Reducing Data Collection Burden in Clinical Practice

Why this topic matters

Clinical teams across the NHS are under significant pressure. The demand for data has grown exponentially, often driven by local and national reporting requirements, research initiatives, and commissioning contracts. While the intent behind data collection is almost always positive, poorly designed or excessive data collection processes can lead to:

• Increased workload and 'trolleyology': diverting staff from direct patient care.
• Reduced morale and engagement: staff may see data collection as an administrative burden rather than a useful tool.
• Poor data quality: hurried or incomplete entry can lead to unreliable data, making analysis meaningless or even misleading.
• Delayed improvements: if data is collected but not analysed or acted upon efficiently, the purpose is lost.
• Shadow systems: staff creating parallel, unofficial systems to manage information, increasing risk and inefficiency.

Addressing data collection burden is not about collecting less data, but about collecting the right data, in the right way, at the right time, with minimal disruption.

Practical explanation

Reducing data collection burden involves a systematic review of what data is collected, why it's collected, how it's collected, and who collects it. It's about optimising processes to be as efficient and impactful as possible.

Key Principles

• Purpose-led: Every piece of data collected should have a clear, justifiable purpose linked to a specific outcome or decision.
• Minimisation: Collect only what is absolutely necessary. Avoid 'nice-to-have' data points.
• Integration: Aim to capture data at the point of care, ideally as a by-product of routine clinical documentation, rather than a separate activity.
• Automation: Leverage technology to reduce manual entry and aggregate data where possible.
• Feedback: Ensure those who collect data receive regular feedback on how it is used and the impact it has.

Common pitfalls

Without a structured approach, efforts to reduce data collection burden can face several challenges:

• Lack of clear ownership: Who is responsible for reviewing and optimising data collection processes?
• 'Just-in-case' data: Collecting data points because they might be useful later, without a defined current need.
• Duplication: Multiple teams collecting the same or similar data for different purposes, often across different systems.
• Resistance to change: Staff may be accustomed to current processes, even if inefficient, and resist new ways of working.
• Technology as a bandage: Implementing new software without first optimising the underlying process.
• Ignoring feedback: Not listening to the frontline staff who are performing the data entry about what works and what doesn't.
• Data hoarding without analysis: Collecting vast amounts of data but not having the capacity or skills to analyse it effectively, leading to no actionable insights.

Step-by-step approach to reducing data collection burden

Step 1: Inventory and Justify

Conduct an audit of all data currently being collected within your area. For each data point:

• What is it? (e.g., patient age, admission date, specific clinical score).
• Why is it collected? (e.g., national audit, local QI project, clinical pathway monitoring, CQC requirement).
• Who uses it? (e.g., clinical lead, audit team, commissioners, individual clinician).
• How is it used? (e.g., daily ward round, monthly report, annual audit submission).
• Where is it stored? (e.g., EPR, local spreadsheet, paper form).
• Is it essential? Can the objective be met without this specific data point, or with a proxy?

Challenge every data point. If the why isn't robust, consider stopping its collection.

Step 2: Streamline Collection Methods

Once you know what to collect, optimise how it's collected.

• Integrate with routine practice: Can data be captured directly from Electronic Patient Records (EPRs) or other existing clinical systems? This is the gold standard.
• Standardise forms: If paper or local digital forms are necessary, ensure they are concise, use clear language, and avoid free-text where structured options (e.g., drop-down menus, tick boxes) are suitable.
• Point-of-care entry: Encourage data entry at the time an event occurs, reducing retrospective data entry which is prone to errors.
• Reduce duplication: Identify where the same data is entered multiple times and seek to consolidate.
• Digitalise: Transition from paper forms to digital solutions where appropriate and feasible, ensuring these are user-friendly.

Step 3: Leverage Technology and Automation

Explore how technology can automate or simplify data collection.

• EPR functionality: Maximise the use of existing EPR capabilities for reporting and data extraction.
• Interoperability: Advocate for better integration between different systems to reduce manual data transfer.
• Report automation: Can routine reports be automatically generated from existing data sources rather than manually compiled?
• Decision support systems: Can prompts or structured input fields within clinical systems lead to more complete and standardised data capture?

Step 4: Governance and Feedback

Establish robust processes to maintain efficient data collection.

• Regular review: Schedule periodic reviews of data collection requirements (e.g., annually) to ensure continued relevance and efficiency.
• Clear roles and responsibilities: Define who is responsible for data entry, quality checks, analysis, and reporting.
• Training and support: Provide adequate training on systems and the importance of accurate data.
• Circulate findings: Regularly share the outcomes and impact of the data collected with the frontline staff. This demonstrates the value of their effort and encourages engagement.

Example in clinical practice: Reducing vital signs collection burden in an outpatient clinic

A cardiology outpatient clinic noticed significant delays and patient complaints due to the time taken for nurses to collect comprehensive vital signs (blood pressure, heart rate, temperature, SpO2, respiratory rate) for every patient, regardless of their presenting complaint.

Initial Observation: All vital signs were taken for all patients, manually recorded on paper, then later transcribed into the EPR.

Application of Step-by-step approach:

1. Inventory and Justify: The team reviewed which vital signs were genuinely critical for specific clinic types (e.g., heart failure vs. follow-up post-PCI). They found that for stable, routine follow-ups, temperature and respiratory rate were rarely actioned. They also confirmed that for new patients or specific high-risk groups, all parameters were indeed crucial.
2. Streamline Collection Methods:
   • They introduced a 'streamlined vital signs' protocol for stable follow-up patients, requiring only BP and HR unless clinically indicated otherwise.
   • They procured automated BP monitors that could wirelessly send readings directly to the EPR, eliminating manual transcription.
   • For patients requiring full vital signs, a dedicated 'vitals station' was set up with integrated devices.
3. Leverage Technology and Automation: The integration of the wireless BP monitor saved approximately 2-3 minutes per patient. For patients needing full vitals, the new station allowed for faster, more accurate readings directly into the system.
4. Governance and Feedback: The new protocol was embedded into nursing guidelines and regularly audited. Nurses received feedback on how the streamlined process allowed more time for patient education. The cardiology consultants confirmed that patient safety was maintained, and clinic flow significantly improved.

Outcome: Reduced data collection time by an average of 40% per stable patient, improving clinic throughput, reducing patient wait times, and allowing nursing staff more time for patient-centred care rather than data entry. Data quality for the critical parameters improved due to direct digital capture.

How Lazomis can help

Lazomis provides a structured environment to support your efforts in reducing data collection burden without compromising quality outcomes.

• QI Project Setup: Use Lazomis to define the scope of your data improvement project, set clear aims, and identify key metrics. This structured approach helps ensure data collection remains focused on tangible improvements.
• Data Collection Tools: If you need to collect specific audit or QI data not available from existing systems, Lazomis can help you design concise, purpose-led digital forms. These can be deployed quickly, reducing reliance on paper and manual transcription.
• Automated Dashboards: Once data is collected (either via Lazomis forms or imported from other sources), our dashboards can automatically visualise key metrics. This provides immediate feedback to data collectors and simplifies reporting, reducing the burden of manual analysis and presentation.
• Feedback Loops: Use Lazomis's reporting features to easily share project progress and data insights with your team, demonstrating the 'why' behind the collection efforts and fostering engagement.

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

Key takeaways