Measuring Clinical Service Productivity in the NHS: A Practical Guide

Why this topic matters

Productivity in healthcare is not simply about doing more with less; it’s about optimising the use of valuable resources – staff, time, equipment, and facilities – to deliver the best possible care to patients. For clinical services, improving productivity can mean reducing waiting lists, enhancing patient access, improving staff experience by streamlining workflows, and ultimately contributing to better patient outcomes. NHS England regularly highlights productivity as a key lever for achieving financial sustainability and meeting escalating patient demand. Effectively measuring productivity moves us beyond anecdotal observations to evidence-based decision-making.

Practical explanation: What is clinical productivity?

Clinical productivity can be broadly defined as the ratio of outputs to inputs. In a healthcare context, this means:

• Outputs: The services delivered to patients. These can be quantified in various ways, such as number of appointments, procedures, patient contacts, discharges, or occupied bed days. For quality improvement, we also consider the quality and effectiveness of these outputs.
• Inputs: The resources consumed to deliver these outputs. This typically includes staff time (clinical and non-clinical), bed capacity, equipment usage, consumables, and facility costs.

Measuring productivity isn't always straightforward due to the complex, person-centred nature of healthcare. Unlike manufacturing, a 'unit of output' can vary significantly in complexity and resource intensity. Therefore, a nuanced approach is required, often using a combination of measures and adjusting for case-mix complexity.

Key Principles of Measurement

1. Define your scope: What specific service or pathway are you measuring? Be clear about the boundaries.
2. Identify relevant outputs: What are the key activities and outcomes of that service? Consider both sheer volume and quality/patient outcomes.
3. Identify relevant inputs: What resources are consistently consumed to deliver those outputs?
4. Normalise for complexity (where possible): Recognise that not all patient interactions are equal. Casemix adjustments or different weighting for procedures can provide a more accurate picture (e.g., using HRG codes, or simple categorisation of new vs. follow-up appointments).
5. Focus on actionable metrics: Choose measures that you can influence and that provide insight into areas for improvement.
6. Regularity and trends: Productivity measurement is most powerful when tracked over time to identify trends and assess the impact of interventions.

Common pitfalls

• "Outputs only" focus: Simply counting activity without considering inputs or quality can lead to perverse incentives and burnout. For example, rushing through appointments without addressing patient needs.
• Ignoring qualitative aspects: High productivity should not come at the expense of patient safety, staff wellbeing, or care quality. Incorporate quality metrics where possible.
• Lack of standardisation: Inconsistent data collection or definitions across different teams or time periods makes comparisons meaningless.
• Poor data quality: Garbage in, garbage out. Ensure the data sources are reliable and accurately reflect activity and resource use.
• Blaming staff: Productivity issues are often systemic, related to processes, pathways, or resource allocation, not solely individual effort. A supportive, improvement-focused culture is essential.
• One-off measurements: A snapshot in time provides limited insight. Trends are more informative.

Step-by-step approach to measuring clinical service productivity

Here’s a practical pathway for NHS clinical and operational teams:

Step 1: Define the Scope and Purpose

• What service or pathway are you focusing on? (e.g., outpatient cardiology clinic, elective orthopaedic theatre list, community mental health team caseload).
• What is the specific question you want to answer? (e.g., "Are we utilising our theatre time efficiently?", "How much nursing time does an acute admission consume?", "Can we see more patients within current staffing levels?").
• What is the desired outcome of this measurement? (e.g., service redesign, business case for additional staff, identifying bottlenecks).

Step 2: Identify Key Inputs

• Staffing: Clinical staff (doctors, nurses, AHPs), administrative staff. Quantify in Whole Time Equivalents (WTEs) or direct hours spent on the service. Consider skill mix.
• Time: Clinic room occupancy, theatre time, bed days, patient contact hours.
• Equipment: Specialist machinery usage, consumable costs.
• Facilities: Square footage, utility costs (often harder to attribute directly to a single clinical service but important for holistic costings).

Step 3: Identify Key Outputs

• Patient activity: Number of new appointments, follow-up appointments, procedures (categorised by complexity), discharges, inpatient admissions, patient contacts.
• Workload units: Health Resource Group (HRG) codes for secondary care provide a standardised measure of activity and complexity. For other settings, local workload models.
• Quality metrics: Patient satisfaction scores, readmission rates, complication rates, waiting times, achievement of clinical targets (e.g., referral-to-treatment targets). These provide context for the 'efficiency' seen in other metrics.

Step 4: Collect and Analyse Data

• Data sources: Patient administration systems (PAS), electronic patient records (EPR), theatre systems, rostering systems, finance systems, local audit data.
• Data extraction: Work with local information and business intelligence teams to extract reliable, consistent data.
• Calculation: Common productivity metrics include:
  • Outputs / Staff WTE (e.g., 'number of outpatient appointments per consultant per week')
  • Outputs / Bed Day (e.g., 'number of procedures per theatre session')
  • Cost per episode of care (e.g., 'total cost of inpatient stay / number of inpatient stays')
  • Patient contact hours / direct clinical staff hours
• Benchmark: Compare your service's productivity with other similar services locally, regionally, or nationally (using GIRFT data, national audits, or published benchmarks where available).

Step 5: Interpret Results and Identify Opportunities

• Look for trends and variances: Are there particular days, clinics, or staff groups that are more or less productive? Why?
• Drill down: If productivity is low, what are the contributing factors? (e.g., high DNA rates, inefficient administrative processes, equipment downtime, staff sickness, skill mix issues, unoptimised patient pathways).
• Identify bottlenecks: Where are the constraints in the system?
• Involve frontline staff: They often have the best insights into operational inefficiencies and potential solutions.

Step 6: Implement and Monitor Improvements

• Develop an action plan: Based on your analysis, define specific interventions to improve productivity.
• Pilot changes: Test interventions on a small scale if possible (e.g., a specific clinic session) before wider rollout.
• Re-measure and iterate: Continue to monitor your chosen productivity metrics to assess the impact of your changes. This is a cyclical process, not a one-off event. This can be integrated into your local Quality Improvement (QI) cycles.

Example in clinical practice: Optimising an Outpatient Clinic

Service: General Medical Outpatient Clinic

Step 1: Define Scope and Purpose

• Scope: All new and follow-up appointments in the General Medical Clinics over a 3-month period.
• Purpose: To understand if current clinic template utilisation and consultant time allocation are optimal, aiming to reduce the waiting list and improve patient access without increasing WTEs.

Step 2: Identify Key Inputs

• Staff: Consultant WTEs allocated to clinics, registrar WTEs, nursing WTEs, administrative support WTEs.
• Time: Allocated clinic slots (e.g., 20 new patient slots, 40 follow-up slots per week).
• Facilities: Number of clinic rooms available and their occupancy rate.

Step 3: Identify Key Outputs

• Patient activity: Actual number of new patient appointments seen, actual number of follow-up appointments seen, DNA rates.
• Quality: Patient experience survey scores for clinic, waiting times from referral to first appointment.

Step 4: Collect and Analyse Data

• Data extracted from PAS for 3 months: booked appointments, actual attendance, clinician present.
• Data extracted from rostering for staff present.
• Productivity calculation:
  • Average actual appointments seen per clinic session.
  • Average actual appointments seen per consultant WTE.
  • DNA rate across all clinics.
• Findings: The data revealed that while clinic templates had 20 new slots, on average only 15 were being used due to administrative scheduling errors and higher than expected DNA rates (15%). Follow-up slots were often underutilised if new patient clinics ran over. Consultant WTEs were consistently fulfilling their contractual PAs, but clinic efficiency was poor.

Step 5: Interpret Results and Identify Opportunities

• Bottleneck: Inefficient scheduling and high DNA rates were limiting actual patient throughput, leading to 'hidden' capacity.
• Opportunity: Improve administrative scheduling processes, implement a robust pre-clinic patient reminder system, and consider adjusting template mix to better reflect demand/capacity.

Step 6: Implement and Monitor Improvements

• Implemented a new pre-clinic call/SMS reminder system for all patients.
• Trained administrative staff on optimising clinic scheduling, ensuring templates were fully booked where possible, and using partial booking where appropriate.
• Adjusted clinic templates to have a slightly higher ratio of follow-ups to new patients based on demand analysis.
• Monitoring: The team continued to track actual appointments seen, DNA rates, and waiting times over the next 3 months. Initial results showed a 5% reduction in DNA rates and a 10% increase in actual patients seen per clinic session, leading to a demonstrable reduction in the outpatient waiting list. This ongoing monitoring highlighted where further refinements were needed.

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

How Lazomis can help

Lazomis offers tools that can simplify the process of measuring and visualising clinical service productivity. Our platform allows for:

• Data Aggregation: Centralise data from disparate sources, making it easier to combine inputs and outputs for calculation.
• Custom Dashboards: Build tailored dashboards to track key productivity metrics over time, allowing for quick identification of trends and variances.
• QI Project Management: Support the structured planning, implementation, and monitoring of improvement initiatives identified through productivity analysis, aligning with PDSA cycles.
• Reporting: Generate clear, concise reports for stakeholders, demonstrating the impact of your interventions and supporting business cases for change.

By providing a robust framework for data collection, analysis, and visualisation, Lazomis can empower clinical and operational teams to move from reactive problem-solving to proactive, evidence-based service improvements.