The key benefits of AI in Electronic health records are improving productivity, providing better-personalized care, and facilitating support for decision-making.
Medical imaging advances and the proliferation of clinical diagnostics and screenings generate large amounts of health data. In large, integrated healthcare delivery systems, EHRs are often regarded as inflexible, difficult to use, and expensive to configure. EHRs cannot capture data about care procedures, patients, administrative processes, etc
In order to improve this misalignment, there are limited options for bridging this gap between systems and procedures. EHR systems that are easily integrated and streamlined can help in this regard because they fit better into workflows. It is time-consuming and difficult to develop such systems. Since open-source EHRs are free, they can provide some respite in such situations. It will require a good deal of programming and infrastructure to provide customized EHR systems again. Due to their design for small medical practices, these open-source systems are imprudently maintained.
Unlike the above two options, AI in EHR is more promising since applying AI can make the EHR systems more flexible and incisive. These systems can be made more physician-friendly with the help of AI.
The Benefits of AI in EHRs
EHR systems powered by AI seamlessly integrate and offer a variety of functionalities. Natural Language Processing (NLP) and machine learning can be used to record the medical experiences of patients, organize large EHR data banks, gauge patient satisfaction, etc. Healthcare providers can utilize machine learning models combined with natural language processing to transcribe speech from voice recognition systems into text.
Large volumes of patient data can be used to train the algorithms, and they can be segmented based on the patient, illness, treatment for illness, etc. By doing this, large databases will be able to search for documents and information more efficiently. In addition to transcription and document searching, machine learning and predictive analytics models also provide healthcare providers with analytics on patient satisfaction and risk prediction.
Applications of AI in electronic health records
Documentation of clinical findings: With the help of AI, healthcare companies are developing NLP-powered tools that integrate with EHRs to capture clinical notes, providing physicians with more time to focus on their patients and treatment plans.
Data extraction: With AI, healthcare providers can extract patient data from various sources, such as fax, clinical data, provider notes, etc.
Predictive analytics: Big data models will alert physicians to potentially lethal diseases. As well as providing decision support and treatment strategies, AI can also enhance medical image interpretation algorithms integrated into EHRs.
