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Top 5 ways AI can Improve Electronic Health Records

January 29, 2022 - Parul Saini, Webmedy Team


Artificial Intelligence (AI) is ready to become a life-changing force in Healthcare. You are using it in everyday life whether you are aware of it or not. AI assists you in discovering the perfect movie on Netflix, shows trending stories on your personalized Facebook feed, and plays your favorite music on demand through Amazon's Alexa. AI has numerous advantages over the traditional method of analytics and decision-making techniques. AI has endless applications in Healthcare.

Artificial Intelligence has been a key driver in digital healthcare transformation with digital applications being used to help patients become better decision-makers for their health while driving efficiencies and reducing costs across the healthcare industry. But what about AI's role in medical health records?

Opportunities of AI in Medical Records

AI is becoming more involved in doing what humans do, but at a faster rate with more accuracy and efficiency and at a lower price. AI can gain information, process it, and give well-defined output. AI performs this by utilizing these mechanisms:

  • Neural Networks
  • Deep Learning
  • Natural Language Processing
  • Voice Recognition
  • Pattern Recognition

As EHRs became increasingly common, physicians have spent more time studying these databases to review clinical data. With more data being stored in EHRs, users could experience a type of information overload. By incorporating artificial intelligence technology to work with these EHRs, physicians will avoid burnout and improve the patient experience.

Top 5 Ways how AI is Improving Electronic Health Records

  • #1 - Improved Personalized Care

    The use of AI in medical records can help identify patterns and perform outcome predictions. Subsequently, this information can be used to tailor specific treatments to an individual, even down to the level of what physician may be best suited to cater to their needs and outcomes that matter most to them. As a practical example, especially during the early phases of the pandemic, patients with pre-existing but non-COVID-19 related conditions could be paired with available caregivers based on their data and the outcomes observed across providers. This could potentially help them avoid long waiting times or keep up with their routine health checks in the case that their regular doctor is not available due to office closures. This not only offers improved patient outcomes but also improves access to care on an individual basis. The use of AI can also enable doctors to be alerted to preventative screenings, vaccinations, or checkups which takes personalized healthcare to a new level.

  • #2 - Decision Support

    AI-based Clinical Decision Support (CDS) tools are being used to improve care delivery. These tools can analyze large volumes of data to provide diagnostic assistance, treatment guidance, and evaluate disease prognosis and progression.

  • #3 - Improved Productivity

    Capturing clinical notes with natural language processing allows clinicians to focus on their patients rather than keyboards and screens. While AI is being applied in EHR systems principally to improve data discovery and extraction and personalize treatment recommendations, it has great potential to make EHRs more user-friendly. Today, customizing EHRs to make them easier for clinicians is largely a manual process, and the systems' rigidity is a real obstacle to improvement. AI, and machine learning specifically, could help EHRs continuously adapt to users' preferences, improving both clinical outcomes and clinicians' quality of life.

  • #4 - Clinical Documentation and Data Entry

    Capturing clinical notes with natural language processing allows clinicians to focus on their patients rather than keyboards and screens. Integrating AI tools with EHR supports data collection and clinical note composition.

  • #5 - Disease Diagnosis

    Artificial Intelligence has a huge potential for improving the process of diagnosing diseases. AI techniques such as support vector machines, neural networks, decision trees, and many more can be successfully used to diagnose many diseases. Deep learning algorithms can perform any diagnosis same as the human professionals.

Innovation will continue to advance AI's role in medical records. It is already being used to analyze large amounts of data to improve productivity, accelerate digital health, improve personalized care and support the clinical decision-making process.

As the healthcare industry embraces technology, the evolution of the data scientist role and the focus on data within healthcare organizations will grow. Patient experience and outcomes will progressively improve, and this will be partially attributable to the data collected within this valuable resource.

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