Database architecture is nearly thirty years old and forms the basis of major EHRs. AI integration with EHR records will unlock the full potential of electronic health records.

Healthcare has a lot of data. With the rise of patient claims, social media and EHR data, the healthcare industry's database is overwhelmed by the amount of information. An increase in database size leads to a more complex situation. The underlying data is becoming less structured, increasing the industry's challenges.

The Significance of an EHR in the Medical Industry around the World

The HITECH Act 2009 introduced electronic health records across the entire healthcare system. This helped to improve data usage by medical providers. Incentives to drive hospitals and clinics to move from paper charts to EHRs were provided by the Act, which was worth $36 billion.

These benefits are well-established and EHR was a crucial tool for the industry. The U.S. has a high use of it. Below is an image that shows the percentage of Americans who have accessed electronic health records.

Even more surprising is the fact that the global markets for HER, which were $ 24.7 billion in 2018, is projected to rise to $ 36.2 million in 2025.

While it is clear that the tool has significant benefits, the tool itself can be problematic. The EHR records are causing a host of problems for clinicians, disrupting their workflow, overloading data and limiting interoperability. EHR records are also a major cause of physician burnout.

According to the American Medical Association (AMA), RAND Corporation's research, EHR was a major cause of physician dissatisfaction and emotional fatigue. Christine Sinsky, ex-vice president of AMA, stated that EHR was not to blame for physician burnout. However, the EHR has allowed others to put new demands on doctors and their practices. Technology is the best way to unlock the full potential of EHR data.

This is where AI and machine learning can be a game-changer in the industry.

How AI & EHR can Enhance Better Healthcare

EHR can be viewed as a treasure trove of information. This includes medical history, treatment plans and medications. However, if this information isn't properly harnessed it can threaten the integrity of EHR. AI and other advanced technologies have the ability to decode electronic information to improve healthcare services.

AI is a powerful tool in diagnosing and medical imaging analysis. These tools can use similar data to make recommendations to doctors and create unique treatment plans.

NorthShore University Health System: A comprehensive healthcare delivery system serving the Chicago area has created the Clinical Analysis Predictive Engine tool within the EHR. This tool assigns each patient a risk score that is tied to multiple predictive models.

Frownfelter is an internationally recognized practitioner and consultant who identifies these as some of the most recent developments in clinical medicine. She stated that "Clinical Prescriptive Analytics is the closest AI to supporting the direct patient care in 2019".

AI can significantly improve the capabilities of EHR

Diagnostic algorithms: Google collaborates with delivery networks to build prediction models using big data. This allows clinicians to be alerted of potentially dangerous conditions like heart failure and other diseases. AI-derived image interpretation algorithms are being developed by many organizations. They also offer the machines that can identify patients at higher risk or those most likely to respond to treatment protocols. This allows for the best patient care possible.

Decision support: Recently, machine-learning solutions have emerged from vendors like Allscripts and Change Healthcare. AI's main goal is to improve the discovery of data that can be used to provide personalized treatment recommendations. This is a crucial goal because EHRs are complex and difficult to use. AI and machine learning are helping EHRs to adapt continuously according to the preferences of the user, improving both the clinician's quality of life and the clinical outcome.

Leveraging data: It was also found that AI solutions provide the human service and the health department with insights that can improve health and decrease substance abuse disorders. Data can be used to connect clinics, hospitals, and community-based organizations, providing a complete picture of each individual and their needs. Research has even indicated that artificial intelligence is the future of data analytics.

Improving interoperability: The problem of data interoperability is a significant one for clinicians around the world. Implementing digital records has not helped either, as many vendors are unable to exchange patient information. Providers have difficulty using EHR to improve patient care.

CCM (Center for Connected Medicine) conducted a survey and found that a third of hospitals and systems felt that interoperability efforts were not adequate even within their own health organizations. Although AI and other advanced technologies are not widely used in the current environment, it is believed that AI technology could improve interoperability and allow for greater advancement.

Flexibility Using AI in the system can help make the EHR Software more flexible and intelligent. EHRs that are currently using AI have many capabilities, such as:

Data extraction from the freetext: Providers can now extract data from faxes with the most recent AI tools. Amazon Web Services recently announced a cloud-based service, which uses AI to extract and index data from clinical notes.

Entry and documentation of clinical information: The NLP captures the clinical notes and allows clinicians to be more focused on their patients and not on their keyboards and screens. AI-supported healthcare solutions may offer AI-supported tools to integrate with commercial EHRs, as well as supporting clinical note composition and data collect

Capturing conversations between physicians and patients:

Saykara, a Seattle-based start-up launched Kara in 2017. The iOS app uses machine learning, voice recognition, language processing, and machine learning to capture the conversations between physicians and patients and turn them into orders, notes, and diagnoses in the EHR. Physicians can also dictate documentation using the Athena Health mobile app. The app will translate the text into appropriate billing codes and diagnostic codes. Artificial intelligence is a tool that can assist clinicians in making better and more sophisticated decisions.

Future EHRs

Future EHRs will integrate telehealth technology. As healthcare costs rise and new delivery methods are being developed, many glucometers and blood pressure cuffs that are used in residential areas would automatically measure and send results to the EHR. This would quickly gain momentum. The electronic report of patients and personal health records is being used to highlight the importance of patient care center as well as the self-management of diseases. All these data sources can be used when integrated into an existing EHR. The future of the medical industry looks brighter with the implementation of AI-electronic healthcare records.

Conclusion

A new type of EHR is necessary for the real transformation of medical industry. Database architecture is nearly thirty years old and the foundation of major EHRs. You can say columns and rows of information. When AI is integrated into the EHR records it will unlock the full potential of electronic medical records.

Acceptance of EHR is increasing. However, it is important to involve staff in the EHR process for safer and better treatment. EHR has unlimited potential. To maximize the quality of patient care at major hospitals in the country, it is important to use the right techniques and solutions to get the best results. AI-powered electronic medical records systems seamlessly integrate and offer multiple functionalities.

NLP, together with machine learning, helps to record the medical experience of patients. It organizes large electronic health records data banks for patient satisfaction and allows you to find important documents. The ML can be combined with NLP to aid healthcare providers in putting speech from voice recognition into the system. It is possible to train algorithms on large amounts of patient data, such as equipment, doctors, and so forth. AI improves document and information searches from the large electronic health record database.