Unlocking the Potential of Electronic Health Records with AI


Major EHRs are built on database architecture, which is almost thirty years old. When AI is integrated with the EHR records, it would help to unlock the potential of electronic health records to an ultimate level.

There is an abundance of data in the healthcare world, and with the emergence of useful data from the patient claims, social media, EHR, the database of the healthcare industry has been overwhelmed with the amassed information. The increased database size is giving rise to a complex scenario, which is the underlying data is getting more unstructured, thus increasing the challenges for the entire industry.

Significance of EHR in the Medical Industry across the World

Since the electronic health records got introduced across the entire healthcare system with the HITECH Act of 2009, it helped improve the data usage among the medical providers. The Act helped to provide $36 billion in the financial incentives for driving clinics and hospitals to the transition from the paper charts to the EHRs.

See also: CI Enables New Healthcare Diagnostic Capabilities

The benefits are well established, and EHR served as a critical tool for the industry. It has high usage in the U.S., and from the below image you can get to know the percentage of the US adults that have accessed the electronic health records:

It is even found that the global markets for HER, which was $ 24.7 billion in 2018 is expected to grow to $ 36.2 billion in 2025.

But, while it has been seen that you get significant benefits by using the tool, the tool itself is far from perfect. As per the findings, EHRs are giving a new range of problems, disrupting the workflow of a clinician, creating an overload of the data, and having limited interoperability. The EHR records have also been identified as the major contributor for physician burnout. The research conducted by the American Medical Association (AMA) and RAND Corporation found EHR as a leading cause of emotional fatigue, physician dissatisfaction, and depersonalization. In contrast with that, Christine Sinsky, former vice president of AMA, said: “It’s not fair to blame all the physician burnout on the EHR, but the EHR has enabled others to place new demands on physicians and their practices.” In such a scenario, technology is the best option for unlocking the potential of the EHR data.

This is where the AI and machine learning can act as a game-changer for the industry.

How AI and EHR Can Enhance Better Healthcare

In many cases, EHR is considered as a goldmine of information ranging from the medical history, treatment plans, and medications, but when this information is not appropriately harnessed, then it can put the integrity of the information in EHR highly at stake. The improved technologies such as AI have a remarkable ability to decode the electronic information necessary to improve the services in healthcare.

As AI excels in categorizing the data, it is increasingly seen as a useful tool for diagnostic purposes and medical imaging analysis. The tools can even use similar information for offering recommendations to the doctors and crafting unique treatment approaches.

NorthShore University Health System, an integrated healthcare delivery system that served the patients in the Chicago metropolitan area, has developed the Clinical Analytics Predictive Engine tool in the EHR, which assigns each of the patients a risk score tied to the multiple predictive models.

These are some of the interesting recent developments in the clinical arena, as stated by Frownfelter, an internationally known practitioner, consultant. She even stated, “Clinical prescriptive analytics is the closest AI getting to support the direct patient care in 2019.”

The capabilities of EHR which can be significantly improved with the help of AI:

Diagnostic algorithms: Google has been collaborating with the delivery networks for building prediction models from the big data. This helps to warn the clinicians of risky conditions such as heart failure, and others. Many organizations are developing the AI-derived image interpretation algorithms, and they are also offering the successful machines that identify the patient at a higher risk and those who are most likely to respond to the treatment protocols. This helps to provide the right patient care at the right time.

Decision support: In recent times, machine learning solutions are emerging from vendors such as Allscripts, Change Healthcare, and IBM Watson. The main aim of AI is improving the discovery of the data for personalized treatment recommendations. It has turned out to be a critical goal, as EHR is complicated and difficult to use. AI and machine learning have been helping EHRs for adapting continuously as per the preference of the user and improving both the quality of life of the clinician and the clinical outcome.

Leveraging the data: It has also been found that the AI solutions are providing the human service and health department with the insights that can improve the health of the population, and reduce the substance abuse disorder. Data has the potential for connecting clinics, hospitals, community-based organizations for providing the complete picture of the individual and the needs. Researchers have even stated that the trend towards the next generation technology on the data analytics spectrum is artificial intelligence.

Improving the interoperability: Data interoperability has been a major problem for clinicians worldwide. The implementation of digital records has also not helped the process either as most of the vendors cannot exchange the patient information. This makes it difficult for providers to use EHR in improving patient care.

A survey from the CCM (Center for Connected Medicine), even found that one-third of the hospitals and the systems reported that the interoperability endeavors are not sufficient even within the same health organization. Presently a limited number of organizations are using AI and other advanced technologies, but it is widely believed that AI technologies could help to improve the interoperability process and extend the right advancement.

Flexibility: Using the AI in the system could help in making the EHR system more flexible and intelligent. The EHRs which are presently using the AI has many capabilities such as:

  • Data extraction from the free text: Here, providers can extract the data from the faxes using the recent AI tools. Amazon Web Services have recently announced a cloud-based service that uses the AI for extracting and indexing data from the clinical notes.
  • Clinical documentation and entry: Capturing the clinical notes within the NLP allows the clinicians to focus on the patients than on the screens and keyboards. Healthcare solutions could offer the AI-supported tools for integrating with the commercial EHRs and supporting clinical note composition and data collection.

Capturing conversation between the physicians and patients: In the year 2017, one of the Seattle based start-up, Saykara has launched a virtual assistant Kara. This iOS app uses voice recognition, machine learning, and language processing for capturing the conversation between the physicians and patients, and turning them into notes, orders, and diagnoses into the EHR. The Athena health’s mobile app also allows the physicians to dictate the documentation. The app can then translate the text into the appropriate billing and the diagnostic codes. Thus, artificial intelligence can help clinicians in taking better, sophisticated decisions.

Future EHRs

Future EHRs would be developed with the integration of telehealth technologies. As healthcare costs rise, and there are new healthcare delivery methods that are being tested, glucometers or blood pressure cuffs used at many residential places would be automatically measuring and sending results from the home to the EHR. This process would fast gain momentum. Electronic report of the patients and the personal health records are being leveraged as it emphasizes the importance of patient care center and the useful self- disease management. These all data sources become useful when they are integrated into the existing EHR, and by implementing the AI-electronic health records, the future of the medical industry seems to be better.


The real transformation of the medical industry requires a new kind of EHR. The major EHRs are built on database architecture, which is almost thirty years old. It can be said columns and rows of information.  So, when AI is integrated with the EHR records, it would help to unlock the potential of electronic health records to an ultimate level. 

The acceptance of the EHR has become more common, and one should not forget to engage the staff in the EHR process for better and safe treatment.  The potential of EHR is limitless, and to improve the patient care in major hospitals across the country, it should be seen that the right techniques and solutions are implemented to reap the best results. AI-powered electronic health record systems effortlessly integrate and offer solutions with various functionalities. It, along with machine learning, NLP helps in recording the medical experience of the patients, thus organizing the large electronic health record data banks for receiving the right patient satisfaction, and finding crucial documents. The ML merged with the NLP can help the healthcare providers in putting the speech from the voice recognition into the system. The algorithms can also be well trained on the large volumes of the patient data like the equipment used for treatment, respective doctors, etc. Overall, AI enhances the document and information search from the extensive electronic health record database.

Robert Jordan

About Robert Jordan

Robert Jordan, a seasoned marketing professional with over 10 years of experience, currently working as Media Relations Manager at InfoClutch Inc., which provides data appending, data licensing, and many more services. He has a deep interest in AI, Big Data, and CRM-related discussions. His passion is writing. Now he works at studyclerk.com as a content writer. Always open for new ideas and discussions.

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