Measuring the effectiveness of an electronic health record (EHR) starts with a specific question: what should the system help your organization do better? “Is the EHR working?” is too broad to measure. A useful evaluation connects the technology to an operational or care-process aim, establishes a baseline, and tracks both intended benefits and possible side effects.
An EHR is one part of a larger system that includes people, clinical practice, policies, data quality, and workflow. Changes in outcomes may have many causes, so avoid attributing every improvement—or problem—to the software alone.
Step 1: Define the goal before choosing metrics
Write a clear aim that names the population, process, and time period. For example: “Over the next quarter, reduce the time from receiving a referral to scheduling it for our cardiology clinic, without increasing staff overtime or reducing patient choice.” The aim points to measures that can test whether the change helped and whether it created unintended effects.
Step 2: Build a balanced EHR scorecard
Choose a small set of measures across domains that matter to your use case. Do not try to track every available dashboard metric.
Reliability and access
- System availability during defined clinic hours.
- Frequency and duration of outages or performance incidents.
- Time to restore key workflows after downtime.
Define how you count partial outages and which functions are essential. A single uptime percentage can hide a disruption that affects scheduling, orders, or results.
Usability and adoption
- Task completion for common workflows, such as documenting a visit or placing an order.
- Time, clicks, or handoffs needed for a defined task, measured consistently.
- Training completion, support requests, and user-reported ease or friction.
- Use of specific functions that support the organization’s stated goal.
Log-ins alone are a weak measure of effective use. High adoption can coexist with workarounds, frustration, or a poorly designed process.
Workflow and workload
- Time spent on documentation or administrative tasks for a sampled workflow.
- Duplicate entry, after-hours work, or unresolved queues.
- Staff workload and satisfaction, interpreted alongside the task being changed.
Measure before and after implementation or configuration changes, and note other workflow changes that may influence the result.
Data quality and safety
- Patient-identity mismatches, duplicate records, or correction rates.
- Completeness and timeliness of selected information, such as results or medication reconciliation.
- Closed-loop follow-up for a defined test, referral, or message workflow.
- Reported safety concerns and near misses, with a consistent review process.
Do not assume that an increase in reported incidents means the system became less safe; reporting may improve when staff have a clearer, more trusted reporting path. Review narratives and operational context, not just counts.
Patient access and experience
- Appointment access or time to complete a requested service.
- Patient ability to use a portal or other digital feature, paired with accessible non-digital alternatives.
- Patient feedback on communication, instructions, and the ease of completing a task.
Segment results when appropriate to identify differences by language, age, disability, location, or other relevant factors. Protect privacy and use reliable sample sizes.
Interoperability and coordination
- Successful exchange of a defined document, result, or referral with an external organization.
- Completeness and usability of received data in the clinician’s workflow.
- Time from receipt to review and action, where relevant.
Transmission alone does not establish that information was understood or used. Test the full workflow from sender through receipt, review, and follow-up.
Cost and resource use
- Licensing, implementation, support, training, and integration costs.
- Staff time, hardware, connectivity, and ongoing maintenance.
- Costs associated with downtime, workarounds, or duplicate systems.
Use a defined period and include both initial and recurring costs. A return-on-investment estimate depends on assumptions; present those assumptions and avoid treating projected savings as guaranteed.
Step 3: Define each metric clearly
For every measure, write down its purpose, numerator and denominator where relevant, data source, owner, reporting frequency, and how missing or unusual cases are handled. Use the same definition before and after a change. If the data are manual or sampled, document the sampling method and limitations.
Step 4: Establish a baseline and choose balancing measures
Collect enough pre-change information to understand normal variation. Then choose at least one balancing measure that could reveal a trade-off. For example, if a new template aims to shorten note completion, also check note corrections, missing information, and clinician workload. If a scheduling change aims to increase utilization, also monitor access delays and staff overtime.
Step 5: Review results and act
- Review the scorecard with clinicians, staff, operations, and IT partners who understand the workflow.
- Look for patterns over time and differences between relevant patient or service groups.
- Investigate unexpected changes before assigning a cause.
- Test one improvement at a time when feasible and document what changed.
- Keep, adapt, or stop the intervention based on evidence and user feedback.
The Institute for Healthcare Improvement describes outcome, process, and balancing measures as complementary ways to understand whether a change made a difference, whether the process is working as intended, and what unintended effects may have emerged.
