Artificial Intelligence (AI) has become pervasive in the healthcare industry and is already used in EHR systems to a considerable extent. However, it needs to be improved and certainly has the ability to do so while streamlining practice operations, simplifying complex tasks, and easing the workload, thereby reducing physician burnout.
AI is very good for clinical predictions, making healthcare effective, with better and timely treatment delivery. How? Let’s find out.
Possible automation through AI is predictive diagnoses and treatment options for diseases with common symptoms. AI also helps increase interoperability and bring silos of information together under one umbrella. It has the potential to organize, filter, and decipher information that can lead to meaningful use of data, and increase the decision support performed by an EHR.
The most beneficial component of AI in healthcare is to reduce medical error, improve clinical outcomes, promote interoperability, and provide a faster turnaround for physician decision making. This will augment the relationship between the patient and the caregiver. For example, devices that can be monitored through an AI-powered EHR will be common across physician practices. Patients with diabetes, high blood pressure and chronic diseases can take their readings in the comfort of their homes. This information will be synced in real time with the EHR and made readily available for referring providers while providing timely alerts to both patients and providers to act upon
Future AI tools to look forward to:
- Voice Recognition: Google, Apple, Amazon all have devices that can perform various tasks for patients Such as speech recognition for history intake, appointment scheduling, and reminders. This features enables providers with clinical note composition, and to keep a record of your interaction with the patients. It does away with paperwork, and also the recording is useful data that can be referred to at any point in time. The clinician will not be paying attention to a monitor, rather, she’ll be focused on the patients.
- Predictive algorithms will enable faster diagnostics, and these predictions can be acquired from past and current patients, as well as from Big Data to create trending analysis for various causes. Practitioners can utilize this information and be made aware of risks such as heart attacks, a sepsis or organ failure, making AI a life-affirming tool.
- AI has the means to cut constant interaction between practitioners and vendors. AI and machine learning technology can help EHRs adapt to the preferences and customize EHR responsibly for the clinicians. This will improve productivity, save time and improve their ability to perform.
AI is a critical goal for all EHRs as it simplifies, streamlines, and augments healthcare, and makes all technology user-friendly as described above. EHRs are infamous for being complicated, clumpy, and frustrating. For larger manual processes, AI is the only answer to enable clinicians’ to perform all tasks in a stress-free environment and for the patients too. Doctors look at their screens for 30.7% of the time while the patient is in the room. With voice recognition and diagnoses prediction, the physician-to-patient gaze can increase.
AI options offered by EHR vendors, at the moment, are not integrated into the EHR, rather, they are given separately and for a higher add-on which increases the cost of running the practice. This way physicians also see the issues with their interfaces without the features and pay a higher price for the integration. One Medical created its own AI-powered EHR system for their practice. However, it took a whole decade to get it to work proficiently. Not everyone has the resources to create their own AI EHR, but it is definitely worth having.
Many vendors have already begun work on their AI-integrated EHRs, and have created a hybrid EHR system, which is used manually, but allows for machine learning and makes information useful. The industry itself is growing and has investments pouring in. However, we have to wait a while for EHRs to be operable automatically and to be smarter. It is also predicted that Artificial Intelligence will not be artificial for very long. The machines will rely less on big data, and more on top-down cognition that will resemble the human order thanks to machine learning.
Machine learning has known to automate and accurately follow directions. The more one uses devices with machine learning capabilities, have seen machines improve tasks with time. They learn to predict, understand, and streamline information fed into them by themselves with use and time. Integrated interfaces are easier to use, and lead to higher patient satisfaction as well.
Since AI has developed a lot in the medical device sector. The widespread use of EHR software as well as reliance on imaging (MRIs, CT scans and X-rays), and reading information has helped providers come to the diagnosis faster. Medical imaging analysis using AI can compare cell structures and tissue segmentation that the human eye can miss. AI-powered platforms in medical scanning can lead to image clarity, and clinical outcomes as well, and this will reduce exposure to radiation. Powering all medical technology through AI will lead to faster diagnoses, error reduction and faster treatment and recovery time. Algorithms can be used to reorganize organ damage in an image which may be missed in a simple viewing of a scan. AI can change the way patients receive medication, and remove the economic burden of long-lasting diseases from the patients. Our reliance on AI has also increased, and therefore all AI run operations in medical practice will be a necessity in the future.
While our reliance on AI will grow with time, and it has a lot of benefits. Many fear that they will be rendered unimportant in the healthcare industry because AI will try to do physicians jobs for them. This fear stems from the way machine learning happened so fast in AI. These machines were supposed to give us decision-making tools, and while they delivered, they also learned to make decisions for us as they understood what was the desired requirement. There are no signs of AI taking control of caregiving yet. However, staff work might be streamlined through AI, and one would only need someone to oversee the tasks performed rather than do it all themselves. This is primarily true for tasks that require measurements and logic. What do you think regarding job takeovers by AI? Is it possible, or something to consider in new developments in IT healthcare? Let us know in the comments below.