Reducing Data Collection Burden in Clinical Teams

Why this topic matters

Clinical teams in the NHS are under immense pressure to deliver high-quality care while navigating increasing workloads. Data collection, though vital, can become a significant source of this pressure. When data collection processes are inefficient, duplicative, or poorly designed, they can consume valuable clinical time, divert focus from patient care, and contribute to staff burnout. This 'data burden' can manifest as:

• Reduced direct patient contact time: Every minute spent on administrative data entry is a minute not spent with patients.
• Increased staff stress and dissatisfaction: Repetitive, manual data entry tasks can be demotivating.
• Compromised data quality: When teams are rushed or disengaged, errors in data entry are more likely, leading to unreliable insights.
• Delayed improvements: If data collection is a bottleneck, the analysis and implementation of improvements are also delayed.
• Inefficient resource allocation: Poor data collection can obscure the true state of services, making it harder to allocate resources effectively.

Addressing data collection burden is not just about efficiency; it's about creating a more sustainable and effective healthcare system that supports both staff well-being and exceptional patient care.

Practical explanation: Unpacking the data collection landscape

Much of the data collected in the NHS serves legitimate and important purposes, such as national reporting for NHS England, clinical governance, commissioning, research, and local quality improvement projects. The challenge often lies in how this data is collected, stored, and used. Key aspects to consider include:

Identifying the 'why' behind each data point

Before undertaking any data collection, it is crucial to understand its purpose. Is it for a national benchmark, a local QI initiative, a CQC requirement, or internal operational tracking? Often, historical data collections are maintained without a clear current justification.

Duplication and fragmentation

Teams frequently find they are collecting the same or very similar data for multiple different reporting streams or departmental needs. This duplication wastes effort and creates inconsistencies. Furthermore, data may be scattered across various systems – electronic patient records (EPRs), spreadsheets, paper forms, and local databases – making consolidation and analysis cumbersome.

Manual vs. automated collection

The most significant burden often comes from manual data entry. While some data points may inherently require human input (e.g., patient experience feedback), many others could potentially be extracted directly from existing digital systems like EPRs, laboratory systems, or prescribing platforms.

Data granularity and 'nice-to-haves'

Teams sometimes collect more data than is truly necessary for a given purpose. While comprehensive data can be valuable, it's important to differentiate between 'need-to-have' and 'nice-to-have' data points. Over-collection increases burden without always yielding proportional value.

Common pitfalls

When trying to manage data collection, teams often encounter several common pitfalls:

• 'Just in case' data collection: Collecting data without a clear plan for its use, hoping it might be useful later.
• Lack of standardised definitions: Different teams or individuals collecting the 'same' data using slightly different definitions, rendering comparison impossible.
• One-off requests becoming routine: A data request for a specific project becoming embedded as a permanent collection without review.
• Technology siloisation: Digital systems that don't communicate with each other, necessitating manual transfer of information.
• Fear of stopping legacy collections: Hesitancy to discontinue long-standing data collections due to perceived compliance risks or 'we've always done it this way' mindsets.
• Underestimation of staff time: Not fully accounting for the cumulative time clinical staff spend on data collection tasks.

Step-by-step approach: Streamlining data collection

To effectively reduce data collection burden, a systematic approach is needed. This framework can guide your team:

1. Map current data collection processes

• Inventory: List all data points currently collected by your team. For each, identify: the data field, who collects it, when, how (system/paper), where it's stored, and its stated purpose.
• Stakeholder identification: Who uses this data? Include clinical staff, managers, auditors, commissioners, and national bodies.
• Visualise the flow: Use process mapping (e.g., swimlane diagrams) to illustrate how data moves through your system, from collection to reporting.

2. Challenge everything: The '5 Whys' for data

• For each data point and collection process, repeatedly ask 'Why is this collected?' If the answer isn't clear, ask 'Why is that important?' Continue until you reach a fundamental requirement or establish it as unnecessary.
• Categorise: Is it mandatory (national/regulatory), clinical safety critical, for local QI, or 'other'?
• Identify duplication: Look for instances where the same information is being captured multiple times or could be derived from existing data.

3. Optimise collection methods

• Prioritise automation: Investigate if data points can be automatically extracted from existing electronic systems (e.g., EPRs). This often requires collaboration with IT and informatics teams.
• Integration: Explore opportunities to integrate disparate systems to reduce manual data transfer.
• Standardisation: Implement clear, concise data definitions and use standardised forms or templates where manual entry is unavoidable.
• Reduce granularity: Can aggregate data suffice instead of individual-level data for certain purposes? For example, weekly counts instead of daily, or broad categories instead of precise numerical values.
• Review frequency: Does data truly need to be collected daily, or would weekly or monthly suffice?

4. Implement and monitor changes

• Pilot changes: Test revised data collection processes on a small scale before wider implementation.
• Training and communication: Ensure all staff understand the 'why' behind changes and are proficient in new methods.
• Performance monitoring: Track key metrics such as data quality, timeliness, and perceived burden on staff. Use this feedback to iterate and refine.
• Regular review cycle: Establish a schedule (e.g., annually) to review all data collections to prevent 'data creep'.

Example in clinical practice: Ward-based audit

A busy surgical ward was conducting a monthly paper-based audit on venous thromboembolism (VTE) prophylaxis compliance, requiring nurses to manually review patient notes daily and transcribe findings onto a paper form. This took approximately 10 minutes per patient per day, leading to significant burden and often incomplete forms.

The QI lead, in collaboration with nursing staff and the informatics team, undertook the following:

1. Mapped the process: Showed nurses spending significant time on transcription, then junior doctors consolidating summaries, and finally a ward clerk manually inputting into a spreadsheet.
2. Challenged the 'why': The core purpose was to ensure VTE prophylaxis was prescribed and administered appropriately, a national safety priority.
3. Optimised collection:
   • An audit of the existing EPR confirmed that VTE risk assessment and prescribed prophylaxis were already being recorded electronically.
   • The informatics team developed a custom report within the EPR that could extract the required VTE prophylaxis data for all patients on the ward, highlighting any deviations from guidelines, with a single click.
   • For the few data points not available electronically (e.g., patient understanding of VTE), a brief, targeted observation form was developed to be completed just once by a nurse during routine care, rather than daily transcription from notes.
4. Implemented and monitored: After a pilot, the new electronic report and streamlined observation were rolled out. Nursing staff now spent less than 5 minutes per patient on specific data collection per week, with the electronic report providing real-time compliance information to the ward manager. Data accuracy improved significantly, and the ward's VTE compliance rate was more reliably tracked.

This example demonstrates how leveraging existing digital infrastructure and critically evaluating data needs can drastically reduce burden whilst enhancing data utility.

How Lazomis can help

Lazomis provides a structured environment that can significantly support NHS teams in managing and reducing data collection burden for quality improvement and audit. Our platform can help by:

• Centralised project management: Use Lazomis to map out your current data collection processes by creating projects for each audit or QI initiative. This allows for clear documentation of 'what' is collected and 'why'.
• Digital data collection forms: Transition from paper-based forms to customisable digital forms within Lazomis. These forms can incorporate drop-down menus, validation rules, and conditional logic to improve data accuracy and reduce manual entry errors. Data can then be automatically collected and stored.
• Integration opportunities: While not a full EPR, Lazomis can often integrate with existing systems via APIs or facilitate the structured upload of data, reducing the need for manual re-entry. This is a topic to discuss with our technical team in line with your local IT policies.
• Analysis and reporting tools: Once data is collected, Lazomis dashboards and reporting features provide immediate insights, reducing the manual effort involved in data analysis and presentation. This helps demonstrate the value of collected data, justifying its collection or highlighting where it can be streamlined.
• Collaboration features: Facilitate discussions and decisions about data collection within your team and across departments, ensuring everyone understands the purpose and necessity of each data point.

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

Key takeaways