Frailty Identification

Frailty identification is the foundation of population health management. It must support:

• Early identification
• Whole-population visibility
• Clinically meaningful assessment
• Proactive intervention
• Longitudinal updating over time

Effective frailty management depends on identifying people earlier across the frailty spectrum, including those living with mild frailty in the community before significant deterioration occurs.

Earlier identification creates opportunities for:

• Prevention of progression
• Falls prevention
• Medication optimisation
• Maintenance of function and independence
• Coordinated multidisciplinary support

General practice remains the natural anchor point for population-level frailty identification for three reasons:

• Continuity through longitudinal relationships over time
• Near-universal population coverage
• A single clinical record bringing together information from across the system

Together, this provides the closest thing the NHS has to a whole-population view of frailty.

However, effective frailty identification cannot rely on general practice alone. Community services, acute trusts, social care, ambulance services, and neighbourhood teams all contribute important information about functional decline, mobility, cognition, deterioration, and vulnerability over time.

Effective population health management therefore depends on creating a shared, continuously updated view of frailty across neighbourhood systems rather than relying on isolated organisational records.

Frailty identification is already taking place across the NHS, but inconsistently, incompletely, and often without coordinated follow-through.

Current approaches generally fall into two broad categories:

• Population segementation tools, such as the Electronic Frailty Index (eFI)
• Clinician-led assessment, such as the Clinical Frailty Scale (CFS)

Both provide important capabilities. However, each addresses only part of the wider identification challenge, explored further in our NHS Frailty Paradox Part 2 and NHS Frailty Paradox Part 3 blog series.

The Electronic Frailty Index (eFI) is a population segmentation tool that supports large-scale frailty identification using routinely collected primary care data (1). It is widely used across the NHS and is advocated as a tool by NHS England because it allows practices and neighbourhood systems to identify people who may be living with frailty across registered populations in a systematic and scalable way (2).

The eFI works by analysing the accumulation of coded health deficits recorded within the primary care record. These include long-term conditions, prescribing patterns, symptoms, clinical events, and markers of functional impairment. As the number of recorded deficits increases, the likelihood of frailty also increases.

One of the major strengths of the eFI is its ability to support population-level visibility. It allows practices and neighbourhood teams to identify potentially vulnerable individuals who may otherwise remain unrecognised until deterioration or crisis occurs.

This makes it valuable for frailty casefinding and population health management.

However, the eFI also has important limitations.

The model depends heavily on the quality and completeness of coded clinical data. Earlier indicators of frailty, particularly declining mobility, reduced resilience, social vulnerability, functional change, and emerging cognitive impairment, are often under-recorded or inconsistently coded within routine records (3).

As a result, clinically evident frailty may not always be fully visible through coded data alone, particularly in earlier stages of frailty progression (3).

Importantly, the eFI does not diagnose frailty. It identifies people at increased statistical risk who may require further clinical assessment and review. Clinical interpretation remains essential.

The Clinical Frailty Scale (CFS) takes a more clinically focused approach. Rather than relying primarily on coded deficits within the electronic record, it uses functional assessment and clinical judgement to assess both the presence and severity of frailty.

The CFS considers how a person is functioning in everyday life, including:

• Mobility
• Physical activity
• Independence
• Cognition
• Ability to perform everyday tasks
• Overall resilience

This gives the scale strong clinical relevance because frailty is fundamentally a functional and biological syndrome rather than simply the accumulation of coded disease.

The CFS is widely used across acute care, community services, geriatric medicine, ambulance services, and urgent care pathways. It supports a shared language for frailty severity across multidisciplinary teams and helps guide clinical decision-making, escalation planning, and care coordination.

However, the CFS also has limitations.

The score depends on clinical judgement, and studies have demonstrated variation between clinicians assessing the same individuals across the full 1 to 9 scale (4). More importantly, increasingly granular numerical scores do not always translate into meaningful differences in intervention or management.

In practice, broader frailty states are often more clinically and operationally useful:

• CFS 1-3 describes people who are fit or not living with frailty, where the focus is typically on prevention, healthy ageing, and reducing future risk.
• CFS 4-5 reflects mild frailty, where earlier intervention may help slow progression, maintain independence, and reduce avoidable deterioration.
• CFS 6 reflects moderate frailty, where care increasingly focuses on protecting function, coordinating multidisciplinary support, and maintaining stability over time.
• CFS 7-8 describes severe frailty, where priorities often shift toward supportive care, symptom management, comfort, and reducing burdensome intervention.
• CFS 9 does not represent a frailty category itself, but terminal illness requiring palliative or end-of-life care.

From a population health management perspective, these broader frailty states are often more useful than precise numerical scoring alone because they align more closely with intervention models, multidisciplinary coordination, and neighbourhood service delivery.

There are also practical constraints. The CFS may be less reliable in people living with stable long-term disability, where reduced physical function does not necessarily represent frailty or declining physiological reserve.

In addition, the scale is usually applied during individual clinical encounters rather than systematically across whole populations. While it provides strong clinical meaning, it does not on its own provide systematic population-level visibility.

Effective frailty identification requires more than population segmentation or clinician-led assessment alone.

To support coordinated population health management, frailty identification must be:

• Systematic across whole populations
• Clinically meaningful
• Capable of earlier identification
• Longitudinally updated over time
• Embedded within routine neighbourhood care

Existing approaches only partially meet these requirements. Population-level models may lack precision, while clinically meaningful approaches often lack systematic reach at neighbourhood scale.

At Target Health Solutions, we believe frailty identification should function as a continuous operational process embedded within everyday care rather than as a one-off case-finding exercise.

The Pathfields Tool was developed as one approach to help bridge the gap between large-scale population identification and clinically grounded frailty assessment (5).

Rather than relying solely on coded disease burden, the model combines multiple markers associated with frailty, including:

• Advanced age
• Dementia
• Care home residence
• Housebound status
• Observed mobility problems

A central design principle was improving identification of people living with mild frailty earlier in the frailty trajectory, before significant deterioration or crisis admission occurs.

Frailty status is then clinically reviewed and grouped into four operationally meaningful states:

• Not frail
• Mild frailty
• Moderate frailty
• Severe frailty

Because frailty status can be updated continuously through routine care activity, this supports a dynamic longitudinal frailty register capable of underpinning population health management across neighbourhood systems.

Further information about the Pathfields Tool and the wider operational model is available on the Pathfields Tool overview page.

1. Clegg A, Bates C, Young J, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age and Ageing 2016;45(3):353-360. doi: 10.1093/ageing/afw039.
2. The Electronic Frailty Index. NHS England, 2019. Available at: https://www.england.nhs.uk/ourwork/clinical-policy/older-people/frailty/efi/
3. Broad, A.; Carter, B.; Mckelvie, S.; Hewitt, J. The Convergent Validity of the electronic Frailty Index (eFI) with the Clinical Frailty Scale (CFS). Geriatrics2020, 5, 88. https://doi.org/10.3390/geriatrics5040088
4. Hörlin E, et al. Inter-rater reliability of the Clinical Frailty Scale by staff members in a Swedish emergency department setting. Acad Emerg Med. 2022 Dec;29(12):1431-1437. doi: 10.1111/acem.14603.

5. Attwood D, et al. The Pathfields Tool: a frailty case-finding tool using primary care IT-implications for population health management. Age Ageing. 2020;49(6):1087–1092. doi:10.1093/ageing/afaa119