Not All ‘AI’ SOAP Notes Are Equal: What Actually Determines Clinical Quality

In the healthcare industry, AI is used in different areas such as drug discovery, surgery, and clinical documentation, including the creation of SOAP notes.

However, there is one important thing about AI-generated SOAP notes, not all AI-generated SOAP notes are equal. This means not all of them are actually usable. In this blog, we will discuss this in detail.

AI Generated SOAP Notes 

AI generates SOAP notes based on recordings of the provider or the conversation between the provider and the patient, and it is connected to the EHR system to get additional information needed to create the SOAP note. There are many benefits that AI SOAP notes have. According to providers who use tools to generate AI SOAP notes, it saves time, reduces errors by 20%, leads to 63% less burnout, and results in a 46% reduction in stress levels. 

Around 60% of providers feel comfortable letting AI draft visit notes, while the other 40% are somewhat skeptical about AI-generated SOAP notes. This is because AI is not yet all powerful enough to create notes like an expert provider who understands the patient’s history and the full context of the visit on which the SOAP note is created.

Why Not All SOAP Notes Are Equal

AI-generated SOAP notes are created using AI models, and their quality depends on multiple factors such as the AI model itself, the hardware it runs on and supports, and the processes used to convert recordings into final output.

Not all AI models of AI SOAP note tools are equally powerful, intelligent, well-trained, or fine-tuned to create SOAP notes. Some AI models may hallucinate more than others, some may not properly understand the context, and some may not be fully connected to the EHR, which limits their ability to use patient history.

In some cases, AI may struggle with accurate transcription, while others may transcribe correctly but fail in speaker diarization. Some models handle most steps well but fail to properly structure the data into a clear SOAP format. Others may not correctly understand accents, mispronunciations, or complex medical terminology, and some may not effectively filter background noise from conversations. 

Some AI SOAP note tools have clinical reasoning capabilities, which help them understand complex voice recordings from providers and correct errors in the recording to generate more accurate clinical notes. In some cases, the AI-generated SOAP notes go through multiple rounds of human review before the final version is created.

Since multiple factors are involved in the process of AI SOAP notes, each step directly impacts the final output. Because of these differences in capabilities and performance, the clinical quality of the SOAP notes generated can vary, which is why not all AI-generated SOAP notes are equal.

What Actually Determines the Clinical Quality of AI SOAP Notes

1. Core AI Model and Its Capabilities: The AI model is the core engine behind SOAP note generation. If the AI model is not specifically designed for clinical documentation and properly fine-tuned for it, then it may not understand medical terminology, accents, or mispronunciations, or perform the task correctly, and in some cases, may hallucinate more. Since not all AI SOAP note generation tools are equal in power, intelligence, or how they work, the clinical quality of the SOAP notes they produce also varies. The clinical quality of AI-generated SOAP notes largely depends on how capable the AI model is.

2. Speech Capture and Speaker Identification Accuracy: The accuracy of transcription and speaker diarization directly impacts the quality of the entire SOAP note. If either of these is incorrect, the final SOAP note will contain errors, and its clinical quality will suffer.

Accurate transcription depends on how clearly the conversation is captured and how well the AI SOAP note tool distinguishes between different speakers (patient and provider). It also depends on how effectively it separates speech from background noise and correctly captures what is being said in the recording.

The devices used for recording also play an important role. For example, if a SOAP note tool is also available as an iOS application, it can use iPhone hardware with built-in noise cancellation capabilities, which helps reduce background noise and improve recording quality.

If the recording is unclear due to noise, overlapping speech, or poor audio quality, it can lead to incorrect transcription or misidentification of speakers. These errors directly carry forward into the SOAP note, affecting its accuracy and overall clinical quality.

3. Context Understanding and Patient History Awareness: In healthcare, context matters the most because in diagnosis, one factor influences another, and if something is ignored, the outcome can change. For example, a patient with past health issues comes to the doctor with chest pain, and their medical history is already available in the EHR or in previously generated SOAP notes. The doctor understands and retains this context across different visits.

For instance, if the patient did not have chest pain two weeks ago but now presents with it, the doctor can interpret this change and relate it to previous health conditions.

However, if an AI SOAP note tool does not have proper access to this EHR data or lacks contextual understanding, it will not generate SOAP notes with the right interpretation. It may treat the symptom in isolation instead of connecting it with the patient’s history, which can lead to incorrect assessment or incomplete documentation and ultimately affect clinical quality.

4. Hallucination Risk in AI SOAP Notes: During different steps of SOAP note generation, there is a chance that AI may hallucinate, either in transcription or speaker diarization. It may add information that is not present in the dictation or recording, or miss important details that are actually in the recording. Even a small hallucination rate of 1–2% in transcription can cause issues, as it may miss critical information such as symptoms, leading to undercoding, or add incorrect details, leading to overcoding.

For example, if AI misses a symptom like chest pain, it can impact ICD or CPT coding and result in undercoding. On the other hand, if it adds a symptom that was never mentioned, it can lead to overcoding and compliance risks.

This becomes dangerous because incorrect SOAP notes, meaning wrong or missing details, can result in claim denials.

5. Clinical Reasoning in AI SOAP Notes: AI model’s clinical reasoning is very important because it acts like the processor of a digital engine that makes SOAP note generation possible. Using this, AI understands the patient’s symptoms, interprets the information in context, and helps structure it into a meaningful SOAP note.

If this reasoning is not present, then what the provider says in the recording about symptoms, diagnosis, and treatment plan will be just text, nothing more. In that case, the SOAP notes created will not have good clinical quality.

6. Final Output Quality and Formatting: The clinical quality of a SOAP note depends on the points discussed above, which determine the capability of the AI tool to create accurate notes. No matter how good or bad those points are, how the data is structured and formatted in the SOAP note ultimately determines its clinical quality.

This is because everything may look correct and well formatted, but it can still have issues. On the other hand, even if everything is correct in the final output, but the formatting of the SOAP note is poor, then it is not usable, and its use in a clinical setting becomes difficult.

So, the final output of an AI-generated SOAP note must have correct structuring and formatting of information to ensure good clinical quality.

7. Human-in-the-Loop Review and Validation: As discussed, AI is not yet fully capable of doing everything without issues or making mistakes, which is why human review is required. The clinical quality of AI-generated SOAP notes greatly depends on whether they have been reviewed by a human expert or not.

This human-in-the-loop approach is a very important factor because it helps ensure that the SOAP notes are accurate and reliable. If they are reviewed by multiple experts, errors and mistakes are more likely to be identified and corrected, making the SOAP note more accurate.

In such cases, the clinical quality is high, and the SOAP notes can be reliably used in clinical settings.

So, the clinical quality of a SOAP note is directly influenced by all the factors discussed above, including the AI model, transcription accuracy, context understanding, hallucination risk, clinical reasoning, output formatting, and human review. When these factors are handled correctly, the clinical quality of the SOAP note improves. 

Why a ‘Perfect-Looking’ or ‘Accurate’ SOAP Note Can Still Be Clinically Weak

We need to understand that even if a SOAP note looks complete, well-structured, or has correct information, it does not necessarily mean that the clinical quality of the AI-generated SOAP note is good. Let’s understand this in detail.

1) “Perfect-Looking” SOAP Note Can Still Be Clinically Weak: Good formatting does not mean that the clinical quality of an AI-generated SOAP note is correct. Good formatting only means that the data is arranged properly, which may look correct on the surface, but it does not guarantee that all important details are captured. Because of this, such a SOAP note may not be reliable for clinical use.

On the other hand, even if the data in the SOAP note is correct, but the formatting is poor, it still affects usability in a clinical setting. So, both accurate data and proper formatting are important for good clinical quality.

2) Highly Accurate Transcription Can Still Create a Clinically Weak SOAP Note: This might sound incorrect, but even if the transcription is accurate, it does not mean that the SOAP note created will be correct, have high clinical quality, or be usable without issues. This is because there are still chances that the AI may hallucinate in later steps, may not have the required level of clinical reasoning, or may produce a SOAP note with poor formatting or missing important details.

How Talisman Solutions Creates AI SOAP Notes with Its SOAP Note Tool

At Talisman Solutions, we have been working with providers, payers, clinics, multi-specialty facilities, and hospitals for more than two decades. We have a team of medical billing experts and other specialists. A few years ago, when OpenAI introduced ChatGPT, we understood the importance of AI and started exploring how in the near future AI could be used in medical billing and the healthcare sector in general.

Since then, we have developed multiple AI tools to improve the quality of our medical billing services, and one of these tools is our AI SOAP note solution. The SOAP notes generated by our AI tool follow a structured process and include the following features:

1) AI Model for SOAP Note Generation: Our AI model for generating SOAP notes is properly trained on real-world data and workflows. This gives it clinical reasoning and context awareness, which helps it create accurate and clinically relevant SOAP notes.

2) EHR Integration for Better Context: Our AI SOAP Note tool  is connected to the EHR system, which helps it understand patient context better and retain information across different patient visits.

3) Handling Real-World Recording Challenges: Our tool can be used with high-quality, noise-canceling microphones to record patient visits or conversations between the provider and patient. It is also available on iPhone as an iOS application, which allows it to use built-in noise reduction capabilities for better recording quality. In addition, it can understand different accents, mispronunciations, and fast-paced dictation, helping ensure accurate transcription and SOAP note creation.

4) Accurate SOAP Note Output: With the help of all the above factors, the AI SOAP note tool provides accurate live transcription so providers can see the output in real time, followed by speaker diarization. It then uses clinical reasoning, context awareness, and error correction to produce clear, well-structured, and clinically strong SOAP notes. We also have a feature in our AI SOAP note tool which allows providers to have their own custom SOAP note templates.

5) Human-in-the-Loop Validation: AI alone is not enough, and clinical review is essential for accuracy and legal validity. Errors can still happen, so we follow a structured human review process.

Once the AI generates the SOAP note:

• The provider first reviews and edits it if needed
• Then it is reviewed by an expert medical billing specialist
• The reviewer compares it with patient details, voice recordings, and the generated note
• After that, it goes back to the provider for final approval

Once finalized, the SOAP note is ready for clinical use. This process ensures that the AI SOAP note created by our AI SOAP note tool is accurate, complete, and ready for claim submission without missing critical information.

Conclusion

As we have seen, there are many factors involved in generating SOAP notes, and one of the most important is the AI model. Not all AI SOAP note tools are built in the same way, and they do not have the same features and capabilities. This is what creates differences in the quality of the SOAP notes they generate.

Along with this, AI alone is not enough, and having a human in the loop is very important to ensure accuracy and reliability. At Talisman Solutions, we focus on all these factors to ensure that the SOAP notes we generate are accurate and have strong clinical quality. That is why our AI generated SOAP notes are reliable and suitable for clinical use.

FAQs About AI SOAP Notes for Clinics and Providers