What Changed? The Most Underused Question in Healthcare Investigations

When healthcare organizations investigate adverse events, near misses, healthcare-associated infections, medication errors, or patient safety incidents, the focus often centers on identifying what went wrong within existing processes. Investigators examine policies, staff actions, compliance gaps, and procedural failures. Yet one of the most powerful and frequently overlooked questions in healthcare investigations is remarkably simple:

What changed?

Change Analysis is a root cause analysis technique that compares conditions before and after an event to identify meaningful differences that may have contributed to the outcome. Traditionally, the method is described as a comparison of the “normal state” and the “event state.” However, in healthcare, Change Analysis offers something even more valuable: a structured way to recognize that adverse events often emerge not because processes suddenly became bad, but because the environment in which those processes operated changed.

In complex healthcare systems, safe performance is often dependent on a delicate balance of people, workflows, technology, patient characteristics, and environmental conditions. When one or more of these factors changes, previously reliable systems may become vulnerable in unexpected ways.

Why Traditional Investigations Miss Change

Many healthcare investigations begin with an assumption that someone failed to follow a process or that a process itself was inadequate. While these explanations are sometimes accurate, they can cause investigators to overlook an important reality of complex systems: the same process may function safely for months or years before a significant event occurs.

If a process has been performed successfully hundreds of times, investigators should ask:

• What was different this time?
• What conditions existed that did not exist before?
• What changed in the system shortly before the event?

Modern patient safety approaches emphasize examining contributing factors and system conditions rather than focusing solely on individual actions (Agency for Healthcare Research and Quality (AHRQ), 2024; Behrhorst et al., 2025). Change Analysis supports this perspective by directing attention toward variations in the environment that may have altered system performance. Rather than asking only, “What failed?” Change Analysis asks, “What changed?”

Staffing Changes

Healthcare staffing is one of the most common sources of system change.

Organizations frequently experience:

• Staff turnover
• Use of agency or travel personnel
• Increased reliance on float staff
• Changes in nurse-to-patient ratios
• New physicians or advanced practice providers
• Adjustments to shift structures

A process that functions effectively with an experienced, stable team may operate very differently when unfamiliar personnel are introduced. Consider a central line maintenance process that has produced excellent outcomes for years. Following an increase in agency nurse utilization, a cluster of central line-associated bloodstream infections occurs. The line maintenance policy may not have changed, but the staffing model did.

In this scenario, the meaningful change may not be procedural failure but rather reduced familiarity with local workflows, equipment, documentation systems, or unit expectations. Healthcare investigations frequently identify human factors such as training, communication, fatigue, scheduling, and staffing as recurring contributors to adverse events (Browne et al., 2008). Change Analysis helps investigators recognize when workforce changes have altered system performance.

Workflow Changes

Healthcare organizations constantly redesign workflows to improve efficiency, throughput, and patient experience.

Examples include:

• New patient admission procedures
• Modified discharge processes
• Updated rounding structures
• Revised medication administration workflows
• New escalation pathways
• Changes in documentation responsibilities

These changes are often implemented with positive intentions. However, even beneficial workflow modifications can create unintended consequences. For example, a unit may streamline patient handoffs to reduce delays. Several weeks later, investigators discover an increase in missed clinical information during transitions of care.

The issue may not be poor staff performance. Instead, the workflow redesign may have unintentionally removed a critical communication checkpoint. Change Analysis encourages investigators to map the timeline of operational changes occurring before an event and determine whether the system’s defenses were altered as a result.

Technology Changes

Healthcare organizations continue to invest heavily in technology to improve safety and efficiency. Yet technology implementation is one of the most disruptive forms of organizational change.

Examples include:

• Electronic health record (EHR) upgrades
• New clinical decision support tools
• Barcode medication administration systems
• Smart infusion pumps
• Automated dispensing cabinets
• Laboratory information systems

Technology often changes how clinicians think, communicate, and complete tasks. Research examining large-scale EHR implementations has demonstrated that workflow patterns can change substantially following technology conversions, affecting efficiency, workload, and clinical operations (Kipp et al., 2019). Consider a medication error that occurs shortly after an EHR upgrade. An investigation focused solely on the clinician’s action might conclude that a prescribing mistake occurred. A Change Analysis approach would also examine:

• Were alert displays modified?
• Did order entry screens change?
• Were workflows redesigned?
• Did clinicians receive sufficient training?
• Were new workarounds emerging?

The critical question becomes not simply why the clinician made an error, but whether technology changes altered the conditions under which decisions were made.

Patient Population Changes

Healthcare systems often assume that a process that works well today will continue working well tomorrow. However, patient populations are not static.

Changes may include:

• Higher patient acuity
• Increased complexity of care
• Aging populations
• Greater prevalence of chronic disease
• Emerging infectious diseases
• Changes in referral patterns

A process designed for one patient population may become strained when the characteristics of patients change significantly. For example, a unit may historically care for stable postoperative patients. Over time, increasing patient acuity leads to more complex monitoring requirements and greater care coordination demands.

If adverse events begin increasing, investigators should evaluate whether patient complexity has outgrown the system’s original design. In these situations, the process itself may not have failed. Instead, the operating environment may have evolved beyond the assumptions under which the process was created.

Environmental Changes

Healthcare environments are dynamic and can change rapidly.

Environmental factors may include:

• Unit renovations
• Construction activities
• Changes in physical layout
• Increased patient census
• Supply shortages
• Seasonal surges
• Infection outbreaks
• Changes in equipment availability

Environmental changes can introduce new risks even when staff continue performing tasks exactly as expected.

A medication storage area relocated during a renovation project may increase selection errors. A temporary isolation unit created during a respiratory virus surge may alter communication pathways and staffing patterns.

Healthcare investigations frequently identify environmental factors as important contributors to safety events (Browne et al., 2008). Change Analysis provides a structured way to determine whether environmental shifts created conditions that increased risk.

Using Change Analysis During Investigations

A practical Change Analysis approach can be built around five questions:

1. What was different immediately before the event?

Identify any operational, staffing, technology, patient, or environmental changes occurring in the days, weeks, or months before the event.

2. When did the change occur?

Establish a timeline. Some changes produce immediate effects, while others create vulnerabilities that emerge gradually.

3. Who was affected by the change?

Determine whether the change affected specific departments, professional groups, patient populations, or workflows.

4. What new risks were introduced?

Assess whether the change created additional workload, communication challenges, complexity, uncertainty, or opportunities for error.

5. Were safeguards adjusted to match the change?

Many organizations successfully implement change but fail to adjust monitoring, training, staffing, or risk controls accordingly.

Moving Beyond “Who Made the Mistake?”

One reason Change Analysis remains underused is that it shifts attention away from individuals and toward evolving system conditions. Modern patient safety science emphasizes understanding how organizational systems contribute to adverse events rather than focusing exclusively on frontline actions (AHRQ, 2024; Behrhorst et al., 2025). Change Analysis aligns naturally with this systems perspective because it recognizes that healthcare performance is influenced by changing conditions, not merely by adherence to procedures.

A nurse, physician, pharmacist, or technician may perform the same task exactly as they have many times before. However, if staffing levels, workflows, technology, patient acuity, or environmental conditions have changed, the system surrounding that task may be fundamentally different. The investigation must therefore examine not only the action itself but also the changing context in which the action occurred.

Conclusion

Many healthcare investigations focus on identifying what failed. Change Analysis encourages investigators to ask a different and often more revealing question: What changed? The answer may reveal that the underlying process was not inherently flawed. Instead, the conditions supporting safe performance evolved in ways that were not fully recognized or managed.

Staffing changes, workflow redesigns, technology implementations, shifts in patient populations, and environmental modifications all have the potential to alter system performance. By systematically examining these changes, investigators can uncover contributing factors that might otherwise remain invisible.

In complex healthcare systems, adverse events frequently occur not because processes are bad, but because the conditions under which those processes operate have changed. Understanding those changes may be one of the most effective ways to understand why an event occurred and how to prevent it from happening again.

References

Agency for Healthcare Research and Quality. (2024). System-focused event investigation and analysis guide. Agency for Healthcare Research and Quality.

Agency for Healthcare Research and Quality. (2024). Root cause analysis. PSNet.

Behrhorst, J., Gale, B., & Van, C. M. (2025). The evolution of root cause analysis. PSNet.

Browne, A. M., Mullen, R., Teets, J., Bollig, A., & Steven, J. (2008). Common cause analysis: Focus on institutional change. In K. Henriksen, J. B. Battles, M. A. Keyes, & M. L. Grady (Eds.), Advances in patient safety: New directions and alternative approaches (Vol. 1: Assessment). Agency for Healthcare Research and Quality.

Kipp, M. A., Hoonakker, P. L. T., Stewart, R., Ashfaq, A., Delaney, B., & Brown, R. L. (2019). Mayo Clinic Registry of Operational Tasks (ROOT): A paradigm shift in electronic health record implementation evaluation. Mayo Clinic Proceedings: Innovations, Quality & Outcomes, 3(3), 286-299.