Why Clinical Content Reviews in Jira Take Longer Than They Should

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How a healthcare content team can turn Jira status history into useful review-and-approval metrics

Most articles about flow metrics use software examples: code review, deployment, cycle time.

But review-heavy teams outside software can benefit from the same approach. Consider a clinical content team working with regulated, evidence-based care guidelines. Every update has to pass through several expert reviews before it can be published. For this kind of team, "How long does approval take?" isn't an abstract process question. It's something leadership may need to answer when planning releases or reviewing how the process works.

This example walks through one such workflow: what the flow looks like, where time can accumulate, which metrics fit the process, and how Time Metrics Tracker can be used to analyze it directly in Jira.

🩺 The team and the work

The team maintains a library of clinical care guidelines. The work of updating them — revised guidelines, new evidence summaries, changed care pathways — is tracked in Jira. But because this content drives clinical and utilization decisions, it can't be published on one person's say-so. Every item moves through a chain of expert reviews:

• Peer Review — the first content review for accuracy and clarity.
• Editorial Review — a second content-focused review.
• Clinical Review — a clinician checks the content for clinical soundness.
• Pending Approval — final confirmation that the required changes are resolved and the item is ready.
• QA — a final check before publication.

The reviewers are senior specialists who also have other work. That makes their availability an important part of the flow.

The question isn't simply whether an item eventually gets published. The more useful question is: where does the time go before it gets there?

🔁 The flow

Here's the workflow from draft to publication:

Draft ➔ Peer Review ➔ Editorial Review ➔ Clinical Review ➔ Pending Approval ➔ QA ➔ Published

🔍 Where does the time actually hide?

Before looking at the status history, the team's understanding of the process might look familiar:

• They know the overall time from draft to publication feels too long.
• When a release is delayed, "it's stuck in review" is the explanation.
• They don't know which review stage contributes most to the delay.
• They can't easily distinguish between time spent actively reviewing an item and time spent waiting for the next reviewer.
• Rework isn't obvious when looking only at overall duration.

The information is already in Jira. Every status transition creates part of the history needed to understand the flow.

The challenge is turning that history into metrics that match the actual workflow.

📐 The metrics that fit this flow

The team set up a small set of Time metrics that mirror the chain.

Setting one up takes a minute. In Jira, open Time Metrics Tracker → Configuration → Time metrics → + Time metric, then set:

• Start and stop statuses — the two statuses the metric measures between (e.g. Peer Review → Editorial Review). For each end, you choose whether the first or last time an item entered or left that status counts — useful when items loop back for a second pass.
• Exclude (pause) statuses — add Blocked and On Hold so time spent there is subtracted, not charged to a reviewer.
• Work schedule — attach a business-hours calendar (e.g. Mon–Fri, 9–5) so weekends and off-hours don't inflate the number.
• Warning / Critical thresholds (optional) — a target turnaround that highlights slow items yellow or red on the grid.

Metric	What it tells the team
Content Review Time	How long an item takes to clear the first content check — often the first place a backlog forms when peer reviewers are stretched thin.
Editorial to Clinical Time	How long it takes to get through the second content pass and reach the clinician.
Clinical Review Time	How long clinical review takes before the item reaches final approval — the gate leadership may suspect is the main bottleneck.
Approval-to-Publish Time	How long approved work takes to become published, including the final sign-off and QA leg.

Warning and Critical thresholds

For stages with a target turnaround, the team can set Warning and Critical times.

For example, if clinical review is expected to hand off within a couple of business days, items that exceed the configured thresholds can be highlighted in the report grid.

📊 What the team can now see

Everything in one grid

The report grid provides one row per guideline, with each review-time metric as a separate column.

The team can:

• sort by any metric to find the slowest items;
• filter by date, issue type, assignee, or label;
• save a configured view as a Preset;
• keep a Preset private or share it with the Jira site;
• export the full filtered report to Excel or CSV.

From there, the same metrics can answer more specific questions.

Which gate is the bottleneck? — Status Contribution

The Status Contribution chart shows how much of the total time is associated with each status.

For this workflow, that can turn:

"Review is slow."

into:

"The clinical-review gate is holding work longer than the other review stages."

That's a much more useful starting point for a staffing or scheduling discussion.

Is the process getting slower? — Trend

The Trend view shows how a metric's typical duration changes over time and compares it with the previous period.

This can help reveal a gradual increase in review time before it becomes the team's new baseline. Warning and Critical thresholds can also appear as reference lines.

Is it an outlier or a pattern? — Scatter Plot

When an average increases, the Scatter Plot helps show whether the change comes from one unusually slow guideline or a broader slowdown across multiple items.

That distinction matters: one outlier may need investigation, while a broad slowdown may indicate that the overall review load needs attention.

One connected view — Flow Insights

Flow Insights brings several perspectives together for a selected metric, project, and period, including trend, status contribution, WIP pressure, and outliers.

This provides a way to assess the health of a metric without having to open multiple separate charts.

A quick question — Rovo agent

For a question such as:

"What's the average clinical review time this quarter?"

The built-in Rovo agent can answer in Jira's chat.

🧭 How to replicate this for your own chain

The same approach can work for other multi-step review and approval workflows — clinical content, medical-device change control, clinical-trial release gates, or service approval chains.

1. Map your workflow. Write down your review and approval statuses in the order an item passes through them.
2. Set up matching Time metrics. Create a metric for the time between each pair of consecutive statuses, and configure them to fit how your team actually works — exclude the statuses that shouldn't count (Blocked, On Hold, Waiting) and attach a business-hours calendar so durations reflect working time.
3. Analyze on the grid, or export. Read and sort your metrics directly in the report grid, or export to Excel/CSV when you need them elsewhere. For a deeper look at any single metric — its trend, which status contributes most, outliers — open it in Flow Insights.

The data is already in your issue history. The setup is a handful of metrics. What you get back is the ability to answer "where does approval time go?" with a specific gate instead of a shrug.

Explore the app on the Atlassian Marketplace.