As the need for good and affordable healthcare mounts, using data to make better-informed healthcare decisions is essential. By deploying artificial intelligence (AI), healthcare organizations can leverage vast troves of data to better anticipate patient needs, staff for them, improve treatment decisions, and reduce health risks in select cohorts.
You may have heard the terms analytics, advanced analytics, machine learning and AI. Let’s clarify:
- Analytics is the ability to record and playback information. You can record the diagnoses for each patient and report on how many have a particular illness.
- Analytics becomes advanced analytics when you write algorithms to search for hidden patterns. You can cluster patients based on similar symptoms.
- Machine learning is when the algorithm gets better with experience. The algorithm learns, from examples, to predict the onset of illness.
- AI is when a machine performs a task that human beings find interesting, useful and difficult to do. Your system is artificially intelligent if, for example, machine-learning algorithms predict the outbreak of illness in a community and construct an outreach plan.
If you’re in healthcare, here’s how to make sense of the terms analytics, advanced analytics, machine learning and AI. Click image to expand.
AI is often built from machine-learning algorithms, which owe their effectiveness to training data. The more high-quality data available for training, the smarter the machine will be. The amount of data available for training intelligent machines has exploded. According to an article on Forbes.com, by 2020 every human being on the planet will create about 1.7 megabytes of new information every second. According to IDC information in enterprise data centers will grow 14-fold between 2012 and 2020.
And we are far from putting all this data to good use. Research by McKinsey’s Global Institute suggests that, as of 2016, healthcare providers typically capture only 10 to 20 percent of the value of in their data. Here’s what it looks like when you use AI and put that data to better use in caring for patients.
Be more efficient
The better we anticipate needs, the better we can care for patients. Hospital lengths of stay forecasts from AI make it easier to spot and help patients who may have problems with recovery. The McKinsey Global Institute found that, with applied AI, hospitals have the potential to reduce expenditures by 8 percent. We can increase the efficiency of care by anticipating patient needs and responding accordingly.
Be more responsive
Responsive healthcare requires the right staff. AI can forecast admissions so that care providers can anticipate spikes in healthcare needs. It can help administrators plan staffing to best meet those needs. According to the McKinsey Global Institute, 60 to 70 percent of hospital costs come from staff labor. AI can increase the effectiveness of hospital staff and save $200 for every patient treated. You build the best possible staff and provide patients with the best possible care.
Be more effective
The better the diagnosis, the more effective the treatment. AI can help predict the risk of readmission and discover patient cohorts. This improves a patient’s care by augmenting the care provider’s decisions. The McKinsey Global Institute reported that using AI to augment treatment decisions can result in a 40 percent reduction in adverse drug reaction and 30 to 70 percent cost savings by eliminating ineffective drugs. You provide patients with better care by making smarter treatment decisions.
According to the McKinsey Global Institute, personalized care can add a year to the life expectancy of patients. AI helps personalize medicine by automatically intervening on behalf of the patient. We can use public community data, for example, to anticipate health risk, develop optimal outreach plans and reduce impact. AI makes it possible for hospitals to anticipate the risk of diseases like diabetes and create optimal outreach plans for individual patients who may not be receiving proper care. You improve the lives of patients by providing better, more personalized care.
Applied AI makes a difference in patients’ lives
If we see AI as just technology, it makes sense to adopt it according to standard systems engineering practices: Build an enterprise data infrastructure; ingest, clean, and integrate all available data; implement basic analytics; build advanced analytics and AI solutions. This approach takes a while to result in better patient outcomes.
But AI can be a way to make a difference in a patient’s life. When AI is seen as a differentiator, the attitude toward AI changes: Run if you can, walk if you must, crawl if you have to. Find an area of patient care that you can make as smart as possible as quickly as possible. Identify the data stories (like optimizing staff or cohort discovery) that you think might make a real difference. Test your ideas using utilities and small experiments. Learn and adjust as you go.
It helps immensely to have a strong Analytics IQ — a sense for how to put smart machine technology to good public use. We’ve built a short assessment designed to show where you are and practical steps for improving. If you’re interested in applying AI to healthcare and are looking for a place to start, take the Analytics IQ assessment.
See more of Jerry Overton’s thoughts in Wired Magazine: Welcome to the Age of AI-Based Super Assistants.
Jerry Overton is a data scientist and senior principal in DXC Technology’s Analytics group. He leads the strategy and development for DXCs Advanced Analytics, Artificial Intelligence and Internet of Things offerings.
Jerry is the author of the O’Reilly Media ebook, Going Pro in Data Science: What It Takes to Succeed as a Professional Data Scientist. He teaches the Safari Live Online training course “Data Science at Enterprise Scale.” @JerryAOverton