Question 1

Who should run this audit?

Accepted Answer

Doctors in training, IMTs, registrars, QI fellows, ward nurses, therapy staff, pharmacists, patient flow teams and discharge coordinators are all well placed to run this audit. Include a named project lead, a consultant supervisor, a referral pathway or specialty lead, and a patient flow or discharge lead where possible.

Question 2

What permissions do I need?

Accepted Answer

Most trusts treat this as clinical audit / service evaluation and do not require ethics approval, but you must register the project with your local clinical audit or governance team before starting. Use only pseudonymised identifiers — do not enter NHS numbers, hospital numbers, names or dates of birth.

Question 3

How do I sample?

Accepted Answer

Use consecutive inpatient specialty referral episodes for adults (≥ 16y) over a defined period, for example two weeks per ward. A sample of 30–50 episodes per ward gives a reasonable point estimate; 100+ across the organisation supports comparison by ward, specialty and urgency.

Question 4

What does the tool measure?

Accepted Answer

Whether inpatient specialty referrals record a clear clinical question and urgency, are made within the locally expected timeframe, are triaged and reviewed within the expected timeframe, result in clearly documented advice with a named action owner, are actioned and escalated where delayed, and what impact any delay has on diagnosis, treatment, discharge readiness, length of stay and patient safety.

Question 5

What happens after data collection?

Accepted Answer

The dashboard updates in real time. Use the Findings tab to interpret results, the Improvement tab to plan actions and PDSA cycles, and the Export centre to generate editable Word, PowerPoint, A0 poster, Excel and ARCP evidence outputs.

Question 6

Do I need to sign in to see the tool?

Accepted Answer

No. Authentication is handled at the main Lazomis QI website. Inside the tool you will already be in your authenticated workspace, or in demo mode with clearly labelled sample data.