep the coder as the person who assigns the code. That configuration is the one the evidence supports.
PicNet builds production AI systems for Australian organisations. .
SOCIAL SHARE CARD GENERATOR