How data integration is key to improving healthcare decision making
The vast amounts of information in various systems, ranging from the supply side to financial systems, must be brought together to aid care.
Data is everything in healthcare. It is the fundamental requirement for developing an in-depth picture of the industry, and opening the door to understanding performance, outcomes, costs and efficiency (or a lack thereof). Researchers want data to conduct studies and discover breakthroughs. Finance teams require it to develop spending forecasts, and determine overall financial performance and organizational health. Suppliers and manufacturers use it to get a robust understanding of product information. Clinicians want to draw on data to improve patient care, procedures and therapies. With so much potential at the industry’s fingertips, most healthcare professionals list data and analytics for improved decision making as a top priority. The vast amounts of data locked within systems can offer insights that will help the industry not only address critical business and operational challenges, but also help ensure the effective delivery of patient care. Efforts are underway to redesign and standardize the way healthcare is delivered, whether it’s a knee replacement, or preventing or treating sepsis. The change is driven in large part by the move to a value-based care model, which requires providers to bear greater financial responsibility (and risk) for the entire episode of care. This necessitates that providers understand the true cost of a procedure, from start to finish. There must be a full accounting of every cost, including location, labor and supplies, and those costs must be balanced against patient outcomes. Provider organizations must be able to answer two critical questions: What does it cost to deliver care? Are we getting the best value (quality and outcomes) for the price? The supply chain lies at the center of this transformation. It houses a trove of data and connects with nearly every other system in the hospital. But the onus is not on the supply chain alone. While supply chain enables organizations to make more informed purchasing decisions, it does not contain all of the data required to address questions regarding quality and outcomes. For example, how can provider organizations understand which products succeed and which fail? A familiar example is bed pads. Bed pads that don’t remove moisture will cause bedsores, requiring an increase in the level of patient care. Supply chain data can tell us the cost of clinical supplies, the inventory level and more, but unless users understand how and where that product is used, they can’t have a complete picture of cost, quality and outcome. In this instance, supply chain and clinical data combined have shown us that the cheaper pads can actually drive up the total cost of care. If the healthcare industry intends to use data as evidence for decision making, it first needs to ensure the data provides a complete and accurate picture of what was used, what was paid, reimbursement, variation and, of course, the outcome. To do this, supply chain data must be fully integrated with clinical data. Imagine the impact of a fully aligned supply chain and clinical environment. Consider implantable devices—quality data enables physicians to make more informed decisions at the point of care. For example, it’s well known and documented that physicians have preferred vendors. The challenge is that implantable costs vary by vendor, and they are often significant. In some cases, the cost of an implant might vary by 40 percent. These devices should not be evaluated on price alone or even on personal preference. We need reliable, complete data to evaluate the efficacy of outcomes, based on the health profile of the individual patient. This data can then be used as evidence to enable clinicians to make decisions based on both cost and outcomes. So what’s the hold up? Data, of course. Data shouldn’t be difficult to muster, but it is. First, the industry still struggles with standardization of its data, which means data doesn’t flow freely among systems. Second, there’s still not an accurate or complete clinical supply capture at the point of care. Sometimes this is process related, where staff don’t record product at the bedside. More often, it’s systems related. Clinicians scan a product at the point of care, only to find the system doesn’t recognize the data, or the data is incomplete. Without access to timely and accurate data, hospital leadership can’t possibly conduct value analysis to spot issues with care delivery, identify variation in costs and quality, and more important, make the necessary changes to improve patient care. There are three key steps providers can take to normalize data and the process to support it. Leverage clean, standardized data. Bad data undermines value analysis activities. Supply chain professionals need to define a set of processes to clean and standardize data in the item master, and deploy a solution that supports those efforts. The ideal solution will not only clean your data now, but also ensures that data remains up-to-date and synchronized with other systems. Deploy a clinical item master. Healthcare needs a clinical item master that can serve as the single source of truth, and makes it easier and faster for clinicians to document product use. The data will help ensure that providers have complete and accurate information on chargeable items. Engage with clinicians. The alignment of supply chain and clinical is more than the integration of back-end systems. It’s a partnership where supply chain leaders and clinicians use data and experience to help redesign the delivery of care. Clinicians can provide knowledge regarding the “whys” of product preferences. Supply chain and value analysis teams can give clinicians visibility around costs and outcomes that will inform their decisions in line with the organization’s long-term performance goals. If an organization can marry supply chain, clinical and finance information, and normalize the data these systems house, it can finally begin to transform healthcare by using data as evidence in decision-making, and more effectively and efficiently support the redesign of care.
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