As advanced AI models continue to transform our abilities to collect, manage, and expedite health data analysis, applying this innovative technology to address health inequalities is emerging as an important focus for patient-centered care and an important application of patient data translations. Recently, following the US Food and Drug Administration’s (FDA) new guidance on increasing patient diversity in clinical trials, a New York-based healthcare provider launched a platform to commercialize AI specifically that is specifically designed to address the quality of care, access, and cost of healthcare needs of communities disproportionately affected by maternal health issues. Yet, as we scale AI to target chronic conditions associated with particular subsets of the total population, a recurring obstacle is that patient data, including health records (EHRs), cannot be accurately assessed due to the native language of the patient. To help facilitate technological innovation that bridges the gap in health inequalities, translations of medical records and patient data are an important way to ensure that care can be provided for the specific needs of diverse patient populations.

While patient data is a fundamental resource leveraged by AI in healthcare today, consistent health inequalities across our population still pose as a barrier to providing more affordable and high-quality care to the communities affected the most. In one recent example of how AI can be used to tackle this problem, the healthcare organization Northwell Health has partnered up with a startup studio called Aegis Ventures with the aim of developing new cutting-edge AI tools to that will help to detect and manage serious complications with pregnancies, including a condition called preeclampsia, which disproportionally affects women of color. By analyzing thousands of previous cases, the AI will use advanced machine learning to be able to assist clinicians in detecting the condition before it occurs. However, in order to accurately assist clinicians with disease detection, a recurring challenge is that patient health records are not consistent in language and for some communities, doctors and nurses can record information to the language preference of the patient. Moreover, providing better quality of care entails a patient-centric approach that might need to consider linguistic and cultural differences to effectively deliver more accurate medical solutions. AI models deployed in healthcare today are able to perform advanced screening and detection functions due to patient medical records being readily available and consistent across language and without focusing on the language represented in the patient data and heath records, it can remain difficult to properly detect crucial information, such as underlying medical conditions. Prior to this development, the US FDA announced a guidance that recommends sponsors to submit a “Race and Ethnicity Diversity Plan” in the early stages of the clinical development process. With the ultimate goal of increasing diversity in clinical trials to be more representative of the population as a whole, the FDA also cites language as being a challenge to patient participation in some communities that are disproportionately burdened by particular disease.

Since many barriers to equity, including language, exist in healthcare, translations for medical records and documents are essential, especially with the rise in innovative AI solutions and shifts towards expanding the diversity in clinical trials. In our recent discussion on AI being used in real-time to analyze patient data and medical records, a similar challenge is presented in that the quality of care that patients received is directly influenced by the quality and accessibility to medical records and data. Patient-centric solutions require tailoring the care to the needs of the patient which cannot be achieved without bridging the language barriers and ensuring the health records can be collected and analyzed by any system. Leveraging translations of patient data and medical records in a crucial approach to connecting more patients to the best care available and in both hospitals and clinical trials, these translations are necessary for upholding patient-focused care. To help meet the needs of a linguistically diverse population and to ensure that crucial medical information is consistent and accessible, language services providers like CSOFT provide end-to-end translation services for the healthcare industry and the clinical trials industry. Learn more at lifesciences.csoftintl.com