Across the healthcare industry, a growing number of providers are using artificial intelligence and machine learning to provide better care for patients, drive greater efficiencies, and create better customer experiences. In fact, 54 percent of healthcare professionals expect widespread adoption of AI across the industry by 2023.
In the process, they are striving to hit key results, which include turning beds over faster in the hospital and reducing readmission rates by taking a data-driven approach to medicine.
Practically speaking, that means rather than simply troubleshooting any issues patients present them with, as has historically been the case, providers are using these technologies to deliver more precise health outcomes while also reducing costs.
Importantly, to be able to do this work as a provider effectively, you have to partner with your peers. That’s because it’s only by tapping into major electronic health records (EHRs), de-identifying patient data, and sharing it consortium style with others, that you can access the vast data sets necessary for effective machine learning. Indeed, being able to tap into those massive data sets is essential for increasing your algorithms’ accuracy in terms of identifying patterns or trends hidden in patient data that can help make life-saving predictions. To achieve better results in predictable outcomes, you need more, and different data.
In this article, we’ll look at some of the ways that providers like you are using artificial intelligence and machine learning to improve health outcomes. Those outcomes can be customer experience oriented, such as reducing wait times or driving down costs, or they can pertain to improving patient health. As we’ll see, they are also being used to enable greater security and better research. Let’s start with research and imaging.
In–depth research leveraging imaging
Given that images account for up to 90 percent of all medical data,ii imaging is an important use case for machine learning. Providers can use cloud–based machine learning technology to provide correct determinations from image recognition, often with a higher degree of accuracy than humans. As such, we’re seeing major investments in this area to enable in-depth research. Microsoft, for example, is making strides with biomarkers and phenotyping, and using the results to drive cancer research forward like never before.
Improving patient care
A second example is the use of Natural Language Processing (NLP) in hospitals and clinics to process information contained in EHR applications. Clinicians who want to investigate what prescriptions a patient is on, for example, can use NLP to search unstructured data, which is often left in doctors’ notes, at scale to find the answer. They can also ask how many patients with similar conditions a facility has in order to look for trends among their patient population. That, in turn, can ultimately lead to better care and reduced costs thanks to greater efficiency.
Being able to use cloud–based machine learning technology to extract significant meaning from massive amounts of unstructured text is helping with critical decision–making. When a doctor or nurse enters a patient’s room, he or she typically begins with a series of important questions. Whether it’s being recorded on audio, notepad, or iPad, there’s great potential in that data — provided it ever gets used. Accordingly, providers are investing a lot of time and money into machine learning so that they can augment and better position diagnosis. In just one such example, they’re using machine learning to distinguish between different heart conditions. While those heart conditions appear to manifest themselves in very similar ways in the eyes of most human doctors, they actually contain many nuanced differences that machine learning algorithms are far better suited to detect. Using machine learning to correctly diagnose the conditions reduces the risk of providers simply opting for whatever diagnosis they are most familiar with when it may not actually be the best option.
Simply put, machine learning is making it possible to read all the structured and unstructured data sitting in EHR applications and analyze what’s really happening.
The insights gleaned from machine learning provide practitioners with a more holistic view of the patient. And in the case of healthcare, more information is better.
Practitioners can search for commonalities or determine what kind of trauma a patient may have been through to determine the critical or long-term care the patient needs to manage their disease or illness. In addition, the practitioner can predict more accurately who’s at risk of certain diseases in the near term. In the case of chronic care this may mean getting in front of chronic conditions when the chronic state is still preventable. By being more accurate in the diagnosis, there’s less repeat treatment and the facility can optimize its code, reduce costs, and most importantly, improve patient outcomes.
Securing data, especially PHI
Finally, one of the greatest opportunities to leverage machine learning is in cybersecurity, where, for example, it’s a vital tool for staving off the growing threat of ransomware. With ransomware attacks happening every day across the healthcare industry, providers can use machine learning to identify potential vulnerabilities and prevent attacks before malicious actors can take action. Over time, healthcare organizations may use machine learning technology to understand where gaps and risks made security incidents occur.
Intelligent algorithms detect risk by flagging out-of-the-ordinary activities and generating alerts to enable timely interventions. By calculating scores for where the data should be moving, and by whom, and in what ways, machine learning systems can help target problem areas long before anyone on an IT or security team would typically notice anything is wrong.
Importantly, machine learning is also a valuable tool when it comes to ensuring compliance around protected health information (PHI) so that you’re never running afoul of regulatory requirements. That in turn can go a long way toward ensuring that any PHI you’re collecting is always secure.
A stronger future with AI
As a provider, you’re probably analyzing your patients from the moment they walk in the door to meet with you. By using artificial intelligence and machine learning, any data you have about a patient is fuel for diagnosing what’s wrong with that person before he or she ever gets examined by a doctor or nurse. That has the potential to save time and money, while helping ensure that patients are getting the best care possible.
By adopting public cloud machine learning services, you have the opportunity to help improve your patients’ outcomes by increasing the chances of delivering the right care the first time you meet with them. Not only that, it’s a way to enable advances in critical areas such as image recognition and data security. These are just a few of the many reasons why embracing this technology makes so much sense. And, when it comes to the potential advancements in healthcare machine learning can facilitate, we’re just getting started.