By Mark Weber, SVP healthcare development, Infor
Big data has arrived, and in healthcare, it has landed on our desks with a resounding thud. The challenge ahead lies in discerning how to analyze information and use it to effectively improve patient outcomes, costs and efficiencies.
Many of us are already influenced by machine learning and artificial intelligence (AI). For example, if buying hiking boots online, items of a similar nature also appear as suggested purchases, like bug spray or sunscreen. The data analytics behind those recommendations includes a wealth of information about the user, including demographics, such as age, gender, education and income level, as well as location and other factors that influence buying decisions. It will only be a matter of time until we are able to apply the same principles to healthcare data.
Imagine a doctor who can review operational and clinical data in real time for a patient who had knee replacement surgery. After the patient goes home, she is given a Fitbit to monitor her step count. If her steps trend downward, it is probably time for someone to intervene because she is potentially in pain or not ambulating correctly. That same physician could also see where she has received care, the cost of the care, and who performed the surgery. Then, the physician could compare her progress against others with similar demographic and health backgrounds by using machine learning and streaming analytics that not only gather relevant data across the entire care continuum—from hospital to rehab facility to home—but draw inferences from that information in real time to truly influence cost and care outcomes. In addition, if the patient had three MRIs that cost $2,000 each and someone with similar demographics and health conditions had one MRI that cost $500—caregivers can explore why that happened and work toward more uniformity.
This idea is inspiring, but a more practical look can be taken for how AI can support the business operations of healthcare as an achievable first step, along with connecting that operational data with remote care, device data and patient EHRs. Here are next steps for creating efficiencies with the power of AI and interoperability:
Step 1: Unlock Human Potential
As a recent Advisory Board report states, “AI works best when paired with humans.” The goal is to use this technology to create efficiencies across the care continuum that not only help staff in their roles, but that free clinicians, caregivers and office staff to focus on more valued activities. AI can help augment and automate human tasks and functions where appropriate, and sooner rather than later it may be able to offer advice, ultimately allowing caregivers to focus entirely on patient care.
Step 2: Optimize the Supply Chain
AI can quickly answer employee queries, buy supply, such as bandages from a certain supplier, and can also track unused supplies to minimize excess inventory. In addition, AI can help alleviate the amount of time—and frustration—nursing and clinical staff spend searching for supplies by not only providing location, but automating future order and delivery.
Step 3: Enhance and Expand Employee Self-Service
For those healthcare employees without regular access to a computer, such as lab technicians, AI can quickly and accurately empower cross-functional self-service. All employees need to do is ask for answers about anything, from paid time off (PTO) balances to company holidays.
Step 3: Automate Financial Processes
AI can augment the payment process, detecting payment, vendor and invoice patterns, and suggesting automating payments for a specific invoice that is approved 99 percent of the time.
Step 4: Maintain a Restful Environment
Employing AI to help manage and maintain hospital equipment can provide staff with needed insights quickly, thus ensuring something as simple as too-bright hallway lights can be adjusted during patient sleeping hours. This not only contributes to patients getting the restful sleep they need, but may in turn boost patient satisfaction scores that can have an impact on a hospital’s reimbursement levels.
Bridge the Gap Between Clinical and Operational Data
Having the ability to capture supply demand automatically from your clinical systems, automate your patient refund process, including status updates of checks, and flow patient information automatically between ADT, point of use, scheduling, case scheduling, and billing systems will help your organization achieve higher cost and care efficiencies. Combining all information through AI or machine learning will help you quickly draw inferences between all that data, and give you the power to positively influence the entire continuum of care.
We are just beginning to unlock the potential of AI in healthcare, and making health data interoperable. And while we are not quite there yet with using machine learning to help adjust treatment and care protocols in real time, the industry is well positioned to take the next step with computing power and streaming analytics to uncover how AI can impact healthcare.