Xiaowei Song, PhD, MSCS1,2,3, Barry Clarke, MD, CFPC2, Grace Park, MD, CFPC4, Kenneth Rockwood, OC, MD, FRCPC, FRCP2,5
1Clinical Research and Evaluation, Fraser Health Authority, Surrey, BC;
2Department of Medicine, Dalhousie University, Halifax, NS;
3Department of Biomedical Physiology & Kinesiology, Simon Fraser University, Burnaby, BC;
4Primary Care and Family Medicine, Fraser Health Authority, Surry, BC;
5Centre for Healthcare of the Elderly, Nova Scotia Health, Halifax, NSDOI: https://doi.org/10.5770/cgj.29.804
ABSTRACT
Background
Comprehensive geriatric assessment (CGA) is the reference standard for diagnosing and managing frailty. By evaluating a broad range of health, functional, cognitive, and social problems, the CGA enables the construction of a deficit accumulation Frailty Index (FI-CGA). Recent advances have integrated the electronic CGA (eCGA) into electronic health/medical records and other digital platforms, allowing automated coding and summarization of CGA data to generate an electronic Frailty Index (eFI-CGA).
Methods
We reviewed over two decades of research on the development, validation, and application of the FI-CGA, eCGA, and eFI-CGA in health-care contexts, conducted following the PRISMA-ScR guidelines. A comprehensive search was performed in MEADLINE and CINAHL databases, including English language publications from 2004 to July 1, 2025. The 38 studies that met all criteria are included in the final review. Data were synthesized descriptively and analyzed thematically.
Results
The evidence suggests that the FI-CGA is a robust, adaptable predictor of adverse outcomes including mortality, hospitalization, and functional decline. Digital adaptations improve feasibility, accuracy, and workflow, supporting wider application in acute, long-term, primary, and community care. The transition from manual to eCGA-based frailty measurements marks a significant advance toward scalable, integrated frailty care. Emerging implementations are targeting earlier detection, risk stratification, and personalized interventions.
Conclusion
The digital eCGA and eFI-CGA tools hold potential to enhance (“geriatrize”) capacity to identify and manage frailty across care settings. Further research is needed to validate them across populations, and leverage innovative technologies to advance frailty care, in these ways promoting healthy aging.
Key words: frailty, Frailty Index (FI), Comprehensive Geriatric Assessment (CGA), electronic Comprehensive Geriatric Assessment (eCGA), electronic Frailty Index based on Comprehensive Geriatric Assessment (eFI-CGA), Clinical Frailty Scale (CFS), primary care, acute care, long-term care, early management, healthy aging
In 2021, the baby boom cohort began to turn 75, the age at which most diseases of aging begin to accelerate.(1) Central to caring for older adults is frailty, a multiply determined, age-related dynamic health state. As health deficits accumulate, the ability to withstand stress related injuries (i.e., “robustness”) declines, as does the ability to repair such damage (“resilience”).(2,3) With frailty, even minor stressors can result in major clinical consequences. Although frailty increases with age, individuals of the same age can exhibit varying levels of vulnerability—this also undergirds the statistical definition of frailty: variability in risk for people with the same exposure.(4) Identifying frailty early, perhaps even before clinical manifestation arise (e.g., in laboratory tests(5) or performance measures such as gait speed or grip strength(6)), has implications for both clinical practice and public health.(7)
A deficit accumulation frailty index (FI) quantifies the degree of frailty by a summary score as the proportion of health deficits an individual exhibits in a defined set of variables.(6,7) These deficits include diverse health measures such as symptoms, diseases, and disabilities. The FI principle recognizes that, arising from the age-related accumulation of multiple health problems, higher frailty levels represent poorer health states/greater biological aging, and correspond to an increased risk of adverse outcomes.(8,9) It also facilitates cross-species translational research on frailty.( 5)
Interest in digitalizing the FI has grown, particularly with the development in 2016 of the electronic Frailty Index (eFI).(10) Using the cumulative deficit model, the original eFI was constructed with 36 items from routine UK primary care electronic health record (EHR) data. Validated internally and externally across large datasets, it demonstrated strong predictive validity for one-, three-, and five-year risks of mortality, hospitalization, and nursing home admission.(10–12) The eFI has achieved wide adoption in UK primary care as a risk stratification tool embedded in policy frameworks. International uptake is widespread, using various EHRs (also called electronic medical records [EMRs]) data including demographics, diagnostic codes, medication lists, and laboratory values.(10,11) In Australia, Canada, China, and the United States,(12,13) eFIs rapidly identify frailty at scale, with accuracy, validity, and usability directly influenced by the quality and completeness of EHR data.(13,14,15) Since the Centers for Medicare and Medicaid made frailty screening a key component of its Age Friendly Hospital scheme,(16) uptake has accelerated.
A newer eFI2 refined prediction by using a wider range of linked EHR and other health measures from hospital, community, mental, and social care, showed improved discrimination for mortality, care home admission, and hospitalization.(17–19) By capturing a greater range of health indicators, the eFI model might yield a more comprehensive and dynamic view of frailty. Still, its ability to aid practice has yet to be demonstrated.(20)
Against this background, Comprehensive Geriatric Assessment (CGA), established as a multidimensional diagnostic and therapeutic process, presents a unique opportunity. It systematically evaluates an older person’s medical, functional, psychological, and social domains, and guides individualized care planning.(21,22) In acute geriatric care, CGA can reduce frailty and improve patient and service outcomes through multifactorial interventions, including exercise, nutritional support, medication review, cognitive and behavioral therapies, and enhanced social support.(23) Much new work focuses on applying the CGA for early frailty identification and intervention in primary care and preventive settings.(24)
CGA-derived data have long supported the development of robust frailty measures, including a Frailty Index based on Comprehensive Geriatric Assessment (FI-CGA). Ongoing digitalization has led to the integration of the electronic CGA (eCGA) into EHRs and other digital platforms, facilitating real-time access and use. Applying standard FI methodology(25) to eCGA data through automated processing permits the eFI-CGA to be calculated automatically. Still, as children of geriatrics, the FI-CGA and eFI-CGA have yet to be reviewed and gain widespread recognition in research and clinical contexts.
The objective of this scoping review is to examine how the FI-CGA, eCGA, and eFI-CGA have been developed, validated, and applied across various care settings, and to identify gaps and directions for future research.
We conducted a scoping literature search of the MEDLINE and CINAHL databases to identify relevant studies describing the development, validation, and use of the FI-CGA, eCGA, and eFI-CGA. Although no formal protocol was developed or registered, the review was conducted and reported in accordance with established PRISMA-ScR guidelines for scoping reviews (https://www.equator-network.org/reporting-guidelines/prisma-scr/). We retrieved original research and review articles published in peer-reviewed journals in English since 2004 when the first FI-CGA paper was published. Last updated on July 1, 2025, the search strategy combined keywords and Medical Subject Headings (MeSH) using Boolean search strings with a truncation symbol “*” to capture word variations: (“frail” OR “deficit accumulation”) AND (“index” OR “FI” OR “risk index” OR “RI”) AND (“old” OR “aged” OR “geriatric” OR “elderly” OR “senior” OR “patient”) AND “comprehensive geriatric assessment” OR “CGA” OR “electronic comprehensive geriatric assessment” OR “eCGA” OR “electronic health record” OR “EHR” OR “electronic medical record” OR “EMR” OR “clinical frailty scale” OR “CFS” OR “electronic frailty index” OR “eFI” OR “FI-CGA” OR “eFI-CGA”) AND (“primary” OR “assisted” OR “acute” OR “hospital” OR “emerg” OR “long-term” OR “home” OR “physician” OR “nurse” OR “healthcare” OR “care setting”).
Titles and abstracts were screened independently by two reviewers. Studies were excluded if not published in English or in peer-reviewed journals, or not original research or review articles. Each of the 98 remaining journal publications underwent full-text evaluation for relevance to the review topics by the same reviewers. Discrepancies were resolved through discussions, with consultation of a third reviewer when necessary. To ensure comprehensiveness, references from the selected articles were also assessed for additional relevant publications.
Through full-text review of each included study, we extracted the following data items: author and year, geographical region, study purpose and design, care setting, sample size and characteristics, frailty assessment tool used, outcomes assessed (e.g., mortality, hospitalization, functional decline), follow-up period, and key findings relevant to frailty measurement and management. Data from each included full-text study were charted independently by two reviewers using a pre-designed data charting form that captured the predefined data items. Discrepancies were resolved through discussion among the reviewers.
Studies were categorized by review theme (e.g., FI-CGA, eCGA, eFI-CGA) and organized according to the extracted data items. Any articles related to more than one theme were discussed under the higher-order theme. Studies were further summarized using figures that illustrated publication trends over time, as well as mapping by care setting, geological region, and sample size.
Final selection yielded 38 original research articles, including 24 on FI-CGA and 14 on eCGA/eFI-CGA. Table 1 summarizes these studies by review theme, with extracted data items organized in chronological order within each theme subhead and corresponding reference citations indicated. Most studies focused on participants aged 65 years and older. Over the past two decades, the FI-CGA has been validated across geriatric, hospital, and home/primary care settings worldwide, and has served as a benchmark for other frailty screening and assessment tools (Table 1A, B; Figures 1–2). Comparisons with alternative frailty measures in relation to clinical outcomes continue. Since the first digitalization of the CGA in the late 2000s, the CGA has been implemented on online platforms and within EHR systems to facilitate frailty care, leading to the development of the eFI-CGA (Figure 1). While early work focused primarily on geriatric care, interest in CGA digitalization has expanded to primary and integrated care settings in Canada, Australia, the United States, and beyond. However, most emerging eFI-CGA studies remain limited by small sample sizes, restricting the evidence on effectiveness (Figure 2).
TABLE 1A Summary of studies on FI-CGA, eCGA, and eFI-CGA: list of studies under reviewa
TABLE 1B Summary of studies on FI-CGA, eCGA, and eFI-CGA: key findings of the studies under reviewa
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FIGURE 1 Summary of studies over time by purpose | ||
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FIGURE 2 Summary of the numbers of studies in results | ||
A Canadian research group first developed the FI-CGA by combining FI-defined deficits collected in a standard CGA form of 12 domains (Appendix 1). These domains included: Medical (chronic diseases, acute illnesses); Function (activities of daily living [ADLs] and instrumental ADLs [IADLs]); Cognition (cognitive impairment); Psychological health (depression, anxiety); Medications; Nutrition; Social, environmental, and quality-of-life factors (support systems, living conditions). The FI-CGA summarizes the 72 components from these domains in a single summary score.(26,27)
The FI-CGA’s predictive validity was assessed against mortality, institutionalization, and functional decline; construct validity compared it with other frailty measures. Validated in a randomized controlled trial, it was a valid, reliable, and practical tool for stratifying outcome risks.(26) Its utility was further confirmed in a longitudinal population secondary analysis, demonstrating its feasibility for quantifying frailty using routinely collected data.(27)
Those FI-CGA scores were calculated as the proportion of CGA deficits present, with each deficit coded as “1” (present) or “0” (absent).(26,27) Creating an FI-CGA for both prospective and retrospective data was later standardized in two publications.(25,28) More recent FI methodology developments suggest that retaining the original discrete coding of input variables (rather than binary coding) can improve frailty accuracy and precision.(29)
Since its introduction, researchers from multiple countries validated the FI-CGA in acute hospital care settings, and continued to examine the prognostic validity of individual CGA domains and as a summative count.(30,31) Studies evaluated its utility for predicting adverse outcomes such as mortality, prolonged hospital length of stay (LoS), non-home discharge, postoperative complications, and functional decline in inpatients with various geriatric conditions.
A prospective hospital cohort study showed that higher baseline FI-CGA scores were seen in patients who died, had longer hospital stays, or were discharged to long-term care facilities.(32) Other investigations used retrospective datasets, including the National Hip Fracture Database (NHFD), interRAI Acute Care (AC), and routine medical records.(33–35) Krishnan et al.(33) reported that the combined FI-CGA score was a more reliable predictor of adverse outcomes—including mortality, LoS, and discharge destination—than individual CGA components. Hubbard et al.(34) identified a threshold of FI-CGA <0.40 that discriminated inpatients unlikely to experience severe in-hospital events such as falls, delirium, pressure ulcers, and death, supporting its use in clinical decision-making. Kim et al.(35) found that the FI-CGA provided greater prognostic value for mortality and non-home discharge risks in older patients with atrial fibrillation and complemented other risk scores.
The FI-CGA has also been applied to home-dwelling older adults with primary care. Burn et al.(36) used an FI-CGA derived from a large interRAI Home Care (HC) dataset to predict five-year mortality and long-term care (LTC) admission: patients with baseline FI-CGA <0.1 were more likely to remain alive and in their own home than those with FI-CGA >0.5.
Collectively, these studies support integrating frailty as graded by the FI-CGA into clinical practice to identify individuals at high risk of adverse outcomes, thereby enabling targeted interventions, care planning, and resource allocation. The CARE-FI study provided further evidence that for patient-reported FI-CGA to predict adverse outcomes in older adults with gastrointestinal malignancies.(37)
The FI-CGA has served as a benchmark in developing/validating other frailty measures. Goldstein et al.(38) compared the FI-CGA with a simplified CP-FI-CGA, designed for care partners (families and community care teams) in urgent situations.
A cross-sectional validation of the CAN (Care Assessment Need) score, based on EHR data, used a 40-item FI-CGA as reference. There, automating frailty screening in primary care EHR systems is feasible.(39) Abbasi et al.(40) reported the convergent validity of an EMR-derived eFI against the FI-CGA, to be strong (r=0.72), supporting their =utility in identifying frailty in primary care.
Liang et al.(41) showed strong correlation between a generic eFI and a manual FI-CGA in hospitalized inpatients, with both indices independently predicting adverse outcomes including prolonged LoS, mortality, and higher health-care costs with comparable performance.
The Clinical Frailty Scale (CFS), an established judgment-based frailty-screening tool introduced to summarize a CGA(42,43) and the five-item frailty phenotype (FP)(6) have also been examined with the FI-CGA. Jung et al.(44) developed an electronic Short Physical Performance Battery (eSPPB) and showed its stronger correlation with the FI-CGA than chronological age. Jung et al.(45) validated the CFS against the FI-CGA and FP in geriatric outpatients. The CFS showed a stronger association with FI-CGA than did the FP.(45) DuMontier et al.(46) used both FI-CGA and CFS as reference standards to validate a veterans’ frailty index (VA-FI) and found moderate associations between the VA-FI with each benchmark scale.
Some studies refer to the FI-CGA interchangeably as “CGA-FI” (see Table 1A, B). These variations reflect adaptations of the FI-CGA tailored to specific cohorts and the precise CGA items used. Despite this variation in item composition or coding (e.g., dichotomous vs. discrete), these models consistently embody the cumulative-deficit and CGA-based approach to frailty measurement.(8) Maintaining a consistent naming convention (FI-CGA) in this review promotes clarity and coherence.
Several prospective cohort studies compared the prognostic value of the FI-CGA and CFS with other frailty assessments or functional and diagnostic markers in geriatric inpatients, focusing on mortality and related outcomes.(47–52) In parallel, a few cross-sectional studies have assessed how these measures correlate with other frailty measures.(53–55)
Some instruments share a conceptual basis with the FI-CGA by incorporating CGA-derived items—for example, many versions of the Multidimensional Prognostic Index (MPI), FI-CGA-10D, and FI-CGA-10D+CM. Others are not CGA-specific, such as the generic deficit-accumulation tools (FI-CD, FI-SOF) and rule-based frailty scales (CSHA-RBFD), Edmonton Frail Scale (EFS), Fried Phenotype (FP), and FRAIL. Comparative evaluations between the FI-CGA and these methods are reviewed below; further details are available elsewhere.(6,8,25–27,42,43,56–66)
In a prospective study of how four indices predicted one-month and one-year all-cause mortality in geriatric inpatients,(47) all methods showed significant prognostic accuracy, with the MPI achieving the highest AUC (0.79 for one month, 0.75 for one year). An evaluation of FI-CGA variants compared CGA item composition: a standard version, using continuous, individual health deficits (MIHD) and simplified domain-based versions (FI-CGA-10D and FI-CGA-10D+CM).(48) The standard FI-CGA demonstrated superior predictive accuracy for six-month mortality. The same group separately compared FI-CGA, CFS, generic FI, FP, and CSHA-RBFD for one-year mortality prediction, finding FI-CGA, CFS, and FI outperformed FP and CSHA-RBFD.(60) Studies by Nishijima et al.(50) in geriatric oncology patients and Stuck et al.(51) in patients with post acute rehabilitation also reported strong correlation and significant prognostic values for FI-CGA-10 and CFS, respectively, for longer and shorter outcomes. In the recent longitudinal analysis, Zeng et al.(52) examined FI-CGA, CFS, FP, FRAIL, and EFS for predicting five-year mortality. All scales identified individuals at increased mortality risk; however, the CFS was the preferred screening tool, while the FI-CGA yielded the highest predictive accuracy (AUC=0.72). Cross-sectional research also shows the FI-CGA’s use in capturing functional decline. For example, one gait analysis study reported stronger correlations between gait impairment parameters and both FI-CGA and FI, compared with CFS and FP.(53) A related balance study in 2020 found that greater postural instability was consistently detected by FI-CGA, which captured a broader spectrum of functional decline than did other frailty measures.(54) Lastly, Patel et al.(55) related CGA rating with cognitive and physical health, offering insight for selecting older adults for kidney transplantation.
In 2008, Gray and Wootton(67) in Australia published on digitally transforming the paper-based CGA into an online format, demonstrating a proof-of-concept for use via an Internet platform. Using the InterRAI Acute Care tool, nurses collected patient assessment data online and uploaded it for remote review. This enabled geriatricians to interact with nurses without physically seeing the patient, generate structured reports, available to authorized clinicians, both within and outside the hospital. Workflow evaluations and clinician feedback suggested that the system was safe and acceptable for remote consultation, supplementing traditional geriatric care.(67)
Subsequently, this group conducted validation studies comparing the online CGA with traditional face-to-face CGA for acute care patients requiring geriatric consultation.(68–70) They assessed whether remote review of online CGA data could support triage decisions, and reported substantial concordance with face-to-face clinical judgment.(68) A time-efficiency analysis showed that online triage decisions took, on average, 62% less time than face-to-face assessment.(69) In 2017, the group’s trial data indicated high agreement between face/online and face/face CGA for key outcomes, including permanent residential care referral, geriatric syndrome detection, and medication recommendations, demonstrating noninferiority of the online method.(70)
These studies highlight the potential of digital platforms for CGA, particularly where traditional CGA is difficult to implement consistently. Still, there is a lack of evidence from large-scale clinical trials or long-term follow-up studies evaluating patient outcomes using this online CGA model.
The CGA has been embedded directly into EHR or EMR systems. The terminology varies internationally. In the United States, “EMR” typically refers to a digital version of a paper chart, while “EHR” refers to a broader, interoperable record that can be shared across settings. In Australia and Canada, the two terms are often used interchangeably, with EMR more common among clinicians. In China, EMRs are complete clinical information resources created by hospitals and serve as the primary data source for EHRs. It appears that both systems can support structured CGA documentation and facilitate its use in clinical decision-making.(71)
Tsubata et al.(72) integrated a structured CGA module into the hospital EMR to assess older patients newly diagnosed with lung cancer and automatically classify them as fit, vulnerable, or frail. The study demonstrated the feasibility of using EMR-based CGA for real-time classification to identify chemotherapy risks associated with frailty, supporting treatment decision-making, albeit with limited grades of frailty.
In 2022, Chu et al.(73) combined CGA items stored in the EHR with routinely collected clinical data (e.g., demographics, diagnoses, medications, laboratory results) to develop machine-learning models predicting fall risk. Among four tested algorithms, XGBoost achieved the best performance, with 73% accuracy in the validation dataset. In 2023, this group used CGA plus EHR data to predict physical function at hospital discharge, comparing three machine-learning models.(74) The eCGA showed excellent predictive capacity, with accuracy rates of 98% for discrimination, 94% for classification, and 87% for prediction in the validation dataset—superior to performance reported for eFI without CGA in other studies, though based on different populations.(75,76)
While some studies were not exclusively frailty-focused, they illustrate the potential of integrating CGA into EHR/EMR systems to enhance predictive analytics and clinical decision-making. As AI matures, this might be useful, although concerns about bias remain.
Cooper et al.(77) reported a quality-improvement initiative to build a bedside FI-CGA for use in acute care hospitals, where comprehensive geriatric input is often needed. An FI-CGA, incorporated into the EHR, enabled access across care services and supported geriatric management. The successful integration of a digitalized CGA-based FI into routine practice highlighted that the tool not only graded frailty, but also provided a uniform and efficient way to communicate complex geriatric concepts, such as vulnerability and reserve, among multidisciplinary teams. In these ways, eFI-CGA could enhance team communication and inform clinical decision-making in hospital care of older adults, but further validation is needed.(77)
Building on the eFI approach for large-scale frailty screening, attention has turned towards enhancing frailty assessment and management at the first point of care. Garm et al. described a novel primary care model, Community Actions and Resources Empowering Seniors (CARES),(78) a Canadian interprovincial collaboration between the authors’ teams. By helping seniors age well through upstream interventions intended, the aim is to reduce the downstream impact of frailty on acute care and emergency resources. CARES integrates standard care with telephone-based wellness coaching provided by trained community volunteers supported by the digitization of CGA into the EMR to enable routine, periodic geriatric assessments. Wellness plans were individualized, focusing on exercise, socialization, and nutrition.(78) At six months, frailty-informed multidisciplinary care plans in 51 community-dwelling older adults were associated with self-reported frailty level improvement in 61% of participants, outperforming the CFS in sensitivity.(79) The pilot work supported CGA integration into EMRs for potential real-time use in primary care.
Following the initial results, efforts progressed toward developing from CGA (see Appendix S1-A in the supplementary material) to eCGA and eFI-CGA solutions for EMR-embedded (see Appendix S1-B in the supplementary material), standalone (see Appendix S1-C in the supplementary material), and web-based (see Appendix S1-D in the supplementary material) developments. Sepehri et al.(80) introduced a standalone eFI-CGA tool that automated FI-CGA calculation, adapted from a widely used paper CGA form, and achieved 100% scoring accuracy. The tool ensured secure data handling and allowed clinicians to record follow-up actions for care planning. With the COVID-19 pandemic, the software was updated with a search function for resuming disrupted assessments and an improved interface for care management documentation, supporting virtual and in-person assessments.(81) The eFI-CGA web application (Fraser Health Authority Surrey, BC / Nova Scotia Health Authority, Halifax Nova Scotia https://efi-cga.ca) was launched to promote widespread adoption, continuity of care, and large-scale analyses.(82) A version for clinical use was offered (https://clinical.efi-cga.ca), enabling local data management and enhanced confidentiality for health-care providers.(82)
In another EHR-embedded protocol, the IT-assisted CGA (i-CGA),(83) like the eCGA, combined the traditional multidisciplinary FI-CGA framework with EHR integration. The system allowed review of prior CGA entries, domain-specific remarks, medication review, and streamlined care planning through reminders, standardized documentation, medication lists, and information on hospital or long-term care availability. In a quasi-experimental quality-improvement study, i-CGA use was associated with reduced mortality in severely frail residents.(83) While the study did not compute an FI score, the richness of CGA data feasibly allows automating an eFI-CGA down the line.
This scoping review examined the development, validation, and application of the FI-CGA, eCGA, and eFI-CGA tools in frailty care. The findings highlight the role of these tools in frailty assessment and management, while suggesting opportunities for their further validation and application to enhance clinical decision-making. The review was limited to studies published in English and indexed in major medical databases, without quality assessment or meta-analysis. These limitations may have excluded relevant non-English publications and restricted cross-study comparisons.
Our review underscores several points. First, the FI-CGA, grounded in the cumulative deficit model(5–9) and encompassing multidisciplinary health-relevant factors assessed through the reference standard CGA,(21–24) provides a continuous, multi-domain measure of frailty with demonstrated predictive validity for important adverse outcomes. Since its introduction in 2004, the FI-CGA has been validated across hospital, primary care, and community settings, supporting both patient care and epidemiological research. The FI-CGA has also served as a benchmark for validating other frailty and risk assessment tools. Comparative studies generally indicate that the FI-CGA offers superior predictive performance, especially using the standard continuous score that allows more accurate grading of frailty, and capturing dose-responsive impacts.
Digital implementation of standard CGA forms streamlines its administration. Automation of CGA item-value coding and FI score calculation, and accelerated data collection and processing, while reducing human error, are each possible. Integrating structured eCGA and eFI-CGA tools into EHRs/EMRs enables wider deployment, particularly in primary and acute care settings where geriatric expertise may be limited.(84–86) During the eCGA session, documentation of findings, care needs, and available resources further supports individualized interventions and advanced care planning. This in turn can enhance care provider and recipient engagement in frailty identification.(87–89) Overall, the eCGA and eFI-CGA tools have the potential to enhance workflow efficiency, standardize data collection and frailty measurement, and enable earlier identification, risk stratification, and management of frailty, thereby allowing more geriatrics-informed care.
Growing interest in primary care implementation reflects this shift. Innovative models in communities and care homes across the United Kingdom, Canada, China, and elsewhere have demonstrated the feasibility of integrating frailty assessment into routine practice. These initiatives underscore substantial progress toward earlier detection, holistic care planning, and targeted interventions.
Much of the current evidence for the FI-CGA comes from single-site studies involving older adults aged 65+ years, whereas the eCGA and eFI-CGA tools, though now reaching implementation and feasibility successes in several sites, remain largely in the early stages of validation. Consequently, large-scale, multi-center, cross-setting validation involving diverse populations remains a critical gap. The proof of the effort is its impact on performance and output. One important focus must be on how effectively general practitioners, who typically do not receive formal CGA training, can apply the eCGA and eFI-CGA in frailty care. This underscores the need for rigorous reliability testing.(90)
Further research is also warranted to determine the extent to which the eFI-CGA can benefit the interpretability of frailty scores, particularly when based chiefly on administrative rather than clinical data. A better understanding of care needs rooted in the impairments recorded on the CGA and giving rise to frailty will provide clinical meaningful insights. Moreover, consensus on the optimal selection of eFI items, tailored to different care purposes and care settings, has yet to be established. In addition, even though FI allows precise risk stratifications (e.g., 0.01 FI increment), it has often been used to categorize individuals and no consensus exists on the cut-points for classifying frailty (e.g., frail vs. non-frail). Previous studies have applied thresholds ranging from 0.2 to 0.3 to define frail, non-frail, or pre-frail groups, depending on the population, the number and type of deficits included, and the intended use of the classification.(2,8) This underscores the ongoing need for context-specific validation of eFI thresholds to enable meaningful cross-study comparisons—a process for which standardization using eCGA-based assessments may be particularly beneficial. As studies mature to include people across the full spectrum of frailty, it will be important to move beyond simple dichotomization of this health state.
Taken together, the eCGA and eFI-CGA in older adult care beyond specialized geriatric clinics are compelling. Both have the potential to be useful tools in “geriatrizing” care approaches, and not only in primary care. They offer scalability without compromising quality, enabling timely, individualized interventions at the first point of contact and supporting a preventive, patient-centered, and standard frailty care. Already we can look to linking them with the FI-Lab, a frailty index based on routine laboratory tests.(5) As frailty becomes increasingly recognized as a common life stage that places a substantial burden on health and social care—particularly in supporting aging at home—broader, distributed care models will be essential.
The CGA-based frailty evaluation remains a cornerstone for effective management of frailty in older adults. The digital evolution provides a pathway toward more accessible, efficient standard, engaging care providers at the first points of contact. Large-scale, multi-center studies by countries willing to make the investments are essential to validate the diverse range of health scores across care settings and populations. We must also learn how to leverage innovative technologies that can promote healthy aging across the life course.
We sincerely acknowledge the eFI-CGA investigators team for input in various aspects of the project development; H. Low for literature search, screening, review, and material organization; K. Harding and M. Moore for assistance with literature search and article retrieval and organization; the Department of Evaluation and Research Services of Fraser Health Authority; and the Geriatric Medicine Research Unit of Nova Scotia Health for research and administrative support.
We have read and understood the Canadian Geriatrics Journal’s policy on conflicts of interest disclosure and declare no conflict of interest with this work and its publication. The work cited here for the development of the eCGA and eFI-CGA is in the public domain.
This research was funded by an operating grant from the Canadian Institutes of Health Research (# CIHR-PJT-156210).
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Correspondence to: Kenneth Rockwood, OC, MD, FRCPC, FRCP, Division of Geriatric Medicine, Dalhousie University, 5955 Veterans’ Memorial Lane, Suite 1421 Veterans’ Memorial Building, Halifax, NS B3H 2E1; and Xiaowei Song, PhD, MSCS, Fraser Health Authority, Surrey Memorial Hospital Critical Care Tower T2-820, Surrey, BC V3T 0H1, E-mail: kenneth.rockwood@dal.ca & xiaowei.song@fraserhealth.ca
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Canadian Geriatrics Journal, Vol. 29, No. 2, JUNE 2026