Electronic health records don’t just collect and store patient information but also provide insights that can help healthcare professionals improve healthcare in several ways. Despite so many benefits, detailed documentation requirements, including regulatory, billing, and revenue cycle requirements, increase the hassles for healthcare providers and make their job more complex.

An answer to this problem is combining Artificial Intelligence with EHR. AI reduces tedious administrative work associated with taking notes and helps healthcare experts provide better treatment based on accurate analysis.

Documentation Using Natural Language Processing

As per American Medical Association, administrative work consumes 50% of doctors’ time. This not just drains them but also leads to dissatisfaction, work-life imbalance, burnout, and errors. 

Natural language processing (NLP) enables automated capturing of clinical notes that reduces the admin work, giving doctors more time to focus on patients. Speech-to-text technologies powered by Artificial Intelligence can document patient problems, diagnoses, and procedures through voice-based commands. 

AI-powered EHR solutions even allow doctors to convert their narratives into actionable information for real-time decision-making. 

Rapid extraction of patient information for faster diagnosis and treatment

For faster diagnosis and decision-making using EHRs, the following things must be ensured:

• Patient data should be easily accessible.
• It should be clear and easy to construe for clinicians.

But sorting through large amounts of EHR data and picking the right information can often be challenging. 

AI-enabled EHR addresses these challenges by allowing the experts to rapidly access, extract, and export patient information.

EHRs are complicated; AI makes them more user-friendly by improving data discovery and extraction.

Google Cloud’s Healthcare Natural Language API uses AI to help clinicians extract the most pertinent patient information they need from the stacks of medical records. Using Machine Learning, it identifies clinically relevant attributes from the medical records.

Achieving EHR interoperability using AI

Interoperability is the best way of achieving the complete potential of EHRs. It enables better-coordinated care at lower costs.

However, achieving interoperability between different devices is not easy as there is fragmentation among different vendors. Some EHR providers cater to large hospitals; some cater to small physician practices. 

In the absence of interoperability, there are always chances of critical information getting lost when shared digitally with multiple health providers, impacting the quality of care and patient outcomes.

AI and ML can solve interoperability issues in clinical documentation. It can collect large amounts of data from clinical notes and faxes. AI algorithms aid in timely diagnosis and identify patients with serious medical conditions. It can also enhance the accuracy of clinical decisions.

Using Predictive Analysis for Better Decision-Making

Artificial Intelligence integrated with EHR enhances patient care in the following ways:

• By identifying risks.
• Evaluating health conditions.
• Book auto appointments and even call emergency services. 

Artificial Intelligence is even capable of correlating every test result and draw conclusions using predictive analytics. Prediction models using big data can be used to warn clinicians of high-risk conditions such as heart failures and sepsis.

Data from wearables can also be directly integrated into EHR to perform real-time predictive health analysis using AI.

Data Visualization

Medical records can get vast over time, even for someone with normal health. Large data sets present great challenges for generic EHRs. With AI, it becomes very easy to traverse even large stacks of data. With the help of advanced capabilities, such as – spotlight, one can fetch the required data in seconds. 

Besides the spotlight feature, normal dataset visualization and correlation can be automatically enabled with a pre-implemented AI. 

In addition to all the above benefits, AI-enabled EHRs keep patient health records more secure and safe by ensuring zero data leaks. A standalone EHR system cannot guarantee such a high level of security. 

Wrap Up

EHRs have been highly beneficial in enhancing the quality of patient records and healthcare in general. With AI bolstering the EHRs, several new possibilities for improving healthcare can be unlocked.