How to Analyse Clinical Audit Results for Meaningful Improvement

Why this topic matters

Effective analysis of clinical audit results is crucial for several reasons:

• Identifying Gaps in Care: It highlights areas where current practice deviates from established standards or guidelines, indicating potential safety concerns or inefficiencies.
• Understanding Variation: Analysis helps to identify unwarranted variation in care delivery, which can be a key driver of poorer outcomes or higher costs.
• Informing Improvement Initiatives: Well-understood data provides the evidence base needed to design and prioritise effective quality improvement projects.
• Demonstrating Impact: By comparing cycles, robust analysis allows teams to demonstrate the impact of their interventions and sustain improvements.
• Meeting Governance Requirements: NHS organisations require evidence of ongoing audit and quality improvement activities to ensure patient safety and effective care.

Practical explanation of analysis principles

Clinical audit analysis doesn't necessarily require sophisticated statistical software, but it does demand a systematic approach. The goal is to turn raw data into understandable information that tells a story about your current practice.

Data Cleaning and Preparation

Before any analysis begins, your data needs to be clean. This involves:

• Checking for Completeness: Are there missing entries? How will you handle them (e.g., exclude, impute if appropriate and justifiable)?
• Identifying Outliers: Are there data points that seem unusually high or low? Investigate if these are true values or data entry errors.
• Standardising Data Entry: Ensure consistency (e.g., all dates in the same format, clear categories for qualitative data).
• De-identification: Ensure all patient-identifiable data is removed or appropriately pseudonymised in line with local Caldicott Guardian guidance and data protection policies.

Descriptive Statistics

These are your starting point, summarising the basic features of your data. Common descriptive statistics include:

• Frequencies and Percentages: How often does a particular event or characteristic occur? (e.g., '85% of patients received appropriate venous thromboembolism prophylaxis').
• Measures of Central Tendency: Mean (average), median (middle value), and mode (most frequent value) help understand the 'typical' value for numerical data.
• Measures of Dispersion: Range (difference between max and min) and standard deviation (spread around the mean) tell you how varied your data is.

Comparative Analysis

Often, you'll want to compare your results against something:

• Against a Standard: The most common comparison in audit, assessing compliance with a target (e.g., NICE guideline, local protocol).
• Against Previous Cycles: Comparing current performance with baseline data or previous audit cycles to assess change over time.
• Between Groups: Looking for differences in performance between different clinicians, teams, wards, or patient cohorts (e.g., 'Did patients admitted on weekends fare differently?').

Visualising Your Data

Data visualisation is paramount for making findings accessible and understandable. Clear charts and graphs can reveal patterns that are hard to spot in tables of numbers. Common visualisations include:

• Bar Charts: Ideal for comparing categorical data (e.g., compliance percentages for different criteria).
• Pie Charts: Useful for showing proportions of a whole, but generally less effective than bar charts for comparisons.
• Line Graphs: Excellent for showing trends over time (e.g., performance across multiple audit cycles).
• Run Charts/Control Charts: Powerful tools from quality improvement for monitoring processes over time and detecting non-random variation. Run charts are simpler to create and interpret; control charts require more statistical understanding but provide more robust signals.

Common pitfalls in audit analysis

Avoiding these common mistakes can significantly improve the quality and impact of your audit findings:

• Poorly Defined Standards: If your audit criteria are vague, your analysis will be inconclusive. Ensure standards are SMART (Specific, Measurable, Achievable, Relevant, Time-bound).
• Insufficient Sample Size: A sample too small may not be representative of the wider population, leading to unreliable conclusions. Consult statistical advice if unsure.
• Interpreting Correlation as Causation: Just because two things happen together doesn't mean one causes the other. Be cautious with causal language.
• Ignoring Context: Numbers alone don't tell the whole story. Consider the clinical context, local challenges, and resource limitations that might explain certain findings.
• Over-reliance on Averages: Averages can hide significant variation. Look at the spread of your data and consider median and mode, especially for skewed datasets.
• Lack of Actionable Insights: The analysis should lead to clear, practical recommendations for improvement, not just a statement of findings.
• Failing to Present Clearly: Complex data presented poorly will not engage your audience or catalyse change.

Step-by-step approach to analysing audit results

1. Revisit Your Audit Objectives: Before you even look at the numbers, remind yourself of the 'why' behind the audit. What questions were you trying to answer? This informs your analytical focus.
2. Clean and Prepare Your Data: As described above, ensure your dataset is accurate, complete, and anonymous. This is non-negotiable.
3. Perform Descriptive Analysis: Calculate frequencies, percentages, means, medians, and ranges for your key audit criteria. This gives you a foundational understanding of your current performance.
4. Compare Against Standards: Directly compare your compliance rates with the pre-defined standards. Clearly identify where you met, exceeded, or fell short of the targets.
5. Look for Trends and Variations:
   • Over Time: If this is a re-audit, compare current data with previous cycles. Did things improve, worsen, or stay the same? Use line graphs or run charts.
   • Between Groups: If your data allows, break it down by relevant factors (e.g., specific wards, differing patient demographics, time of day/week). Are there significant differences that warrant further investigation?
6. Drill Down into 'Why': When you identify areas of low compliance or significant variation, start to ask 'why?'. This moves you from 'what' happened to 'why' it happened. This often involves qualitative review (e.g., reviewing patient notes for underlying reasons, discussing with staff).
7. Synthesise Findings and Formulate Conclusions: Bring all your analytical pieces together. What are the key messages? What is the overall picture of compliance and quality?
8. Develop Actionable Recommendations: Based on your conclusions, propose specific and realistic actions for improvement. These should be linked directly to your findings.
9. Visualise and Present Your Results: Create clear, impactful charts and graphs. Structure your presentation to tell a compelling story. Focus on the most important messages.
10. Reflect and Plan Next Steps: Consider what you've learned from the analysis process itself. What might you do differently next time? What does this mean for the next audit cycle?

Example in clinical practice: Management of Sepsis in A&E

An audit was conducted on adherence to the 'sepsis six' bundle in an Accident & Emergency (A&E) department. The standard was 95% compliance with all six elements within one hour for patients presenting with suspected sepsis.

Initial findings (Baseline Audit):

• Overall compliance with all six elements within one hour: 55%
• Breakdown of individual elements:
  • Oxygen administration: 98%
  • Blood cultures: 65%
  • Lactate measurement: 60%
  • IV fluids: 70%
  • Broad-spectrum antibiotics: 58%
  • Urine output monitoring (initial): 75%

Analysis:

1. Comparison to Standard: Overall compliance (55%) is significantly below the 95% standard.
2. Identifying Gaps: Oxygen administration is high, suggesting awareness of immediate critical care. However, blood cultures, lactate, and antibiotics are notably low.
3. Drilling Down (Example): Why are blood cultures and lactate low? Investigation might show:
   • Blood Cultures: Difficulty accessing venepuncture equipment on busiest shifts, junior staff sometimes prioritising fluid cannulation over drawing bloods early, lack of clear huddles at triage.
   • Lactate: Point-of-care testing machine often in use by other teams, delays in sending to lab, misunderstanding of its urgency.
4. Visualisation: Bar charts comparing each element's compliance to the 95% standard would visually highlight the biggest deficits. A run chart could track overall compliance over several weeks if data were recorded more frequently.

Conclusions and Recommendations:

• Conclusion: Significant improvements are needed in initiating key diagnostic and therapeutic elements of the sepsis six bundle, particularly blood cultures, lactate, and antibiotics.
• Recommendations:
  • Introduce a 'sepsis pack' at triage with all necessary blood culture bottles and lactate tubes.
  • Implement dedicated sepsis lead nurse/doctor for each shift to oversee initial management for suspected cases.
  • Provide targeted refresher training for junior staff on the urgency and correct procedure for immediate sepsis bundle elements.
  • Relocate a dedicated point-of-care lactate machine to the A&E resuscitation area.

This resource supports, but does not replace, clinical judgement. Local policy, formulary and specialist advice should be followed.

How Lazomis can help

Lazomis offers various tools to streamline the clinical audit process, from data collection to analysis and reporting:

• Lazomis Data Entry Forms: Design custom, user-friendly forms for efficient and accurate data capture, reducing errors during the collection phase.
• Lazomis Dashboards: Automatically pull your audit data into visual dashboards, allowing you to instantly see compliance rates, trends over time, and compare performance against targets. Customise charts (bar, line, pie) to clearly convey your findings.
• Automated Reporting: Generate professional reports from your analysed data with embedded graphs, ready for presentation to audit committees or for sharing with colleagues, saving valuable time and ensuring consistency.
• Secure Data Storage: Lazomis ensures your pseudonymised audit data is stored securely and compliantly, meeting NHS data governance standards.

By centralising your audit workflow on Lazomis, you can dedicate more time to interpreting your results and implementing improvements, rather than manual data handling and chart creation.