Over the past 12 months, the healthcare industry has seen many providers take advantage of their supply chain data to make better decisions not only for cost-cutting measures, but to improve and, at times, standardize patient care. We’ve even seen supply chain involvement in predictive analytics, using data to better anticipate which products will be needed and enable providers to not lose speed to patient care if a product is discontinued or backordered.
But these opportunities to take advantage of data for the benefit of quality patient care and optimization for the hospital come with a renewed focus on and accountability for the integrity of that data. So much of our efforts to transform the industry, and establish a sustainable model for the future, hinges on accurate, trust-worthy data.
Healthcare IT systems drive massive amounts of data. Our industry’s challenge is not a lack of data, but rather the quality of that data. Too often we cannot access the data we need when we need it, or worse yet, the data is inaccurate, incomplete, or simply outdated. When inaccurate or “dirty” data drives our industry’s activities, the outcomes suffer — leading to a host of issues, including a lack of visibility, increased risk for more errors, clinician distrust, and an inability to accurately measure results.
Take, for example, one of the more critical initiatives in healthcare today: the investment in electronic health record (EHR) systems. These implementations often cost hospitals and health systems millions of dollars. One reason that EHRs are so important, beyond the potential for clinical gains and the essential requirement to digitize health records, is that they provide an opportunity for reimbursement improvement and revenue growth. Faulty data can derail EHR programs, adding additional costs in time, dollars and resources, and can even disrupt clinical care. Organizations must be able to rely on the integrity of their EHR outputs.
Incorrect data in the supply chain also has an adverse affect on value analysis activities. As the U.S. healthcare market shifts from volume-based to value-based care, value analysis activities are taking center stage within hospitals and health systems. Value analysis allows us to review product equipment and technologies to evaluate their clinical efficacy, safety and impact on organizational resources.
Hospital leaders know that this inaccurate data is hindering their ability to achieve significant progress in delivering accountable and informed care. But the sheer volume of data and its ongoing churn presents a significant challenge in ensuring accuracy. On average, changes are made each year to one-third of the 30 million plus medical-surgical products on the market in the U.S. GHX has found that bad data contributes to over two million transactional errors each month. Corrections to hospital databases occur at a rate of 192,000 edits each month.
Allowing this data to continue as the foundation of our strategic, operational and IT initiatives means we will inevitably fall short of our goals, and further delay the sort of true, transformative change that is crucially needed in healthcare.
So what can we do to drive data integrity from the ground up? Hospitals and health systems can begin by establishing a master data management strategy through which product data is cleansed and corrected. Enrich item master data by assigning charge codes to the products procured. Hospitals should also implement a robust content management solution to continually monitor for changes and updates, and then systematically correct product data inaccuracies. The rewards are substantial, not only in terms of improved efficiency and better analysis, but in multi-million-dollar savings for an organization.
Integris, one of the largest healthcare organizations in Oklahoma, is a great example of the operational improvements and costs savings that can be realized by making a concerted effort to ensure clean, accurate data. Like many healthcare corporations, Integris understood the never-ending, uphill battle of maintaining accurate product data. But it also saw the potential that lies in finally getting a handle on its data. The organization initiated both a master data and contract management strategy that improves data quality and allowed Integris to achieve approximately $3M in annual savings through better contract compliance and lower non-file spend, driving down price exceptions by 25 percent and contract exceptions by 65 percent.
By cleaning up the item master and maintaining its integrity over time, we can turn previously “bad data” into business-critical information that helps set strategic direction, feeds EHRs, and serves as the foundation for efficient and effective value analysis efforts.
Clean, accurate data is a goldmine for insight that allows us to improve operational performance, reduce costs, and ultimately provide better-informed, higher-quality patient care. We must ensure that we maximize the efficacy of our health IT investments by ensuring the accuracy and integrity of our data at all times. We have tremendous opportunity to get healthcare right, and it all begins with data.
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