Medication Errors Quality Improvement Project: A Nursing Example With SWOT, SMART Objectives and PDSA

What Did the Assignment Ask?

The brief asked for a quality improvement proposal on a problem in a UK healthcare setting, taken from the case for change to how success would be measured. Our team chose medication administration errors on an acute medical ward in a large NHS trust and answered the nine parts below, in this order.

1. Describe the quality problem in its setting, with national evidence for why it matters and who it affects.
2. Analyse the organisation's position on the problem with a SWOT.
3. Set an aim and SMART objectives.
4. Review the literature on the problem and the interventions, and name the gap.
5. Choose a quality improvement model and justify it.
6. Explain how data will be collected and how success will be measured.
7. Identify the risks that could stop the project and how each will be managed.
8. Set out the plan as a Gantt chart.
9. State the expected outcomes, then conclude with recommendations.

Written by our team to a nursing brief. It is a proposal, so nothing below reports results from a project that ran.

If your brief asks for the root-cause step in full, our fishbone diagram example with Pareto and control charts shows it on a worked case.

Why Are Medication Administration Errors the Problem to Fix?

Because administration is the stage where most medication errors in England happen. The national estimate by Elliott et al. (2021) puts the total at 237 million errors a year, 54.4 per cent of them at administration, and the cost of definitely avoidable adverse drug events at £98.5 million a year, with 1,708 deaths caused or contributed to.

Where medication errors happen in English hospitals, by stage of the medicines process

Chart data

Item	Value (%)
Administration	78.5%
Prescribing	8.5%
Discharge prescriptions (transition)	7.1%
Dispensing	2.9%
Monitoring	2.9%

The totals cover every setting: 38.4 per cent of errors were in primary care, 41.7 per cent in care homes and 19.9 per cent in hospitals, and primary care has no administration figure because the study treated doses taken at home as the patient's own. Most errors, 72 per cent, had little or no potential for harm, and the authors stress significant uncertainty in their burden estimates. For one ward the case is narrower and stronger: hospitals account for about 47.3 million errors a year, 37.1 million of them, roughly 79 per cent, at administration (our calculation from Table 3 of the accepted manuscript). Definitely avoidable adverse drug events that lengthened a hospital stay cost an estimated £14.8 million a year and caused or contributed to 1,081 deaths, so this proposal argues from the hospital figures.

On the ward the errors are mostly ordinary: wrong time, omission and wrong dose are the commonest, and the median error rate in 91 observation studies was 19.6 per cent of opportunities when wrong-time errors are counted (Keers et al., 2013a). Intravenous doses carry more risk. With wrong-time errors excluded and more than one error allowed per dose, the same review found a median of 53.3 per cent for intravenous doses against 20.1 per cent across all routes, and UK studies put intravenous errors at 101 per 1,000 administrations across prescribing, preparation and administration (Sutherland et al., 2020).

Who Does the Problem Affect?

Four groups carry the cost.

• Patients bear the harm: a missed or wrong dose can mean deterioration, a longer stay and less trust in the ward.
• Nurses give most doses, and the NMC Code requires them to "take measures to reduce as far as possible, the likelihood of mistakes, near misses, harm" (standard 19.1) and to be open when something goes wrong (standard 14). Blame makes both harder, so NICE asks for a "fair blame" culture that encourages reporting (NICE, 2015, recommendation 1.1.1).
• Pharmacists and doctors own the prescription, the chart and the supply, which decide whether a dose can be given correctly.
• The organisation answers to the Care Quality Commission, whose Regulation 12 requires "the proper and safe management of medicines" by staff who are "suitably trained and competent".

SWOT Analysis of Medication Safety on the Ward

The SWOT treats the ward, not the whole trust, as the setting: strengths and weaknesses sit inside the ward, opportunities and threats outside it.

SWOT analysis of medication safety on an acute medical ward

Helpful Harmful Internal

Strengths Internal, helpful

• Written medicines policy: the change tightens practice against a standard staff already use
• Pharmacists on the ward: chart review and coaching at the point of care
• A multidisciplinary team: nurses, pharmacists and doctors each own part of the process

Weaknesses Internal, harmful

• Uneven checks and records: checks and dose records vary from round to round
• Interrupted medicine rounds: each interruption linked to 12.7% more clinical errors in one study
• Unreliable incident counts: reports cannot give the project its baseline

External

Opportunities External, helpful

• A newer reporting system: LFPSE replaced the NRLS on 30 June 2024
• A learning-first framework: PSIRF looks at the factors behind each incident
• Electronic prescribing records: EPMA dose records can be audited without paper charts

Threats External, harmful

• Short staffing and workload: linked to omitted doses in national incident reports
• Change fatigue: a checklist seen as paperwork is often the first to lapse
• Temporary and new staff: may not have been shown the ward's checks

The four lists below are the detailed SWOT analysis behind the grid, with the evidence for each point.

Strengths

• Written medicines policy: the project tightens practice against a known standard, and the cycle 1 checklist comes from the joint RPS and RCN administration guidance (Royal Pharmaceutical Society and Royal College of Nursing, 2019, still the edition in issue).
• Pharmacists on the ward: a ward pharmacist can review charts before the morning round and coach at the bedside, the change tested in cycle 3.
• A multidisciplinary team: nurses, pharmacists and doctors each own part of the process, so a group drawn from all three can change what a nurse cannot change alone.

Weaknesses

• Uneven checks and records: checks and records vary between rounds, though the RPS and RCN guidance expects every dose given, withheld or declined to be recorded, with a reason when not given (principle 17).
• Interrupted medicine rounds: each interruption was associated with a 12.7 per cent increase in clinical errors in an observational study of 4,271 doses (Westbrook et al., 2010).
• Unreliable incident counts: reports show what staff choose to report; Sutherland et al. (2020) set aside prevalence studies built on spontaneous reports alone.

Opportunities

• A newer reporting system: since the NRLS was decommissioned on 30 June 2024, incidents go to the Learn from Patient Safety Events service (LFPSE), which NHS England built to support higher quality and more timely data.
• A learning-first framework: the Patient Safety Incident Response Framework (PSIRF), which takes the place of the Serious Incident Framework (2015), centres incident response on how an incident happened and what contributed, which suits a systems project.
• Electronic prescribing records: where the ward uses electronic prescribing and medicines administration (EPMA), timed dose records can be audited without paper charts.

Threats

• Short staffing and workload: in 72,390 administration incident reports made in England and Wales in 2016, "workload" was the staffing term most likely to accompany a report of an omitted dose (Härkänen et al., 2020).
• Change fatigue: a checklist that staff see as paperwork is often the first thing to lapse.
• Temporary and new staff: agency and newly started nurses may not have been shown the ward's checks.

So the project changes how the round is run and recorded, and uses the pharmacist the ward already has.

What Are the Project's Aim and SMART Objectives?

The aim is to reduce medication administration errors on one acute medical ward by 30 per cent from the ward's own baseline within 12 weeks, measured by direct observation of medicine rounds. The SMART objectives turn that aim into one outcome target, two process targets and a fixed timetable for the work.

• Specific: every dose on the ward's medicine rounds is given with the checklist's checks and recorded as given, withheld or declined, with a reason.
• Measurable: observed administration errors per 100 doses fall by 30 per cent from the baseline measured in weeks 1 and 2; the share of audited doses with a complete record closes at least half the gap between its baseline and 100 per cent; the checklist is used on at least 90 per cent of observed rounds by week 10.
• Achievable: the changes use what the ward already has: a one-page checklist, a reorganised round and the ward pharmacist's time.
• Relevant: the aim serves the improvement aim of the NHS Patient Safety Strategy (July 2019, updated 2021); the WHO's Medication Without Harm challenge, launched in 2017 with a five-year goal of halving severe avoidable medication-related harm globally; and the duty in CQC Regulation 12(2)(g).
• Time-bound: baseline in weeks 1 and 2, three PDSA cycles in weeks 3 to 10, evaluation in weeks 11 and 12, with the error rate plotted after every observed round.

The outcome is observed, not taken from incident reports, because reporting rises when staff trust the process: across a decade of NRLS data, reporting rates were not associated with hospitals' mortality or patient satisfaction, while confidentiality and feedback to staff increased them (Howell et al., 2015). A target to cut reported errors would reward the silence the project is trying to end. The baseline is the ward's own: Elliott et al. took 18.6 per cent of administered doses as the hospital error rate, the mean of five UK studies in specific patient groups, so a ward starting near that rate would need to reach about 13 errors per 100 doses to meet the target.

What Does the Evidence Say About Reducing Administration Errors?

The evidence says administration errors come from systems more than from careless individuals. The best-known single fix, a "do not interrupt" vest, has not been shown to cut errors, and PDSA tells a team most when it runs several small cycles with frequent data, which most published PDSA projects have not done. Each finding shapes a choice in the plan.

A systematic review of 54 studies classified the causes with Reason's model of accident causation (Keers et al., 2013b). Slips and lapses were the commonest unsafe acts, ahead of knowledge-based mistakes and deliberate violations, and the conditions behind them were mostly organisational: unclear prescriptions and records, supply and stock problems, high workload, ward equipment, patient factors, fatigue and stress, and interruptions. Training alone targets knowledge, which is not where most errors start; a checklist, a protected round and a pharmacist's review target the conditions.

Westbrook et al. (2010) observed 98 nurses giving 4,271 doses in two Sydney teaching hospitals: interruptions occurred in 53.1 per cent of administrations, and nurse experience gave no protection against clinical errors. Two cluster randomised trials have tested a "do not interrupt" vest. In Adelaide, a bundle of vests, diversion strategies, education and reminders cut non-medication interruptions from 50 to 34 per 100 administrations. That trial did not measure errors; extrapolating from their 2010 data, the authors put the potential absolute reduction in clinical errors at 1.8 per cent, and only 48 per cent of nurses surveyed backed the bundle as policy (Westbrook et al., 2017). In 29 French units, the vest "had no impact on administration error or interruption rates": errors occurred in 7.09 per cent of opportunities with the vest and 6.23 per cent without, and interruptions were not associated with the error rate (Berdot et al., 2021). So this project tests a protected round without a vest, and judges it on errors, not interruptions.

PDSA has been used successfully on a medicines process. On an internal medicine ward in a tertiary care hospital in a developing country, medication reconciliation compliance rose from 4 per cent to 77 per cent in the first month and 96 per cent by month four, and stayed above 90 per cent for six months (Sabeen Ahmed, Aziz and Sethi, 2025). That is reconciliation, not administration, but the pattern carries over: changes tested in sequence, with data throughout. Fidelity limits what can be claimed for the method in general: a review of 73 published PDSA projects found fewer than 20 per cent documented a sequence of iterative cycles, and only 7 of 47 used quantitative data at monthly or more frequent intervals to guide the next cycle; a better understanding of how the method is used, the authors argue, is essential "to draw reliable conclusions about their effectiveness" (Taylor et al., 2014).

The gap. Keers et al. (2013b) found few studies of the causes of intravenous administration errors, and little on latent conditions such as local working culture and managerial decisions. Sutherland et al. (2020) found only eight UK studies with usable intravenous error data and concluded that it is "time to focus on reducing administration error". The reviews leave a practical gap: UK ward-level work that tests administration changes in documented cycles against observed error rates. This proposal is designed to add one such example.

Why PDSA, and How Will the Cycles Run?

PDSA is the testing method inside the Model for Improvement: plan a small change, do it, study the data, act on what was learned. It fits this problem because the causes are several and local, the ward needs changes that survive a busy round, and 12 weeks allow three short cycles, each judged against a prediction before the next begins.

The Model for Improvement, developed by Associates in Process Improvement and used by the IHI, asks what we are trying to accomplish, how we will know that a change is an improvement, and what change will result in improvement; the aim, the measures and the driver diagram answer them in turn.

Why not Lean or a one-off audit? The Health Foundation's guide says the evidence "has not yet evolved to the point where one approach can be recommended above another", so the choice rests on the problem (Health Foundation, 2021). An audit checks practice against a standard at one point in time, so here it only sets the baseline. This problem needs changes to an ordinary round, tested by the nurses who run it and measured often enough to show whether each helped: small, sequential, measured PDSA cycles.

What Does the Driver Diagram Show?

A driver diagram links the aim to what drives it and to the changes worth testing; the Health Foundation guide recommends one for capturing the key issues and the activities needed to tackle them. Three drivers here have a change tested in a cycle. The fourth, staffing, is outside the ward's control and is recorded weekly beside the run chart.

Driver diagram: from the aim to the changes tested

Point of comparison	What drives it	Change idea to test
Reliable checks at the bedside	What drives it The same checks on every dose, and every dose recorded as given, withheld or declined	Change idea to test Cycle 1: a one-page administration checklist on the drug trolley
Undisturbed medicine rounds	What drives it Fewer interruptions while doses are prepared and given	Change idea to test Cycle 2: a protected round, with a named nurse taking calls and questions
Clear, current prescriptions	What drives it Charts that are legible, complete and reviewed before the round	Change idea to test Cycle 3: ward pharmacist chart review before the morning round, then a two-minute huddle
Enough staff on the round	What drives it Staffing and skill mix on each shift	Change idea to test Outside the project's control: recorded each week beside the run chart

The Three PDSA Cycles

Each cycle tests one change, states a prediction and ends in a decision to adopt, adapt or abandon, as the IHI's guidance on testing changes advises: early cycles "should be scoped as small as possible", and most changes need "many PDSA cycles, planned and executed in a sequence".

Baseline, three PDSA cycles and the decision

1. 1 Baseline audit and observation, weeks 1 to 2 Write the error definitions first, then observe one round each weekday and audit 20 charts a week.
2. 2 Cycle 1, weeks 3 to 5: administration checklist One bay's morning round for three days, then the whole ward. Prediction: record completeness rises first.
3. 3 Cycle 2, weeks 6 to 8: protected medicine round A named nurse takes calls and questions during the round. Prediction: interruptions halve and wrong-time errors fall.
4. 4 Cycle 3, weeks 9 to 10: pharmacist chart review Charts reviewed before the morning round, then a two-minute huddle. Prediction: chart-led errors fall.
5. 5 Decide, weeks 11 to 12 Adopt, adapt or abandon each change, and write the keepers into the ward routine.

Cycle 1 tests the checklist, built from the administration checks in the RPS and RCN guidance (principle 15): the patient's identity, an unambiguous prescription, allergies, timing and route, the medicine and its expiry, and that the dose has not already been given. Record completeness should rise before the error rate moves, because a checklist works only once it is used; a skipped item prompts a question to staff before any rewording.

Cycle 2 tests a protected round. Because one vest trial measured only interruptions and the other found no effect on errors, the change is organisational: a named nurse takes calls and questions during the round, and a sign on the trolley asks others to wait. If interruptions fall but wrong-time and omission errors do not, the change has done what the Adelaide bundle did and no more, and it is adapted or dropped.

Cycle 3 brings in the ward pharmacist to review charts for unclear or incomplete prescriptions before the morning round, then lead a two-minute huddle on the day's high-risk medicines. It needs another team's time, so it runs last.

How Will Data Be Collected and Success Measured?

By mixed methods. A trained observer watches one medicine round each weekday in the two baseline weeks and two rounds a week after that, counting errors per 100 doses; a weekly audit of 20 charts checks the records; and staff huddles and a short survey at week 12 explain the numbers. Success is judged on a family of outcome, process and balancing measures.

Observation is the core because it counts errors nobody reports, and NICE NG5 lists health record review and direct observation of medicines administration among the methods organisations should consider (NICE, 2015, recommendation 1.1.4). The observer, a nurse or pharmacist not giving the doses, records what happened against the chart. Definitions, such as what counts as a wrong-time dose, are fixed before the baseline, because observed error rates vary widely with the definitions used (Keers et al., 2013a).

• Outcome measure: observed administration errors per 100 doses, with wrong-time, omission and wrong-dose errors also counted separately. The IHI's page on establishing measures gives "adverse drug events per 1,000 doses" as its medication example; observed errors per 100 doses is what a 12-week ward project can collect.
• Process measures: checklist use on observed rounds, the share of audited doses recorded as given, withheld or declined with a reason, and interruptions per round.
• Balancing measures: round length in minutes and late doses of time-critical medicines, because a protected round could delay other care. The IHI frames a balancing measure as the check on whether a change is "causing new problems in other parts of the system".
• Context and secondary signals: staffing on each observed shift, and incident reports to LFPSE, which should rise as staff see the project acting on what they report.

Each observed round is one point on a run chart. The ten baseline rounds give the ten points NHS Lothian's analysts wait for before fixing a baseline median, and real change then shows as a shift: "six or more consecutive data points either all above or below the median" (NHS Lothian Analytical Services, 2017). The qualitative strand explains the numbers: each Friday huddle asks what got in the way of the checks, and the week 12 survey asks which changes staff would keep. Ethics are checked at the planning stage, including the burden on staff being observed (Hunt et al., 2021): the project is registered with the trust's audit or QI team, and staff are told that observation is for learning, not performance management.

What Could Stop the Project, and How Is Each Risk Managed?

Six risks could stop it: staff resistance, workload, being observed, poor data, cost and temporary staff. Each has a mitigation built into the plan, and the ward manager reviews the list weekly so a growing risk is caught within a cycle.

• Staff resistance: an imposed change feels like criticism. Mitigation: ward nurses draft the checklist in week 2 and see each cycle's results at the Friday huddle.
• Workload: a longer round on a short-staffed shift is the quickest way to lose the change. Mitigation: round length is a balancing measure, the checklist stays on one page, and training fits a 15-minute huddle slot.
• Being observed: staff may work more carefully while watched, which flatters the results. Mitigation: daily observation in the baseline weeks lets the novelty wear off before the first change, and observers rotate.
• Poor data: missed observations and loose definitions make the run chart unreadable. Mitigation: written definitions, trained observers, and a pharmacist checking a sample of the coding weekly.
• Cost and staff time: there is no budget line. Mitigation: existing staff and printing, with the pharmacist's cycle 3 time agreed with the pharmacy lead before week 1.
• Temporary and new staff: agency and newly started nurses miss the huddles. Mitigation: a two-minute checklist briefing at the start of each shift they work.

What Is the 12-Week Plan?

The plan runs for 12 weeks: two weeks of set-up and baseline, eight weeks for the three PDSA cycles, and two weeks to evaluate and report. Observation runs every week, so the Gantt chart shows it as one bar across the project.

The 12-week plan as a Gantt chart

Weeks

1 2 3 4 5 6 7 8 9 10 11 12

1. Set-up, governance and staff engagement

   Weeks 1–2

2. Baseline observation and chart audit

   Weeks 1–2

3. PDSA cycle 1: administration checklist

   Weeks 3–5

4. PDSA cycle 2: protected medicine round

   Weeks 6–8

5. PDSA cycle 3: pharmacist chart review

   Weeks 9–10

6. Observation, chart audit and run chart

   Weeks 1–12

7. Evaluation, staff survey and report

   Weeks 11–12

Timeline data

Phase	Start (week)	End (week)
Set-up, governance and staff engagement	1	2
Baseline observation and chart audit	1	2
PDSA cycle 1: administration checklist	3	5
PDSA cycle 2: protected medicine round	6	8
PDSA cycle 3: pharmacist chart review	9	10
Observation, chart audit and run chart	1	12
Evaluation, staff survey and report	11	12

Because measurement never stops between cycles, the run chart shows whether a gain from cycle 1 holds while cycle 2 is tested, and the final evaluation reads the whole series, not a before-and-after pair.

Expected Outcomes and Recommendations

If the cycles work as predicted, patients receive more doses on time and at the right dose, staff get a predictable round and a way to discuss errors without blame, and the organisation gets observed data and a tested change to spread. These are expected outcomes; only the run chart would show whether they happened.

• Keep only what the data supports. A change that moved the process measures but not the error rate is adapted or dropped, however popular it was.
• Write the keepers into the ward routine, including the induction for new and temporary staff.
• Spread one ward at a time. A second ward repeats the baseline before adopting the changes, because its error profile will differ.
• Share the learning with the trust's patient safety team under PSIRF, setting the observed data beside LFPSE reports.

Several changes depend on bedside and handover communication, which our communication in healthcare assignment sample covers.

How a Marker Grades This

The aim has a number, a baseline and a date. The measures form a family, with an observed outcome. The PDSA cycles carry predictions and decisions, not just the four stage names. The national data are used precisely: administration's share of errors, the hospital figures for a hospital setting, and the study's own warning about uncertainty. Marks are lost for a SWOT that could describe any ward: on a real ward each point carries that ward's evidence, such as last quarter's chart-audit figure, the pharmacist's hours on the ward or the agency fill rate. Marks also go for citing papers that do not support the sentence they sit in, and for a conclusion that claims the project worked when it has not run.

See also our critical analysis of national health policy and the role of registered nurses, a research proposal example on self-medication among healthcare professionals for the research route, and more nursing and healthcare assignment samples.

Need a quality improvement project or another nursing assignment built around your own ward and brief? Message us on WhatsApp with the brief, the word count and your deadline, or first read how our nursing and social science assignment help works.

Sources

• Berdot, S., Vilfaillot, A., Bezie, Y., Perrin, G., Berge, M., Corny, J. et al. (2021) 'Effectiveness of a "do not interrupt" vest intervention to reduce medication errors during medication administration: a multicenter cluster randomized controlled trial', BMC Nursing, 20(1), 153. https://doi.org/10.1186/s12912-021-00671-7
• Care Quality Commission (no date) Regulation 12: Safe care and treatment. Guidance on the Health and Social Care Act 2008 (Regulated Activities) Regulations 2014. https://www.cqc.org.uk/guidance-providers/regulations/regulation-12-safe-care-treatment
• Elliott, R.A., Camacho, E., Jankovic, D., Sculpher, M.J. and Faria, R. (2021) 'Economic analysis of the prevalence and clinical and economic burden of medication error in England', BMJ Quality & Safety, 30(2), pp. 96–105, first published online 11 June 2020. https://doi.org/10.1136/bmjqs-2019-010206. Stage, setting, prevalence and Table 3 figures from the accepted manuscript: https://eprints.whiterose.ac.uk/id/eprint/161973/. The accepted manuscript's abstract gives 71 per cent of errors in primary care; its results and the published abstract give 38.4 per cent, the figure used here.
• Härkänen, M., Vehviläinen-Julkunen, K., Murrells, T., Paananen, J., Franklin, B.D. and Rafferty, A.M. (2020) 'The contribution of staffing to medication administration errors: a text mining analysis of incident report data', Journal of Nursing Scholarship, 52(1), pp. 113–123. https://doi.org/10.1111/jnu.12531
• Health Foundation (2021) Quality improvement made simple: what everyone should know about health care quality improvement. 3rd edn. https://doi.org/10.37829/HF-2021-I05
• Howell, A.M., Burns, E.M., Bouras, G., Donaldson, L.J., Athanasiou, T. and Darzi, A. (2015) 'Can patient safety incident reports be used to compare hospital safety? Results from a quantitative analysis of the English National Reporting and Learning System data', PLoS ONE, 10(12), e0144107. https://doi.org/10.1371/journal.pone.0144107
• Hunt, D.F., Dunn, M., Harrison, G. and Bailey, J. (2021) 'Ethical considerations in quality improvement: key questions and a practical guide', BMJ Open Quality, 10(3), e001497. https://doi.org/10.1136/bmjoq-2021-001497
• Institute for Healthcare Improvement (no date) Model for Improvement, with the pages Establishing measures and Testing changes. https://www.ihi.org/library/model-for-improvement; https://www.ihi.org/library/model-for-improvement/establishing-measures; https://www.ihi.org/library/model-for-improvement/testing-changes
• Keers, R.N., Williams, S.D., Cooke, J. and Ashcroft, D.M. (2013a) 'Prevalence and nature of medication administration errors in health care settings: a systematic review of direct observational evidence', Annals of Pharmacotherapy, 47(2), pp. 237–256. https://doi.org/10.1345/aph.1R147
• Keers, R.N., Williams, S.D., Cooke, J. and Ashcroft, D.M. (2013b) 'Causes of medication administration errors in hospitals: a systematic review of quantitative and qualitative evidence', Drug Safety, 36(11), pp. 1045–1067. https://doi.org/10.1007/s40264-013-0090-2
• NHS England (2019, updated 2021) The NHS Patient Safety Strategy. https://www.england.nhs.uk/patient-safety/the-nhs-patient-safety-strategy/
• NHS England (no date) Learn from patient safety events (LFPSE) service. https://www.england.nhs.uk/patient-safety/patient-safety-insight/learning-from-patient-safety-events/learn-from-patient-safety-events-service/
• NHS England (2022) Patient Safety Incident Response Framework. https://www.england.nhs.uk/patient-safety/patient-safety-insight/incident-response-framework/
• NHS Lothian Analytical Services (2017) A common analytical approach supporting quality improvement in Lothian. https://qilothian.scot.nhs.uk/s/A-Common-Analytical-Approach-supporting-Quality-Improvement-in-Lothian.pdf
• NICE (2015) Medicines optimisation: the safe and effective use of medicines to enable the best possible outcomes (NG5), published 4 March 2015. https://www.nice.org.uk/guidance/ng5/chapter/Recommendations
• Nursing and Midwifery Council (2018) The Code: professional standards of practice and behaviour for nurses, midwives and nursing associates, effective 31 March 2015, updated 10 October 2018. https://www.nmc.org.uk/standards/code/read-the-code-online/. This version remains in force; the NMC's consultation on a new draft Code will now launch by the end of November 2026 (Future Standards, last updated 11 September 2026).
• Royal Pharmaceutical Society and Royal College of Nursing (2019) Professional guidance on the administration of medicines in healthcare settings, January 2019, review date January 2023. https://www.rcn.org.uk/workingwithus/-/media/royal-college-of-nursing/documents/working-with-us/endorsements/professional-guidance-on-the-administration-of-medicines-in-healthcare-settings.pdf. The Royal College of Pharmacy, as the Royal Pharmaceutical Society is now named, still issues this January 2019 edition from its Safe and secure handling of medicines page.
• Sabeen Ahmed, A., Aziz, A. and Sethi, S.M. (2025) 'Improving medication reconciliation compliance in a tertiary care hospital of a developing country: a quality improvement initiative using the PDSA cycle', BMJ Open Quality, 14(4), e003487. https://doi.org/10.1136/bmjoq-2025-003487
• Sutherland, A., Canobbio, M., Clarke, J., Randall, M., Skelland, T. and Weston, E. (2020) 'Incidence and prevalence of intravenous medication errors in the UK: a systematic review', European Journal of Hospital Pharmacy, 27(1), pp. 3–8. https://doi.org/10.1136/ejhpharm-2018-001624
• Taylor, M.J., McNicholas, C., Nicolay, C., Darzi, A., Bell, D. and Reed, J.E. (2014) 'Systematic review of the application of the plan-do-study-act method to improve quality in healthcare', BMJ Quality & Safety, 23(4), pp. 290–298. https://doi.org/10.1136/bmjqs-2013-001862
• Westbrook, J.I., Woods, A., Rob, M.I., Dunsmuir, W.T. and Day, R.O. (2010) 'Association of interruptions with an increased risk and severity of medication administration errors', Archives of Internal Medicine, 170(8), pp. 683–690. https://doi.org/10.1001/archinternmed.2010.65
• Westbrook, J.I., Li, L., Hooper, T.D., Raban, M.Z., Middleton, S. and Lehnbom, E.C. (2017) 'Effectiveness of a "Do not interrupt" bundled intervention to reduce interruptions during medication administration: a cluster randomised controlled feasibility study', BMJ Quality & Safety, 26(9), pp. 734–742. https://doi.org/10.1136/bmjqs-2016-006123
• World Health Organization (2017) Medication Without Harm: WHO Global Patient Safety Challenge, launched 29 March 2017. https://www.who.int/initiatives/medication-without-harm

All sources accessed 13 October 2026. The three NHS England pages were read from Internet Archive copies of 30 July 2026 and the NICE recommendations from a copy of 22 September 2026; the NMC pages, the CQC guidance, the IHI pages, the WHO page, the Royal College of Pharmacy page and the NHS Lothian guide were read live on the access date.