Quality Improvement Report Template (SQUIRE 2.0)

Quality Improvement Report: [Project title]

Report template for a healthcare quality improvement (QI) project, written for an internal governance committee, a learning event or a journal submission. The numbered body sections follow the 18 items of SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence, Ogrinc et al., BMJ Quality and Safety 2016), grouped as the guideline groups them, with original wording written for this collection. Use it together with the official SQUIRE 2.0 checklist and its Explanation and Elaboration paper. The worked example (a fictional ward at a fictional hospital) shows how the sections connect. Replace every [bracketed] value and every row marked Example. This is a reporting format, not clinical advice; check the current official guideline text and your organisation's governance, ethics and privacy rules.

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Revision history

Contents

• Title and abstract: 1. Title, 2. Abstract
• Introduction: 3. Problem description, 4. Available knowledge, 5. Rationale, 6. Specific aims
• Methods: 7. Context, 8. Interventions, 9. Study of the interventions, 10. Measures, 11. Analysis, 12. Ethical considerations
• Results: 13. Results
• Discussion: 14. Summary, 15. Interpretation, 16. Limitations, 17. Conclusions
• Other information: 18. Funding
• 19. SQUIRE 2.0 cross-reference, 20. Sustainability and spread plan, 21. Glossary and references, Appendix A. PDSA log, Appendix B. Data collection sheet

1. Title

[Improving outcome or process] in [setting] through [main change]: a quality improvement project

Example: Getting discharge medicines ready before 11:00 on an adult medical ward: a quality improvement project at Halvermoor District Hospital (fictional).

2. Abstract

Example: On Ward 6, a 28-bed adult medical ward, 41.6% (197 of 474) of planned discharges had their discharge medicines ready on the ward by 11:00 over a 12-week baseline. Using the Model for Improvement, a multidisciplinary team tested four changes in sequential PDSA cycles over 12 weeks: a ward pharmacist at the morning board round, a "tomorrow's discharges" list, prescribing on the day before discharge, and a ward medicines cupboard for common discharge items. The weekly proportion rose above the baseline median from week 13 and stayed above it for 12 consecutive weeks, a shift on the run chart. Over weeks 17 to 24 the proportion was 70.1% (225 of 321). The balancing measure, discharge prescriptions needing a pharmacist correction, was 17.7% at baseline and 18.4% in weeks 17 to 24. The changes are now standard work on Ward 6 and are being tested on two further wards.

Introduction

The four items below explain the problem, what was already known, why the team expected the changes to work, and what the project set out to achieve. Write them so that a reader from another organisation can judge whether the problem looks like theirs.

3. Problem description

[Describe the nature and significance of the local problem: what happens now, what should happen, how often the gap occurs, and the effect on patients, staff and flow.]

Example: Patients on Ward 6 who were medically fit to leave often waited on the ward for discharge medicines. Baseline data from 12 weeks (474 planned discharges) showed that 41.6% had medicines ready by 11:00. Late medicines delayed bed availability for patients waiting in the emergency department and the acute medical unit, and patients and families reported uncertainty about when they could leave.

4. Available knowledge

[Summarise relevant evidence and experience: published studies, national or professional guidance, previous local projects and benchmarking. State where the evidence is thin or comes from different settings.]

5. Rationale

[Explain the informal or formal theory, framework or logic model that links the changes to the expected improvement, and the assumptions it rests on.]

Example: The team's theory was that medicines are late because the prescription is written on the morning of discharge, then queues in pharmacy with that day's other work. Moving prescribing to the day before, and giving the pharmacist early sight of likely discharges, should move the work out of the morning peak. The driver diagram below shows the aim, primary drivers, secondary drivers and change ideas (CH-01 to CH-04) that the PDSA log in appendix A tests.

6. Specific aims

[State the purpose of the project and of this report.]

Example: To increase the proportion of planned discharges from Ward 6 with discharge medicines ready on the ward by 11:00 from a baseline of 41.6% to 70% within 12 weeks of the first test of change, without an increase in discharge prescriptions needing pharmacist correction. This report describes the first 24 weeks (12 baseline, 12 improvement).

Methods

The six items below describe the setting, the changes, the approach to studying them, the measures, the analysis and the ethical review. Give enough detail that another team could repeat the work and judge how much of the result depends on local conditions.

7. Context

8. Interventions

Team. [List roles: project lead, clinicians, pharmacist, nursing, patient partner, data analyst, sponsor. Say how often the team met.]

Describe also what did not change: the discharge criteria, the clinical decision about fitness for discharge and the prescribing rules stayed the same, so that any change in the measures can be attributed to the workflow changes.

9. Study of the interventions

[Describe the approach chosen for assessing the impact of the interventions and for judging whether the observed effects were due to them. Include how you checked that each change was carried out as intended.]

Example: The team used the Model for Improvement with sequential PDSA cycles (appendix A). Impact was studied as a time series with weekly data plotted on a run chart against the baseline median. Fidelity was checked for each change: the pharmacist's attendance at board rounds (CH-01) and the completion of the 16:00 list (CH-02) were recorded on the ward board and counted weekly. Two short staff huddles after weeks 18 and 22 collected views on what helped and what got in the way.

10. Measures

Data quality. [Describe how the data were validated: for example, the project lead checked 10 records a week against the paper ward board for the first four weeks, and the definition was fixed before baseline collection began.]

11. Analysis

[Describe the analytic methods used to draw inferences from the data, and how variation over time was handled.]

Example: M1, M2 and M3 were plotted weekly on run charts. The baseline median of M1 (42.3%) was calculated from weeks 1 to 12 and frozen, then extended across the improvement period. Non-random variation was judged with commonly used run chart rules (Perla, Provost and Murray, 2011): a shift (six or more consecutive points above or below the median, with points on the median neither breaking nor adding to the run), a trend (five or more consecutive points all going up or all going down), too few or too many runs, and an astronomical data point. Staff huddle notes were grouped into themes by two team members independently.

12. Ethical considerations

[State the ethical review route and outcome, data protection measures and any conflicts of interest.]

Example: The project was registered with the hospital's audit and QI office (reference [QI-2026-041]). Using the organisation's research decision tool, the office confirmed it was service improvement and did not need research ethics committee review. Only aggregate counts left the ward. The team declared no conflicts of interest.

13. Results

13.1 How the interventions evolved

[Describe the sequence of PDSA cycles and how each changed the next. The full log is in appendix A.]

Example: CH-01 began in week 13 with one consultant team and spread to both teams in week 14 after the first cycle showed that the pharmacist could cover both board rounds in 30 minutes. CH-02 was adapted in its second cycle: the list moved from a paper sheet to a column on the electronic ward board because the paper sheet was lost twice. CH-04 started in week 17 with 12 pre-packed items chosen from a month of dispensing data.

13.2 Outcome measure M1

The run chart shows weekly M1 against the frozen baseline median. All 12 points from week 13 to week 24 sit above the median, which meets the shift rule (six or more consecutive points on one side). Weeks 12 to 21 also form a trend: ten consecutive points, each higher than the one before.

Dashed line: baseline median (42.3%). Solid grey line: aim (70%). The weekly counts behind each point are in appendix B.

13.3 Process and balancing measures

13.4 Context, unintended consequences and missing data

[Describe how context interacted with the changes, any unexpected benefits or problems, and how much data was missing and why.]

Example: The hospital flow programme started in week 10, before the first change, and introduced a daily site meeting that did not involve Ward 6 pharmacy staff; M1 did not change in weeks 10 to 12. Staff reported an unexpected benefit: earlier conversations with patients about their medicines. Data for two days in week 20 were missing because of an electronic prescribing outage; those discharges were excluded from both numerator and denominator.

Discussion

The four items below summarise the findings, interpret them against other work and the costs, set out the limitations and state the conclusions and next steps.

14. Summary

[Summarise the key findings, their relevance to the rationale and aims, and the particular strengths of the project.]

Example: Moving discharge prescribing to the day before, with early pharmacist involvement, was followed by a sustained rise in the proportion of discharges with medicines ready by 11:00 from 41.6% to 70.1%, meeting the aim in weeks 19 to 24 without a rise in prescription corrections. The project's strength is that it measured fidelity for each change and a balancing measure from the start.

15. Interpretation

[Discuss the nature of the association between the interventions and the outcomes, comparison with other work, the effect on people and systems, reasons for any differences from what was expected, and costs, opportunity costs and trade-offs.]

16. Limitations

[List the limits to generalisability, factors that might have limited internal validity, and the efforts made to minimise them.]

Example: The design is a single-ward time series without a control ward, so a concurrent hospital-wide effect cannot be excluded; the flow programme that began in week 10 is the main alternative explanation. The absence of change in weeks 10 to 12 and the step pattern that follows the change dates make it less likely. The definition of a planned discharge relied on the ward board, which was updated by many staff.

17. Conclusions

[State the usefulness of the work, how it will be sustained, the potential for spread to other settings, implications for practice and further study, and suggested next steps.]

Example: The four changes are now standard work on Ward 6. The team will monitor M1 monthly for six months and is testing CH-01 and CH-03 on two surgical wards with different pharmacy cover.

Other information

Add here any information required by the target journal or committee that does not fit the SQUIRE items, such as patient partner involvement or data availability statements.

18. Funding

[List sources of funding that supported this work and the role, if any, of the funder in the design, implementation, interpretation and reporting.]

Example: No external funding. Staff time was provided by the hospital within normal duties. The pre-packed stock for CH-04 was funded from the pharmacy budget.

19. SQUIRE 2.0 cross-reference

20. Sustainability and spread plan

21. Glossary and references

21.1 Glossary

21.2 References

1. Ogrinc G, Davies L, Goodman D, Batalden P, Davidoff F, Stevens D. SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence): revised publication guidelines from a detailed consensus process. BMJ Qual Saf. 2016;25(12):986-992. doi:10.1136/bmjqs-2015-004411. Published simultaneously in several journals.
2. SQUIRE 2.0 Explanation and Elaboration. BMJ Qual Saf. 2016;25(12):e7. Listed on the EQUATOR Network page: https://www.equator-network.org/reporting-guidelines/squire/
3. Langley GJ, Moen RD, Nolan KM, Nolan TW, Norman CL, Provost LP. The Improvement Guide: A Practical Approach to Enhancing Organizational Performance. 2nd edition. Jossey-Bass; 2009. Model for Improvement summary: https://www.ihi.org/library/model-for-improvement
4. Perla RJ, Provost LP, Murray SK. The run chart: a simple analytical tool for learning from variation in healthcare processes. BMJ Qual Saf. 2011;20(1):46-51. doi:10.1136/bmjqs.2009.037895.
5. [Local and published sources cited in sections 4 and 15]

Related templates in this collection: healthcare/pdsa-cycle for each test of change, healthcare/clinical-audit for criterion-based audit before and after a project, healthcare/root-cause-analysis when the project starts from a serious incident, business/kpi-scorecard for ongoing monitoring and project/lessons-learned for the close-out review.

Appendix A. PDSA log

Appendix B. Data collection sheet

Example data: every row above is fictional. Totals: weeks 1 to 12, 197 of 474; weeks 13 to 16, 86 of 161; weeks 17 to 24, 225 of 321.

Standard and licence note

Structure aligned with SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence), Ogrinc G and colleagues, BMJ Quality and Safety 2016;25(12):986-992, published simultaneously in several journals and listed by the EQUATOR Network. This template uses the SQUIRE item numbers and short item names only; every heading explanation, guidance note and example is original text, and no checklist wording is reproduced. Use the official SQUIRE 2.0 checklist and the Explanation and Elaboration paper when you submit, and check the EQUATOR Network page for the current version. The Model for Improvement is cited from Langley and colleagues (Associates in Process Improvement) and the run chart rules from Perla, Provost and Murray (2011). The organisation, ward and all figures in the examples are fictional. This is a reporting format, not clinical advice; follow your organisation's governance, ethics and privacy rules and consult qualified professionals. Template licence: MIT.