By Gillian Hammah (Dr)
Artificial Intelligence (AI) is no longer confined to tech hubs in Silicon Valley—it’s solving critical challenges in Ghana’s healthcare system.
While AI is reshaping industries globally, one area where its impact is life-changing is in bridging language gaps between doctors and patients, reducing administrative burdens, and automating medical coding.
This article explores how AI-driven medical dictation software is tackling these issues, with lessons applicable to healthcare systems worldwide.
The Problem: Language Barriers and Burdensome Documentation
In Ghana, where over 80 languages are spoken, communication gaps between doctors and patients are alarmingly common. A 2022 study found that 64 percent of patients and 81 percent of healthcare workers in urban Ghana faced language barriers during consultations, leading to misdiagnoses, inefficient care, and breaches of confidentiality. Doctors often rely on gestures or untrained translators, risking errors and eroding patient trust.
Meanwhile, physicians spend hours manually documenting symptoms and assigning medical codes. Ghana’s healthcare system uses the International Classification of Diseases (ICD) coding system to track diagnoses, but memorising codes like ICD-10 is time-consuming. Doctors frequently resort to manually searching symptoms to assign the relevant ICD-10 code and populate it into the Lightwave Health Information Management System (LHIMS) manually. With the country transitioning to ICD-11 – a more complex system – doctors face even greater pressure. Globally, studies show that physician burnout is a significant issue as physicians spend twice as much time with 75 percent suggesting that the EHR contributes to their burnout.
The AI Solution: Speech Recognition, Translation, and Automated Coding
Imagine a tool that listens to a patient’s symptoms in Twi, translates them into English for the doctor, generates a clinical note, and auto-fills the correct ICD code – all in seconds. This is now possible with AI-powered medical dictation software.
Here’s how it works:
- Automated Speech Recognition (ASR):
The software will transcribe consultations in real time, supporting languages like Twi, Dagbani, Hausa and Ewe. For example, a patient describing “Me yɛm yɛ me yaw” or “Me yafunu yɛ me yaw” (“my stomach hurts” in Twi) is instantly converted to text.
- Machine Translation (MT):
The transcribed text is translated into English and populated into Ghana’s Lightwave Health Information Management System (LHIMS) for doctors to review. The LHIMS is a web-based software platform that is designed to manage patient health records electronically across healthcare facilities in Ghana.
- AI-Powered Medical Coding:
A large language model (LLM), such as Llama 3.1, analyses the translated text, identifies symptoms, and assigns the correct ICD-10 or ICD-11 code automatically. This eliminates manual searches and reduces errors.
Global Precedents and Local Potential
Similar tools are already cutting documentation time globally. For instance, Nuance’s Dragon Medical One, used in U.S. hospitals, reduced clinicians’ documentation time by up to 67 percent (Source: Nuance Communications). In Ghana, a pilot program could replicate these efficiencies while addressing unique local challenges:
Accuracy in Multilingual Contexts: Training AI models on Ghanaian accents and dialects ensures reliable translations.
Seamless ICD Transition: Automating coding helps doctors adapt to ICD-11 without memorising thousands of new codes.
Cost Savings: Misdiagnoses due to language barriers cost patients unnecessary tests. AI reduces these expenses while protecting confidentiality.
Potential Outcomes: A Healthier Future for Ghana
Integrating AI-powered medical dictation tools isn’t just about adopting new technology, it’s about redefining healthcare delivery in Ghana. By tackling language barriers, automating tedious tasks, and modernising coding practices, this innovation will advance healthcare in Ghana in three key ways:
- Smoother Transition to ICD-11:
Manual coding errors account for 30 percent of claim denials globally (Source: American Health Information Management Association). Insurance providers require specific details to assess and process claims accurately, yet the most common reasons for claim rejections are missing or incorrect information, including ICD codes.
Another vital reason automated ICD coding is needed is to decrease re-admit rates of patients who have to repeatedly return to hospitals because they were misdiagnosed the first time due to the wrong ICD code. Less repeat out-patients saves money and time. An AI-powered medical coding model will significantly improve the efficiency and accuracy of coding assignments before, during, and beyond the transition to ICD-11, enhancing compliance and efficiency in Ghana’s healthcare system.
- Inclusive Care for Language Minorities:
Patients who are more comfortable communicating using their local dialect or who speak less common dialects can gain equitable access to care without needing to worry about violation of privacy with the use of an interpreter. AI acts as a neutral “translator,” preserving trust and privacy.
- Doctors Regain Time for Patients:
One doctor can see over 80+ patients a day. In emergency department visits, doctors must memorise symptoms for multiple patients and retroactively record documentation. If AI cuts documentation time by even 30 percent (as seen in global pilots), Ghanaian doctors could redirect more hours per week to critical care instead of administration.
Challenges and the Path Forward
Adoption hurdles include internet reliability, model training costs, and resistance to new workflows. However, partnerships between tech providers like Aya Data, healthcare institutions, and policymakers can overcome these. For example, offline-compatible ASR models and localised training datasets ensure accessibility.
Wrapping Up
Ghana’s healthcare system stands at a crossroads – one where AI can bridge gaps in communication, efficiency, and care quality. Developing AI solutions tailored to West Africa’s unique challenges, from multilingual speech recognition to seamless ICD coding, will be instrumental in helping transform patient outcomes and operational efficiency.
