It doesn’t matter how much you know about the development of the healthcare industry worldwide, there’s one fact that couldn’t escape your attention: According to the AI in medical record review, Artificial Intelligence took EHRs to new heights. Are there challenges? Sure, there are some. But are there more opportunities for patients and doctors? There can’t be any doubts about it.
Today, several EHR systems have already introduced AI instruments to boost their capabilities and performance. Among them are such known names as Epic Systems, Cerner, McKesson, Athenahealth, NextGen Healthcare, and eClinicalWorks. It is also estimated that by 2030 the healthcare AI market will reach $208.2 billion, if not more. This is why considering the development of an AI-based system or the improvement of an already existing one is a must-do.
What Are EHRs?
Simply put, they are digital copies of one’s medical history, treatment records, and any other health-related data. The difference between paper-based and electronic records is that the latter are stored and managed digitally. Thus, the records are accessible by anyone with relevant access to the systems and from any geography or device. Step-by-step, electronic health records are replacing paper-based ones since they can deliver a more comprehensive view of one’s health as well as allow healthcare practitioners to make more informed decisions and treatment options.
Because of the EHR benefits, many private hospitals and clinics are now choosing to develop their systems, while public and state institutions keep using 3rd-party solutions. The reasons behind why to develop own systems are clear:
- Accessibility
- Efficiency
- Accuracy
- Decision support
- Security
- Interoperability
AI has become a superpower for computer systems, enabling them to analyze, think, and make decisions. No wonder, AI is now on its way to improving existing EHR systems.
How AI Enhances Medical Records and Empowers Personnel
- AI assists with data analysis. Artificial Intelligence models can analyze large patient data sets quickly to define correlations, trends, or anomalies that are typically overlooked by humans. This is how AI helps in early disease uncovering and fast treatment.
- AI supports clinical decision-making. Digital health records supported by Artificial Intelligence ensure real-time clinical decision support to physicians and healthcare providers. One of the most obvious examples is the capability of such EHRs to alert regarding medical interactions and suggested treatments depending on one’s medical history. This is especially helpful in the case of the most complex conditions.
- AI applies NLP. Natural Language Processing integrated into EHRs enables the latter to understand data and pull out required insights from medical sources as well as unstructured notes. This allows doctors to see a patient’s condition from a more comprehensive angle.
- AI ensures predictive analysis. Artificial Intelligence models are capable of predicting patient outcomes, progression of conditions, and readmission risks. This approach is called proactive due to the fact it allows healthcare practitioners to potentially save lives as well as reduce healthcare costs.
- AI offers text-to-speech features. More complex EHR systems feature built-in assistants powered by AI. These are capable of voicing patient records and saving time on data searches. The text-to-speech function is often used for clinical notes and prescriptions as well as helps staff double-check the entered data, thus reducing failures.
- AI assists with scheduling. This is a typical case for telemedicine-integrated systems, where Artificial Intelligence, once based on notes of a doctor, supports scheduling follow-up appointments and sends reminders about upcoming visits.
- AI creates personalized treatment plans. Until now, many healthcare practitioners were relying on their knowledge (40%), experience (40%), and a patient’s medical history (20%) to provide a matching treatment plan. With AI in EHR systems, the picture has changed. Now, AI helps to deliver tailored treatment plans based on data patterns (50%) and a patient’s medical history (50%). A more customized approach helps to ensure better results.
- AI analyzes medical images. ML algorithms and AI models have shown their efficacy in analyzing MRIs, CT scans, X-rays, and other medical images. Unlike humans, AI is rapid in detecting tumors, fractures, and diverse anomalies. This information assists clinicians and radiologists in making diagnoses more accurately.
- AI ensures billing. Artificial Intelligence models deal with unstructured and structured care data, finding billing information, composing comprehensive billing reports, also conducting insurance verification and one’s eligibility check.
Bottom Line
Artificial Intelligence continues to advance, and so does its role in electronic health records. AI promises to improve productivity, grow the adoption of health records as well as polish custom care. So far, healthcare practitioners and patients see the introduction of AI into the medical records domain as a positive sign.
As long as the healthcare industry embraces new technological advancements, this will ensure a progressive improvement in data analysis, patient experiences, and treatment outcomes. Let’s see what the future brings.
