Patient Flow Metrics Every Clinical Team Should Understand

Why this topic matters

Effective patient flow ensures that patients receive the right care, in the right place, at the right time, preventing unnecessary delays and improving outcomes. For clinical teams, metrics are not just operational figures; they reflect patient journeys and the efficiency of care delivery. Prolonged waits, delayed discharges, or inefficient clinic scheduling can lead to poorer patient experience, increased risk of harm, and staff frustration.

At a national level, NHS England places significant emphasis on optimising patient flow to meet performance targets and improve capacity. Local teams that grasp these metrics are better positioned to respond to challenges, contribute to organisational objectives, and demonstrate the tangible impact of their efforts. This understanding also fosters a culture of continuous improvement, moving beyond anecdotal evidence to data-driven decision-making.

Practical explanation of key metrics

Patient flow metrics can seem complex, but many are straightforward and directly relevant to clinical practice. Here, we focus on those that individual departments and wards can influence and monitor.

Length of Stay (LoS)

This is the duration, usually in days, a patient spends in a hospital or on a specific ward. It is typically calculated from admission to discharge. LoS can vary significantly by speciality, condition, and patient demographics. It's often categorised into different types: total hospital LoS, ward LoS, or even 'excess' LoS (beyond clinically indicated).

• How it's used by clinical teams: Monitoring average LoS helps identify patients with unusually long stays, allowing for early intervention to address potential blockers (e.g., awaiting diagnostics, social care, or specialist review). A change in average LoS can signal an improvement or deterioration in care pathways.
• Key consideration: Comparisons should be made against similar cohorts (e.g., patients undergoing the same procedure) or national benchmarks (e.g., GIRFT reports) to identify unwarranted variation.

Discharge to Assess (DTA) Pathway Performance

DTA pathways facilitate prompt discharge for patients who are medically optimised but require ongoing assessment or support in a community setting. This frees up acute beds for patients requiring hospital care.

• How it's used by clinical teams: Measuring the proportion of eligible patients discharged via DTA, and the speed of their transfer, highlights efficiency in discharge planning and collaboration with community teams. It helps identify delays in assessment or provision of community resources.
• Key consideration: Local DTA pathways and criteria should be well understood. Performance should be measured against local targets and national best practice guidance.

Bed Occupancy Rate

This metric represents the percentage of available beds that are occupied over a given period. While often viewed at a hospital-wide level, it is highly relevant for individual wards and departments.

• How it's used by clinical teams: High bed occupancy (e.g., consistently above 90-95%) can indicate pressure on resources, leading to delays in admissions from the emergency department (ED) or other wards. Understanding ward occupancy helps teams anticipate demand fluctuations and plan staffing levels. Extremely low occupancy might signal over-provision or under-utilisation.
• Key consideration: Sustainable bed occupancy varies, but above 85% is often cited as leading to reduced flexibility and potential congestion. This metric needs to be balanced against patient safety and quality of care.

Ward Transfers and Moves

The number of times a patient is moved between different wards or even within the same ward during their hospital stay.

• How it's used by clinical teams: Frequent moves can be disruptive, leading to patient anxiety, delayed care, and increased risk of clinical handover errors. Monitoring transfer rates can highlight issues such as 'boarder' patients (admitted to a specialty ward they don't belong to) or a lack of appropriate bed availability.
• Key consideration: While some transfers are clinically necessary, unnecessary moves can be minimised through better bed management and proactive discharge planning.

Clinic Utilisation/Did Not Attend (DNA) Rates

These metrics relate to outpatient services. Clinic utilisation measures the proportion of available appointment slots that are filled. DNA rates track the percentage of patients who miss their appointments without prior cancellation.

• How it's used by clinical teams: High DNA rates lead to wasted clinician time and longer waiting lists for other patients. Monitoring these metrics allows teams to investigate causes (e.g., transport issues, poor communication, complex booking processes) and implement targeted interventions (e.g., text reminders, flexible appointment times).
• Key consideration: The impact of DNA rates is felt beyond the individual clinic, affecting diagnostic pathways and overall patient flow through the system.

Waiting List Sizes and Times

For elective procedures or specialist consultations, these metrics track the number of patients awaiting care and the duration of their wait.

• How it's used by clinical teams: These are direct indicators of unmet demand and potential barriers to timely care. Clinical teams are vital in validating waiting lists, ensuring appropriate prioritisation, and identifying opportunities to streamline pathways or increase capacity.
• Key consideration: National targets for waiting times (e.g., 18-week referral to treatment) provide important benchmarks. Understanding the clinical urgency of patients on the waiting list is paramount.

Common pitfalls

• Data overload without insight: Collecting vast amounts of data without a clear understanding of what it means or how it relates to clinical practice can be overwhelming and unhelpful. Focus on a few key metrics that are actionable.
• Blaming individuals: Metrics should highlight system issues, not be used to blame staff. The goal is to improve processes, not assign fault.
• Ignoring local context: National benchmarks are useful, but local population demographics, deprivation indices, and available community resources can significantly influence performance. Interpret data within your specific context.
• Lack of standardised definitions: Ensure everyone understands how a metric is defined and calculated (e.g., what constitutes 'admission time' or 'discharge ready'). Inconsistent definitions lead to unreliable data.
• Only looking backwards: While historical data confirms trends, the real power of metrics lies in their ability to inform proactive decision-making and forecast future demand.
• Working in silos: Patient flow is an organisational-wide issue. Improvements require collaboration across departments, specialties, and with community and social care partners.

Step-by-step approach to using metrics for improvement

1. Define your focus area: What specific patient flow challenge are you trying to address? (e.g., high LoS on a specific ward, long clinic waiting times).
2. Identify relevant metrics: Based on your focus, choose 2-3 key metrics that will help you understand the problem and measure improvement (e.g., average LoS for stroke patients, DNA rate in cardiology clinic).
3. Establish baseline data: Collect current data for your chosen metrics over a defined period (e.g., the last three months). This is your starting point.
4. Set clear, achievable targets: What is your desired improvement? (e.g., reduce average stroke LoS by 1 day, decrease cardiology DNA rate by 5%). Ensure targets are SMART (Specific, Measurable, Achievable, Relevant, Time-bound).
5. Analyse and interpret: Look for patterns, trends, and outliers. Engage your team in discussion: Why are we seeing these numbers? What are the underlying causes?
6. Implement interventions: Design and implement specific changes to address the identified issues. This could involve process redesign, communication improvements, or technology adoption.
7. Monitor and evaluate: Continuously track your chosen metrics after implementing changes. Is there an improvement? Is it sustained? Use tools like run charts or control charts to visualise progress.
8. Iterate and share: If an intervention doesn't work, learn from it and try another. Share your successes and lessons learned within your team and across the organisation. Celebrating small wins encourages further engagement.

Example in clinical practice: Reducing ED breaches on an acute medical unit (AMU)

An Acute Medical Unit (AMU) team consistently reports high numbers of 4-hour ED target breaches due to delays in admitting patients to inpatient beds. The Clinical Lead decides to focus on improving patient flow through the AMU.

• Focus area: Reducing AMU LoS to facilitate timely patient flow from ED.
• Metrics: Average AMU LoS, Proportion of patients discharged before midday, Average time to senior review.
• Baseline: Average AMU LoS 2.8 days; 15% of discharges before midday; Average time to senior review 4 hours.
• Interventions:
  • Proactive discharge planning: Daily 'board rounds' with multidisciplinary team (MDT) to identify patients discharge-ready or requiring social care input the following day.
  • Early senior review: Rota changes to ensure a consultant or registrar reviews all new admissions within 2 hours.
  • Therapy 'pull' model: Physiotherapists and Occupational Therapists proactively assess patients in the ED who are likely to come to AMU, flagging potential discharge needs earlier.
• Monitoring: The team monitors LoS and midday discharge rates weekly, using a run chart. Time to senior review is tracked via electronic patient records.
• Outcome: After 3 months, average AMU LoS reduced to 2.2 days, midday discharges increased to 30%, and average time to senior review dropped to 1.5 hours. ED breaches related to AMU capacity decreased by 20%.

This example demonstrates how a focused approach to understanding and utilising key metrics can lead to tangible improvements in patient flow and ultimately better patient care and staff morale. 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 to support clinical teams in monitoring and improving patient flow. Our customisable dashboards can help you visualise key metrics relevant to your department, such as average Length of Stay, bed occupancy trends, and DTA pathway performance. By integrating with existing NHS data sources (subject to local IG and data sharing agreements), Lazomis can provide near real-time insights, allowing your team to identify issues quickly and track the impact of interventions. Our project management features also help structure your quality improvement initiatives, ensuring that your data-driven changes are planned, implemented, and evaluated effectively. This supports a robust, evidence-informed approach to improving patient flow within your clinical area.