Hidden Costs of Poor Data Quality in Emergency Departments

Poor-quality data in EDs can lead to costly errors, boarding delays and inefficiencies—integrated analytics offers a clearer path to better outcomes.

Poor data quality is costing EDs more than they may realize.

Data isn’t just bits and bytes. When effectively curated into relevant information, it can be the lifeblood of decision-making, especially in emergency medicine. Poor data quality can silently drain resources, increase errors, and erode trust in your emergency department (ED) operations. d2i was founded on the belief that purpose-built analytics solutions can help you solve these problems, making sure you have timely and relevant information you and your department can trust.

Data has become the bedrock of modern medicine. Clinical judgment based on intuition, empiricism, and anecdotes has been transformed into evidence-based knowledge. Since the passage of the Affordable Care Act (ACA) and the subsequent proliferation of electronic health record (EHR) systems, data has permeated every facet of clinical care.

In EDs and other acute-care settings where high volumes and rapid decisions are the norm, high-quality, integrated data is vital. EDs face the dual challenge of improving outcomes while simultaneously capturing and curating data to accurately measure, monitor, and drive performance improvement.

With the rise of value-based care models, success is defined not only by efficiency but by measurable, reportable performance tied to outcomes and cost savings. Fragmented, incomplete, and inaccurate data not only undermines its own integrity, but it also undermines trust in the very initiatives it is meant to support. Poor data quality does more than create reporting challenges. It erodes trust, causing clinicians and leaders to spend time debating the numbers instead of acting on them.

Challenges of Fragmented Data

In emergency departments, poor data quality manifests in ways that directly impact clinical operations, department design, and performance attribution:

• Operational fragmentation across systems: Data from EHRs, billing platforms, staffing schedules, and patient satisfaction systems are seldom integrated in a way that optimizes clinical operations. Even with interoperability standards like HL7 and Health Information Exchanges (HIEs), data often remains siloed, hindering the ability to fully understand department flow, resource utilization, and patient throughput dynamics.
• Inconsistent or incomplete clinical attribution: When data isn’t accurate or integrated, it is difficult to attribute metrics — such as boarding rates, left-without-being-seen (LWBS) rates, or return visit rates — to specific processes, staffing models, or care pathways. Discrepancies in clinical documentation, time stamp accuracy, or care transition records often create blind spots for clinicians and administrators. These data gaps erode confidence, hinder performance improvement efforts, and make it challenging to implement targeted sustainable solutions.
• Delayed decision-making: Even when data is technically available, its integrity and relevance cause clinicians and department leaders to spend valuable time reconciling fragmented data sets, delaying rapid-cycle decisions that are essential for optimal patient flow, medical decision-making, and safe, timely discharges.
• Revenue leakage and compliance risk: Data challenges not only compromise clinical operations but also impact downstream revenue cycle integrity. Missing documentation tied to diagnosis-related groups (DRGs), clinical quality measures, or risk adjustment factors can lead to underbilling, denials, lost revenue, regulatory penalties, and reputational harm.

Data quality is best understood as a matter of fitness for use, that is, whether the data is complete enough, plausible enough, and conformant enough to support the decision being made.

In an ED, these dimensions are not abstract. They determine whether a boarding metric is believable, whether a clinician attribution report is fair, whether a throughput delay is measured at the right operational moment, and whether a quality-improvement initiative is targeting the right process.

Boarding is a practical example. The same timestamp logic that ED leaders use to manage daily throughput is now central to external reporting.

CMS has finalized the ECAT eCQM, with voluntary reporting beginning in CY 2027 and mandatory reporting beginning in CY 2028 for the CY 2030 payment determination. ECAT also replaces the chart-abstracted Median Time from ED Arrival to ED Departure for Discharged Patients and Left Without Being Seen measures.

How d2i Solves These Challenges

d2i Performance Insights for Emergency Medicine™ is a purpose-built analytics platform for emergency medicine, used by hundreds of EDs to address data quality and integration gaps that plague emergency medicine operations.

• Unified data platform: Your team gets one curated view of ED performance instead of reconciling EHRs, billing systems, scheduling platforms, and patient satisfaction surveys separately. d2i aggregates and normalizes those sources behind the scenes, so the blind spots created by siloed data disappear from your daily view.

  Enhanced analytics: Instead of sorting through raw data dumps, you get validated, retrospective insight you can act on. Benchmark your department against peer institutions, spot trends early, and identify targeted opportunities for improvement without added manual work.

• Improved physician and staff workflow: Your clinical and administrative teams spend less time reconciling conflicting reports and more time with patients. Customized reporting and targeted feedback give your staff a clear, trusted basis for continuous quality improvement.

  Support for value-based care initiatives: You gain the structured foundation needed to track compliance with clinical guidelines, monitor quality and safety metrics, and stay aligned with evolving reimbursement models

The value of trusted analytics becomes clearer when organizations can use it not only to identify variation, but to change behavior and measure the impact.

Emergency Care Specialists, a physician-led group treating more than 450,000 patients a year, worked with d2i to standardize chest pain care for low-risk patients. The result: HEART Score documentation rose from 32.5% to more than 75%, more than 4,000 unnecessary hospitalizations were avoided, and the organization saved an estimated $39 million, all from cleaner, more trusted data

The ROI of High-Quality Data

Reliable, high-quality ED data creates value across clinical, operational, and financial domains. d2i clients such as ECS have used analytics to make faster, more informed decisions, reduce unnecessary utilization, and support better patient outcomes. Operationally, trusted analytics reduces manual reconciliation, streamlines performance measurement across systems, and helps leaders address ED crowding, staff burnout, and safer discharge workflows.

Financially, organizations can connect cleaner documentation and measure logic to fewer billing errors, more complete coding, a reduction in unnecessary admissions, and stronger payer conversations. In today’s value-based care landscape, this level of data transparency provides a distinct competitive advantage by enabling organizations to demonstrate measurable quality and efficiency in payer negotiations and performance-based contracts.

Earlier economic modeling found that reducing mean ED boarding time by one hour could generate roughly $9,700 to $13,300 in additional daily revenue by recapturing LWBS and ambulance-diversion demand. The point for ED leaders is not to overfit to another hospital’s estimate. It is to build local ROI from local flow, utilization, staffing, documentation, and payer data.

That is the business case: Analytics should be tied to measurable operational and financial levers, not a vague promise that dashboards will “improve performance.” A high-quality ED data infrastructure can create value in four areas:

• Avoided utilization
• Recovered capacity
• Revenue integrity
• Reporting readiness

That means measuring admissions avoided through standardized pathways; reduced boarding hours; fewer LWBS; less manual reconciliation and chart abstraction; cleaner documentation to support coding, denials prevention, and payer conversations; and earlier validation of ECAT logic before public reporting and payment implications begin.

Pairing analytics with d2i Performance Advisory™ can sharpen that ROI story. Start with a trusted baseline, identify which bottlenecks are under ED control versus hospital-wide capacity constraints, estimate the operational and financial impact, and then prioritize interventions that can be tracked over time.

Data Quality Prioritization in 2026 and Beyond

The strongest ED data-quality programs should focus less on adding another dashboard and more on strengthening the data foundation behind decisions. Start with governance. Define how key operational measures are calculated across systems and ensure that leaders trust the data they use to make decisions. Then test the data in the context in which leaders will use it. A metric can be technically populated yet operationally misleading if the time stamp is delayed, handoffs are not captured, or physician attribution varies by site.

From there, connect measurement to action. Clean ED data should support daily throughput huddles, physician and APP feedback, variation analysis, documentation improvement, value-based care reporting, and targeted advisory work. The goal is not simply cleaner reports. It is a trusted, shared operating picture that lets emergency medicine leaders move faster without second-guessing the numbers.

As emergency and acute care operations become more data-dependent, the stakes of poor data integration are too high to ignore. Flawed data silently erodes clinical quality, operational efficiency, and financial performance. The good news? These challenges are solvable with better data.