Augmented EHR System provides reports & dashboards for the healthcare providers to analyze the EHRs & provide meaningful data points to take corrective actions, attend to complaints, etc. thereby providing data to patients quickly & efficiently and also reducing the hassle of visiting & wait times at the doctor’s office
About the Problem
The customer is a provider of a secure, cloud-based platform that leverages AI and augmented analytics to directly solve the problem of physician burnout, due to an outdated user experience and increasing cognitive burden on healthcare providers due to EHR.
To be able to eradicate this cognitive burden, reduce stress & to enable the healthcare workers to provide high quality service to their patients, we helped our customer build a product that not only records & documents EHRs using a state of the art AI Engine, but also provides an interface that healthcare providers can use to see patient history, schedules, follow ups, etc. which creates a complete package.
The Automated EHR Generation AI Engine continuously listens to patient conversations with healthcare providers (in-person or digital). The engine then analyses the conversation & creates the Electronic Health Record (EHR), in addition it also creates notes, reminders, medicine prescriptions with dosage & other details. The Patient’s medical report, history, lab reports are available for physicians to review.
While the AI Engine analyses the conversation it latches onto every word and makes detailed notes of the conversation & creates the EHR:
It is also a medium to have the patient’s EHR available in one place including:
Once the EHRs have been generated, they are marked ready for review for the healthcare provider. The healthcare provider then just has to go through the various sections of the EHR already pre-filled to make sure everything is in order. Make changes or adjustments, assign tasks to nurses or other healthcare providers and finally approve & save the EHR.
The platform also provides reports & dashboards for the healthcare providers to analyse the EHRs & provide meaningful data points to take corrective actions, attend to complaints, etc. thereby providing data to patients quickly & efficiently and also reduce the hassle of visiting & wait times at the doctor’s office.
Under the Hood
The AI Engine’s efficiency is as good as the training data that is being supplied to it. The more training data that the AI Engine can consume, the better the productivity & efficiency of the EHR system.
Based on the changes & edits done by the healthcare providers before approving & saving the EHR, the changes are recorded and fed back into the AI Engine as training data. This AI Engine is thus a self learning engine which generates its own training data. Every month, the training data is overhauled & reviewed by subject matter experts to keep upgrading the efficiency of the AI Engine.
Optimistic concurrency control (OCC), also known as optimistic locking, is a concurrency control method applied to transactional systems. In this blog, we talk about implementing concurrent data updates with Optimistic Locking.
Explainable AI is a key concept in Machine Learning/AI to explain why your model is making the predictions. It helps us understand how good a model is. In this blog, we cover how you can use a game theory-based method called Shapley Values to explain what's happening inside the ML model.