Most healthcare organizations treat a downtime event as something to survive and move past. The EHR comes back online, the reconciliation gets done, the staff returns to normal workflows, and the event is filed away in organizational memory as a difficult period that was managed. Occasionally a brief after-action discussion happens. Rarely does that discussion produce specific, measurable changes to the preparedness program.
This is a significant missed opportunity. Every downtime event, including planned maintenance windows, generates data that is more specific, more honest, and more actionable than any external assessment or policy review can produce. It captures how the organization actually behaves under downtime conditions rather than how it intends to behave. It reveals which procedures held up and which broke down. It shows where staff were confident and where they improvised. And it produces the raw material for a continuous improvement cycle that, compounded over multiple events, produces a qualitatively better preparedness program year over year.
Building a system for collecting, analyzing, and acting on downtime data is not a complex undertaking. It requires intention, a defined data collection process, and an organizational commitment to use what the data reveals rather than filing it alongside the policy documents it is meant to improve.
The Data Categories That Matter Most
Not all downtime data has equal value for program improvement. The data categories that most reliably reveal genuine preparedness gaps and drive meaningful improvement are:
Activation timing data. From the moment the EHR goes offline to the moment each department’s downtime workstations are active and staff are using them is the most important operational metric in any downtime program. This number reveals whether the activation protocol is working and whether staff know what to do immediately when an event begins. Activation timing should be captured for each department separately, because variation across departments is as informative as the overall facility average. A department that consistently activates in under five minutes has a different program profile than one that takes 25 minutes, and understanding why the difference exists produces targeted improvement rather than generic retraining.
Workflow completeness data. During an event, which workflows were completed electronically using dbtech’s eForms and downtime workstations, and which reverted to paper? This data reveals where the electronic backup is genuinely embedded in staff practice and where paper workarounds are still the default. Departments or workflow types where paper is consistently used despite electronic alternatives being available indicate a training gap, a forms library gap, or a usability issue that the data helps pinpoint.
Forms completion accuracy data. After recovery, the post-outage reconciliation process reveals whether the documentation captured during the downtime period is complete, accurate, and structured in a way that integrates cleanly with the EHR. High rates of missing fields, data entry errors, or forms that required manual correction before EHR import indicate specific forms configuration problems that can be addressed through targeted updates to the eForms library.
Recovery timeline data. The time from EHR restoration to completion of data reconciliation is a measure of both the quality of downtime documentation and the efficiency of the recovery process. Tracking this metric across multiple events reveals whether the recovery process is improving as staff become more familiar with it or whether a persistent bottleneck is preventing efficient recovery regardless of how the downtime period itself was managed.
Staff knowledge assessment data. Spot interviews conducted in the days following a downtime event, asking staff to describe what they did and what they wish they had known, produce qualitative data that quantitative metrics cannot capture. These interviews consistently surface gaps in staff knowledge and procedure familiarity that activation timing data alone does not reveal, because some staff compensate for limited familiarity with the downtime system by using workarounds that do not appear in the metrics.
Building the Data Collection System
Collecting useful downtime data requires a defined process that begins during the event, not after it. The data that is collected in real time during an event is significantly more accurate and more complete than data reconstructed from memory in the days following recovery.
The data collection system should include:
- A downtime event log maintained by the designated downtime coordinator or administrator on call during the event, capturing activation times by department, any workflow failures observed, and any patient safety concerns that arose during the outage period
- Observer notes from department leads, who should be specifically asked during the event to document the specific moments where procedures broke down, staff expressed uncertainty, or the downtime system did not support the workflow that was needed
- An automated activity log from dbtech’s system, which captures workstation access timestamps, eForms completion data, and any technical issues with the HL7 feed or the downtime infrastructure during the event
- A post-recovery reconciliation report that documents the volume of records reconciled, the time required, and any data quality issues identified during the reconciliation process
- A brief staff survey completed within 48 hours of EHR restoration that asks specific questions about what worked, what was difficult, and what information or tools would have helped
This data should be compiled into a standardized event summary report within one week of the event, while the details are still fresh and before the organizational memory of the event begins to fade.
The Analysis Process That Produces Actionable Findings
Raw downtime data does not improve a preparedness program. Analysis that translates data into specific findings does. The analysis process should address three questions for each data category:
What does the data show that is different from what the program intended? The gap between intended performance and actual performance is where the actionable findings live. If the activation protocol specifies a 10-minute target and the data shows three departments consistently activating in 20 to 25 minutes, the gap is specific and addressable. If the forms completion accuracy data shows that a particular form type consistently generates missing fields, the gap points to a specific forms configuration issue that can be corrected.
What is the most likely explanation for the gap? Not all gaps have the same cause, and treating them as if they do produces interventions that do not address the actual problem. A department that activates slowly because the workstation is in an inconvenient location has a different problem than a department that activates slowly because the staff member who knows the activation procedure is rarely on the same shift as the event. Root cause analysis applied to each gap produces targeted interventions rather than broad, generic ones.
What specific change would close the gap, and how will the organization verify that it worked? The finding is only useful if it produces a specific action item with an owner, a deadline, and a verification mechanism. The verification mechanism is essential: it ensures that the next event’s data is compared against the improvement that was supposed to have occurred, creating the continuous improvement cycle rather than a one-time response to each event.
Using Trend Data Across Multiple Events
The power of downtime data compounds over time. A single event produces findings about that event. Multiple events over one to two years produce trend data that reveals patterns the individual event analysis cannot surface.
Trends worth tracking across events include:
- Whether activation timing is improving, stable, or degrading over time, which is the most direct indicator of whether the training and practice program is working
- Whether the same departments or the same workflow types appear in the findings repeatedly, which indicates systemic gaps that targeted remediation has not yet resolved
- Whether post-recovery reconciliation time is decreasing as staff become more familiar with the process, or whether it is stable or increasing despite the expected learning curve improvement
- Whether the types of gaps that surface are shifting from technology and configuration gaps in early events to training and culture gaps in later ones, which is the expected pattern of a maturing program and indicates that the foundational infrastructure investments are working
This trend data should be presented to the downtime program governance committee and to executive leadership on an annual basis, alongside the program’s investment history, so that the relationship between investment decisions and measured outcomes is visible. The organizations that sustain downtime preparedness investment most consistently are the ones where leadership can see that the data shows the program improving, not just the ones that are told it is. To discuss how dbtech supports downtime data collection and program improvement, contact our team or request a demo.