Using AI to Analyze Health Records
In the current digital age, many medical facilities keep an electronic health record (EHR) for every patient, which contains everything from medical history and test results to current treatment plans. They also allow for patient information to be easily shared with other healthcare providers or organizations (such as a pharmacy or laboratory) that are involved in a patient’s overall care.
EHRs are a vital tool for diagnosing and preventing disease because of all the information they contain. A natural language processing (NLP) AI can be used to sift through notes in an EHR to pick up symptom patterns (such as pain) that could indicate that a larger issue or condition is present. However, there are limitations with current models, since different terms for the same condition or abbreviations may confuse the AI. So, NLP can also be used to make EHR notes more succinct by eliminating redundant phrases that have been written, which would make it easier for medical professionals to sift through an EHR in order to find specific information. They could also be used to develop more efficient search algorithms to look for specific terms in an EHR.
On the idea of helping with a clinician's workflow, AI scribes are being developed that can transcribe conversations between a clinician and a patient. This could help decrease the amount of time spent on EHR data entry at the end of a workday. Similarly, AI voice assistants that can complete tasks issued by a voice command could allow a physician to have more time for patient interaction.
Since AI algorithms are adept at finding patterns in data, they could use a combination of patient data, such as family history, previous conditions, and symptoms to create a diagnosis or risk analysis for certain conditions. This could help catch diseases such as cancer early, and also speed up the diagnosis process by narrowing down the possibilities of what a patient could have, since some symptoms could apply to different conditions.
In a similar vein of thought, AI algorithms could use data on what treatments have or have not worked for a particular patient with a chronic illness to make suggestions on what treatments could be tried in the future. Also, these algorithms could analyze new drugs and determine whether they could be beneficial or cause side effects for a patient.
To conclude, AI has many potential applications in healthcare, but it does have many limitations at the moment. There are also concerns relating to the ethics of using this technology and potential inaccuracies in the results produced by an AI. These issues need to be considered and overcome in order for AI to become a staple in medicine.
Sabrina Mei is a Student Ambassador in the Inspirit AI Student Ambassadors Program. Inspirit AI is a pre-collegiate enrichment program that exposes curious high school students globally to AI through live online classes. Learn more at https://www.inspiritai.com/.
