One of the key feasibility considerations is the availability of data. The success of this project will rely heavily on having access to a large and diverse dataset of medical faxes that can be used to train the AI LLM model. If access to sufficient datails limited, it may impact the accuracy and effectiveness of the solution.
There are several regulatory and legal considerations that were considered for processing sensitive medical information which ensure HIPAA regulations. PHI must always beprotected; therefore, you must account for anonymizing patient data beforesending it to the AI engine. Besides, all other HIPAA procedures, likeencryption, secure connections, authentication, access rights, and expiringsessions, will add to the development budget.
However, if AI analysis happens on the device without syncing to the cloud, such data de-identification might be unnecessary. As long as all other HIPAA-relatedprecautions are in place, there’s no need to anonymize PHI.
Develop algorithms or AI modules that continuously monitor the AI system's outputs and comparethem to predefined standards or expected outcomes.
Set up automated alerts for when discrepancies or errors are detected during these audits.
Model inaccuracy, conflicting information, and wrong responses will have potential affects on patientcare.
We want to ensure that we are not implementing a bad model, which would be one that falsely categorized or mislabels information which could
affect the retrieval of medical records within the Electronic Health Record (EHR) systems.
It’s important to understand the cost of the model if it wrongly mislabels or miscategorizes information. To mitigate this, implementation of a continuous refinement process would be ideal.
Therefore, a robust system must be in place to ensure adhesion to well defined rules and
Potentially delaying decision-making or causing frustration. If the AI provides
different answers for the same clinical question, it could impact the decision-making process for clinic staff. Inconsistencies in medical summaries could lead to suboptimal patient care. In a clinical setting, inconsistency in AI responses could disrupt established workflows and processes. Clinic staff may need to spend extra time reconciling conflicting information, potentially increasing their administrative burden.
To mitigate this, assign confidence scores to AI responses. If the model is uncertain about an answer, it can indicate this to users. Human reviewers can also prioritize reviewing low-confidence responses.
A single wrong response from the LLM could lead to mislabeling, misplacement, or
misinterpretation of a faxed document. Consequences may include delayed patient care, errors in medical records, and potential legal or compliance issues.
To mitigate, encourage clinic staff to report and correct any errors, creating a feedback loop for improvement. Continuously train and fine-tune the LLM using a diverse dataset to improve accuracy.