Ophthalmology charts have two features not found in other departments. One is recording anterior-segment and fundus findings by drawing them on a schema (a diagram of the eye). The other is reading many examination values—visual acuity, refraction, IOP, OCT, and more—and summarizing them as findings. Both are directly tied to the quality of care, but in outpatient clinics that see many patients a day, the documentation burden accumulates. This article explains how to handle ophthalmic schema entry in the electronic chart, and how "AI findings drafts"—in which AI drafts findings from examination results—can streamline documentation.
Why schemas are indispensable in ophthalmology charts
In ophthalmology, positional information—"where" and "what kind" of change—is important. The location of corneal opacity, the degree of lens opacity, the site of retinal hemorrhages or tears: these are hard to convey accurately in text alone, and a drawing shares them at a glance.
- Sharing location: Physicians and staff reviewing later can intuitively grasp where the lesion is
- Comparing the course: Placing the drawing next to the previous schema shows whether a lesion has grown or shrunk
- Patient explanation: Explaining while showing the diagram deepens patient understanding
- Information at referral: Findings can be conveyed accurately in referral letters and pre-operative handovers
On the other hand, workflows that scan and import paper schemas, or substitute general-purpose drawing tools, leave issues such as drawing taking time and past diagrams being hard to compare side by side. In an ophthalmology electronic chart, whether ophthalmology-specific schemas and templates are available as standard greatly affects usability.
The burden of summarizing examination values as findings
In ophthalmology outpatient care, an orthoptist (ORT) performs multiple examinations before the physician's consultation. The physician reads those results, documents findings for each eye (R/L), and adds the assessment and plan.
Even for a single patient on a routine visit, the following information must be integrated into findings.
| Information | What documentation requires |
|---|---|
| Visual acuity (uncorrected and corrected) | Record values per eye and check change since last visit |
| Refraction and corneal curvature | Record values per eye |
| IOP | Values per eye and assessment against the target |
| OCT and fundus | Review images and put findings into words |
| Previous chart | Comparison with previous findings and plan |
Even if transcription of values is eliminated by device integration, the step of "reading the values, capturing change, and writing findings" remains. Accumulated across dozens of patients a day, this is what leaves charting unfinished after clinic hours.
What is an AI findings draft? Drafting findings from examination results
An AI findings draft is a mechanism in which AI creates a draft of findings based on imported examination results and the previous chart. Rather than writing from scratch, the physician reviews the draft, corrects or adds to it as needed, and finalizes it.
The flow of an AI findings draft is roughly as follows.
- Importing examination data: Values and images measured by the ORT are imported into the chart
- AI drafting: Based on per-eye values and change since the last visit, the AI creates a draft of the findings
- Physician review and correction: The physician checks the draft against images and the schema and corrects it
- Finalization: The physician confirms the content and saves it as the chart record
What matters here is the premise that judging and finalizing findings is always done by the physician. The AI handles routine parts such as reading values and putting them into text, and the physician concentrates on clinical judgment—this division of roles is the basis for using AI safely. How to think about what AI can be entrusted with is explained in detail in Where to Draw the Line on Delegating Work to AI.
Combining voice SOAP and past-chart reference
AI findings drafts become even more effective when combined with other input methods.
- Structuring SOAP from voice: AI structures the chief complaint and patient's statements (S) and the assessment and plan (A/P) from the consultation conversation. Combined with the examination-based findings draft (O), a draft of the entire SOAP comes together
- Schema entry: While viewing images, the physician draws lesion locations on the schema, linking the written findings with the diagram
- Past-chart reference: Previous findings and schemas are displayed side by side to check for changes
For how to use voice input itself, see How to Reduce Ophthalmology Chart-Documentation Time with AI Voice Input; for how to think about accuracy, see How Accurate Is Voice-to-SOAP Generation?.
Schema and findings-draft features of AI Karte Ophthalmology
"AI Karte Ophthalmology," developed by Pottech, provides features that support ophthalmic documentation on a single foundation.
- Ophthalmic chart and schema: Ophthalmology templates, schema entry, AI findings drafts, and past-chart reference
- Ophthalmic examination entry: 11 types—including visual acuity, refraction, IOP, corneal curvature, and OCT—entered and recorded as history for each eye
- Device integration: Automatic import from OCT, fundus cameras, tonometers, and autorefractometers via DICOM
- AI assistant: Automatic SOAP structuring from voice, diagnostic suggestions and differential diagnoses, examination trend summaries, and personalized explanations for patients
- Document creation: AI automatically drafts ophthalmic documents such as referral letters, medical certificates, surgical explanations, and consent forms
Because everything from importing examination data to AI drafting of findings, illustrating on the schema, and the physician's finalization is completed within the same chart, back-and-forth between screens and manual work is reduced. And because the receipt computer is integrated, documented care flows directly into billing that supports ophthalmology-specific codes. For the overall picture of why AI Karte is strong in ophthalmology, see Why AI Karte Is Strong in Ophthalmology; for feature details, see the AI Karte Ophthalmology page.
In closing
Ophthalmic documentation carries two burdens: recording location with schemas, and turning many examination values into findings. If ophthalmology-specific schemas and templates reduce the effort of illustration, and AI findings drafts take on the drafting of text from examination results, physicians can spend their time reviewing images and making clinical judgments. The AI is only the drafter; the physician finalizes—by keeping this premise, you can build a documentation system that achieves both efficiency and safety.
Through the provision of AI Karte, Pottech serves as the optimal business partner for clinics—supporting not only better working conditions for physicians, nurses, and medical clerical staff, but also helping clinics achieve to the fullest what they want to accomplish.
For details, please do not hesitate to contact us.