Patients leaving without being seen (LWBS) is a drain on an emergency department’s resources. Deep analysis of why it’s happening using data that is fit-for-purpose can benefit operations overall.

Using data that is fit-for-purpose and specific to each ED is required to optimize performance.

Have you ever noticed a disconnect between your EHR reports and what you see every day in your emergency department (ED)?

Have you ever adjusted your staffing or added clinical services to meet reported demand, only to later discover that the data you used was incomplete and did not reflect the whole picture?

Do your reports accurately reflect site-specific operational and clinical nuances and variations?

If you staff more than one ED, are each site’s specific nuances taken into consideration separately when generating and comparing metrics?

Why does this matter?

Understanding how operational and clinical nuances impact key performance metric calculations is crucial to avoid incorrect conclusions about ED performance, as well as the impact performance is having on outcomes, cost, and patient experience. Identifying these nuances helps to accurately pinpoint areas for improvement. The methods used to calculate metrics like census, turnaround times for admitted patients, returns, CT utilization, and holding hours can significantly affect decisions regarding provider performance improvement, clinical staffing adjustments, and the appropriateness of clinical care.

At d2i, we understand that the backbone of effective emergency medicine analytics lies in the granularity and precision of the data collected and analyzed. Our approach to data differs fundamentally from standard practices, which often overlook the subtleties of each site’s emergency department. This is why we dive deep, ensuring that during the setup of each client’s dashboards and reports, we ask the necessary questions so that all data in our reports accurately reflect the unique operational and clinical realities of that specific ED.

Traditionally, hospitals have relied on standard, generalized EHR reports to make critical decisions in emergency medicine; especially right after an upgrade or EHR change. These reports often fail to capture the nuances specific to individual ED operations, leading to inaccurate site-by-site comparisons, inefficiencies, and misinformed policies. For instance, without a detailed understanding of patient flow and staff allocation, these reports fail to accurately identify true bottlenecks, mistakenly attributing issues to the wrong sources.

Fundamental Need for Fit-for-Purpose Data in Emergency Medicine

Data that is “fit-for-purpose” is crucial for accurate and actionable insights. This type of data is comprehensive enough and organized in such a way as to measure all relevant metrics for specific business activities and KPIs. It is captured to reflect the unique design, workflows, and business rules of each department. Additionally, it allows for drill-down to the lowest level of detail, enabling answers to questions about aggregate numbers. Fit-for-purpose data is also integrated with related data sources, normalized, and easily filterable, providing a balanced view of performance and quickly answering all “why” questions.

Consider the scenario of patients classified as left without being seen (LWBS) and the importance of correlating that metric with the “Door to First Provider” time. Standard reports might highlight a high correlation without factoring in whether a medical screener was used during certain hours. Other considerations might include breaking out psychiatric-specific diagnoses with different intake and triage processes. In contrast, d2i’s approach analyzes factors like the use of medical screening examinations (MSEs), final diagnosis, time of day, day of the week, triage acuities, and patient arrival method. Applying distinct inclusion and exclusion criteria makes the metric correlation more accurate.

How do the hours and staffing of ED observation areas affect this metric? Additionally, how are psychiatric patients managed differently when admitted? Answers to these questions help determine nursing and other staffing needs, as well as the costs associated with emergency department holds.

Understanding these nuances enhances d2i’s predictive and prescriptive analytics capabilities. Predictive analytics can forecast demand, but conclusions tied to historical trends are nuanced since arrival patterns vary. Balancing over- and understaffing must consider department goals like patient safety and constraints such as bed capacity. Accuracy is also essential for prescriptive algorithms that balance multiple factors, such as productivity and cost, to fine-tune staffing levels and planning for anticipated surges effectively.

The Impact of Getting It Right

The implications of using nuanced, high-quality data are profound and broad. For emergency medicine groups, it can mean being able to negotiate hospital as well as managed care contracts more precisely, optimizing staffing, determining when observation or other flex units need to be accessed to handle ED patient overflow, or assessing the impact of poor workflow when treating psychiatric patients. By addressing the unique challenges of EDs through curated data analytics, d2i helps these critical healthcare hubs filter out the “data noise.”

In this article, we’ve established the importance of building a solid foundation of data that considers site-specific nuances. This approach is not just about collecting numbers but about understanding the story behind each statistic. As we continue this series, we will further explore how d2i’s data-driven insights can transform emergency medicine practices by providing clarity and precision that generic data approaches and EHR systems alone simply cannot match.

At d2i, we are committed to pushing the boundaries of what data analytics can achieve in emergency medicine. To learn more about how our analytics solutions can benefit your practice, contact us for more information or to request a demo.