Every medical organization struggles to publish fresh, unique content that adds value to their digital presence. Sourcing this content is difficult, time consuming, and expensive, but the fruits of this content are profitable, and sustain very well over time. This is where the industry can leverage AI to discover a better, faster, and more efficient solution to content creation.
Our AI research tasks is a medical content engine that utilizes NLP, pub med articles, machine learning and patient medical records to publish unique blog content to expand to and attract future patients of the same surgical case and treatments.
Annotation: NLP will be implemented to structure and annotate data from pub med and patient health records. Redacting patient name and personal identifier characteristics, and requiring annotated data from patient notes based upon multiple internal visits as well as referral visits and medications.
Amount of Data: Depending on the size of the patient population in the EHR, there should be enough data to train an NLP model. As patients continue to visit the hospital for their treatment, new data will be inputted at high volumes. This will be key, as this is "no cost" new data to fine tune the accuracy of the blog generator module.
Retraining: It is in my opinion that an AI NLP model should consistently be retrained and measured. once the accuracy is 99.999%, a monitoring process should verify it is sustaining these levels in regards to HIPAA compliance, unique medical content generation, SEO, and website traffic.