Logically, in the times of a pandemic, top medical professionals’ responsibilities are to treat and save lives. However, these are just two of many more responsibilities they have to deal with daily. No wonder medics frequently suffer from burnout. By the way, that’s the case for about 47% of US medical professionals, according to Physician Burnout & Depression Report 2022 by Medscape. Curiously, 60% of the respondents name excessive paperwork as the leading cause of burnout.

So is it possible to lift this burden off doctors’ shoulders? Fortunately, it is. You can entrust repetitive daily tasks to machines, and machine learning in healthcare is the route to follow. Let’s see how it works.

Healthcare automation in action

Healthcare automation is already in full swing. So what technologies are there to facilitate healthcare automation? The technology stack comprises medical image analysis, natural language processing (NLP), healthcare BI, and more. So let’s dive in to see how these solutions save clinicians’ time and spare them excessive paperwork.

Natural language processing

Natural language processing (NLP) is a branch of AI that aims to bridge the gap between humans and machines, helping the latter to understand the former. The technology works in two directions—it can translate unstructured data into a machine-friendly format and retell the machine format in a natural language for humans to use. It also enables doctors to save time on routine tasks, allowing them to dictate information on the go. Then it can be stored in patients’ EHRs or sent to other clinical systems, which solves the issue with interoperability in healthcare.

What’s more, NLP in healthcare also fosters cost reduction and improves the quality of care and doctor-patient relationships. However, to deliver value for doctors and patients, NLP solutions need a well-developed digital environment. Otherwise, introducing these tools may require additional integration efforts, which often results in lengthy deployment and increased costs.


A chatbot is the amalgamation of NLP and machine learning for healthcare. Chatbots can lend doctors a hand in reducing the number of hospital visits, triaging, and acquiring patients.

To understand how it works, let’s look at Grace from Providence St. Joseph Hospital in the State of Washington. This chatbot solves the triaging puzzle with a series of simple yes-no questions and a set of pre-developed scenarios. With this set of tools, Grace decides on the steps to take to offer a patient the most suitable care option. It can be calling the emergency line, scheduling a visit to a particular specialist, or following simple self-help recommendations at home. Grace even provides an estimate of costs the person is likely to incur in case of hospitalization or a doctor visit.

Robotic process automation (RPA)

AI-driven RPA tools can capture and reproduce human behavior when dealing with routine repetitive tasks—processing transactions, communicating with other digital systems, working with data, and more. RPA has at least one unbeatable advantage: it can be deployed without disrupting a healthcare facility’s operations, by using the existing digital environment and lowering the risks of human error.

In a clinical setting, RPA proves valuable for automating several lengthy and effort-intensive functions and streamlining business process management. Here are the top three candidates for robotic process automation in healthcare:

  • Patient relationship management. RPA bots can easily schedule appointments matching diagnoses, doctor availability, patient location, and other choice-driving factors. Moreover, they can manage follow-ups and send patients reminders on the next appointment, prescription drug pick-ups, and more.
  • Healthcare cycle improvements. RPA tools can generate valuable insights from available data to help clinicians improve diagnosing and deliver personalized treatment plans.
  • Claim management. It is yet another lengthy process requiring vigilant attention and concentration, which are not always there due to the human factor.
  • Clinical trial management. Equipped with RPA, clinical trial management software can streamline patient recruitment, secure data management, and more.

RPA is a powerful technology that can automate various healthcare workflows, from onboarding and first-appointment scheduling to payment. While the technology is promising, you’ll still need to get prepared to prevent potential issues before deployment. Rushing is not the best strategy here. It’s better to make grounded decisions, cooperating with the key stakeholders and technical specialists in due time.

Computer vision in diagnostics

Computer vision is yet another alliance between ML and data science, which proves beneficial for several medical areas, from precision medicine to diagnostics.

Computer vision tools use deep learning (DL) and convolutional neural networks (CNNs) to mimic humans in image classification and recognition with an accuracy of up to 90%. In some cases, machine-powered recognition outdoes doctors. That is the case with the new AI-driven algorithm for lung cancer detection by Google.

When precisely calibrated, medical image analysis facilitates diagnostics and allows doctors to make effective treatment decisions without investing much time. In diagnostics, computer vision tools let doctors detect a problem early, reduce false positives and negatives, and design the most suitable treatment plans.

In some cases, AI algorithms can even help practitioners personalize treatment. For example, the Cleveland Clinic, Ohio, deployed an AI algorithm trained to combine patient records, health history, and CT scans. Having analyzed the data, the tool builds mathematical models to evaluate how well patients would react to offered treatments. Doctors then weigh the results and offer the most suitable and scientifically grounded treatment.

Predictive analytics

Predictive analytics is a unique field located at the crossroads of statistics, data mining, and machine learning. Data scientists study hospital workflows and other data sets in question to formulate a problem and develop suitable predictive models for solving it. After model assessment and validation, the tool is released to work its magic—to look into a mass of historical data to detect dependencies and make predictions about future events.

Predictive analytics proves efficient for personalized health management. Analyzing patients’ medical histories with the help of this type of analytics, doctors get a clear visualization of the likelihood of complications with risk scores. Then they can assess the number of at-risk patients and prepare for their potential hospital stays in time.

Predictive analytics also helps resolve health challenges on a larger scale—in communities. Predictive analytics can plunge into large health-related datasets to evaluate population health and detect risk-prone groups of people in need of special protection schemes.

On a final note

With all these AI-powered automation solutions, there comes a legitimate question: is there a chance that healthcare professionals will be ousted by smart machines? That is highly unlikely.

However smart, machines can only perform repetitive tasks; creative problems are out of their reach. Still, with such tasks, machines are far more efficient than humans: they work faster and don’t make mistakes associated with fatigue and lack of concentration. So why not let them bring these benefits to your clinic?

In fact, automation doesn’t rob healthcare professionals of their work. It spares them the time they can dedicate to solving complex professional problems and to self-education in order to deliver top-quality care and improve patient outcomes.


By admin

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