By Steve Cavolick
Because it’s the most wonderful time of the year, I wanted to write a letter to Santa on behalf of all those organizations seeking an enterprise-class Analytics environment. I was going to request best practices, organizational adjustments, and critical technology needed for success.
But Oracle put an early present in my stocking by announcing a $30 billion acquisition of electronic medical records company Cerner, so I want to cover that instead and use it as a segue into discussing efficiencies in healthcare.
2021 was the largest year for merger and acquisition activity ever, topping the $5 trillion mark. Not surprisingly, healthcare and technology were the two largest sectors. While the Oracle purchase is just a drop in the global M&A bucket, it clearly shows the strong connection between data, AI and the future of healthcare.
Other technology behemoths (Google, Amazon, and Apple) are also making a push into the healthcare arena to improve results and reduce manual tasks with cloud and AI. In fact, Oracle cited a Mayo clinic study that shows medical practitioners spend at least one hour inputting data for every one hour of patient time. This impacts the cost of healthcare, with administration now comprising 25% of all healthcare spending. In 2020, terms, that means about $1 trillion was spent on administrative costs.
Healthcare in the United States is complicated. Hospitals, physician groups, and payers are separated, which means there are many steps, hand-offs, and communications in the billing cycle and appeals of claims. In addition, the industry is highly regulated. Compliance with regulations such as HIPAA or participating in new markets such as Medicare Advantage lead to additional administrative procedures where inefficiencies can hatch.
So faced with labor shortages, and visit and procedure numbers that are returning to pre-pandemic levels, where can efficiencies be created at individual organizations with analytics? In our recent experience with healthcare partners, LRS has observed operational and technological shortfalls that cause consequential pains on results and morale. Here are some areas of low-hanging fruit where manual processes are rampant that we would start modernizing with analytics:
Scheduling: Nursing schedules should not be kept in Excel and require nursing supervisors to manually input data into the “Master” spreadsheet. Use AI models to intelligently create schedules based on current and predicted future census numbers. The same principle should be applied to operating room scheduling.
Revenue Cycle Management: Instead of manually inputting data and manually reviewing claims, let AI models learn why claims are rejected and fix errors before submission. AI models can also improve cash flow by predicting how long it will take to process a claim and when an insurance company is likely to pay based on billing code.
Clinical/Financial Reporting: It sounds simple, but it is not uncommon for a hospital or health system to have multiple EHRs and multiple billing systems. Having a governed single repository or virtualized data storefront gives knowledge workers the ability to answer questions without having to request access from IT. There is power in being able to understand cost discrepancies in similar procedures by different doctors or knowing the precise demand for procedures so you can plan expansion into other service lines.
Other areas rife with manual processes we see include calculating patient leakage, HR (turnover, on-/offboarding), and matching inventory to census numbers. Before any of these can occur, you must have a modern data architecture that allows for the democratization and browsing of governed and secure data.
If you are ready to build AI applications that eliminate manual processes and reduce back office errors, our data science team can help. If you’re not quite there yet, the LRS Big Data and Analytics group has over 20 years of experience implementing modern information architectures.
Not sure how to get started? Our strategic offerings can help align business and technology teams, discover the right use case, and determine an ROI. If you are interested in understanding how we can help you find value in your data, please fill out the form below to request a meeting.
About the author
Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.