Electronic health records have become the industry standard in recent years for their ease of use and ability to deliver healthcare information to both patients and providers quickly. Since EHR integration has been implemented in hospitals and doctor’s offices, the standard of healthcare has been raised and patients have more access to their medical records than ever.
However, these systems are always able to be improved to benefit the changing needs of the medical industry. Recently, machine learning and automation have become more prevalent as EHR technology advances. In this article, we will discuss how EHRs utilize machine learning to predict potential issues in patient health, organize unstructured information, assist with decision-making, and automate data entry. Once fully integrated into all EHRs, these innovations have the potential to revolutionize the way we administer and receive healthcare.
Automation and machine learning do not eliminate the need for human decision-making or data entry. Machine learning technology makes it easier for healthcare professionals and data scientists to access data and communicate with patients without performing small, repetitive tasks that slow down the healthcare process. Because these tasks can now be automated, your healthcare system will work much more efficiently, and care standards will improve. Let’s explore how EHRs use machine learning to improve everyday healthcare.
EHR-Integrated Predictive Technology Makes for Better Patient Outcomes
When EHR integration allows healthcare providers to consult predictive models, they can present a more holistic care plan
When it comes to your healthcare, you will want to pursue the best options possible that will result in a positive outcome. Care organizations operate by the same motives. They want to ensure that the care provided is the best possible. Because massive healthcare systems can be overwhelming, it is essential to consider machine learning as an important part of EHR integration. By using predictive care models, doctors can make predictions based on data collected by the machines used in hospitals or other care offices to make more informed decisions. For example, with machine learning, data from ICU machines can be collected and aggregated to make predictions about potentially serious conditions that could disrupt patient care in the future, like internal bleeding or blood clots. By learning from the data produced by machines, automation will predict negative outcomes before they happen, thus providing healthcare professionals and patients with the data they need to prescribe continued care.
Because EHRs can now use machine data to predict and prevent negative outcomes, patient satisfaction rises. Patient satisfaction usually goes hand in hand with a provider's revenue, making machine learning a smart financial and medical move for most practices.
Recognizing and Organizing Unstructured Information
By identifying and integrating EHR information, all information stored in the system is optimized and easily accessible
Another important feature of EHR integration is storing information in a structured manner, such as a patient profile where both patients and providers have access. However, in some cases, not all patient data is structured correctly within the EHR. If important diagnostic information is stored in unstructured formats, it can be difficult to access or could be more easily lost. Machine learning can remedy situations like this by extracting and then structuring the data in a patient portal. This automation can take shape as part of a natural language processing system or an image recognition system. When this task is fully automated, there is no longer a need for a data scientist to find and restructure the necessary information manually.
Assisting Healthcare Providers in Decision-Making Based on Input Data
Automation has the ability to assist providers as they navigate unclear diagnostic challenges
Often, diagnoses are challenging and can be subject to a lack of clarity or subjective decision-making. These circumstances are never ideal when it comes to a patient’s health. Thankfully EHR integration has helped limit uncertainty by providing doctors with more patient data than ever to make the most informed decision possible. Machine learning expands on the EHR system and will further help doctors make a complex diagnosis. This is achieved by harvesting data from machines and using that data to model the complex decision-making process that is making a diagnosis. With the added help of machine learning, doctors make more efficient and personalized diagnoses by consulting a model. The model helps provide much-needed clarity when it comes to very complex diagnoses. Once fully integrated into all EHRs, these innovations have the potential to revolutionize the way we administer and receive healthcare.
Automation and machine learning do not eliminate the need for human decision-making or data entry. Machine learning technology makes it easier for healthcare professionals and data scientists to access data and communicate with patients without performing small, repetitive tasks that slow down the healthcare process. Because these tasks can now be automated, your healthcare system will work much more efficiently, and care standards will improve.
Assistance With Patient Record Documentation and Data Entry
Automated natural language processing allows EHRs to identify the most important information
Natural language processing is a powerful automation tool that is useful for doctors as they are assessing a patient’s needs. EHRs will use natural language processing to locate and highlight the pertinent information needed for the doctor to make a diagnosis or to answer any patient questions. A natural language processor can scan large portions of medical text and pull the required information, without a doctor having to locate it on the spot. This example of machine learning improves the doctor/patient relationship by allowing the doctor to spend more time discussing care options with their patient, rather than scanning dense medical text on a computer screen. Much like the other facets of machine learning discussed, natural language processing will lead to a higher patient satisfaction rate benefiting the healthcare system and the patient’s health.
Machine learning has shown great potential in improving EHRs and the healthcare industry as a whole. When machine learning becomes more common, that patient satisfaction will likely rise along with positive patient outcomes assisted by automation and data-based models. Simply put, machine learning and automation hold great promise for the future of EHR integration.
As healthcare technology rapidly advances, providers are always looking for the most cutting-edge technology to assist their patients. Iron Bridge remains at the forefront of these EHR integrations with its leading IT solutions in data technology, aggregation, public health management, and patient access systems. Contact the team today to learn more about Iron Bridge’s innovative health IT solutions.