Manual ICD Coding: A Costly Bottleneck
Correctly assigning ICD codes is time-consuming, error-prone, and increasingly hard to staff. In a typical hospital, it is one of the most resource-intensive administrative tasks — and one of the most consequential for revenue and compliance.
- Coding errors: incorrect or imprecise codes due to human error, especially in complex cases with multiple diagnoses
- Time cost: each case can require several hours of specialist work
- Over- or under-coding: wrong classifications lead to financial losses or legal exposure
- Rule changes: ICD system updates require constant retraining of coding staff
- Skills shortage: qualified coding specialists are hard to find, with a training period of 6–12 months
- Quality audits: regular checks are needed to catch and correct errors after the fact
Neither Rules nor AI Alone Are Enough
Rule-based systems
Traditional systems attempt to match clinical text to ICD codes using fixed rules. They perform adequately on common codes but fail to generalise, cannot handle rare or new codes, and have no understanding of clinical nuance or context.
Pure AI approaches
General-purpose AI models understand language, but applied alone they lack the precision and reliability needed for production-ready clinical coding. They produce plausible-sounding but unchecked suggestions — without the validation mechanisms healthcare demands.
Transparent AI Coding You Can Trust
Our system combines multiple complementary AI methods into a single pipeline — each checking the others — and accompanies every suggestion with a plain-language explanation your team can read and verify instantly.
"Mr. Mustermann was admitted with cough, fever, and difficulty breathing. The doctors diagnosed acute bronchitis and treated him with antibiotics for a bacterial infection. There were no pre-existing conditions and no complications. This clearly results in code J20, Acute Bronchitis."
Beyond the text explanation, the system highlights the key terms in the original report — so the coding specialist can see at a glance which passages drove the decision, and confirm or correct it in seconds rather than re-reading the entire document.
Measurable Impact
| Metric | Manual | Automated |
|---|---|---|
| Processing time per case | 2–4 hours | 2–5 minutes |
| Daily throughput | 15–25 cases / person | 200+ cases |
| Coding accuracy | 80–90% | >95% |
| Error rate | 10–20% | <3% |
| Response to rule changes | Weeks to months | Automatic |
| Traceability | Sample audits only | 100% explained |
| Scalability | Linear — requires more staff | Virtually unlimited |
For a hospital with 100 discharges per day, this means: instead of 10–15 full-time coding specialists, only 2–3 reviewers are needed to validate the automated suggestions — with coding completed on the same day.