Machine learning models, referred to as EHRs (electronic health records) or EMRs (electronic medical records), help clinicians better manage patient health records through population management, diagnostics, and smart documentation. Clinicians also use these EHR data-based models to perform other clinical tasks.
EHR management places a heavy burden on clinicians: studies have shown that over half of a clinician’s workday can be spent on their use! The massive amount of data that EHRs require cause disruption of workflows and have limited interoperability. They also suffer from data overload when the models are not optimal. However, machine learning-based models continuously improve, helping clinicians spend less time maintaining records.
Data utilized in EHR systems includes all kind of clinical data: administrative and billing data, patient demographics, progress notes, vital signs, medical histories, diagnoses, medications, immunization dates, etc. However, artificial intelligence models go further, using the data above to analyze clinician preferences and patient feedback to improve the system.
A good machine learning model should be trained with large amounts of data from many different EHR systems. These include reports involving patient treatments, the outcomes of those treatments, and the equipment used during a patient’s visit. Additional data may include demographic information on patients.
External tools like predictive analytics and language processing also help system management and improvement.
Further external data to use in a good EHR model includes feedback forms from the Consumer Assessment of Healthcare Providers and Systems (CAHPS). Similar data from accredited health organizations are also useful.
As mentioned, EHR management imposes challenges in data input and overload as well as interoperability difficulties.
In addition to the vast amounts of EHR data, clinicians question which goals the AI-powered EHR management system should aim for. In the same vein, clinicians question which tools are most likely to help the systems reach their goals.
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