As artificial intelligence goes on to create shockwaves throughout the healthcare ecosystem, its steps into the domain of EHR (Electronic Health Records) has been fascinating. This is definitely due to the numerous advantages both systems provide. Now, visualize you leverage a basic EHR for patients. One patient is prescribed an MRI contrast agent prior to the scan. What you might not be aware of is that they are susceptible to an allergic reaction or scenarios that could cause the dye to negatively impact the patient. Probably the data was included in the patient’s EHR but was buried at such a depth that it would’ve been not possible to search for it in particular. 

An AI-based EMR, in contrast, would have been capable to undertake analysis of all records and decide if there was a potential of any conditions that may make the patient vulnerable to adverse reactions and caution the lab prior to any such dyes being leveraged. 

Here are other advantages of AI-driven EHR to assist you in comprehending how they make contributions to the domain: 

• Improved diagnosis: Maintenance of comprehensive records is really beneficial for making enhanced, more educated diagnoses. But, with artificial intelligence included in the dance, the solution can then detect even the most minimal alterations in health statistics to assist practitioners in confirming or disproving it. Further, these systems can also caution doctors with regards to any anomalies or abnormalities and directly link them to reports, documentation, and conclusions furnished by other practitioners, ER staff, and other allied healthcare professionals. 
• Predictive analytics: A few of the most critical advantages of AI-driven EHRs is that they can undertake analysis of health conditions, tag any risk aspects and automatically go about scheduling appointments. There solutions also assist doctors to corroborate and correlate evaluation outcomes and assist in setting up treatment plans or subsequent medical investigations to provided improved and more robust determinations about the well-being of patients. 
• Condition mapping: Limitless pre-existing conditions may hamper medical diagnoses and the protocol followed can be challenging or even hazardous. This can be simply tended to by AI-driven EHRs that can assist practitioners in ruling out any such potentialities on the basis of factual data. 

Now, let’s observe a few of the hurdles: 

• Real-time access: For information to be accessible to AI, the massive amounts of data produced by a hospital on an everyday basis are recorded in proper data centers. 
• Data Sharing: Obviously, the entire point of EHRs is to make information available and accessible. Unluckily, that isn’t really possible until you have attended to the storage and that it is in the needed formats. Unprocessed information is not impossible for artificial intelligence to go through but it does not boil down as a totally differing activity – one that has a toll on the time taken to carry out AI’s other, more critical goals in this context. 
• Interoperability of data: It is not adequate to just be able to record data, the aforementioned data must be also interpretable throughout a wide array of devices and formatting. 

Artificial intelligence has a ton to provide with regards to Electronic Health Records and the medical domain in general.