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The move to value-based care has sparked a healthcare revolution. Instead of focusing on the volume of healthcare services, health systems are implementing population health programs aimed at improving patient outcomes through personalized care delivery and reducing costs by eliminating wasteful practices. Because laboratory testing is uniquely positioned to influence both of these aims, health systems must reconsider how they manage both the testing itself and the data it generates in order to develop a successful population health program.

Although laboratory testing makes up only 

, its impact on care is widespread. Lab tests are the highest volume diagnostic activity and are required to accurately diagnose and treat most health conditions. About 

 are based on lab results. The lab data hold great potential to personalize care, but health systems face several challenges that limit their ability to capitalize on it.

Major Challenges Impacting Laboratory Testing

Lab test ordering is not always precise. In some cases, unnecessary tests are ordered and performed. In others, the most appropriate testing is not performed. In the case of both under-and overutilization of lab testing, accurate diagnoses are delayed, leading to negative health outcomes and high costs.

For decades, patients have been over- or under-tested, misdiagnosed and overprescribed because doctors, pharmacists and laboratories haven’t had access to complete, actionable patient information. In the rare cases when all of this information has been available, there has been no efficient way for clinicians to glean insights that could help them personalize care.

Additional challenges include the ever-expanding number of tests, lack of best practices to help providers select the best testing protocols and ineffective processes to maximize the positive impact on patient outcomes. One study of U.S. family physicians found that while tests are ordered in about one-third of all patient encounters, in nearly 15% of these cases, the physicians 

  the tests they ordered, and 8% were confused by the results.

This imprecise approach to lab testing results in subpar outcomes for patients and escalating costs that tax the entire health system. The U.S. healthcare system wastes an 

r on unnecessary or inefficiently delivered services and missed prevention opportunities. Under and over-utilization of lab tests can 

 by prompting unnecessary or delayed treatment and procedures due to incorrect or missed diagnoses.

In response, many health systems have identified test utilization or lab stewardship programs as critical priorities to identify and reduce clinical variation in lab ordering. These programs are designed to identify and address patterns such as expensive repeat genetic tests, excessive frequency of testing and failure to order recommended tests. The challenge these programs face, however, is the lack of an efficient way to manage and analyze the avalanche of data that this volume of testing produces. Many disparate enterprise systems house the required data, compounding the complexity. The resulting process consists of time-consuming, manual data uploads that then have to be manipulated into a format for analysis, which leads to outdated conclusions that are ineffective at driving timely changes in patient care.

Advanced Lab Strategies to Personalize Healthcare

To sustain an effective lab stewardship program, lab directors and hospital administrators need a continuous, automated process for identifying clinical variation in real time and alerting medical staff to problematic testing trends that require a response. Such a process requires modern integration protocols, such as Fast Healthcare Interoperability Resources, and advanced technology, such as artificial intelligence and machine learning, to analyze and drive action from the vast volumes of lab testing data generated in most health systems.

The process starts with live patient test results from the laboratory information system. The data are integrated into an analysis engine that applies clinically validated rules to evaluate whether patients are getting appropriate testing. Machine-learning models trained by large volumes of normalized lab data over time bring the various patient data points together without requiring significant manual effort. By organizing data from multiple sources into comprehensive patient profiles, health systems can instantly identify problematic utilization patterns, allowing leadership to zero in on trends at particular locations, in specific departments or with individual physicians and to follow through with the necessary action to improve diagnostic processes and patient outcomes. Not only does this type of process allow for a quick response to testing trends across the health system, it also enables health systems to proactively update rules when test compendiums change, or new utilization guidelines are developed, so that new ordering processes can be incorporated into physician workflows automatically.

Precise lab testing enables providers to make timely, accurate diagnoses and deliver on the promise of personalized, high-value care. Lab stewardship programs support precision testing by setting clinical guidelines, monitoring testing trends and taking action to ensure providers follow the best ordering practices. A successful lab stewardship process requires an advanced technological solution built to turn massive volumes of static lab data into actionable healthcare insights. By implementing a real-time test utilization solution to ensure the right patient gets the right test, health systems can reduce spending on unnecessary testing and, most importantly, improve patient outcomes.

 to improve lives with high-value care. Brad has been building and leading advanced cloud technology companies for more than two decades. A well-known innovator in the healthcare technology market, he has been named to the Becker's Hospital Review list of Top Entrepreneurs in Healthcare and the Indianapolis Business Journal’s 40-Under-40 list of business leaders.

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CEO at hc1

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Transforming Lab Data into Personalized Healthcare Insights