Artificial Intelligence (AI) & EMR: Present and Future

The healthcare industry is a huge sector with a dominance of advanced technologies including Artificial Intelligence (AI) and Machine Learning (ML). The technology also helps in understanding treatment variability and enhancing patient outcomes.

There is plenty of healthcare information in the form of new databases created through patient claims, electronic medical records (EMRs), social media, and new customer engagement channels. One such area of applying Artificial Intelligence and Machine Learning is with electronic medical records (EMR).

The AI technology allows clinicians to gain valuable insights into improving diagnostics and care processes. The electronic medical records usually cover the specific interactions of the patient with specific healthcare activities that result in billing.

There are opportunities to deploy EHR data for more precise and effective interventions at the right moment in the patient care journey. However, with the growing sizes of databases, the underlying data become unstructured, finding insights from such a huge amount is really a big challenge. EMR in the healthcare industry’s journey towards digitalization.

However, the benefits of electronic health records bring a lot of challenges like cognitive overload, monotonous documentation, burnout of the users. Here make Artificial Intelligence (AI) and Machine Learning (ML) the hero entry and change the game completely.

Artificial Intelligence (AI) & EMR: Present Healthcare

The main objective of Artificial intelligence is to mimic human cognitive functionalities. it is bringing a paradigm shift to healthcare, backed by the increasing availability of healthcare data and rapid progress of analytics techniques. According to research, the use of AI applications for healthcare in the current date is significant and going to increase in the near future.

Artificial Intelligence (AI) will also greatly apply across more and more types of healthcare both structured and unstructured forms. The popular AI techniques include machine learning ways for structured data, such as the classical support vector machine, neural network, and modern deep learning. Natural Language Processing (NLP) is used for unstructured data. The critical disease areas that use AI applications are Cancer, Neurology, and Cardiology. Artificial Intelligence helps in stroke, in the three major areas of early diagnosis, treatment, and prognosis evaluation.

Artificial Intelligence (AI) & EMR: Future Healthcare

Electronic medical records are a great source to gain complete details about the patient. Collecting and evaluating the data for its accuracy and reliability will be a continual challenge for providers and developers. The data quality, integrity issues, multiple formats, structured & unstructured inputs, and incomplete records have even made it complex to get actionable insights.

In a futuristic view, AI will help in creating intuitive interfaces to open new opportunities from electronic medical records (EMR) to generate in-depth insights for healthcare professionals. AI will help to automate time-consuming routine processes to save precious time and provide patient information collected but not visible to the healthcare professionals for making decisions. The analytics on EMR data is delivering successful risk-scoring tools, specifically when researchers implement ML and NLP techniques to find out the connections between datasets.

The specialty healthcare providers deal with inefficient data collection and improper information delivery process. The doctors, paramedical staff, nurses, and technicians could see the data in a raw format. Hence, it was a crucial need to capture biomarker data and other biographic information for the treatment of patients with breast and ovarian cancer patients. It meant detailed diagnosis and downstream healthcare delivery negatively impacted by:

  • Improper treatment of the patient leads to high medical treatment costs for patients.
  • Lack of patient care as there was a dependency on the doctor’s experience that leads to inconsistency in healthcare delivery.

The advanced AI techniques will help healthcare providers to extract authentic data from EMR Files in the form of PDF or images.  It can also help the team to dive in-depth and can the EHR and EMR files. By using the techniques like image recognition, sentiment analysis, and intent detection, healthcare providers can deliver a great experience and gain the below insights:

  • It helped the decision making of the physician decision-making and improved the quality of patient care.
  • AI can easily identify new cancer patients.
  • It will deliver a better understanding of the disease area and the right pattern of treatment.

Additionally, you will be accessing well-tailored information dashboards that will be facilitating easy dissemination of patient insights to physicians. The dashboards comprise detailed information, and previously inaccessible lab results along with physician encounter notes, that resulted in inaccurate decision making. The AI solutions will also help the hospital network to tap into a new source of revenue i.e. information from anonymized patient data for the purpose of research and development for the manufacturers, and improve the quality of treatments delivered to the patients.

Final thoughts

Artificial Intelligence and Machine Learning are in vogue. In the era of continuous transformation with immense power and potential, AI has made its strong place across the healthcare ecosystem making successful results. Learning the pros and cons, iterative nature of these approaches is vital for the proper usage of the AI tools. Simultaneously, organizations should have a fundamental understanding of these methods. It is essential to make a note that there is an ‘accuracy versus interpretation’ phase in ML models.  

It means in many cases the exact algorithms usually fail to provide insights that are easy to interpret and there is a need to design intervention programs around them to modify the outcome of interest. AI requires human involvement to nurture, and amplify patient care. It is a perfect amalgamation of humans, tools, and resources to deploy advanced technologies that will boom in a new era of clinical quality and bring great breakthroughs in patient care.

About Net Health

Net Health provides software and analytics for specialty medical providers across the continuum of care. Net Health’s interoperable solutions ensure compliance, improve outcomes, empower providers and inspire care. The company serves over 14,000 facilities, including 98 percent of the largest hospital chains, two-thirds of skilled nursing facilities and many leading hospice organizations and private practices. Net Health is a portfolio company of The Carlyle Group, Level Equity and Silversmith Capital Partners.

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Christophe Rude

Christophe Rude

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