Month: January 2018

Correcting Patient Coding Can Save Millions

Healthcare

Physician burnout is a real thing. Between seeing increasing numbers of patients, documenting visits and reviewing health records, more tasks are expected of healthcare providers. According to a Mayo Clinic Study, 54% of physicians reported at least one symptom of burnout. While there are many new technologies to assist with patient care, using new technology does not always integrate well with the face-to-face, personal nature of providing medical care. Some physicians perceive that technology slows them down, as they do not have the time between patients to update codes and clinical details in documentation. But the reality is that taking the time to ensure proper coding reduces the likelihood of redundant tests, the risk of medical errors, and can significantly impact value-based contract reimbursements. So in this sense, taking the time to input patient information, document and code patient conditions is critical.

From a financial standpoint, healthcare CFOs understand the benefits of accurate patient coding. The transition to value-based payment models in healthcare is accelerating under the Medicare Access and CHIP Reauthorization Act (MACRA). The intent is to provide financial motivation for providers to manage more patients under risk-bearing or coordinated care contracts. The two methods of managing physician reimbursement under MACRA—Advanced Alternative Payment Models (APMs) and Merit-Base Incentive Payment Systems (MIPS)—both hinge on correct clinical documentation to assign accurate risk adjustment scores.

From a provider’s standpoint, risk adjustment scores such as the Hierarchical Condition Category (HCC), impact physician documentation practices and requirements. This means that all chronic conditions need to be reported on an annual basis for all patients, regardless of the care setting, in order to correctly estimate the risk score for a provider’s patients. Annually, this information is reported to the Centers for Medicare and Medicaid Services (CMS) in a Risk Adjustment Processing System (RAPS) submission. In many cases, this submission is compiled from claims data; but while claims data carries a great deal of information on a patient’s medical history, it often understates the true “risk” of the patient population. In financial terms, this means that providers who understate their patient risk receive a lower reimbursement from CMS. In other words, because of inadequate patient coding, healthcare providers are leaving money on the table.

How can healthcare organizations solve the problem of underreported patient risk? We ventured to take on this problem, and sought to find a solution that any provider group, large or small, could use. Understanding that not everyone has the budget for an expensive platform or a team of auditors to check through exhaustive patient records, we wanted to see what technology could do to ensure patient risk is accurately coded and reported—without putting an additional burden on physicians. 

We used machine learning and natural language processing of unstructured data, encompassing millions of files, including doctor’s notes and faxes. Within the many silos of data were notes captured of patient conditions that were not always reflected in the claims coding in the patient’s electronic health record. This technology enabled us to shortcut the work that would normally be undertaken by an entire auditing team manually reviewing patient files. To give an idea of the efficacy of a process like this, in this case we were able to discover $1.3 million in under-represented risk reimbursements for the provider group. 

For healthcare CFOs who are looking to make sure patient coding is accurately reflecting population risk adjustment for CMS reporting, there 2 options to ensure your organization isn’t leaving money on the table. 1) Invest in hiring additional people to educate, input, and audit correct patient EHR codes or 2) invest in a technological solution to ensure you’re not losing out on reimbursements due to under-reported patient risk. Healthcare organizations who form a strategy to ensure correct risk adjustment reporting can see millions in additional deserved reimbursements on their bottom line. 

For a free demo, contact us.

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BetaXAnalytics is a healthcare data consulting firm that helps payers and providers to maximize their CMS reimbursements and helps employers to reduce their healthcare spending through proven strategies to contain costs. For more insights on using data to drive healthcare, pharmacy and wellbeing decisions, follow BetaXAnalytics on Twitter @betaxanalytics, Facebook @bxanalytics and LinkedIn at BetaXAnalytics.

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How To Make Data-Driven Decisions When You Don’t Have Data

Data

In 1934, T.S. Eliot famously lamented the empty soul of modern work life. Though he wrote “Choruses from the Rock” over 80 years ago, he hits a nerve in our present-day struggles by asking, “Where is the wisdom we have lost in knowledge? Where is the knowledge we lost in information?” In current times, we have so much data at our fingertips, but does that mean we are making better decisions? Today, the core of data analytics is simply using information to make well-informed decisions. The only difference today from 80 years ago is that we simply have more information available to make decisions and more sophisticated methods to use this information. 

A question that I get time and time again from managers is “How do I make data-driven decisions when I don’t have any data?” As a decision maker, it’s incredibly frustrating to feel hampered by a lack of data.  Despite wide availability of information, companies might not put data into the hands of decision makers for a couple reasons. Maybe the organization does not have an effective way of capturing data—this happens in companies that have older technology in key areas of the business. Or maybe the data they have is too messy—for instance, perhaps they can track customer quotes online, but they have no way of cleaning up the 30 different customer quotes that actually were generated by the same person. In other cases, data is kept sectioned off in certain parts of a company, but it is not shared widely with people whose decisions depend on the information. For whatever the reason that managers feel like they do not have access to information to make an informed decisions, there are a few guidelines you can follow to ensure that you are making the right decisions.

The key is not to get more data – it’s to get the right data.

It’s important to keep in mind you can have all the data in the world and still not have the information you need. The key is not to get more data – it’s to get the right data. In research from the book Stop Spending, Start Managingexecutives reported wasting an average of $7,731 per day—or $2.8 million per year—on wasteful “analytics.” The first step to making sound decisions is to recognize what that “right” data is for your business. Once you identify this, you can cut your time looking at reports significantly because now you have a strategy. You know exactly what you need to see to make a decision, and you can see through the noise of mountains of data that don’t add value to your decisions. 

Executives reported wasting an average of $7,731 per day—or $2.8 million per year—on wasteful “analytics.”

If you don’t have access to the data you need at work, here are some steps you can take:

1.      Identify your business goals.  Here’s your opportunity to start at square one and holistically rethink how your decisions are made. This entails taking a 50,000 foot view of your business to make sure that you’re asking the right questions. We often get in the habit of process, and we repeat process patterns of looking at old reports that don’t tell us what we really need to know. If your business unit always looked at a set group of metrics, it’s easy to get tunnel vision and to see it as a bad decision to stop looking at a certain report. But I recommend taking a step back to ensure you’re asking the following questions before even looking at any data:

·        What are the business objectives for which we are responsible? (In other words,what are our goals?)

·        What are the crucial areas of the business that we need to be tracking?

2.      Identify which data you need to track progress on your goals. What data do you need to see to be able to track progress on these goals and to make sound decisions? In most cases, every business goal you cite has one or multiple metrics that will help you to gauge progress against that goal.

3.      Examine your data access. Identify which of these must-have pieces of data you have access to. For the data you don’t currently have access to, identify how you can get access. This can be as easy as requesting access from another department, or as hard as implementing a way to capture new data.

4.      If needed in the short term, identify proxy data for the information to which you don’t have access. When you can’t access crucial data, is there a proxy measure that would tell you the same thing? For instance, if you have no way today of tracking the number of customers who are calling with a particular complaint, can you poll your front line customer service representatives to identify trends in complaint themes? Finding a short-term proxy for needed data will provide you with some useful information. The proxy is not a perfect solution, but in the short term it’s better than using no information at all.

5.      Start the process of gaining access to the data that you need. As simple as this sounds, if you’re in a situation where you don’t have access to crucial data, the goal is to exit this reality as soon as possible. Whether this means insourcing or outsourcing to gain access to data you need, there’s simply no business case for continuing to manage without the right information.

The guiding principle of how to manage your data is to identify what data aligns with your goals—if you don’t have access to this data today, the best place to be is somewhere on the track to gaining access to this data. Identifying proxy data is a bridge to dealing with an undesirable situation, and moving towards one that puts you on the right path. But it is important to not accept a lack of data within your company simply because it’s “the way it’s always been done.” If you find yourself clamoring for meaningful metrics, creating a process to get this data involves some work–but there are huge rewards for your business in the end.Y

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BetaXAnalytics is a healthcare data consulting firm that helps payers and providers to maximize their CMS reimbursements and helps employers to reduce their healthcare spending through proven strategies to contain costs. For more insights on using data to drive healthcare, pharmacy and wellbeing decisions, follow BetaXAnalytics on Twitter @betaxanalytics, Facebook @bxanalytics and LinkedIn at BetaXAnalytics.

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How did I gain weight working for a wellness company?

Here I share my personal experience while working in the corporate #wellness industry, and what I discovered to be critical for building a true culture of corporate #wellbeing.

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Some things my mom taught me:

—All things are possible
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