What Makes a Useful Clinical Dashboard? A Guide for NHS Teams

Why This Topic Matters

In an increasingly data-rich environment, the ability to extract meaningful information is paramount. Useful clinical dashboards can:

• Highlight Performance: Quickly identify areas of good practice and those requiring improvement, enabling targeted intervention.
• Support Decision-Making: Provide timely, relevant data for operational and strategic decisions.
• Drive Quality Improvement: Act as a central tool for monitoring the impact of QI initiatives and sustaining gains.
• Enhance Accountability: Offer transparency regarding performance against agreed metrics and standards.
• Facilitate Communication: Present complex information clearly, fostering shared understanding across multidisciplinary teams.

Conversely, poorly designed dashboards can lead to information overload, misinterpretation of data, wasted resources in their creation and maintenance, and a general distrust in data-driven approaches. Getting dashboard design right is crucial for leveraging the potential of NHS data.

Practical Explanation: Defining 'Useful'

A 'useful' clinical dashboard is one that not only displays data accurately but also enables its users to answer key questions, make informed decisions, and take effective action. It's less about the sheer volume of data, and more about its relevance, clarity, and timeliness.

Key characteristics of a useful clinical dashboard include:

• Targeted Audience and Purpose: It is designed with a specific user group (e.g., ward nurses, department leads, trust board) and a clear objective (e.g., monitoring patient flow, tracking clinical outcomes, managing staff workload) in mind.
• Key Performance Indicators (KPIs): It focuses on a limited number of essential metrics that directly relate to its purpose. These KPIs should be SMART: Specific, Measurable, Achievable, Relevant, and Time-bound.
• Clarity and Simplicity: Data is presented in an intuitively understandable format, avoiding jargon and excessive complexity. Visualisations (charts, graphs) are chosen appropriately to convey insights quickly.
• Actionable Insights: It doesn't just present numbers; it highlights trends, deviations from targets, and areas requiring attention, prompting users to investigate further or take action.
• Timeliness and Accuracy: The data is up-to-date and reliable, reflecting the current state of affairs. Outdated or erroneous data undermines trust and usefulness.
• Accessibility and Usability: It is easy to access, navigate, and interact with. Users should be able to drill down for more detail or filter data if needed, without extensive training.
• Contextualisation: Data is presented alongside relevant benchmarks, targets, or historical trends to provide context and aid interpretation.

Common Pitfalls to Avoid

Many dashboard initiatives falter due to common errors. Being aware of these can help teams proactively design for success:

• Information Overload (The 'Dashboard Graveyard'): Too many metrics, charts, or colours can overwhelm users, making it impossible to discern key messages. Dashboards should be concise.
• Lack of Clear Purpose: Dashboards created without a defined problem or question to answer often end up unused or misleading. Start with 'what problem are we trying to solve?'
• Irrelevant Metrics: Including data points that don't directly relate to the dashboard's purpose or the user's role. This wastes screen space and cognitive load.
• Poor Data Quality: Dashboards are only as good as the data feeding them. Inaccurate, incomplete, or inconsistent data will lead to incorrect conclusions and erode confidence.
• Lack of User Involvement: Dashboards designed in isolation by technical teams without input from end-users often miss critical requirements or present data in an unhelpful way.
• Static or Outdated Data: If data isn't refreshed regularly, the dashboard quickly loses its relevance. Manual updates can be prone to delays and errors.
• Ineffective Visualisations: Choosing the wrong chart type (e.g., a pie chart for trend data, a bar chart with too many categories) can obscure insights rather than reveal them.
• Neglecting Context: Presenting raw numbers without targets, benchmarks, or historical comparisons makes interpretation difficult. For example, knowing you have 10 readmissions means little without knowing if that's high or low for a given period or compared to a previous baseline.

Step-by-Step Approach to Designing a Useful Clinical Dashboard

Creating an effective clinical dashboard is an iterative process. Consider these steps:

Step 1: Define the Purpose and Audience

• What problem are you trying to solve or what question are you trying to answer? Be specific. E.g., 'Reduce 30-day re-admission rates for patients with heart failure' or 'Monitor bed occupancy and patient flow in acute medical wards'.
• Who is the primary user? Their role, responsibilities, and decision-making needs will dictate the content and level of detail.
• What decisions will they make based on this dashboard? This ensures actionability.

Step 2: Identify Key Performance Indicators (KPIs)

• Based on your purpose, what are the 3-5 most critical metrics that will tell you if you are succeeding or failing? Resist the urge to add too many.
• Ensure these KPIs are measurable, and consider if the data is readily available and reliable.
• Examples: Length of stay, 4-hour ED target breaches, medication error rate, patient experience scores, staff sickness absence.

Step 3: Source and Validate Data

• Identify the source systems for each KPI (e.g., EPR, PAS, e-prescribing system, HR system).
• Establish robust data extraction and transformation processes.
• Critically assess data quality. Work with data teams to address any discrepancies or gaps before dashboard creation.
• Agree on data refresh frequency.

Step 4: Choose Appropriate Visualisations

• For trends over time, use line charts or area charts.
• For comparing categories, use bar charts.
• For proportion of a whole, use pie charts (sparingly, for 2-3 categories) or stacked bar charts.
• For a single key number or target, use 'gauge' charts or big number displays with a clear target indicator.
• Use colour thoughtfully – often to highlight 'red/amber/green' status or to distinguish categories, not just for aesthetics.

Step 5: Design and Prototype

• Sketch out layouts – even on paper – before digital development. Think about information hierarchy.
• Group related metrics together. Place the most important KPIs prominently (e.g., top-left).
• Add titles, clear labels, and units of measurement. Include a 'last updated' timestamp.
• Provide context: targets, benchmarks, and comparisons (e.g., to previous month/year) are essential.

Step 6: Test, Iterate, and Refine with Users

• Share prototypes with target users. Gather feedback on clarity, relevance, and usability.
• Ask: 'Does this help you answer your key questions?', 'Is anything unclear?', 'What would make this more useful?'.
• Be prepared to make changes based on feedback. Dashboard design is rarely perfect on the first attempt.

Step 7: Implement and Monitor

• Deploy the dashboard. Announce its availability and provide brief training or support resources.
• Regularly review its use and effectiveness. Are people engaging with it? Is it driving improvement?
• Be prepared for ongoing maintenance (e.g., data source changes, new requirements).

Example in Clinical Practice: Acute Medical Unit (AMU) Flow Dashboard

Purpose: To improve patient flow and reduce avoidable delays in the Acute Medical Unit.

Audience: AMU Charge Nurse, AMU Lead Consultant, Operational Manager, site Clinical Flow Manager.

Key Metrics:

• Total patients in AMU: Current number (big number, RAG rated against capacity).
• Patients waiting for a ward bed: Number of patients, average wait time (line chart over 24 hours), longest waiting patient (individual entry).
• Patients awaiting discharge: Number, average time since 'ready for discharge' status, reasons for delay (top 3 categories).
• New admissions in last 4 hours: Number.
• 4-hour target breaches (ED to AMU): % breaches, trend over time (line chart).
• Time from admission to senior review: Average, and target (gauge chart).

Visualisation Ideas:

• Large, clear numeric displays for current patient counts and RAG status.
• Line charts for trends (e.g., wait times over shifts).
• Bar charts for reasons for delay.

Interactivity: Clickable elements to show patient lists for waiting or delayed patients; filter by consultant team or acuity.

Actionable Insight: The AMU Charge Nurse can see real-time bed pressures, identify specific patients with prolonged waits, and collaborate with the ward allocation team or discharge coordinators. The consultant can quickly identify patients who haven't had a senior review within the target.

How Lazomis Can Help

Lazomis provides a structured approach to quality improvement and data management, which can significantly enhance your ability to create and utilise useful clinical dashboards:

• KPI Tracking: Lazomis tools help you define, track, and monitor your chosen KPIs against targets, forming the core of your dashboard data.
• Project Management Integration: Dashboards become more powerful when linked to active QI projects. Lazomis helps connect the 'what' (dashboard data) with the 'why' and 'how' (QI interventions).
• Data Visualisation Guidance: Our platform offers intuitive interfaces for data input and basic visualisation, helping teams to move from raw data to clear charts quickly, reinforcing good design principles.
• Collaboration: Facilitate multidisciplinary input into dashboard design and interpretation, ensuring that the dashboards reflect real-world clinical needs.

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