Artificial intelligence in recent months has captured the world’s imagination all because of ChatGPT. For the very first time, many of us could actually interact freely with an intelligent machine that in seconds gave us the answers we were looking for. However, AI has been around for quite some time and no industry has been able to be immune to its power, AI in healthcare is no exception.
AI in healthcare has numerous applications but for small and medium medical practices it is the value it delivers when AI is integrated into EHR systems.
In this article, I would like to focus on both the opportunities and challenges of integrating AI in EHR systems. iTech has its own EHR product called RehabOne which has been adopted by many medical organizations in the US.
Why Healthcare Organizations cannot ignore the opportunities that AI provides
Many healthcare organizations have implemented some form of AI integration into their EHR systems, but the specific use cases may vary depending on the organization’s size, resources, and priorities.
Here are a few examples of AI applications that have been implemented by medical practices
Growing Patient Data: With the increasing number of patients and corresponding data, EHR systems become more complex and require significant time to analyze, interpret, and report. AI can help manage large amounts of EHR data and automate tasks that were previously manual, allowing for a more efficient analysis and faster turnaround time.
EHR data extraction: Electronic Health Records (EHRs) contain a vast amount of information about a patient’s medical history, treatments, and outcomes. Document processing using AI in EHRs involves using machine learning and natural language processing techniques to extract and analyze information from EHRs to improve healthcare outcomes.
AI algorithms can extract data from unstructured EHR documents, such as clinical notes, discharge summaries, and radiology reports. This information can then be used for analytics, research, and clinical decision-making. DocExtract from iTech uses AI and Natural Language Processing (NLP) to extract information from physical documents and categorize and store information digitally.
Cost Savings: AI technologies can help healthcare organizations reduce costs associated with administrative tasks, errors, and unnecessary tests or procedures.
Improved Efficiency: AI can help automate routine tasks and reduce the workload of healthcare providers. This, in turn, can lead to improved efficiency and productivity in the healthcare system. Virtual assistants and chatbots can help patients access healthcare information and services more easily. For instance, a chatbot can schedule your appointment with a doctor.
The more advanced applications of AI in EHR
Clinical Decision Support: Some medical practices are using AI-powered clinical decision support systems to provide real-time guidance to healthcare providers. These systems can suggest treatment options, identify potential drug interactions, and alert providers to potential risks or complications.
Enhanced Diagnosis: AI technologies, such as machine learning and natural language processing, can help improve diagnosis and treatment plans. By analyzing medical records, test results, and symptoms, AI can provide healthcare providers with insights to make more informed decisions.
Many medical practices are using NLP to analyze unstructured patient data, such as clinical notes or radiology reports. NLP can help providers to extract valuable insights from this data and make more informed decisions about patient care.
Reducing Errors and Improving Safety: AI algorithms can be trained to identify potential errors, such as drug interactions, incorrect dosage, or improper diagnoses. AI can also detect anomalies in vital signs or medical history and alert healthcare providers in real time, leading to a reduction in medical errors and improved safety.
Personalized Treatment: AI algorithms can help healthcare providers personalize treatments and therapies for individual patients. By analyzing a patient’s medical history, genetics, lifestyle, and other factors, AI can suggest tailored treatment plans for better patient outcomes.
Overall, integrating AI into Electronic medical record systems can help healthcare organizations to provide more accurate, efficient, and personalized care, leading to improved patient outcomes and reduced costs.
AI Challenges in Implementation
In the United States, the integration of AI into EHR systems is an area of active research and development. There is significant potential for AI to improve healthcare outcomes and reduce costs, but there are also a number of AI challenges that must be addressed to fully realize these benefits.
One of the main challenges in the US is the fragmented nature of the healthcare system, which can make it difficult to integrate AI into EHRs. EHR systems are often siloed within individual healthcare organizations or networks, making it difficult to share data and develop interoperable AI tools. Addressing this challenge will require efforts to improve data sharing and interoperability across the healthcare system.
Read about how iTech created an integration layer for a medical practice legacy system for interoperability.
Another challenge in the US is ensuring patient privacy and security in AI-enabled EHRs. As AI tools become more prevalent in healthcare, there is a risk that sensitive patient data could be compromised. Regulations such as HIPAA (Health Insurance Portability and Accountability Act) are in place to protect patient privacy, but additional measures may be necessary to address the unique risks posed by AI.
The regulatory landscape in the US can also present challenges to AI integration in EHRs. AI-enabled tools may be subject to FDA regulation, and compliance with these regulations can be complex and time-consuming. Additionally, AI tools may need to be validated and tested to ensure their safety and effectiveness, which can add to the time and cost of implementation.
Finally, there is a need to address the skills gap in the US healthcare system. Many healthcare providers may not have the necessary training or expertise to effectively use AI-enabled EHRs, which can limit the adoption of these tools.
Addressing this challenge will require efforts to provide training and education to healthcare providers, as well as the development of user-friendly AI-enabled EHR interfaces.
What is involved in AI Integration into EHR for small and medium practices
Integrating AI into EHRs can be a complex process, but there are several steps medical practices can take to make the transition smoother.
Choose the Right AI System: Medical practices should select an AI system that fits their needs and budget. This could include cloud-based solutions that offer low upfront costs and scalable infrastructure.
First, assess your needs and identify areas where AI can provide the most significant benefits. This could include automating routine tasks, analyzing patient data, or improving diagnosis and treatment.
Ensure Compatibility: Do remember that integrating AI into existing EHR systems will depend on their level of maturity. Integration may require additional software or hardware, which should be taken into account when selecting an AI solution.
Develop Data Management Strategy: AI relies heavily on data, and any organization should develop a data management strategy that addresses data quality, privacy, and security. This could include regular data backups, data encryption, and access controls.
Train Staff: Smaller practices should provide staff with training on how to use the AI system and EHRs effectively. This could include providing access to tutorials, training sessions, and user manuals.
Test and Validate: Smaller practices should test and validate the AI system before deploying it in a live environment. This could include running simulations or using test data to validate the system’s accuracy and functionality.
If you are a medical practice or a healthcare organization looking to implement AI in EHR systems then the iTech AI platform can help you to do that. Know more about our services by booking a free demo with our senior team.
Biju Narayanan
Biju is an emphatic people management leader and works by the vision that change is the door to new opportunities and innovation. As Director, he has been guiding iTech on a path of innovation for over 19 years. iTech is a full-service custom software company with a large portfolio of successful domestic and international projects including Fortune 500 organizations. Biju specializes in the healthcare, sports and logistics industries with particular focus on AI and ML. Outside of work, you may find him hitting a lethal jump smash on the badminton court and he is also a creative artist.
