The healthcare business is in desperate need of transformation. There are various ways that technology may improve clinical workflows, improve experiences, and elevate overall care, from chronic conditions like heart disease and diabetes to risk assessment.

Artificial intelligence is developing as a plausible answer to such challenges, as physicians nowadays experience high rates of burnout and people demand more attention from their doctors. It frees up doctors’ time, allowing them to provide care with greater attention and efficiency.

Predicting senior falls, altering oncology, increasing access to care in distant places, and much more are all examples of how the subject, which includes machine learning and deep learning, is already making ripples in the world of healthcare today.

1) Diagnosis is the First Step in the Treatment Process

When it comes to medical diagnoses, a variety of algorithms have shown promise.1 These include Machine Learning (ML) methods, such as neural networks or clustering algorithms, in which a computer is trained on a huge data set. The data could be X-rays with the words “illness present” or “disease not present” pre-labeled by an expert. The computer updates the settings of its algorithm with each iteration. After the algorithm has been trained, it is validated on a portion of the dataset that was kept back throughout the training process.

When it comes to tasks that demand more general knowledge, humans will always beat machine learning. Giving medical practitioners more tools is the goal of AI diagnosis.

Not only will providers have access to these tools, but when the machine malfunctions, a doctor will be able to provide input that can be used to improve the system’s accuracy. The algorithm will profit from the collective wisdom of the medical community.

 

 

  2) Natural Language Processing in Electronic Medical Records

Most people recognise the potential of an electronic medical record (EMR), yet doctors now spend more than twice as much time inputting data as they do talking with patients. Natural Language Processing (NLP) is a branch of AI that is intended to assist in this endeavour.

NLP seeks to derive meaning from spoken or written language in order to offer meaningful information to a computer system. Rule-based approaches were utilised in earlier attempts at NLP, whereas modern NLP algorithms incorporate Machine Learning.

3) Scheduling of Nurses

The majority of nursing units schedule nurses by hand. Creating a timetable that works for you might be a difficult undertaking. Overstaffing is costly, whereas understaffing causes safety concerns and burnout. Hospitals frequently compensate by adding agency nurses to their roster, which is even more costly.

An algorithmic method could optimise the schedule by not only arranging the right nurses at the right times, but also fairly and transparently accounting for the nurses’ preferences. As a result, there is better safety at a lower cost, and the management has more free time.

4) Patient Surveys for Sentiment Analysis

To gauge patient happiness, most healthcare institutions use a patient satisfaction survey. A numerical score is useful, but what if we could explain a patient’s feelings in a more nuanced way? Gratitude, rage, contentment, and frustration could be measured instead of “strongly agree” to “strongly disagree.”

Sentiment analysis assigns an emotion to unstructured material, such as survey responses, using Natural Language Processing and Machine Learning. Reviewing patient survey responses and quickly exposing issues that need to be addressed is one apparent application. Aggregating and monitoring how this data evolves over time allows you to understand the impact of process improvements on a larger scale.

5) Robotic Nurse Assistant

You might not want a robot to take the position of a nurse, but what if a robot could take care of some of the tedious and time-consuming tasks, allowing the nurse to spend more time with their patients?

Moxi, a robot built by Diligent Robotics to transport supplies and samples, has proven to be highly popular. The robot isn’t meant to be scary, and it goes about its business quietly and effectively.

6) Automated Pharmacy

Patient safety is jeopardised by medication errors. A pharmacy robot may not be as human as Moxi, but it is surely willing to operate continuously and with extreme precision.

UC San Francisco has established a robotic pharmacy that dispenses medications and delivers them to nurses’ stations using robots with a claimed accuracy of 100 percent. Prescriptions are received, filled, and delivered by the machine using autonomous robot “tugs.”

This not only improves patient safety, but it also transforms the pharmacist’s function. More time spent advising patients and talking with doctors regarding patient care implies less time spent filling orders.

7) Financial Interactions

Financial interactions account for a significant portion of the patient experience. Improving this is a fantastic way to enhance the patient experience.  Patients demand transparency and simplicity, as well as flexibility in some circumstances. Calculating the cost of an operation may be simple for an algorithm with sufficient previous data to evaluate, but when government rules and insurance contracts are included in, even R2-D2 would give up.

So, where shall we begin? Insurance companies are starting to employ AI to seek for indications of fraud in the financial sector. Although it may be a stretch to call this a patient happiness issue, fraud has been related to patient damage.

Patient satisfaction is an extremely important metric for hospitals. For improving the quality of patient-centered health care the key drivers behind patient satisfaction is a critical initiator. Patients appear to want speedy and tailored medical care, and AI systems may be able to meet those needs. As proven by the Penn State researchers’ machine learning model, AI has the ability to speed up the process of addressing and enhancing patient satisfaction.

Presently, AI technology is in the middle of a “hype cycle” — a period of over-inflated claims which will eventually give way to a “trough of disillusionment.” Hopefully, this article leaves you with a realistic picture of the tools available and the actual, incremental changes that can be made. Whether or not those changes improve the doctor-patient relationship, and in turn, the patient experience and satisfaction.