Evidence-based Contingency Planning for Electronic Health Record Downtime

Downtime for electronic health records (EHRs) can result in serious patient safety hazards unless there are robust contingency plans in place. Downtime—any period of unavailability or decreased functionality in the EHR system—is inevitable and results from events internal or external to the information technology (IT) infrastructure of the organization. It may be caused by system failures; software or hardware issues; interruptions to internet connectivity; or catastrophic events, such as natural disasters and terrorism, including cyber-attacks. Downtime may also be planned, as in the case of system upgrades and updates. While EHR vendors work to prevent downtime events from a hardware and software perspective, downtimes will continue, and healthcare providers must have contingency plans in place.

This project researched downtime procedures in two hospital settings that require rapid communication of information: the laboratory (lab) and emergency department (ED). Archived and real-time data of downtime events were analyzed, and interviews of lab and ED personnel regarding downtime events were conducted. In addition, a computer simulation model was developed to assess the performance of various downtime contingency plans.

The specific aims of this project were as follows:

• Collect and analyze data to quantify how lab and ED operations—and subsequently patient safety and cost—are impacted by EHR downtime. 
• Develop a discrete-event simulation that replicates lab and ED operations during EHR uptime and downtime. Calibrate and validate the simulation with data from the first aim. 

A review of 3 years of patient safety reports identified 76 of 80,381 incident reports related to downtime. Of these, 46 percent indicated that downtime procedures were either not followed or were not in place; nearly 27 percent indicated that downtime procedures were successfully executed; and 26 percent had insufficient information to determine if downtime procedures were present or followed. Interviews with stakeholders confirmed that downtime is disruptive to organizational processes and adds considerable stress to the workforce. Consequences of downtime were identified and included delay of care, increases in medical errors, and disruption in communication.

The simulation model was built to document the expected difference in key performances indicators for uptime versus downtime; for example, turnaround time for lab results. It was then used to predict the impact and benefits of two potential downtime strategies: implementing a limited lab testing menu and adding additional staff during downtime. By adjusting variables in the simulation model, the project team found that at the point in which a limited lab menu reduced workload by 40% or more, there would be a significant reduction in the time to see a physician in the ED. When adjusting staffing models, the addition of one staff member and one additional lab technician would achieve significant benefits including reduction in lab turnaround time and critical results reporting time. The team concluded that the use of a simulation model allows users to compare various strategies for downtime contingency plans without impacting clinical care and can inform and improve current practice and plans.