Revolutionizing Healthcare: The Benefits of AI in EHR


Key Takeaway:

  • AI has the potential to significantly improve healthcare through advanced AI-powered EHR software.
  • AI can extract valuable insights from medical records, standardize data entry, and improve patient outcomes while reducing healthcare costs.
  • AI can also help healthcare providers make better-informed decisions through clinical decision support and analysis of large data sets.
  • However, proper implementation and collaboration between healthcare firms and data scientists is necessary to realize the full potential of AI in EHRs.

Electronic Health Records (EHRs) have become an essential tool for healthcare providers, helping to improve patient care and streamline administrative tasks. As healthcare continues to evolve, the use of artificial intelligence (AI) in EHRs has emerged as a potential game-changer.

With a CAGR of 29%, the healthcare AI industry is expected to grow from its 2022 forecast of $5 billion to $70 billion by 2032. Dissatisfaction with overly complicated and ineffective EHRs and the need to gain actionable insights from stored data are driving the demand for AI-powered EHRs.

AI in EHRs can help healthcare providers to analyze data more efficiently, identify patterns, and predict potential health issues. In this blog post, we will discuss the benefits of using AI in EHRs and how it is transforming healthcare.

What are EHRs?

Electronic Health Records (EHRs) are digital versions of patients’ medical records. They contain all the information that healthcare providers need to provide care to patients, including medical history, allergies, test results, and diagnoses. EHRs can be accessed securely by authorized personnel, making it easier for healthcare providers to share information across different facilities.

Why AI in EHRs?

AI-powered EHR systems provide solutions with a range of features and allow smooth integration. The recording of patient medical experiences, the organization of sizable EHR data banks for the discovery of crucial records, the assessment of patient satisfaction, and other tasks can all be aided by machine learning and Natural Language Processing (NLP).

In order to convert voice recognition system speech into text, healthcare professionals can use machine learning models along with NLP. The algorithms can be properly separated based on the specific patient, sickness, treatment for illness, etc., and trained on a huge amount of patient information on the patient’s treatment, equipment utilized in treatment, respective healthcare professional, etc.

This will improve the ability to find information and documents in the sizable databases. Not only do machine learning and predictive analytics models give healthcare providers with data on patient satisfaction or aid in patient risk prediction, but they also enable medical transcribing and document search. These AI-based EHR system applications are generally categorized and succinctly described below.

Use Cases of AI for EHR

The use of Artificial Intelligence (AI) in Electronic Health Records (EHRs) has the potential to transform healthcare, providing healthcare providers with real-time insights that can help improve patient outcomes and reduce healthcare costs. Here are some of the key use cases of AI for EHRs:
  1. Predictive Analytics: AI can analyze patient data, identifying patterns and predicting potential health issues. For example, data analytics can help identify patients who are at high risk of developing chronic conditions, such as diabetes, heart disease, or hypertension. This can allow healthcare providers to intervene early, potentially preventing or delaying the onset of these conditions.
  2. Clinical Decision Support: AI can provide healthcare providers with real-time information, such as medication interactions or potential diagnoses. This can help healthcare providers make better-informed decisions, leading to improved patient outcomes. For example, AI can alert healthcare providers to potential drug interactions or adverse reactions, helping to prevent harm to patients.
  3. Natural Language Processing: AI can help healthcare providers to extract information from unstructured data, such as physician notes. This can help improve the accuracy of patient records, leading to better clinical decision making. For example, AI can analyze physician notes to identify potential health issues that may have been missed or overlooked.
  4. Image Analysis: AI can analyze medical images, such as X-rays or CT scans, identifying potential health issues. This can help healthcare providers to diagnose conditions more accurately, leading to improved patient outcomes. For example, AI can analyze medical images to identify potential cancerous tumors or other abnormalities.
  5. Resource Allocation: AI can help healthcare providers to allocate resources more efficiently, focusing on those who need it most. For example, AI can help identify patients who are at high risk of hospital readmission, allowing healthcare providers to allocate resources to prevent readmission and reduce healthcare costs.
  6. Population Health Management: AI can help healthcare providers to manage population health more effectively, identifying trends and patterns in patient data that can help inform public health initiatives. For example, AI can help identify areas where specific health issues are more prevalent, allowing public health officials to allocate resources to address these issues.
  7. Patient Engagement: AI can help improve patient engagement, providing patients with real-time information and insights that can help them manage their health more effectively. For example, AI can provide patients with personalized health recommendations or reminders to take medication or attend appointments.
  8. Scheduling appointments for telemedicine: AI can help in telemedicine-integrated EHRs by scheduling follow-up appointments based on the doctor’s notes and notifying the patient app about the appointments in advance.

Advantages of Artificial Intelligence for Medical Records

1. Boost efficiency and output

Recently, AI softwares have been created that can assist healthcare professionals (HCPs) in extracting medically-relevant insights from the free text contained in, for instance, medical records or insurance claims.

Healthcare Natural Language API – One such tool, made available by Google Cloud, creates structured data representations of the medical information kept in these data sources for use in automation and downstream analysis. This extracted data may consist of:

  • Medications, techniques, and conditions are examples of medical concepts.
  • Functional characteristics like subjects, temporal linkages, and certainty assessments
  • Relations between dosage and side effects of medications

Despite the progress being made in this area, obtaining data in a standardized manner that considers the full patient journey from an integrated perspective remains a problem for AI-based technologies.

Healthcare organizations are beginning to engage closely with data analysts to determine what data is valuable and how to develop value from it, which eventually leads to value for the patient, in order to maximize AI in electronic medical records.

2. Speed up digital health

Despite the fact that many doctors find it frustrating to record patient medical information electronically and claim that the time it takes to finish entering data is time they would rather spend with their patients, they still believe that this is the future.

In some hospitals, scribes are used to attend visits and record the visit while the doctor attends to the patient. In order to create digital scribes—machine-learning algorithms that can take a dialogue between a doctor and patient, deconstruct the text, and utilise it to fill out the pertinent information in the patient’s electronic medical record (EMR)—a number of businesses are striving to build AI.

Additionally, this can assist standardize data entry, which is difficult with EMRs, and reduce the risk of practitioner burnout. For instance, the ‘notes’ field might be used to enter a strawberry allergy rather than the ‘allergies’ form, which could skew the findings.

3. Better individualized care

AI in electronic medical records can be used to spot trends and make prognoses about outcomes. This information can then be utilized to individually tailor therapies, down to the level of which doctor may be best suited to meet a patient’s needs and achieve the goals that are most important to them.

On the basis of their data and the results seen across providers, for instance, patients with pre-existing diseases that are unrelated to COVID-19 could be matched with carers who are available, particularly in the early stages of the pandemic.

If their regular doctor is unavailable due to office closures, this may enable them to skip lengthy wait periods or continue with their regular health checks. This not only gives better patient outcomes, but also makes healthcare more easily accessible to each individual.

AI can also alert clinicians about preventative exams, immunizations, or checkups, elevating personalised healthcare to a new level.

4. Decision assistance

Clinical decision support (CDS) systems based on AI are being utilized to enhance healthcare. Large data sets can be analyzed by these technologies to aid in diagnosis, direct treatment, and assess disease prognosis and progression.

Despite the fact that CDS tools have a number of benefits, because there are so many of them, it is important to carefully implement their design to ensure that they achieve their intended goal of giving HCPs less work rather than adding to it.

One thing is certain: for the CDS design and implementation process to be effective, individuals from all facets of the healthcare industry must participate.

How much does AI-based EHR cost?

Development of custom Electronic Health Record (EHR) software that incorporates artificial intelligence often has a price tag of $400,000 to $450,000 to create.

Key cost variables:

  • The complexity and planned range of AI functioning.
  • The EHR data quality thresholds.
  • ML algorithms’ complexity and anticipated accuracy.
  • The quantity of data to be processed and stored, as well as the number of data sources.
  • How many and how complicated the integrations are with other products (like patient portals or lab information systems).
  • UI and UX specifications.
  • Criteria for security.
  • Expenditures related to compliance (such as those for registering SaMD capabilities with the FDA).
  • Utilization of cloud services (such as ML tools and cloud hosting).
  • Costs associated with infrastructure, upkeep, and support.


The use of AI in EHRs has the potential to transform healthcare, improving patient outcomes and reducing healthcare costs. As AI continues to evolve, we can expect to see even more applications in EHRs, helping to improve the way we deliver healthcare.

While there are still challenges to be addressed, such as data privacy and security, the benefits of AI in EHRs are clear. The future of healthcare looks bright, with AI playing a key role in delivering better patient care.