The Role of AI and Machine Learning in EHR/EMR Systems

Electronic health records have equipped the healthcare industry with speed, authorized access, and easy data sharing. EHR and EMR systems today are being widely adopted by many healthcare institutions across the world.

Though digitization of the healthcare industry is still in its transitional stage, a new trend has emerged to change the healthcare industry forever.

Yes, you have guessed it right!

The integration of AI in major decision making across various industries, including Healthcare has the potential to improve patient care and the efficiency of clinics and hospitals.

The role of AI and machine learning in EHR/EMR systems is huge, and in this blog, we will see exactly that.

How Artificial Intelligence is Simplifying the Lives of Medical Practitioners

Do you know that zen healthcare professionals spend around 62% of their time reviewing patients’ health records?

Well, as surprising as it may sound, clinical data reviewing is one of the most time-consuming processes for many medical practitioners. But, AI in healthcare, especially in EHR systems, can help practitioners in simplifying their practices. Let’s see how:

• Automated Clinical Documentation

Natural Language Processing (NLP) is an AI-powered tool that can automate some of the most important tasks doctors spend doing. Tasks like data entry, transcribing notes, and extracting important data from medical records can be streamlined. This way, AI can reduce the monk manual entry burden for healthcare professionals and, at the same time, help them with accurate data entry in EHR system software.

• Clinical Decision Support

Integrating AI into EHR systems can help healthcare professionals analyze patient EHR data and recommend care or treatment plans. It can help doctors make better clinical decisions based on data-backed AI recommendations.

• Population Health Management

AI has the potential to analyze large amounts of data in almost no time. Through these techniques, AI can help healthcare providers identify health patterns and address the healthcare needs of common people.

• Predictive Analysis

One of the things that AI, like humans, is its predictive nature. But, since AI analyzes large amounts of data, its algorithms can easily predict patient outcomes and the risk factors that can lead to them. This enables practitioners to intervene and alter care plans according to their needs.

• Interoperability

The interoperability challenge that many EHR systems face can be easily overcome with AI. By identifying data patterns and standards, it can facilitate patient data sharing and continuity of care.

Applications of Machine Learning

Although artificial intelligence can assist healthcare professionals in providing better care, one of its applications can be the catalyst in reshaping the modern-day landscape of the healthcare industry.

Machine learning in the healthcare industry can help practitioners in minimizing human error and improve patient safety with clinical decision support. Below are some of the applications of Machine Learning in EHR systems:

• Disease Prediction

Machine learning models are trained on huge amounts of data. When integrated into EHR systems, they can help practitioners predict the risk factors for serious diseases like diabetes, cancer, and heart disease. ML models can also help with preventive measures, early intervention, and personification of healthcare real plans.

• Improved Data Discovery and Extraction

ML algorithms can be trained for data discovery, which can be hard to identify for humans. Also, these algorithms can even assist healthcare professionals in clinical research by extracting specific data from EHR systems. This could be pivotal in care delivery by predicting readmission risks, detecting diseases, and improving patient outcomes.

• Medication Adherence

EHR systems can provide healthcare professionals key insights into their patients’ medication adherence. Integrating ML algorithms in EHR systems can easily identify patients who are at high risk of medication non-adherence. This can help them develop more personalized medication plans and improve the adherence rate of patients.

• Personalized Recommendations

Integrating machine learning in EHR systems can enable them to learn and improve on their own. So, when a patient comes for a consultation, a doctor can leverage the power of ML algorithms to curate personalized care plans for the patient for better care delivery.

• Data Extraction and Interpretations

Machine learning is a powerful tool that can help extract and interpret data in EHR systems to enhance clinical research and care delivery. It can help understand patients’ health and develop personalized care plans. 

What are the Challenges in Wider Adoption of AI in EHR?

AI in EHR has benefits that can change the complete picture of the healthcare industry and the way we approach healthcare. However, many small healthcare practices and even hospitals are resistant to wider adoption of AI in EHR systems, and for good reasons.

The first and foremost concern is the cost, as AI implementation in EHR systems is costly. Additionally, technical challenges like data quality, interoperability, and lack of data standardization can further complicate its adoption. 

Ignoring all the challenges mentioned above, regulatory barriers and resistance to adoption act as major barriers to the wider adoption of AI in EHR.

Challenges in Implementing Machine Learning in Healthcare

Just like AI, implementing machine learning in healthcare also has many advantages. However, since these models need to be trained on huge amounts of data, their implementation is driven by many challenges. 

Patient data security and privacy are one of the major challenges, as training ML algorithms can compromise the privacy of crucial patient health information. Additionally, complying with the laws and regulations can complicate its implantation process.

Future Trends that Can Be Expected in a Few Years

One of the most anticipated trends is the integration of AI in wearable medical devices to improve the accuracy of data and provide personalized care delivery to patients. AI-powered virtual assistants are also a trend that healthcare professionals are waiting for to enhance their healthcare practice and reduce the burden.

There are many upcoming trends in healthcare with AI and ML, such as resource management and treatment recommendations, but only the future holds the key to their becoming a reality.

Conclusion

The role of AI and machine learning in EHR/EMR systems is huge and can potentially change the healthcare industry’s entire landscape. Recent advancements have given us an idea of how it can revolutionize the industry and play a significant role in reshaping healthcare and care delivery in the future.