What if AI could use digital health data to correctly identify the early signs of a serious health issue that is extremely hard to diagnose, yet widespread in hospital settings? In one recent example of healthcare innovation, a cutting-edge AI system that analyzes electronic health records (EHRs) and real-time patient data is being used in emergency departments to diagnose early signs of sepsis and help provide insight into more-cost effective and data-driven diagnostics. Digital data collection is a rapidly growing sector of the life sciences and is enabling companies and institutions to address leading health challenge through a more accurate and patient-focused lens. Yet, as sophisticated as AI and data-driven health solutions are, the standard of care that patients receive, and the accuracy of results is entirely contingent on the quality of the digital data that is being analyzed. To meet this challenge, the scalability of advanced clinical care is reliant on the availability and accessibility of digital health data that comprehensibly represents the cultural and linguistic diversity of the patients that the care has been designed for.
Advancements in digital data collection have drastically reshaped our ability to provide precision diagnostics and incorporate emerging medical technology like AI. In a recent example of such innovation, Duke University developed an AI system called Sepsis Watch that has been deployed in hospitals with the main function of analyzing real-time data and patient medical records to determine if patients are presenting early onset sepsis. As the MIT Technology Review has reported, Sepsis Watch has been developed as a deep learning model that simultaneously analyzes EHRs, real-time data like blood pressure and vital signs, and 86 additional variables including demographics and comorbidities, to determine if a patient is developing sepsis. Duke Health has added that one of the major challenges despite sepsis being a relatively common infection today, is that diagnosing this sepsis remains difficult and time consuming because there exists no singular diagnostic marker to determine the likelihood the infection is developing. Moreover, the Sepsis Watch AI model has proven so far to dramatically reduce the costs and manpower associated with diagnosing a potentially life-threatening condition like sepsis. Specifically, the Duke Institute for Health Innovation (DIHI) claims that leveraging patient data in this case is a huge success, given that mortality rates of sepsis are nearly 30% and treating sepsis is the single most expensive cost to Medicare. Yet, the degree to which medical AI models like Sepsis Watch can predict a condition before it develops is largely due to the software’s ability to accurately analyze EHRs and compute results which can easily be interpreted by doctors.
Digital health documents like EHRs include a swath of important information that can be widely accessed throughout healthcare systems and provide crucial data that directly influences the type and level of care a patient will receive. However, to ensure that digital documents can match the quality of care that should be provided, translations are often needed ensure that essential health information is not left out and that the document itself can be interpreted by medical staff. Since state-of-the-art technology only works as well as the data on which it analyzes, translations in this space for both medical technology vendors and healthcare workers are an important step in facilitating patient-focused care. Furthermore, software localization for digital health documents is crucial in assuring that the format and usability of resources like EHRs are not hindered when translated across different languages. To support advancements in clinical care that are able to reach more diverse populations, language experts like CSOFT Health Sciences specialize in digital data translations and software localization solutions for the healthcare sector. Learn more at lifesciences.csoftintl.com.