If you’re a clinician in a medical office, you may be wondering whether AI technology can help you make better decisions in your practice. You might also be interested to know that it can improve patient care delivery, interoperability, image interpretation, and clinical decision-making. Read on to learn more about the benefits of using this technology in your EHR.
Artificial intelligence (AI) is a key component in improving interoperability in EHRs. It’s intended to cut down on tedious tasks, such as data entry, while enabling better decisions. To be successful, AI must be implemented properly.
The benefits of electronic health information exchange services include reduced costs and improved patient outcomes. However, the benefits aren’t limited to just these. Interoperability also improves the quality of care and enhances the patient experience.
Improved interoperability with EHR technology allows medical professionals to access the right information at the right time to make the best possible decisions. For example, an AI-equipped EHR can automatically enter back-end data into the system, allowing physicians to see more patients. Moreover, it can facilitate collaboration among doctors.
AI can also automate reminders to remind patients when it’s time to take medication. In addition, technology can augment the retrieval process of the medical record, reducing errors caused by inaccurate or incomplete patient information.
Improve clinical decision-making
The introduction of AI technology in EHR systems has the potential to change the way patients are diagnosed and treated. AI can be used to help health care providers reduce administrative burden and find the right treatment plans. However, there are still many questions about its adoption in healthcare.
Several factors need to be considered to ensure a successful deployment. These include software and human factors engineering. Increasingly, tools will run on mobile devices, so special attention needs to be paid to this factor.
Healthcare AI tools must also be carefully evaluated to determine their accuracy in the context of the institution they are working within. If they do not perform as expected, then they will need to be modified.
Another consideration is the clerical and cognitive load involved in the deployment of such a tool. A successful solution would minimize the number of clicks required, eliminate unnecessary menu navigation, and deliver just-in-time diagnostic assistance.
Improve patient care delivery
When AI technology is integrated into EHR Software, it can improve patient care delivery. Increasingly, healthcare providers are turning to artificial intelligence for help with their administrative tasks. This can make it easier for physicians to extract meaning from patient data.
Healthcare organizations are discovering that machine learning is powerful in predicting health risks. It also offers insights into how patients respond to treatment. And, it can expand treatment options.
In order to better understand how healthcare providers can benefit from AI, researchers conducted a study. They compared the time and accuracy of AIoptimized patient records to standard patient records. The researchers found that the AI system was effective at extracting relevant data in a shorter amount of time.
As a result, physicians estimated that they saved an average of 14.5 minutes per new patient encounter. However, it is difficult to translate this into actual patient visits.
Researchers used a linear mixed-effects model to assess the time that clinicians needed to answer standardized clinical questions. A random effect was included to account for the variability in responses among participants.
Improve image interpretation
Using AI technology to improve image interpretation is becoming the norm in medical imaging. With the increased use of data and more advanced technology, radiologists and physicians have the ability to spend more time doing patientcentered care. Compared to traditional radiologist workflows, AI-enhanced workflows offer improved accuracy and efficiency.
For AI solutions to become useful in the medical imaging industry, data must be available to train the algorithm. In order to achieve this, the data must be properly prepared. The volume of medical images gathered annually is immense. Moreover, a variety of different imaging protocols and clinical sites can make the preparation process challenging.
To address this, researchers are developing tools and platforms to help with the data preparation process. These include automated image segmentation, which helps with measurement and labeling of images. Depending on the task of an AI algorithm, the type of image annotation can vary.
An important aspect of the data curation process is ensuring the metadata is correct and structured. This is essential for the training of AI algorithms and ensuring that they are valid and appropriate.