The Value of Lab Data in the Healthcare Ecosystem
Medical laboratories already drive more than 70% of clinical decisions, but they should be driving more decisions across the healthcare ecosystem.
Having more touch points with patients than any other unit or function in health care, labs are positioned to play an integral role in determining personalized interventions for individual patients. Lab data, aggregated and actionable, should also be used more broadly: by public health officials, by insurance companies, and by hospitals seeking insights about potential risk factors and improved population health.
Current challenges
Population health initiatives rely on predictive modeling based on the integration of clinical, financial, and operational data. This modeling requires a cumbersome and time-consuming effort to make raw data legible and actionable. Some of the difficulties arise from incomplete or unstructured information, some from technological incompatibility across systems. Even the most optimistic of data analysts acknowledge the massive and complex effort required to not only translate data into information, but more importantly, into knowledge.
The information living in a medical laboratory is structured, detailed and longitudinal across every single patient seen by the provider (including patients in the community who aren’t necessarily seen at the hospital). This kind of holistic information can help provide visibility into population health immediately, avoiding the fragmented picture offered, for example, by insurance claims alone, or by other disparate systems in our healthcare.
Lab Data and Public Health
Consider the problem of water lead contamination. According to the CDC, “no safe blood level of lead in children has been identified,” and exposure to lead poisoning “can affect nearly every system in the body.” On top of the cognitive and physical toll caused by lead poisoning, one study determined that an additional $50.9 billion in lost economic productivity results from children’s preventable exposure to lead.
Lab data can help protect populations from lead poisoning by providing an early and reliable warning of contamination. With analytics which aggregate and map high prevalence of critical test results, public health officials or other decision makers could see whether an area is disproportionately affected by lead poisoning—and then work quickly to address the source.
Lab Data for Payer Reporting
Payers have mountains of data at their disposal. Only having claims data to analyze, however, limits their ability to analyze the diagnostic detail and history of their patient populations. To fully analyze (and influence) their members’ care, payers must add lab results to the picture.
Beyond analytical accuracy, payers also need detailed lab data as part of quality reporting efforts such as the Healthcare Effectiveness Data and Information Set (HEDIS). To compete in the marketplace, payers must demonstrate their results here; labs are now sharing meaningful data directly with payers to support these initiatives and improve health at a larger population level.
Lab Data for Providers
Labs offer a numeric, quantitative way of evaluating patient populations across a variety of criteria (e.g., demography, provider, time, disease state, etc.). Decision-makers can quickly and easily identify and track areas of opportunity, as well as drill down into the sources of poor quality or higher cost.
In particular, histograms on specific lab tests over time provide critical insights for identifying and tracking chronic disease populations. Providers can identify how the result distribution is changing within a certain population, but also provide that knowledge back to physicians who are also now tasked with preventative health. So, physicians can easily identify patients that need to return for testing to prevent adverse events.
As hospitals and insurance companies become more accountable for the quality and cost of health care, and as health care reform raises expectations for public and preventative health, laboratory data must be converted into knowledge to successfully manage population health.