- Automation, AI, Robotics… How these relate?
- What’s wrong with the current EHRs
- Voice recognition algorithms streamline taking notes
- Automation can fuel personalized, granular interface
- Automation integrates different players of the healthcare network through EHR
- Automation increases the accuracy of clinical decisions and security
This article is a part of our healthcare tech series. Check out installments on the EHRs design and a more in-depth piece on big data analytics (automated solutions, included) for healthcare providers.
Whatever people feel about automation, fear or excitement, it is here — and businesses are already taking advantage of it. In general terms, automation happens when a machine does repetitive tasks that stand between employees and work they do enjoy. Automation can be applied for different aspects of business: it can improve employee workflows, electronic system security, enhance decision-making, and, generally, make everything quicker.
Automation, AI, Robotics… How these relate?
As we said, automation is a process of setting up machines to do things automatically. It’s, for instance, a call from a bank bot that tells you you’ve forgotten your card in the ATM. Or robotic arm that works on the assembly line.
Artificial intelligence is software that is designed to operate as human, to analyze, see patterns, and forecast. It’s sensors in the airport that navigate you to your gate. It’s predictions about the proceeding of global warming.
Automation software can be based on the AI: smart home devices can automatically increase the temperature in your home when the temperature outside drops.
Robotic automation has nothing to do with giant human-like robots — at least, in software: it’s a small AI-based software that can act, for instance, as janitors of claim processing, detecting, through machine learning algorithms, unusual, out-of-pattern things: claim errors and fraud.
What’s wrong with the current EHRs
The main idea of automation is to render into humans the things humans do best: performing soft skills and applying their higher cognitive function — and let the machines do the rest. Machines are always focused, they can process huge amounts of data without tiring, they, if the algorithms are written properly, aren’t biased.
Despite the fact that the current level of worldwide EHR market revenue grew 6% from the previous year, EHRs continue to be a headache for physicians: surely, they say they “somewhat satisfied” with their systems and then admit a system becomes one of the causes for burnout. There’s even a term “EHR-related stress” for this. What’s happening?
- The more time doctors spend filling up forms in EHR, the more dissatisfied they feel about their work.
- The more frustrated doctors feel doing insufficient and unsatisfactory work and limiting their time with patients (which is satisfying and sufficient), the more mistakes and errors they make, both within their human interactions and working with their systems.
- Human interactions become shallow — doctors spend only 18 seconds listening to patients before interrupting them.
We’ve already written about how human-centric design can make EHRs easy-to-use, now let’s dive in into what functionality should be integrated into these systems to make everyone more productive and happy with their jobs.
Voice recognition algorithms streamline taking notes
The most time-consuming thing in working with EHR is filling up patient data and notes about the visit — most physicians agree there is a place for improvement. Data entry shouldn’t be the main occupation of a doctor during the visit. Nowadays technology allows converting voice into text more or less accurately — yet, only a few doctors are using scribing technology as a way to enter patient data. Transcript-to-note AI algorithms, on the other hand, would remove unnecessary, non-clinical related bits of conversation and turn it into a note. Then, through NLP (natural language processing) induced search may be able to find these particular note if patients’ state improved or worsen — or for research purposes.
Automation can fuel personalized, granular interface
Doctors need to click a lot before finding the needed information in EHR. It’s already proved that displaying fewer data on EHR increases physicians’ productivity, so customized solutions seem like a perfect opportunity to help providers focus on interactions with patients. Automation comes in handy when doctors’ workflow changes depending on the patients’ health, displaying functions that might be useful in real time. Triggered by the input, the automated engine may propose medication for certain diagnosis, “open line” with lab specialists if test results are not understandable, or quick dive-in into a patient’s medical data. The way medical and historical data are displayed in EHRs interface can be improved too, and their understandable visualization with main trends, patients’ self-reports, visit times, or any other relevant to the current diagnosis events may help doctors understand health issues better.
Automation integrates different players of the healthcare network through EHR
Collaboration between labs and physicians are a must if we want to streamline patient-doctor interactions. It’s a question of how fast diagnosis is established — and if X-ray images, blood sample analysis, screening results, etc. would appear at the EHR’s patient profile fast, physicians are able to analyze them faster. Moreover, physicians are often struggling with interpreting lab results. So AI-powered analytical engine that will process these results and show them to physicians in a form of tips may improve the accuracy of diagnosis and reduce time spent calling lab professionals and, therefore, patient waiting time. Alternatively, if data analysis wasn’t sufficient enough, an automated system may provide physicians with an option to request assistance from labs.
Automated ordering is another cool feature that improves the clinical power of EHR. Instead of waiting for nurse’s requests for certain lab tests, AI engine analyses patients’ historical data and the risks described there and supply a request to the lab without humans interfering. There was a study that proved that automated HCV and HIV screening through EHRs was more efficient than nurse-driven procedures.
Physicians spend about an hour a day on refill management. Here’s how the process generally looks like: physicians get a message about refill for a patient named John. But they know nothing about John, so they have to look him up in the patient’s profile. They need to respond to these messages quickly: patients are waiting, — and if they cannot do so, they’re getting frustrated. Automation can help them get through that. If EHR system analyzes the request for a refill and shows relevant “Johns” — Johns who are prescribed with a certain drug, the process would be much faster.
Automation increases the accuracy of clinical decisions and security
Earlier, we’ve written an article on big data analytics and how it helps organizations across the healthcare network to improve patients’ outcomes and make more value-based, data-driven decisions.
Right now, the amount of healthcare data is rather huge and it is the most precious source for improving patient outcomes. Automation as a part of big data analytics is a great help for doctors, because, while working with AI algorithms and gathering insights from them require technical knowledge, automated solutions do not.
Among other things, automation can be a wonderful tool to keep connected to a discharged patient, prioritized alarms can reduce nurses’ fatigue and software robots can ensure clinical documentation is compliant. One of the main advantages of automated solutions, except for reducing crazy amounts of manual labor, is that they’re cutting down the spendings by reducing the amount of claims denials. In general, 36% of healthcare activities can be automated, — and, according to multiple proofs, automation reduces death rates and improves both patients and doctors satisfaction.
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