Where Can We Reduce Medical Spending With Automation
Easily?
During our years working for hospitals and insurance providers, there were many similar issues that caused unnecessarily increased costs. Healthcare providers, like many other industries, are facing drastically increased costs and decreased margins. Unlike tech companies that had the ability to develop technology to help their basic operations automate and scale, hospitals and healthcare providers have not. This has caused multiple administrative and analytical costs to grow year over year because of the lack of automation and process improvement. Some of the biggest costs that we have seen heavily weigh on healthcare providers are billing and financial processes, fraud detection and third-party contract management. Automation and process improvements are needed in these three areas if healthcare providers want to start reducing some of their biggest costs.
Billing And Financial Analysis
Managing billing and financial analysis can be very tedious tasks that require a combination of accounting discipline and entrepreneurial spirit. They are necessary practices that need to happen to manage expenses and revenue.
The problem is that even in billion dollar healthcare organizations lack automated billing and financial processes. Instead, hundreds of thousands to millions of dollars are often manually managed in excel spreadsheets. The processes to get the data and then slice and dice wastes analysts time. There are lots of ways that these financial tasks can start to weigh on the financial teams as they are not scalable. As hospitals and insurance providers merge and grow the problem amplifies and becomes even more difficult to manually manage. Typically, hospitals will approach this problem by increasing their staff rather than create a process or system that can continue to scale basic financial analytics and billing. This leads to increased operational costs that are difficult to manage without laying off employees(this isn’t even considering the costs of hiring, firing, rehiring…).
The solution is automation(mic drop….).
Okay, automation is easier said than done.
It requires a combination of buy-in from multiple stakeholders to financially back the projects and trust from directors and manager who read the reports and output of these automated systems. However, when executed well, these systems can reduce hundreds of hours of manual work.
It seems complicated, but often times automation, when done well really isn’t. Many times it actually simplifies the overall workload on your analysts. There might need to be some upskilling in basic SQL. However, we are living in a data driven world and SQL is the data language(even NOSQL databases have SQL layers because that is what we humans understand).
If your team doesn’t have that the skill set to build the tools themselves, then find it. Automation consultants and engineers can be found both internally and externally. Once the main systems are built it can be maintained by people with a lesser knowledge in programming and automation. Automation will help provide consistency, reduce waste and allow your analysts to focus on more important work.
Fraud Detection
Fraud detection and adjudication are necessary practices of insurance providers. This is because healthcare fraud costs billions of a dollars a year. it comes from patients and also healthcare providers that charge millions of dollars of upcoded and wasteful procedure purely to bolster their bottom line. In fact, there are even consultants and websites that specialize in helping healthcare providers bill creatively and max out their claims. These practices don’t increase costs for the insurance provider, they increase cost for the patients at the end of the day.
Insurance providers have to pay analysts to spend the time to manually go through claims to look for possible patterns of abuse. If you notice, the same problem in billing is occurring here. The problem is the manual step. Healthcare billing and claims processing have gotten too big to effectively handle manually. The solution of manually processing claims just don’t scale. This is where the tools of automation, big data, data warehousing, and analytics work well. They allow a specialist to create systems to munge the data effectively and scale even as the data grows.
Currently, most insurance providers have teams for fraud detection but they often can only go through a small percentage of claims( even billion dollar companies usually only go through about 5–10% of claims manually).
Sometimes they will even hire outside firms to again manually go through and look for low hanging easy fruit. For instance, billing consultants found that 78% of 99215 codes in Wisconsin (highest level established patient office visit) were incorrectly used. This is a very easy issue to spot and it becomes very expensive very fast.
All of this is usually limited to manual processes. Getting data pulls from databases into excel and then slicing and dicing the output. The beauty of automated methods is that they can quickly reduce the number of claims required to manually process.
What is even better, once you have created a system, you can replicate the results at a regular cadence. Even as analysts leave and new hires take their place, it is much easier to train them to understand the results of a system that works rather than have them have to relearn what to look for in the raw data. This increases the number of claims an insurance provider adjudicates while at the same time improving the efficiency of the process.
Many think you need complex machine learning algorithms to detect fraud when you first start developing models and your data isn’t even classified yet. However, when starting out, it is important to focus on cutting the number of targets claims your analysts will look at by 60,70 or 80%. This doesn’t take a complex algorithm. It requires developing basic business rules that can help sort through the false positives.
After properly managing claims and tracking which ones are fraud and which aren’t then it is easier to develop a machine learning model because your data is classified(fraud, not fraud).
Fraud detection and adjudication is a slow and costly process for many healthcare providers. Automation and well-executed analytics offer thousands to hundreds of thousands of dollars of saving.
Third Parties And Contract Management
One of the ways to get some systems automated and integrated is to use third-parties. Hospitals, insurance providers and other healthcare institutions are not tech companies. They don’t focus on developing technical tools to help in their day to day operations and this is ok! What is not ok is constantly signing contracts with new third parties for redundant features. For instance, paying for multiple data visualization tools is pointless like Tableau, Quilk, and OBIEE all provide report features and yet some companies use all three and more. Similar things can be said about relying on multiple financial systems.
Having redundant third-party contracts causes several issues. One, and most obvious is uncontrolled operational costs. Besides the upfront costs of signing multiple contracts it also requires more employee to manage the systems, manage the contracts and deal with the billing. These contracts are also difficult to break without paying a hefty fee for breaking the contract early. Thus, it is very important to reduce redundancy because the costs will be more than the cost of the contract.
The second issues are that these systems don’t have easy ways to access the data behind them. Lacking access to your own data is a problem. It is difficult to make good financial decisions if you don’t know what is happening in your institution. When you rely on third-party software, you need to be aware of the terms of your contracts and the features of the software. Otherwise, your expenses will grow from nowhere and you won’t be able to make good decisions.
Administrative costs like billing, fraud detection, and contract management are driving up health care costs for insurance providers, healthcare systems and patients. With a little bit of automation and process management, many of these costs can start to be mitigated. Automating processes like billing and financial analysis can be done with a combination of SQL and python. Contract management requires a combination of process improvement and analysis of the previous contracts. In the end, it can all lead to scalable cost savings that can occur systematically and don’t constantly need manual intervention.
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