Most health insurance companies feel like they are trapped by their Healthcare Effectiveness Data and Information Set (HEDIS) reports. These annual reports are essential for financial survival, and there’s no way around that. But they don’t have to spell misery. Successful data management and data exchange can make all the difference in the entire HEDIS reporting experience—and to the bottom line.
The formula for successful data management and exchange involves several elements. These are:
- Take the time up front to get the project set up right.
- Know what can lead to trouble and take steps to avoid what can go wrong.
- Ensure you have a good plan for the project’s success going forward.
- Explain to clients why the data you are gathering is important for them to achieve their HEDIS reporting goals.
- Be accessible to the client.
Take the time up front to get it right
Any data management or data exchange project manager knows that it’s not easy to build a successful project, even with proper planning. There is a whole host of complicated requirements issued by the Centers for Medicare & Medicaid Services for the filing of a HEDIS report. That’s why a poorly planned project is doomed to failure. It’s also why it is essential to take the time necessary—at the very start—to lay things out for success.
Poor planning for a HEDIS report can be compared to recklessly planning a trip. At first, everyone is excited and can’t wait to go. Then, when the group arrives at the airport, it realizes that someone in the group left his or her ID at home. Now you aren’t going anywhere, because one important piece of planning was missed.
It’s the same with data management and exchange. Even though a health plan might be exciting up front, it’s essential to itemize what will be needed in advance so that essential details won’t bring the project to a standstill down the road.
Plan ahead—or pay later in time wasted, money spent, and headaches.
Examples of what can go wrong
When it comes to successful data exchange, an old adage comes to mind: garbage in, garbage out. One of the most prevalent things that can go wrong in a poorly planned data exchange project—especially a HEDIS reporting project—includes misunderstandings about formatting. If the data is formatted incorrectly from the start, there will be data exchange problems.
Examples of what can go wrong in a data management and exchange project are everywhere. In 2013, healthcare.gov, the U.S. federal health insurance exchange website set up for Americans to apply for insurance coverage, ran into serious problems. Poor planning resulted in health insurance companies receiving faulty or incomplete data from the exchange, Bloomberg reported.
In 2014, the launch of the Oregon health insurance exchange “failed to deliver a functioning consumer website,” due to “excessive optimism, weak oversight,” and poor project management according to InformationWeek.
For health insurance companies seeking to have their HEDIS reports done correctly and on time, it falls on the data management professionals to ensure that hospitals and doctors know in advance what the data exchange requirements are under HEDIS. This will prevent provider abrasion later, when the health data management vendor is forced to continually go back and ask for corrections or clarifications to the data.
Vendors should make it very clear as to what they are looking for in a HEDIS report and what the meaning is behind the elements. If a health plan doesn’t understand why elements are needed in the data, they are left to guess what’s important, and this can spell disaster.
If a health plan interprets on its own how HEDIS data need to be formatted, it can inadvertently and unintentionally get it wrong—and the end product (in this case, the HEDIS report), won’t turn out as expected. Taking the time to have these data formatted correctly in advance doesn’t take much additional time, and what little time it does take is well worth it in the end, when things go smoothly for obtaining HEDIS reporting results.
When a data manager explains how the data need to be formatted before the project starts and the health plan signs off on it, it makes it that much easier to determine where the problems lie when things go wrong. It will make it that much easier to determine if the vendor is falling short or if the problem lies with the health plan itself. It also makes it easier to address that problem and fix it.
The federal Agency for Healthcare Research and Quality (AHRQ) says good data collection begins with the hospitals and doctors, and it’s important to understand the challenges that hospitals and doctors face up front and help them address those challenges. According to AHRQ, the most common problems include:
- How to ask patients and enrollees questions about race, ethnicity, and language and communication needs
- How to train staff to elicit this information in a respectful and efficient manner
- How to address the discomfort of registration/admission staff (hospitals and clinics) or call center staff (health plans) about requesting this information
- How to address potential patient or enrollee pushback respectfully
- How to address system-level issues, such as changes in patient registration screens and data flow
How to implement a successful data management or data exchange project
After data are correctly gathered and recorded in the most useful format, planning ahead for the HEDIS reporting project is the next key to success.
“In reality, project management is rarely straightforward,” writes Jennifer Lonoff Schiff in a blog post titled 9 Secrets to Project Management Success.
Schiff writes that staffing problems, missing deadlines, and scope-of-work changes are some of the reasons why projects fail.
Info-Tech Research Group advises project managers to get buy-in from the entire organization when starting a data management project. “[Master data management] (MDM) can be difficult and expensive,” Info-Tech notes. “Organizational buy-in and an understanding of the organization’s data environment are imperative to the success of a[n] MDM implementation.”
The size and scope of a data management project determine the scope of its budget and the amount of time that needs to be spent up front. A good plan should be scalable. Even if a project has a limited number of data elements, it should still be scalable to any size.
When it comes to the staff needed to accomplish the data management project—even with a project manager who is tech-savvy—it is always important to have details worked out up front.
Working with a lead developer who understands the nuances of the data is also critical. Regardless of who the point person is, it is still critical to talk in advance about the information that will be exchanged. These advance discussions are imperative to make any data management project successful.
Project managers who fail to have these discussions up front will notice that there can be redundancy in efforts, wasting both time and money. If a project is well laid-out in advance, it is much easier for another staff member to watch over it while that lead manager is on vacation or out of the office. It makes it more reasonable and simpler for another staff member to pick up where the lead left off when you have a good implementation plan in place.
What does good planning for a data management project entail?
For a data management and data exchange to succeed, you must know what the company’s partners need and must make sure they know what the data manager needs. Successful project management is ultimately about making sure information is clear and clean, ensuring that the data shared are what is needed to accomplish the end goals. Making sure data are created with clarity will ensure success 100 percent of the time.
Be clear about who is responsible for what—including deadlines. "When multiple people are collaborating on the same task, assignments, deadlines and other important details often get lost in translation," explains Fred Mouawad, founder and CEO of Taskworld, a task management platform, in the aforementioned CIO.com blog post. To avoid confusion, he says, "determine which team members are responsible for which pieces of work [up front], and enforce accountability. An online task management program is a simple way to do this."
Explaining why these data will help the client achieve its data management goals
To get a good HEDIS report, it’s essential to make sure that clients understand the ultimate goal of a data exchange project. A vendor shouldn’t go overboard with these explanations, but nonetheless, these discussions must take place. If discrepancies or issues occur during the process, a vendor should ask the provider if it was clear in its instructions to the client regarding the data management and exchange. It should ask, “Did we miss anything on our side?”
Data project managers should do everything they can to remedy misunderstandings and to help the client deliver the data that are needed.
Being accessible to the client for assistance and to answer questions
Maintaining a good relationship with the client before, during, and after a data management project is also key to success. “It's common for small business owners and independent consultants to focus their resources on gaining new business,” MBO Partners notes in a blog post titled 6 Tips for Building and Maintaining Long Lasting Client Relationships.
Health insurance plans are looking for data management firms that have put customer service at the top of their priority list. Customer service and the ability to interact well with clients comprise a critical part of working hand in hand with them to accomplish a successful HEDIS data management project.
Closing
One of the things often missed regarding data management function in general is that people might look at it with an immediate vision of it being entirely technical. But most data exchange is rooted in exchange requirements that the client will be expected to deliver in a way that contributes to success. A somewhat unglamourous but nonetheless imperative part of any data management project is thinking about the end result. The end goal needs to always rule all aspects of the project. All parties involved in the data exchange project need to understand the end goals explicitly before the project goes live.
Preplanning, the first requirement of a successful data management or data exchange project, must be done with a high degree of accuracy before the project starts. The project is bound to fail otherwise. Essential elements that need to be understood before the project starts include what the clients will deliver and what processes will be used, including how, where, and to what end the deliverable is going to be used.
The interactions and interfaces in data exchange can be a weak point in any project if they are not managed well. Any company that wants to establish a successful data management and/or exchange project will find the biggest differentiator of any project is understanding the importance of planning ahead.
What are you doing to ensure a successful data management venture?
