The administration that goes into running a hospital or health system involves a large amount of resources. Unfortunately, much of this administration is done without enough regard for the efficiency that can be achieved with technology. In some cases, administrative tasks may be determined to be overly burdensome and skipped altogether when an automated solution may allow this hidden value to be properly mined. There are a number of existing technologies and applications that can be applied to healthcare that alleviate the burden of administration and claims processing while exposing additional benefits.
The U.S. is a world leader in the cost of healthcare administration. The New England Journal of Medicine estimated in a study that the embedded cost of administration in healthcare is 30%. The New York Times puts this into context for an average American family—they’ll pay $19,000 for healthcare coverage every year, $5,700 of which goes to hospital administration. These are clearly significant numbers.
This isn’t to equate all administration with bloated waste. There is certainly value in the work that is being done, and it won’t be possible to automate it all away. But strategically automating labor-intensive business processes will allow for better management of an organization’s revenue by reducing wasteful spending.
A large driver for automation comes down to the dollars and cents—finding how automation solutions can make each dollar go farther. But that isn’t to say it’s the only consideration; it also helps an organization put their people to work where they are really needed.
What Can Be Automated?
A broad question with a broad answer, in general terms anything involving the transmission, processing, reporting and analysis of data is ripe for automation, and any rote tasks or standard processes can be automated. When it comes to automating financial solutions for hospitals and healthcare organizations, there are a number of specific answers.
How to Get Started
The first step is to figure out where your attention is best served. Maybe you’ve been thinking about some of these things already, or maybe they are beyond what you’ve considered before. In either case, it’s a good exercise to take stock of where you’re at today and where you would like to be.
There Are Some Helpful Questions That You Can Ask to Guide This Brainstorming:
Try a direct “What areas of low-hanging fruit are there to strategically improve our business?” See what things you think employees spend too much time doing where technology could help. Put these ideas together as an initial list.1
Look at the flip side: “What kinds of activities should our employees be able to focus their time on?” Examples might be for providers to focus directly on patient care, and for administrative staff to focus on strategy. Get a sense of the areas where employees add real value. Then think about all of the things they do that fall outside of these areas. How many of these can be automated?2
A different approach: “What domain knowledge can be harnessed into an automated solution?” It is too common for important domain knowledge to be in the hands of a few employees and not documented anywhere. Building an automated solution around a formalization of domain knowledge will help document this important information in addition to the potential for efficiency gain. Once there is a formal process, it will be easier to understand niche areas of the organization and identify potential for further improvement.3
Machine Learning Tips
Successful Machine Learning Projects Will Carefully Navigate a Few Things:
They need to bring together three disciplines: statistics, for developing the right model; programming, for correctly implementing the model; and domain knowledge, for understanding the context, important pieces, and limitations throughout the project. Of these, the programming portion is generally the least prone to error; with the others, a bad model is more easily disguised.1
The collection of data to build the model off of is going to be the most labor-intensive part of the process. It’s important to understand:
1. What data would ideally be available for the model,
2. What it will cost to get data from disparate sources into the model,
3. How to pragmatically choose data sources balancing the data quality and the cost to collect the data in order to build a responsible model.
A machine learning solution is iterative; it is not something to “set and forget”. While the model does the legwork, a robust, careful solution will continue to tweak and consider alternative models to see what best serves the business over time.3
If you think it’s time to take a closer look at how automation can streamline your business, it’s a good idea to begin working through your options and what a comprehensive solution might look like. Our company of 30 brilliant and agile developers is here to help you every step of the way. With a proven track record of implementing solutions and a deep knowledge of the healthcare industry, let us guide you on the path to streamlining your business and making each dollar go further.
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