The healthcare sector has been dependent on technology for years now! From the addition of surgery tools and clinical machines to test robots, technology has changed and defined each era of the medical revolution.
With the changing times and technology, medical practices have brought one single digital record from dozens of paper-based medical records. Furthermore, with the advancements in the new-age technologies of AI and machine learning, EMR development has become the need for many medical facilities to build structured and transparent medical practices.
In this blog post, we will learn the benefits Artificial Intelligence and machine learning can add to Custom EMR Development.
Applications of AI/ML in Electronic Health Records
Applications of artificial intelligence and machine learning in electronic health records that can be expected in the near future are:
- AI-Based New Drug Development
AI and machine learning algorithms are already helping pharmacists in the development of new drugs at a faster rate. These technologies can further help healthcare providers by directly integrating these applications into EHRs. These new drugs can then be suggested by AI, depending on the diagnosis and treatment plans.
- Clinical Decision Support
AI makes its prediction by studying data and identifying patterns. This way, these technologies can help healthcare providers make better decisions by providing clinical decision support during the entire care plan.
- Risk Stratification
Risk stratification has immensely helped healthcare providers in resource and equipment allocations. Further application of AI and machine learning in these strategies can improve the results significantly and offer a more personalized approach to patient care. It will reduce the burden on healthcare providers by directly integrating them into the records.
- Personalized Treatment Algorithms
Personalized treatment is something that has become a growing need of people. AI and machine learning algorithms trained on extensive data are today assisting doctors by allowing them to offer better and personalized treatment.
- Improving Medical Quality and Safety
AI and machine learning’s application in the healthcare system can help improve the quality of the medical services provided by healthcare providers. By offering more accurate predictions in medical imaging, diagnosis of complex medical data, etc., it has already helped in improving the overall safety of care delivery and medical decisions.
The Benefits of AI and ML in EHRs
The complex and unstructured healthcare industry has been systematically compiled into a single digital sheet in the form of electronic health records or EHRs. Integration of artificial intelligence and machine learning in these records can benefit the delivery of healthcare in a holistic and personalized manner.
Here are some of the benefits of AI and ML in EHRs:
- Medical Image Analysis:
Machine learning frameworks like Generative Adversarial Networks with generative AI techniques are helping the healthcare industry in creating higher-quality medical images. By improving the resolution of the images and efficiently training with AI algorithms it can help healthcare professionals in better image analysis and provide quality care.
- Natural Language Generation
Generative AI applications in natural language generation in electronic health records could further enhance communication between care teams and improve care coordination by giving a brief summary of a patient’s entire medical records. This will allow healthcare providers to easily navigate through the situation and develop a personalized care plan with faster and better patient outcomes.
- Predictive Analysis and Decision Support
Predictions and decision support are gaining importance as the number of chronic conditions arises. Integrating AI models into the EHR systems can assist doctors through real-time predictive analysis of critical conditions of patients to make decisions. Furthermore, its integration into the system will also help in generating personalized care plans completely based on their conditions and medical history.
- Automated Clinical Documentation
Automated clinical documentation uses Artificial Intelligence and Natural Language Processing to automatically generate clinical notes and reports from patient encounters. Integration of these in EHRs can help reduce the burden on providers by eliminating the clinical documentation process. This enhances patient care by providing better results at a faster rate.
- Population Health Management
Population health management depends heavily on data as it is a data-driven approach. The integration of AI in this can help healthcare providers to achieve better results at a faster rate. It uses various tools and can directly show the readings in the EHR, which will further allow timely intervention.
- Interoperability
The proficiency of the healthcare system depends on the smooth exchange of information. Here, EHRs need to be interoperable, but since every healthcare facility functions in its own way. Here, AI and ML can help systems to easily integrate information of similar patterns and structure them for other systems to communicate effectively.
What is the Limitation in the Adoption of AI and ML in Custom EMR Development?
Artificial Intelligence and machine learning have shown us the potential to revolutionize the healthcare industry with its integration with custom EMR development. However, there are some factors that are acting as major obstacles to its adoption throughout a healthcare setting.
Here are some limitations in the adoption of AI and ML:
- Limited Data: Training AI and ML requires large amounts of data, which at the moment is very structured in the modern-day healthcare system.
- Lack of Standardization: EMR adoption is still finding its way to every healthcare facility. However, the lack of standardized structure in data formats and coding systems makes it difficult for AI and ML to train effectively to provide better and more accurate results.
- Regulatory Challenges: Before the deployment of AI and ML models, they will have to go through various regulatory measures. This process is usually slow and can be costly if proper measures are not followed or implemented.
Conclusion
Artificial intelligence and machine learning are the future of the human race moving forward. Its development and advancements in the healthcare system have further enhanced delivery and made it more efficient. However, integrating these models in custom EMR development can further streamline the healthcare process and make personalized and holistic healthcare delivery the new norm of the 21st century.
