Using AI to Improve Electronic Health Records

Large, integrated healthcare delivery networks’ electronic health record systems are frequently seen as monolithic, rigid, challenging to use, and expensive to configure. Making delivery networks support clinicians rather than hinder them becomes more difficult as they expand and deploy wide enterprise Electronic Health Records software platforms. Making existing EHR systems more adaptable and intelligent through the use of AI is a promising strategy. Some delivery networks are moving in this direction by utilizing AI to help with clinical decision support, clinical documentation and data entry, and data extraction . In the end, AI should assist physicians in customizing EHRs. To their unique requirements and working methods, making them simpler to use and more useful during the care process. That might lessen clinician burnout and enhance patient care.

Making delivery networks support clinicians rather than hinder them becomes more difficult as they expand and deploy wide enterprise medi emr platforms. It is uncommon for EHRs to be able to efficiently capture all of a clinician’s knowledge or make it readily accessible because clinicians’ knowledge extends far beyond their clinical domain. Examples include knowledge of care procedures, patient context, and administrative process. Additionally, regulatory, billing, and revenue cycle requirements in the US make the electronic healthcare workflow even more complex and cut down on the amount of time clinicians have to interact with patients. You have three options regarding this;

Option #1-AI

There aren’t many ways to fix this misalignment of systems and processes. One is to incorporate more integration and streamlining into the design of EHR systems from the start. A concierge medical practice in the U.S. cities, developed its own EHR system. That is very compatible with the methods it uses to provide care and build patient relationships.

Option#2-AI

Another choice is to use an EHR that is open source. However, the majority of them today are made for small medical practices, aren’t easily scalable, and require a lot of configuration. Even though the software is free, it requires a significant amount of programming and IT infrastructure to implement and customize it to the specific practice. Open source EHRs are also less frequently updated and less carefully maintained than commercial ones. Which means they can quickly become outdated. Finally, reimbursement guidelines and regulatory requirements are subject to rapid change. Relying on either internally developed or open source systems. To meet those requirements introduces compliance risks as well as financial difficulties.

Option#3-AI

The third and most promising choice is to use AI to enhance the adaptability and intelligence of current EHR systems. Some delivery networks are moving in this direction, sometimes working together with the provider of their EHR platform. Although EHRs’ AI capabilities are currently quite limited, we can anticipate them to advance quickly. They consist of:

Data Extraction

Taking data by using the EHR providers can already extract data. Human “abstractors” review provider notes and extract structured data . While using AI to help them identify key terms, gain new insights, and work more efficiently. A cloud-based service that uses AI to extract and index data from clinical notes.

Algorithms for Diagnosis

In order to alert clinicians of high risk conditions like sepsis and heart failure, Google is working with delivery networks to develop prediction models from big data. Startups like Google, and many others are developing AI-derived image interpretation algorithms. A “clinical success machine” that identifies patients. Who are most at risk and those who will respond to treatment regimens the best. Each of these could offer decision support by being integrated into EHRs.

Clinical Data and Documentation

Natural language processing allows clinicians to capture clinical notes while allowing them to concentrate on patients rather than keyboards and screens. Nuance provides commercial EHRs with AI-supported tools that support data collection and clinical note creation.

Clinical Decision

In the past, decision support, which suggests treatment strategies, was general and rule-based. Today, machine-learning solutions from vendors, change Healthcare. These solutions learn based on new data and enable more individualized care.

Natural language processing, machine learning for clinical decision support, integration with telehealth technologies, and automated imaging analysis are among the new features being added by companies .Although it will probably happen gradually, this will offer integrated interfaces, access to the data stored within the systems, and many other advantages.

AI has a lot of potential to make EHRs more user-friendly.

Even though it is primarily used in EHR systems to improve data discovery and extraction and personalize treatment recommendations. This is a crucial objective because EHRs are cumbersome, challenging to use, and frequently blamed for contributing to clinician burnout. EHRs can currently be largely customized manually, and the rigidity of the systems makes progress in this area difficult. AI, and in particular machine learning, could assist EHRs in continuously adapting.

To be useful, all of these features must be tightly integrated with EHRs. The majority of today’s AI options are “encapsulated” as standalone products, which don’t offer as much value as integrated ones and force time-constrained doctors to learn new user interfaces. But to make their systems simpler to use, well-known EHR vendors are starting to incorporate AI capabilities.

Wrap up- AI

Future EHRs should be created keeping telehealth technologies in mind as well as the EHR. Home devices like glucometers or blood pressure cuffs.  That automatically measure and send results from the patient’s home to the EHR are gaining popularity. As healthcare costs rise and new healthcare delivery methods are tested. Some businesses even offer more sophisticated products, like the smart t-shirts, which are used in clinical studies. And at-home disease monitoring and can measure a number of cardiovascular metrics.

As healthcare providers stress the significance of patient-centered care and self-management of diseases. Electronic patient reported outcomes and personal health records are also being used more and more. All of these data sources are most helpful. The majority of delivery networks will most likely opt for a hybrid approach. Waiting for vendors to develop AI capabilities in some areas and relying on outside or internal development. For AI offerings that enhance patient care and provider productivity. But they probably won’t have the option of starting over. However necessary and desirable, it appears likely . That it will take many years for the transition to significantly better and smarter EHRs to be fully realized.

Get tips to boost Revenue of your medical practice with EHR by reading previous blog.