Is That Data Valid? Getting Accurate Financial Data in Healthcare

financial data

I’ve been in many meetings with healthcare finance experts where the topic turns to the validity of the data they’re viewing. And they’re right to be concerned — after all, finance experts need accurate, timely, actionable financial data to make informed decisions about their organizations.

Accessing high-quality data to make strategic decisions, however, is not an easy task in the healthcare industry for many reasons. Some of the key challenges include the following:

  • Data is stored in many different, siloed source systems. 
    Healthcare data is typically stored in a number of siloed source systems (also referred to as transactional systems) across the organization. Examples of source systems include EHRs, ERP systems (general ledger, human resources, and supply change, for example), costing systems, patient satisfaction systems, and Excel and Access databases. While each source system provides a specific functionality for that particular department’s needs, as a whole, source systems don’t have the ability to share information between each other. This lack of interconnectedness limits the ability for the organization’s data analysts to evaluate data on an enterprise level to make improvement recommendations.
  • Health systems need to store large amounts of data.
    The healthcare industry requires the storage of enormous quantities of data to provide billing and patient care. To give an example of how much data storage is required, one of my colleagues has worked with a hospital CIO that plans for future storage growth by estimating 100 MB of data generated per patient, per year. Multiply the data a single patient produces by thousands of patients, and the amount of data that needs to be stored is in the terabytes. Sifting through so much data to analyze and report on it requires time-intensive efforts by analysts who typically have a backlog of requests in their queues.
  • Healthcare data is highly volatile. 
    Business definitions are complex and metrics are constantly changing in the healthcare industry, which results in high volatility of the data. Take, for example, the length of stay (LOS) metric, a key financial measure that’s also reported by clinicians. Herein lies the problem — there may be two different LOS definitions. And decisions can be skewed if users either don’t know which metric to use or do not know the definition of the metric that was reported. Here’s how it happens: Clinicians calculate LOS by how long a patient physically stays in the bed. But from a financial perspective, LOS is calculated on a 24-hour scale that ends at midnight. Because of the discrepancy in LOS definitions, users aren’t able to compare apples to apples.

With all of the challenges involved with pulling timely, accurate data to make informed business decisions, it becomes difficult, if not impossible, for leaders to feel confident that they’re viewing reliable data. And without good insight into how their operations are going, it’s difficult to know where the dollars are being spent or where to make improvements in order to provide the value-based care that’s so critical in today’s healthcare environment.

Overcome Current Data Challenges with an EDW and Analytics

It is possible, however, for health systems to overcome their data challenges by adopting two key solutions — an enterprise data warehouse (EDW) and sophisticated analytics applications.

  1. Healthcare Enterprise Data Warehouse
    An EDW overcomes the challenges of accessing the volumes of siloed data. It does this by functioning as a layer on top of all of the other transactional application databases already in place. The EDW doesn’t replace the individual sources systems; these systems continue to deliver the department-specific value they were designed to provide. Instead, the EDW pulls data in its raw format from all of the various source systems and stores it in an easily accessible central repository that serves as a single source of truth. It’s like a one-stop shop where all of the information can be accessed whenever it’s needed. Then, when an analytic use case arises, users from different departments can analyze the same data sets using different definitions based on their department’s needs. This functionality lets a financial analyst define LOS based on the midnight census and a clinician define LOS as the time a patient physically stays in the bed. Besides enabling departments to define their own definitions, the organization can also set up an enterprise-wide definition that is available to all users.
  2. Sophisticated Analytics Applications
    Sophisticated analytics applications provide value to a healthcare organization’s data in a way that individual source systems can’t. That’s because analytics applications are designed to work with the EDW, so that users from different departments can drill down and through the data to gain deep insights to the data. With this knowledge, analysts are then able to provide recommendations to drive quality improvements and cost reductions.

Data drill down and its importance

Being able to drill down into data means that users who are seeking answers to specific questions are able to click down through the various levels of data that are aggregated in the EDW. For example, a CFO can review a dashboard that displays information about general ledger data at a facility level (a specific hospital in a system), an organizational or department level (cardiology or oncology, for example), and even down to the account level (a labor expense, for example). The CFO can then identify which expenses and/or revenues have been rising faster than anticipated.

Data drill through and its importance

While data drill down enables users to access various levels of aggregated data, data drill through enables users to click through to the lowest level of data (account level in our example above). Then they can click through to the lowest level of detail (transactional level) from the source systems that have been loaded into the EDW. For example, a CFO will be able to see that labor costs have been rising. But to determine precisely when the increased labor costs occur and if they’re related to utilization, the CFO can also drill through to the shift data.

Healthcare’s Financial Data Made Actionable

In today’s environment of value-based care, leaders need to access cross-organizational data that will give them the answers they need to make informed decisions about their operations. Waiting for busy analysts to track down data from siloed source systems to run reports from Excel or Access doesn’t offer the same time to value that an EDW provides. By adding sophisticated analytics solutions to the EDW, users can then drill down and through the data sets to gain insights they’ve never been able to access before — and in near real-time. Using the EDW and analytics solutions simultaneously, health systems will have access to transparent, reliable, timely data that can be used to drive confident, well-informed decision-making.

I’d love to hear from you about how you’ve tackled the need to access reliable financial data at your healthcare organization. How would an EDW and analytics solutions enable you to make informed strategic decisions?

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