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M.J. Pereira1, E. Chong2,3, J.A.D. Molina1, S.H.X. Ng1, E.F. Goh3, B. Zhu4, M. Chan2,3, W.S. Lim2,3


1. National Healthcare Group, Health Services and Outcomes Research, Singapore; 2. Tan Tock Seng Hospital, Department of Geriatric Medicine, Singapore; 3. Tan Tock Seng Hospital, Institute of Geriatrics and Active Ageing, Singapore; 4. Tan Tock Seng Hospital, Department of Nursing Services, Singapore

Corresponding Author: Michelle Jessica Pereira, National Healthcare Group, Health Services and Outcomes Research, Singapore, michelle_jessica_pereira@nhg.com.sg

J Frailty Aging 2022;in press
Published online May 26, 2022, http://dx.doi.org/10.14283/jfa.2022.40



Background: The Emergency Department Interventions for Frailty (EDIFY) program was developed to deliver early geriatric specialist interventions at the Emergency Department (ED). EDIFY has been successful in reducing acute admissions among older adults.
Objectives: We aimed to examine the effectiveness of EDIFY in improving health-related quality-of-life (HRQOL) and length of stay (LOS), and evaluate EDIFY’s cost-effectiveness.
Design: A quasi-experiment study.
Setting: The ED of a 1700-bed tertiary hospital.
Participants: Patients (≥85 years) pending acute hospital admission and screened by the EDIFY team to be potentially suitable for discharge or transfer to low-acuity care areas.
Intervention: EDIFY versus standard-care.
Measurements: Data on demographics, comorbidities, premorbid function, and frailty status were gathered. HRQOL was measured using EQ-5D-5L over 6 months. We used a crosswalk methodology to compute Singapore-specific index scores from EQ-5D-5L responses and calculated quality-adjusted life-years (QALYs) gained. LOS and bills in Singapore-dollars (SGD) before subsidy from ED attendances (including admissions, if applicable) were obtained. We estimated average programmatic EDIFY cost and performed multiple imputation (MI) for missing data. QALYs gained, LOS and cost were compared. Potential uncertainties were also examined.
Results: Among 100 participants (EDIFY=43; standard-care=57), 61 provided complete data. For complete cases, there were significant QALYs gained at 3-month (coefficient=0.032, p=0.004) and overall (coefficient=0.096, p=0.002) for EDIFY, whilst treatment cost was similar between-groups. For MI, we observed only overall QALYs gained for EDIFY (coefficient=0.102, p=0.001). EDIFY reduced LOS by 17% (Incident risk ratio=0.83, p=0.015). In a deterministic sensitivity analysis, EDIFY’s cost-threshold was SGD$2,500, and main conclusions were consistent in other uncertainty scenarios. Mean bills were: EDIFY=SGD$4562.70; standard-care=SGD$5530.90. EDIFY’s average programmatic cost approximated SGD$469.30.
Conclusions: This exploratory proof-of-concept study found that EDIFY benefits QALYs and LOS, with equivalent cost, and is potentially cost-effective. The program has now been established as standard-care for older adults attending the ED at our center.

Key words: Geriatrics, emergency medicine, health services research, cost analysis.




Acute hospitalizations can lead to negative consequences among frail older adults. This vulnerable group has greater propensity to experience adverse health outcomes during acute hospitalizations resulting in disability or death, as well as higher economic and resource burden (1, 2). Amongst the recent transformation in healthcare systems designed to meet rising demands in delivering optimal management of multidimensional and complex care needs of a rapidly aging population, is the shift of resources to the Emergency Department (ED) (3, 4). Novel healthcare models designed for frail older adults at the ED have been successful in mitigating iatrogenic and nosocomial complications, reducing unnecessary acute admissions and shortening length of stay (LOS) (5).
Whilst there is growing evidence to support clinical effectiveness in delivering geriatric-centric care services at the ED, data on economic and other efficacy benefits remain scarce. Two studies have reported the cost-neutrality of a multidisciplinary team or geriatric advanced practice nurses (APNs) at the ED (6, 7). Another study reported that increasing resources for ‘front-door’ geriatrics care (i.e. acute medical and short stay unit) can aid in shortening LOS (8). We found a published protocol of an ongoing trial examining an ED-based geriatric assessment and care coordination intervention with an embedded cost-effective analysis (CEA) (9). However, there are no published CEA of similar interventions at present, which is an important metric for prioritizing the allocation of finite healthcare resources (10).
We successfully implemented the Emergency Department Interventions for Frailty (EDIFY) program and reported clinical effectiveness in reducing acute hospital admissions and frailty attenuation among older adults (11). EDIFY’s multidisciplinary team, comprising a geriatrician, senior resident, APN, pharmacist, and physiotherapist, identified older adults with the potential to avoid acute admissions and provided early integrated geriatric interventions at the ED. However, we have yet to determine the program’s ability to benefit patient-reported outcome measures and resource utilization. This provided the impetus for this exploratory proof-of-concept study to examine health-related quality-of-life (HRQOL) and LOS benefits, and cost-effectiveness of delivering front-door geriatric care. Our primary hypothesis is that EDIFY results in better outcomes in quality-adjusted life-years (QALYs) and LOS, and is cost-neutral. As a secondary hypothesis, we additionally propose that EDIFY delivers cost-effective care for older persons attending the ED.



Setting and Design

This study was set in the ED at Tan Tock Seng Hospital, a 1700-bed tertiary public hospital in Singapore serving a resident population of 1.4 million (12). We recruited patients (aged 85 years or older) who were planned for acute admission under the care of the geriatrics service following a review by the ED physician, but were assessed by the EDIFY team to be suitable for discharge or transfer to a low-acuity care area [i.e. subacute care unit (SCU), short-stay unit (SSU), or community hospital (CH)]. From July 2018 to August 2019, eligible participants were recruited in alternating weekly blocks into EDIFY-intervention and standard-care groups, respectively. Details on patient recruitment and eligibility criteria have been previously reported (https://doi.org/10.1016/j.jamda.2021.01.083) (11).
We performed a quasi-experiment that used a single-consent Zelen’s design (11). Written informed consent was obtained from patients or their legally acceptable representative (if patients lack mental capacity for consenting). Ethics approval was granted by the Domain Specific Review Board of the National Healthcare Group, Singapore (Reference: 2017/01076). We adhered to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement for reporting of our cost-effectiveness findings (13).

EDIFY Interventions

Patients in the intervention group received early comprehensive geriatric assessments (CGA), frailty education and counselling, and relevant care planning by the EDIFY team. Additionally, they would be assessed for suitability for safe discharge or transfer to a low-acuity care area.
Patients assessed to require an acute admission after EDIFY interventions were admitted and cared for by the inpatient geriatrics team while those transferred to the SSU continued to receive care from the EDIFY team. Patients discharged home from ED or SSU received a personalized discharge information sheet and telephonic follow-up consultation within 5 days post-discharge by the EDIFY team, if required.

Baseline Characteristics

We collected baseline information including demographics, premorbid functional status using Modified Barthel Index (MBI) (14) and Lawton’s Instrumental Activities of Daily Living (iADL) (15), comorbidities using Charlson Comorbidity Index (CCI), frailty status using the locally-validated Clinical Frailty Scale-Algorithm (CFS-A) (16) to score the Clinical Frailty Scale (CFS) (17), and cognitive status using the Abbreviated Mental Test (AMT) (18).

Outcome Measures

Our first outcome measure was HRQOL, as measured using the five-level version of the EuroQOL five-dimensional (EQ-5D-5L) questionnaire at baseline, 3- and 6-month (19). Subsequently, a crosswalk methodology was implemented to calculate index scores from EQ-5D-5L responses according to a published Singaporean EQ-5D-3L value-set (18, 19). Those who passed away were assigned scores of zero from the time-point when their deaths were reported. These Singapore-specific index utility scores were then used to calculate QALYs gained during the study. LOS, our other outcome measure, for patients who were assessed by the EDIFY team and required transfer to acute wards, SCU, and SSU, was extracted from the hospital’s administrative database.
The public healthcare financing model in Singapore incorporates patient co-payments. In the ED setting, all patients co-pay a pre-determined amount for their ED treatment at public hospitals (approximately $120 Singapore dollars with slight variations depending on the specific institution; SGD-USD average exchange in Dec 2021: 0.733), and the government subsidizes the rest. In the inpatient setting, patients have a choice to decide on the level of government subsidy for the cost of their healthcare by selecting their desired level of patient-density and amenities range for their inpatient accommodation, denoted by different accommodation classifications. For this study, we obtained hospital bill records (in Singapore dollars prior to government subsidy) for all patients’ ED attendance and any subsequent admissions from the hospital’s administrative database. Care delivered by attending teams for patients in acute or SCU, and the EDIFY team for patients in SSU, were assumed to be subsumed in billings. Hospital billings usually consist of healthcare professionals’ attendance fee, taking into account salary and time effort of the relevant staff, daily ward charges, procedures, medications, diagnostic tests. As EDIFY in ED was not considered standard-care, this care component was not chargeable in an ED attendance and not accounted for in the treatment cost for EDIFY patients. Hence, average programmatic cost due to EDIFY interventions can only be estimated. Time spent by the EDIFY APN on screening ED electronic medical records to identify potentially suitable patients was factored into EDIFY’s programmatic cost. Estimated durations for screening, intervention and telephonic follow-up performed by the EDIFY team, and the associated unit manpower cost obtained from the hospital’s human resource department were also used for computation of EDIFY programmatic cost. The resultant manpower costs from screening, intervention and telephone follow-up performed by the relevant EDIFY team healthcare professionals were summed up. No other expenses, besides manpower costs, were incurred due to the implementation of the EDIFY program at the ED. Unsubsidized hospital bills represented treatment cost for both groups, taking the hospital’s perspective for the study. Due to the short follow-up period of the study, no discounting of treatment cost was performed. Healthcare utilization at outpatient settings such as specialist or allied health clinics, or community services at 1-, 3- and 6-months post-discharge were also recorded to estimate post-discharge between-group differences, if any.
QALYs gained, LOS and treatment cost for both intervention groups were compared. The incremental QALYs gained and treatment cost associated with EDIFY were compared to standard-care to determine cost-effectiveness.

Statistical analysis

QALYs gained and treatment cost associated with EDIFY compared to standard-care at 3-month and overall were modelled through Seemingly Unrelated Regressions (SURs) (22). This allows for between-group differences for QALYs gained and cost to be determined, while accounting for the respective variances and covariance simultaneously. Using the suest command, specifically, QALYs gained was examined using simple linear regressions adjusted for baseline index utility score and treatment cost was fitted with log-link Gamma generalized linear models (GLM). Intervention group was treated as an explanatory variable in all models. The complete case analysis was treated as the base case. Missing data analysis was conducted with logistic regression to examine the nature of missingness of data and factors associated with missingness.
Thereafter, multiple imputation (MI) was performed for missing QALYs according to completeness, using predictive mean matching of factors associated with missingness and baseline index utility score, by treatment group (21, 22). Additionally, post-hoc sensitivity analysis examined the potential extent of parameter, sampling, imputation, modelling and methodological uncertainties in several scenarios to test the veracity of main results (23–25). Namely, adding EDIFY programmatic cost to treatment cost and increasing it incrementally by $50 in a deterministic sensitivity analysis, bootstrapping samples to increase precision of estimates, modifying the specifications of SUR models and using an EQ-5D-5L value-set for QALYs computation (28). Lastly, the impact of EDIFY on LOS was investigated using Poisson models similar to those undertaken in the primary paper (11), and Negative Binomial models to account for potential overdispersion.
Statistical analysis was performed using Stata V16.0 (Stata Corp, College Station, TX), and a p-value cut-off of 0.05 was deemed statistically significant.



Recruitment and Baseline Characteristics

There were 100 participants who were recruited (EDIFY = 43; standard-care = 57). Details of patient recruitment can be found in Supplementary Table 1 of our previous article (11). Between-group differences, follow-up and analysis details, and outcome results have also been previously reported. Participants were predominantly female (66.0%) and of Chinese ethnicity (90.0%), with a mean age of 90.0 years. Other baseline characteristics were largely similar (Appendix 1). Overall, whilst baseline data was complete, only 61 participants provided complete data for all follow-up time-points. Upon closer examination of missing data, we found that our data was missing at random. Moreover, upon examining the factors which were associated with missingness of data, we observed that participants who were single or widowed, ex- or current smokers or have a monthly household income of less than $2,500 were less likely to provide complete data.

Unadjusted Results

Unadjusted QALYs gained for the EDIFY-intervention group were better than the standard-care group at 3-month (0.030 ± 0.055 vs. -0.004 ± 0.066) and overall study period (0.086 ± 0.146 vs. -0.016 ± 0.196) (Figure 1). Highest QALYs gained were observed among patients in the EDIFY-intervention group who were discharged home (0.086 ± 0.083) or transferred to the SSU (0.088 ± 0.145) for overall study period. Only 1 out of 5 patients in the EDIFY-intervention group, who required acute care, had completed QALYs data (3-month: 0.104, overall study period: 0.241). In addition, EDIFY-intervention patients who were successfully discharged home incurred the lowest average hospital bills, whilst those who required acute care had the highest average, which was even higher than standard-care. This trend was similarly observed for LOS.

Figure 1. Unadjusted results (means, standard deviations) of quality-adjusted life-years (A and B), hospital bills (C) and length of stay (D), by intervention groups (and EDIFY disposition sub-groups)

Abbreviations: QALYs, quality-adjusted life-years; SSU, short-stay unit; SCU, subacute care.


Details on cost elements of the EDIFY program are as follows. On average, 20 minutes was required for medical records screening, and the time spent by the geriatrician, senior resident and APN for EDIFY interventions was 20, 25 and 60 minutes per patient, respectively. Of the 28 patients discharged home or transferred to SSU, 18 patients were followed-up twice telephonically for an average of 15 minutes by the APN. Information on pharmacist and physiotherapist inputs were unavailable, as these services were incorporated into the EDIFY program towards the end of the study. After applying unit manpower costs, provided by the hospital’s human resource department that covers staff salary and time effort, to the durations spent by EDIFY physicians and APNs, EDIFY’s programmatic cost approximated SGD$469.30 per patient.
There were low post-discharge utilization visit rates at specialist clinics (1-month: EDIFY-intervention = 2.0 ± 1.7 vs. standard-care = 2.0 ± 1.2; 3-month: EDIFY-intervention = 1.6 ± 1.4 vs. standard-care = 2.1 ± 1.4; 6-month: EDIFY-intervention = 1.8 ± 1.3 vs. standard-care = 2.1 ± 1.5), allied health clinics (EDIFY-intervention and standard-care maximum = 1.0), or community services (EDIFY-intervention and standard-care maximum = 2.0), without between-group differences detected.

Model Results

For complete case analyses, there were significant incremental QALYs gained for EDIFY-intervention patients at 3-month [coefficient = 0.032, 95% Confidence Interval (CI) 0.010 – 0.054, p = 0.004] and for the overall study period (coefficient = 0.096, 95% CI 0.035 – 0.157, p = 0.002) (Table 1). In the analysis incorporating imputed HRQOL data, we observed only overall incremental QALYs gained for EDIFY-intervention patients (coefficient = 0.102, 95% CI 0.041 – 0.162, p = 0.001). Furthermore, EDIFY reduced LOS by 17% (Incident risk ratio = 0.83, p = 0.015) in a Poisson regression adjusted for age, gender, baseline CFS, without dispersion issues in post-hoc checking (Chi-square p = 0.318). No significant findings were observed in other LOS models adjusted for age, gender and/or baseline MBI.

Table 1. Cost-effectiveness results from complete case and imputed data

Abbreviation: CI, confidence interval; QALYs, quality-adjusted life-years. * p-value <0.05; † Seemingly unrelated regressions using suest command on STATA (Treatment cost modelled with generalized linear models, gamma family, log link; QALYs gained modelled using simple linear regressions); ‡ Seemingly unrelated regressions using mysuest command on STATA (Treatment cost modelled with generalized linear models, gamma family, log link; QALYs gained modelled using simple linear regressions).


There was no between-group difference in treatment cost. Moreover, in our deterministic sensitivity analysis, there remained no between-group differences despite adding EDIFY’s programmatic cost to treatment cost (coefficient = 0.91, p = 0.563). Moreover, the maximum EDIFY programmatic cost-threshold was SGD$2,500 before EDIFY-intervention patients incurred higher treatment costs (coefficient = 1.30, p = 0.053), exceeding the estimated EDIFY programmatic cost of $469.30 by 5.3 times. Main results were consistent with bootstrapped samples, specification changes made using linear models (sureg command) for treatment cost, and using another locally applicable value-set for QALYs calculations (Table 2).

Table 2. Summary results of sensitivity analysis

Abbreviations: QALYs, quality-adjusted life-years; EDIFY, result favors EDIFY; N.S., no significant findings.



Findings from this exploratory study suggest that front-door geriatric interventions for older adults with the potential for acute admission avoidance, by a team of geriatrics experts, have clinical and cost benefits. Notably, EDIFY improved HRQOL for the overall study duration across models performed and reduced hospital resource utilization, namely in LOS. In terms of treatment cost, EDIFY was also found to be cost-neutral. As EDIFY delivered better clinical benefits, without any negative impact on treatment cost, EDIFY can be considered cost-effective in delivering front-door geriatric care at the ED. These key findings support our study’s primary and secondary hypotheses and provide the foundation for future studies to corroborate and build on evidences supporting the delivery of front-door geriatric care.
EDIFY’s ability to improve HRQOL may be attributable to the front-loading of geriatrics interventions right from the ED along with the avoidance of unwarranted acute admissions, thus preventing known complications including prolonged hospitalization, disability, iatrogenesis, institutionalization, and mortality (29-31). Additionally, we previously reported that patients receiving EDIFY interventions appear to have significantly lower odds [Odds Ratio (OR) = 0.21, 95% CI = 0.06 – 0.71, p = 0.01] of progressing into a more severe CFS category at 6-month (11). Also, with a recent systematic review and meta-analysis reporting a consistent inverse association between frailty or pre-frailty and quality-of-life (32), it is unsurprising that the interventions rendered by EDIFY that promotes the preservation of frailty status also contribute to better HRQOL.
Another significant benefit of the program is the shorter LOS among older adults receiving EDIFY interventions. There remains a paucity of data supporting front-door geriatric care services and LOS reduction. The Geriatric Emergency Department Intervention (GEDI) service is a nurse-led physician-championed model of ED care that reported reduction in ED LOS [Hazard Ratio (HR) = 1.42, 95% CI = 1.33 – 1.52] (33). However, its ability to significantly reduce in-hospital LOS was not sustained. A recent case control study reported that the ED Geriatric Intervention Team (GAT), which consisted of advanced practice providers, care management, and occupation therapy, was successful in significantly reducing in-hospital LOS (mean time in days: 4.49 vs. 5.52, p < 0.05) (34). Hence, delivering a front-door geriatrics care service comprising multidisciplinary care provisions, similar to the EDIFY program, appears necessary to achieve reduction in LOS as demonstrated by various models of care across other care settings (35-38). Additionally, the potential to reduce resources required, in terms of lower LOS, is desirable for hospitals with high workload and would be of interest to hospital administrations.
Costing results for the EDIFY program are reflective of other studies with similar modest financials (6, 7). Although, we did not specifically collect cost information on post-discharge healthcare utilization, from the lack of between-group differences detected, post-discharge cost will unlikely differ between EDIFY and standard-care. Hence, the post-discharge cost benefit is in EDIFY’s favour, as EDIFY improved post-discharge HRQOL, without incurring additional cost, compared to standard-care. Similarly, cost information of pharmacist and physiotherapist involvement was also unavailable, but the impact is likely to be low. To alleviate concerns of financial prudence, we further gauged how much EDIFY’s operating costs could increase in our deterministic sensitivity analysis, without incurring a higher economic burden for the healthcare provider. This cost-threshold analysis is potentially useful for the consideration of future service expansion, such as enhanced post-discharge care, greater pharmacist and physiotherapist involvement, dietician or medical social worker referrals if necessary, or up-scaling the care model for wider implementation by other healthcare providers and policymakers.
To our knowledge, this is the first exploratory study examining cost-effectiveness of an ED-based intervention combining CGAs with discharge planning and education at the front-door. As economic resources for healthcare are becoming increasingly finite globally, an appreciation of the cost-effectiveness of any novel models of care is essential for informed decision-making by clinicians and policymakers alike. In light of the scarcity of such information for the area of geriatric emergency medicine, it is important for any available economic findings to be disseminated to support challenges faced by healthcare policymakers. Other strengths of the study include complete baseline data, efforts to understand the nature with appropriate statistical management of missing follow-up data, and extensive sensitivity analyses to ensure the accuracy of main conclusions made.
There were several study limitations and results should be interpreted with care. Firstly, our study participants were not randomized. Nevertheless, our recruitment process was still successful in producing two comparison groups with comparable baseline characteristics. Secondly, this single-center study only had a modestly sized cohort of an advanced age, of which 43 were in the intervention group (complete data = 30), hence these early results require further verification with larger scale replication studies to ensure reproducibility and for wider generalizability. However, the EDIFY program did show clinical effectiveness of acute admission avoidance, even though the study did not achieved its pre-planned sample size, as previously acknowledged in the primary paper (11). Thirdly, we did not collect direct medical and indirect costs post-discharge. However, similar low rates of post-discharge outpatient healthcare utilization would mean that there would not be substantial between-group differences in direct medical cost, and thus unlikely to impact on between-group treatment cost differences. Nevertheless, informal home-based care cost borne by patients and families, a pertinent cost burden, was not accounted for, but an undertaking for a societal costing perspective beyond that of the hospital was beyond the scope of the study. Other limitations include the short 6-month follow-up and the lack of patient experience measures. Nonetheless, we still view this study as a positive contribution to the limited efficacy and economic evidence base surrounding ‘front-door’ geriatrics emergency care. Future research involving high quality, randomized, adequately powered trials with good and longer follow-up, based at multiple centres, incorporating patient experience measures are critically needed to expand the much needed evidence base in this area.



This exploratory proof-of-concept study showed that EDIFY has greater QALY and LOS benefits, and equivalent costs as current standard-care, leading to potential cost-effectiveness. Early geriatric specialist interventions at the front-door of acute hospitals via EDIFY has now been established as standard-care for older persons attending the ED at our center. Our results pave the way for future studies to explore the efficacy, feasibility and cost-effectiveness of similar front-loading multidisciplinary interventions at the ED which combine CGAs with discharge planning and education.


Acknowledgments: This work was also supported by the Ng Teng Fong Healthcare Innovation Program (Project Code: NTF_JUL2017_I_C2_CQR_02), National Healthcare Group, Singapore, which had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Disclosure statement: The authors have no relevant financial or non-financial interests to disclose.

Conflicts of Interest: None declared by the authors.

Ethical standards: The study was approved by the Domain Specific Review Board of the National Healthcare Group, Singapore (Reference: 2017/01076).




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