Demand and Capacity Analysis for Service Improvement in the NHS

Introduction

In the complex environment of the National Health Service, delivering timely and high-quality care hinges on a delicate balance between the demand for services and the capacity to provide them. Achieving this balance is a perennial challenge, impacted by demographic shifts, evolving healthcare needs, and resource constraints.

Demand and capacity analysis is a fundamental toolkit for NHS clinicians and managers seeking to understand these dynamics. By systematically examining how patient demand interacts with available resources, teams can identify bottlenecks, predict future pressures, and develop evidence-based strategies for service improvement.

Why this topic matters

Optimising demand and capacity is crucial for several reasons:

• Improved Patient Outcomes: Reducing waiting times, ensuring timely access to diagnostics and treatments, and enhancing continuity of care directly impact patient health and experience.
• Operational Efficiency: Identifying and addressing imbalances can prevent costly delays, reduce wasted resources, and better utilise staff time and specialist equipment.
• Financial Sustainability: Efficient service delivery contributes to better financial management, ensuring resources are allocated effectively to meet patient needs.
• Staff Morale and Wellbeing: Reduced pressure from unmanageable demand and clearer pathways for patient care can alleviate staff stress and improve working conditions.
• Regulatory Compliance: Meeting national waiting time targets (e.g., RTT, A&E four-hour standard, cancer waiting times) is a key aspect of CQC and NHS England oversight.

Understanding and applying demand and capacity principles supports the ongoing drive for continuous improvement within the NHS, moving beyond reactive problem-solving to proactive service design.

Practical explanation

Demand and capacity analysis is a systematic process of quantifying the need for a service (demand) against the ability to deliver that service (capacity). It's not just about counting patients and staff, but understanding the flow, variability, and different components that constitute both sides of the equation.

What is Demand?

Demand represents the need for a service from patients. It's often more complex than just a simple count of referrals or attendances. Key aspects include:

• Volume: The sheer number of patients requiring a service over a given period (e.g., daily A&E attendances, weekly clinic referrals).
• Variability: How demand fluctuates by day of week, time of day, season, or in response to external factors (e.g., flu outbreaks, public holidays).
• Complexity/Intensity: The average time or resource required per patient. Not all patients are equal; a complex cardiology assessment takes longer than a routine follow-up.
• Type of Demand: Breakdown by new vs. follow-up, urgent vs. routine, specific diagnostic tests, or therapy types.
• Source of Demand: Where referrals originate (e.g., GP, internal wards, emergency department).

What is Capacity?

Capacity is the maximum output a service can realistically deliver over a given period. It's influenced by:

• Staff Availability: The number of clinical and non-clinical staff, their skill mix, and their available working hours (excluding breaks, training, admin).
• Physical Resources: Number of clinic rooms, operating theatres, diagnostic scanners, beds, chairs for day units.
• Equipment: Availability and functionality of specialist equipment.
• Time: The available planned operating hours of a service, adjusted for planned and unplanned downtime.
• Process Efficiency: How effectively patients move through a pathway, including administrative steps, handovers, and support services.

Capacity needs to be calculated for each distinct stage of a patient pathway, as a bottleneck at any single stage can limit overall throughput.

Common pitfalls

Successfully implementing demand and capacity analysis requires careful attention to detail. Common challenges include:

• Poor Data Quality: Inaccurate, incomplete, or inconsistent data on demand, activity, and resource utilisation makes meaningful analysis impossible. Relying on estimates without validation is risky.
• Lack of Granularity: Analysing data at too high a level (e.g., entire hospital rather than specific pathway) can mask critical bottlenecks within individual services.
• Ignoring Variability: Treating demand as a constant average, rather than accounting for daily/weekly/seasonal fluctuations, leads to under- or over-resourcing.
• Focusing on One Bottleneck: Fixing one part of a pathway without addressing others can simply shift the bottleneck elsewhere, failing to improve the overall patient journey.
• Underestimating Hidden Demand: Not accounting for patients waiting to enter a service (e.g., on a GP referral list) or those who would access a service if it were more readily available.
• Ignoring Non-Clinical Capacity: Underestimating the impact of administrative processes, porters, cleaning staff, or IT systems on overall capacity.
• Lack of Clinical Engagement: If clinicians are not involved in defining demand and capacity, the analysis may lack credibility or miss crucial practical nuances.
• Short-Term Focus: Reacting only to immediate crises rather than using analysis to plan for future demand and build resilience.

Step-by-step approach to Demand and Capacity Analysis

This framework provides a structured way to undertake demand and capacity analysis:

Step 1: Define the Scope and Pathway

• Identify the Service Area: Clearly define the specific service or pathway you are analysing (e.g., elective orthopaedics, urgent care pathway, community mental health assessment).
• Map the Patient Journey: Create a process map of the entire patient pathway, from referral to discharge. Identify each key stage, decision point, and resource consumed.
• Stakeholder Engagement: Involve clinical staff, operational managers, data analysts, and patient representatives early to ensure all perspectives are captured.

Step 2: Collect and Validate Data

• Demand Data:
  • Patient numbers per stage (new, follow-up, urgent, routine).
  • Arrival patterns (daily, weekly, seasonal).
  • Referral sources and appropriateness.
  • Waiting list data (volume, average wait, length of longest wait).
  • Patient acuity/complexity distribution.
• Capacity Data:
  • Staff numbers and whole-time equivalents (WTE), skill mix, and available clinical hours.
  • Number of clinic rooms, theatre sessions, diagnostic slots, inpatient beds.
  • Equipment availability and maintenance schedules.
  • Process times for each stage (e.g., average consultation time, time in theatre, diagnostic reporting time).
• Data Validation: Ensure data accuracy, consistency, and completeness. Work with your local informatics or audit teams.

Step 3: Quantify Demand and Capacity

• Calculate Demand: Using historical data, determine average and peak demand for each stage of the pathway, segmenting by type if necessary. Project future demand based on demographic changes, commissioning plans, or new treatment pathways.
• Calculate Available Capacity: For each stage, quantify the maximum number of patients that can be processed per unit of time, considering staff, facilities, and processes. Account for planned and unplanned absences, training, and non-clinical duties.
• Capacity Utilisation: Calculate the percentage of available capacity that is currently being used.

Step 4: Identify Gaps and Bottlenecks

• Compare Demand vs. Capacity: Overlay demand and capacity profiles. Where demand consistently exceeds capacity, a bottleneck exists.
• Waiting List Analysis: High or growing waiting lists are clear indicators of demand/capacity imbalance.
• Queueing Analysis: Examine where patients wait, for how long, and why. Think beyond a single bottleneck – a queue may be forming due to upstream or downstream issues.
• Process Variation: Identify parts of the process with high variability in time or resource use, as these can create inefficiencies.

Step 5: Develop and Test Solutions

• Increase Capacity:
  • Optimise staff rotas and skill mix.
  • Extend operating hours (e.g., evening clinics, weekend theatre lists).
  • Improve estates utilisation.
  • Review equipment availability.
  • Redesign roles or introduce new professional groups (e.g., ANPs, Physician Associates).
• Manage Demand:
  • Review referral pathways and criteria.
  • Implement 'advice and guidance' services (e.g., via e-RS).
  • Patient self-management support.
  • Digital solutions for remote monitoring/consultations.
• Improve Flow/Efficiency:
  • Streamline administrative processes.
  • Reduce unwarranted variation in care pathways (e.g., through GIRFT recommendations).
  • Implement parallel processing where appropriate.
  • Optimise scheduling and appointment systems.
• Modelling: Use modelling tools or simple spreadsheets to simulate the impact of proposed changes before full implementation.

Step 6: Implement, Monitor, and Review

• Phased Implementation: Start with pilot projects where possible.
• Monitor Key Metrics: Track waiting times, patient flow, staff utilisation, and patient satisfaction.
• Regular Review: Periodically reassess demand and capacity. Healthcare environments are dynamic; what works today may need adjustment tomorrow.

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

Example in clinical practice: Optimising an Outpatient Orthopaedic Clinic

An orthopaedic department consistently struggles with long waiting lists for new patient appointments, leading to patient dissatisfaction and clinician burnout. A demand and capacity analysis is initiated.

1. Scope and Pathway: Focus on new patient referrals to the general orthopaedic clinic.
2. Data Collection:
   • Demand: Review 12 months of referral data: average 250 new referrals/month, with peaks in spring/autumn. 30% trauma-related, 70% elective. Average new patient consultation time (from previous audit) is 30 minutes.
   • Capacity: 4 consultant clinics/week, each 4 hours, booking capacity 8 new patients/clinic (allowing for 30 mins per patient) = 32 new patients/week. 2 specialist physiotherapist-led clinics/week, 4 hours each, booking capacity 8 new patients/clinic = 16 new patients/week. Total current capacity: 48 new patients/week = ~192 new patients/month.
3. Quantify:
   • Demand: ~250 new patients/month.
   • Capacity: ~192 new patients/month.
   • Gap: -58 new patients/month (demand exceeds capacity by ~30%).
4. Identify Bottlenecks: The primary bottleneck is insufficient clinic slots for new patient referrals. Further investigation shows some slots are 'lost' to follow-up conversions or urgent add-ons, exacerbating the problem.
5. Develop Solutions:
   • Capacity:
     • Re-allocate consultant time: Add one protected new patient clinic per week (8 more slots/week).
     • Develop advanced practice practitioners (APPs) to run dedicated 'minor orthopaedic' new patient clinics (e.g., common soft tissue injuries) under consultant supervision (potential for 10-12 new slots/week).
     • Explore weekend clinics once a month with locum support to clear backlog (16-24 slots/month).
   • Demand:
     • Implement a robust 'Advice and Guidance' service for GPs, reducing inappropriate referrals by 10%. Initial audit suggests 15% of referrals could be managed in primary care with specialist advice.
     • Review referral criteria to ensure clear guidelines for primary care.
   • Efficiency:
     • Optimise scheduling system to ensure new patient slots are prioritised and protected.
     • Review length of new patient appointments; could some be shorter for straightforward cases?
6. Implement and Monitor: Introduce APP clinics and A&G first. Monitor waiting list length, average wait time for new patients, and referral appropriateness. Present findings to the orthopaedic clinical governance meeting and develop a plan.

This analysis pinpoints the quantitative shortfall and allows for targeted interventions, moving beyond simply stating "we have too many patients."

How Lazomis can help

Lazomis provides a structured environment that can significantly support NHS teams in undertaking robust demand and capacity analysis:

• Project Management Tools: Use Lazomis to set up and manage your demand and capacity improvement projects, assigning tasks, tracking progress, and managing stakeholders during each step of the analysis.
• Data Visualisation and Dashboards: Integrate data from various sources (EPR, PAS, local spreadsheets) into customisable dashboards within Lazomis. This allows for real-time tracking of demand trends, capacity utilisation, and waiting list metrics, helping to identify and visualise bottlenecks more clearly.
• Process Mapping: Lazomis can host and facilitate collaborative process mapping exercises, helping teams to create detailed visual representations of patient pathways and identify points of friction or delay.
• Resource Allocation Planning: Use Lazomis to model the impact of different staffing scenarios or service redesigns, helping teams to simulate how changes might affect capacity and patient flow before implementation.
• Documentation and Reporting: Centralise all your analysis findings, action plans, and monitoring reports within Lazomis, making it easy to share insights with stakeholders, report to governance committees, and demonstrate CQC compliance.

Key takeaways

• Demand and capacity analysis is essential for optimising NHS services and improving patient care.
• It involves quantifying patient need (demand) against available resources (capacity) across the patient pathway.
• Successful analysis requires good quality data, granular understanding, and accounting for variability.
• Common pitfalls include poor data, ignoring variability, and a lack of clinical engagement.
• A structured step-by-step approach helps identify bottlenecks and develop targeted solutions.
• Solutions can focus on increasing capacity, managing demand, or improving overall process efficiency.
• Continuous monitoring and review are vital, as healthcare environments are dynamic.