Singapore's regional health systems-a data-driven perspective on frequent admitters and cross utilization of healthcare services in three systems

Assessing the performance of non-specialised private hospitals in Malaysia - an upper-middle-income medical tourism destination country using the Pabón-Lasso model

BACKGROUND

The government has rapidly promoted the privatisation of healthcare to improve systemic performance, based on the theory that markets improve efficiency. This study aims to measure the efficiency of private hospitals following their expansion and venture into the medical tourism industry through extensive governmental support.

METHODS

Inpatient utilisation of 101 private, non-specialised hospitals in Malaysia in 2014 and 2018 from the Health Informatics Centre, Ministry of Health Malaysia database was studied using paired samples t-test, analysis of variance (ANOVA), and the Pabón-Lasso model.

RESULTS

Better quantitative performance was found among larger hospitals, those with hospital accreditation, and those participating in medical tourism activities. There is a scale effect of efficiency between smaller and larger hospitals. However, when compared within respective size categories, Category 1 (small hospitals with less than 100 beds) has the highest percentage of efficient hospitals (39.3 per cent in 2014 and 35.7 per cent in 2018 in Sector 3 of the Pabón Lasso graphs).

CONCLUSION

This study has found that a higher bed occupancy rate (BOR) and longer average length of stay (ALoS) are associated with larger private hospitals, hospital accreditation, and participation in medical tourism activities in Malaysia. There is a need to expedite strategic hospitals partnership for resource optimisation and capacity pooling towards producing better performance.

Understanding community-dwelling older adults' preferences for home- and community-based services: A conjoint analysis

BACKGROUND

Older adults' preference for home- and community-based service programs has been highlighted as an essential but usually ignored ingredient in current care models. Disentangling how preferences contribute to older adults' decision-making could facilitate finding optimal ways to deliver home- and community-based services in times of increasing scarcity.

OBJECTIVE

To identify Chinese community-dwelling older adults' preference structure for home- and community-based services and thus to optimize service provision.

METHODS

Conjoint analysis, a preference-based technique, was employed to study older adults' preferences. A stepwise qualitative approach was first adopted to identify the attributes and attribute levels of home- and community-based services. Scenarios were defined through an orthogonal fractional factorial design, and a cross-sectional survey was conducted through a face-to-face, anonymous questionnaire. Conjoint analysis was performed to determine preference weights representing the relative importance of the identified attributes, and cluster analysis was performed to identify clusters of participants with similar preference structures. All data analyses were performed using SAS v9.4 and SPSS 22.0.

RESULTS

A total of 321 of 350 invited participants completed the questionnaire. Four attributes were identified and used to create the conjoint scenarios: care-giving attitude, price, technical care-giving skills, and the type of service provider. Care-giving attitude was the most valued attribute for older adults when making decisions (relative importance score = 48.28), followed by price (relative importance score = 21.618), technical care-giving skills (relative importance score = 19.518), and finally, the type of service provider (relative importance score = 10.585). Three preference phenotypes were identified by applying cluster analysis: "price-oriented", "comprehensively balanced", and "attitude-oriented".

CONCLUSION

The present study underscored the importance of considering attributes valued by Chinese older adults in the design and delivery of home- and community-based services. The preference structure, including the utility score of the attribute levels, differs among older adults. The findings could inform future research and practice and suggest incorporating flexibility during the service delivery stage.

Predictors of frequency of 1-year readmission in adult patients with diabetes

Diabetes mellitus (DM) is the third most common chronic condition associated with frequent hospital readmissions. Predictors of the number of readmissions within 1 year among patients with DM are less often studied compared with those of 30-day readmission. This study aims to identify predictors of number of readmissions within 1 year amongst adult patients with DM and compare different count regression models with respect to model fit. Data from 2008 to 2015 were extracted from the electronic medical records of the National University Hospital, Singapore. Inpatients aged ≥ 18 years at the time of index admission with a hospital stay > 24 h and survived until discharge were included. The zero-inflated negative binomial (ZINB) model was fitted and compared with three other count models (Poisson, zero-inflated Poisson and negative binomial) in terms of predicted probabilities, misclassification proportions and model fit. Adjusted for other variables in the model, the expected number of readmissions was 1.42 (95% confidence interval [CI] 1.07 to 1.90) for peripheral vascular disease, 1.60 (95% CI 1.34 to 1.92) for renal disease and 2.37 (95% CI 1.67 to 3.35) for Singapore residency. Number of emergency visits, number of drugs and age were other significant predictors, with length of stay fitted as a zero-inflated component. Model comparisons suggested that ZINB provides better prediction than the other three count models. The ZINB model identified five patient characteristics and two comorbidities associated with number of readmissions. It outperformed other count regression models but should be validated before clinical adoption.

One-Bed-One-Team—Does an Integrated General Hospital Inpatient Model Improve Care Outcomes and Productivity: An Observational Study

Introduction

With the increasing complexity of healthcare problems worldwide, the demand for better-coordinated care delivery is on the rise. However, current hospital-based practices remain largely disease-centric and specialist-driven, resulting in fragmented care. This study aimed to evaluate the effectiveness and feasibility of an integrated general hospital (IGH) inpatient care model.

Methods

Retrospective analysis of medical records between June 2018 and August 2019 compared patients admitted under the IGH model and patients receiving usual care in public hospitals. The IGH model managed patients from one location with a multidisciplinary team, performing needs-based care transition utilizing acuity tagging to match the intensity of care to illness acuity.

Results

5,000 episodes of IGH care entered analysis. In the absence of care transition in intervention and control, IGH average length of stay (ALOS) was 0.7 days shorter than control. In the group with care transition in intervention but not in control, IGH acute ALOS was 2 days shorter, whereas subacute ALOS was 4.8 days longer. In the presence of care transition in intervention and control, IGH acute ALOS was 6.4 and 10.2 days shorter and subacute ALOS was 15.8 and 26.9 days shorter compared with patients under usual care at acute hospitals with and without co-located community hospitals, respectively. The 30- and 60-days readmission rates of IGH patients were marginally higher than usual care, though not clinically significant.

Discussions

The IGH care model maybe associated with shorter ALOS of inpatients and optimize resource allocation and service utilization. Patients with dynamic acuity transition benefited from a seamless care transition process.

Importance of telephone follow-up and combined home visit and telephone follow-up interventions in reducing acute healthcare utilization

Purpose

Transitional care is increasingly important in reducing readmission rates and length of stay (LOS). Singapore is focusing on transitional care to address the evolving care needs of a multi-morbid ageing population. This study aims to investigate the impact of transitional care programs (TCPs) on acute healthcare utilization.

Design/methodology/approach

A retrospective, longitudinal, interventional study was conducted. High-risk patients were enrolled into a transitional care program of local tertiary hospital. Patients received either telephone follow-up (TFU) or home-based intervention (HBI) with TFU. Readmission rates and LOS were assessed for both groups.

Findings

There was no statistically significant difference in readmissions or LOS between TFU and HBI. After excluding demised patients, TFU had statistically significant lower LOS than HBI. Both interventions demonstrated statistically significant reductions in readmissions and LOS in pre–post analyses.

Research limitations/implications

TFU may be more effective than HBI in patients with lower clinical severity, despite both interventions showing statistically significant reductions in acute healthcare utilization. Study findings may be used to inform transitional care practices. Future studies should continue to examine the comparative effectiveness of transitional care interventions and the patient populations most likely to benefit.

Originality/value

Previous studies demonstrated promising outcomes for TFU and HBIs, but few have evaluated their comparative effectiveness on acute healthcare utilization and specific patient populations most likely to benefit. This study evaluated interventional effectiveness of both, which might be useful for informing allocation of resources based on clinical complexity and care needs.

Implementing an Individual-Centric Discharge Process across Singapore Public Hospitals

Singapore is one of the first known countries to implement an individual-centric discharge process across all public hospitals to manage frequent admissions-a perennial challenge for public healthcare, especially in an aging population. Specifically, the process provides daily lists of high-risk patients to all public hospitals for customized discharge procedures within 24 h of admission. We analyzed all public hospital admissions (N = 150,322) in a year. Among four models, the gradient boosting machine performed the best (AUC = 0.79) with a positive predictive value set at 70%. Interestingly, the cumulative length of stay (LOS) in the past 12 months was a stronger predictor than the number of previous admissions, as it is a better proxy for acute care utilization. Another important predictor was the "number of days from previous non-elective admission", which is different from previous studies that included both elective and non-elective admissions. Of note, the model did not include LOS of the index admission-a key predictor in other models-since our predictive model identified frequent admitters for pre-discharge interventions during the index (current) admission. The scientific ingredients that built the model did not guarantee its successful implementation-an "art" that requires the alignment of processes, culture, human capital, and senior management sponsorship. Change management is paramount, otherwise data-driven health policies, no matter how well-intended, may not be accepted or implemented. Overall, our study demonstrated the viability of using artificial intelligence (AI) to build a near real-time nationwide prediction tool for individual-centric discharge, and the critical factors for successful implementation.

Integration of health services for the elderly in Asia: A scoping review of Hong Kong, Singapore, Malaysia, Indonesia

Against the backdrop of rapid ageing populations, there is an increasing recognition of the need to integrate various health services for the elderly, not only to provide more coordinated care, but also to contain the rapid cost inflation driven primarily by the curative sector. Funded by the Asia-Pacific Observatory on Health Systems and Policies, this scoping review seeks to synthesize the received knowledge on care integration for the elderly in four Asian societies representing varying socioeconomic and health-system characteristics: Singapore, Hong Kong, Malaysia, and Indonesia. The search for English-language literature published between 2009 and 2019 yielded 67 publications in the final sample. The review finds that both research and practice regarding health service integration are at a preliminary stage of development. It notes a marked trend in seeking to integrate long-term elderly care with curative and preventive care, especially in community settings. Many distinctive models proliferated. Integration is demonstrated not only horizontally but also vertically, transcending public-private boundaries. The central role of primary care is highly prominent in almost all the integration models. However, these models are associated with a variety of drawbacks in relation to capacity, perception, and operation that necessitate further scholarly and policy scrutiny, indicating the robustness and persistence of siloed healthcare practices.

Shifting care from hospital to community, a strategy to integrate care in Singapore: process evaluation of implementation fidelity

Background

Accessibility to efficient and person-centered healthcare delivery drives healthcare transformation in many countries. In Singapore, specialist outpatient clinics (SOCs) are commonly congested due to increasing demands for chronic care. To improve this situation, the National University Health System (NUHS) Regional Health System (RHS) started an integrated care initiative,the Right-Site Care (RSC) program in 2014. Through collaborations between SOCs at the National University Hospital and primary and community care (PCC) clinics in the western region of the county, the program was designed to facilitate timely discharge and appropriate transition of patients, who no longer required specialist care, to the community. The aim of this study was to evaluate the implementation fidelity of the NUHS RHS RSC program using the modified Conceptual Framework for Implementation Fidelity (CFIF), at three distinct levels; providers, organizational, and system levels to explain outcomes of the program and to inform further development of (similar) programs.

Methods

A convergent parallel mixed methods study using the realist evaluation approach was used. Data were collected between 2016 and 2018 through non-participatory observations, reviews of medical records and program database, together with semi-structured interviews with healthcare providers. Triangulation of data streams was applied guided by the modified CFIF.

Results

Our findings showed four out of six program components were implemented with low level of fidelity, and 9112 suitable patients were referred to the program while 3032 (33.3%) declined to be enrolled. Moderating factors found to influence fidelity included: (i) complexity of program, (ii) evolving providers’ responsiveness, (iii) facilitation through synergistic partnership, training of PCC providers by specialists and supportive structures: care coordinators, guiding protocols, shared electronic medical record and shared pharmacy, (iv) lack of organization reinforcement, and (v) mismatch between program goals, healthcare financing and providers’ reimbursement.

Conclusion

Functional integration alone is insufficient for a successful right-site care program implementation. Improvement in relationships between providers, organizations, and patients are also warranted for further development of the program.

Conducting a Cost-Benefit Analysis of Transitional Care Programmes: The Key Challenges and Recommendations

Transitional care encompasses a range of services designed to promote care integration as patients transfer between different locations or different levels of care. Transitional care programmes have been proven to produce positive outcomes in reducing hospital readmissions and improving patients’ health outcomes. However, little is known about the benefits of the programmes on healthcare cost and the published results have been inconsistent. With increasing healthcare expenditures and limited public healthcare resources, cost-benefit analyses become paramount in informing healthcare resource allocation decisions. This perspective paper describes the approaches used in estimating the total costs of a bundle of transitional care services from an academic medical centre, identifies the key methodological challenges encountered in the process of cost-benefit analysis, and recommends potential solutions to tackle these challenges. By providing a comprehensive perspective on the methodological challenges, this paper encourages program evaluators to take these possible challenges into consideration for future cost-benefit analyses.

Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore

OBJECTIVE

We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. Subsequently we apply machine learning algorithms to predict which HUs will persist as PHUs, to inform future trials testing the effectiveness of interventions in reducing healthcare utilisation in PHUs.

DESIGN AND SETTING

This is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.

PARTICIPANTS

Patients who had at least one inpatient admission to the AMC between 2005 and 2013 were included in this study. HUs incurred Singapore Dollar 8150 or more within a year. PHUs were defined as HUs for three consecutive years, while THUs were HUs for 1 or 2 years. Non-HUs did not incur high healthcare costs at any point during the study period.

OUTCOME MEASURES

PHU status at the end of the third year was the outcome of interest. Socio-demographic profiles, clinical complexity and utilisation metrics of each group were reported. Area under curve (AUC) was used to identify the best model to predict persistence.

RESULTS

PHUs were older and had higher comorbidity and mortality. Over the three observed years, PHUs' expenditure generally increased, while THUs and non-HUs' spending and inpatient utilisation decreased. The predictive model exhibited good performance during both internal (AUC: 83.2%, 95% CI: 82.2% to 84.2%) and external validation (AUC: 79.8%, 95% CI: 78.8% to 80.8%).

CONCLUSIONS

The HU population could be stratified into PHUs and THUs, with distinctly different utilisation trajectories. We developed a model that could predict at the end of 1 year, whether a patient in our population will continue to be a HU in the next 2 years. This knowledge would allow healthcare providers to target PHUs in our health system with interventions in a cost-effective manner.

Right-Site Care Programme with a community-based family medicine clinic in Singapore: secondary data analysis of its impact on mortality and healthcare utilisation

OBJECTIVE

Stable patients with chronic conditions could be appropriately cared for at family medicine clinics (FMC) and discharged from hospital specialist outpatient clinics (SOCs). The Right-Site Care Programme with Frontier FMC emphasised care organised around patients in community rather than hospital-based providers, with one identifiable primary provider. This study evaluated impact of this programme on mortality and healthcare utilisation.

DESIGN

A retrospective study without randomisation using secondary data analysis of patients enrolled in the intervention matched 1:1 with unenrolled patients as controls.

SETTING

Programme was supported by the Ministry of Health in Singapore, a city-state nation in Southeast Asia with 5.6 million population.

PARTICIPANTS

Intervention group comprises patients enrolled from January to December 2014 (n=684) and control patients (n=684) with at least one SOC and no FMC attendance during same period.

INTERVENTIONS

Family physician in Frontier FMC managed patients in consultation with relevant specialist physicians or fully managed patients independently. Care teams in SOCs and FMC used a common electronic medical records system to facilitate care coordination and conducted regular multidisciplinary case conferences.

PRIMARY OUTCOME MEASURES

Deidentified linked healthcare administrative data for time period of January 2011 to December 2017 were extracted. Three-year postenrolment mortality rates and utilisation frequencies and charges for SOC, public primary care centres (polyclinic), emergency department attendances and emergency, non-day surgery inpatient and all-cause admissions were compared.

RESULTS

Intervention patients had lower mortality rate (HR=0.37, p<0.01). Among those with potential of postenrolment polyclinic attendance, intervention patients had lower frequencies (incidence rate ratio (IRR)=0.60, p<0.01) and charges (mean ratio (MR)=0.51, p<0.01). Among those with potential of postenrolment SOC attendance, intervention patients had higher frequencies (IRR=2.06, p<0.01) and charges (MR=1.86, p<0.01).

CONCLUSIONS

Intervention patients had better survival, probably because their chronic conditions were better managed with close monitoring, contributing to higher total outpatient attendance frequencies and charges.

Characterization of high healthcare utilizer groups using administrative data from an electronic medical record database

Background

High utilizers (HUs) are a small group of patients who impose a disproportionately high burden on the healthcare system due to their elevated resource use. Identification of persistent HUs is pertinent as interventions have not been effective due to regression to the mean in majority of patients. This study will use cost and utilization metrics to segment a hospital-based patient population into HU groups.

Methods

The index visit for each adult patient to an Academic Medical Centre in Singapore during 2006 to 2012 was identified. Cost, length of stay (LOS) and number of specialist outpatient clinic (SOC) visits within 1 year following the index visit were extracted and aggregated. Patients were HUs if they exceeded the 90th percentile of any metric, and Non-HU otherwise. Seven different HU groups and a Non-HU group were constructed. The groups were described in terms of cost and utilization patterns, socio-demographic information, multi-morbidity scores and medical history. Logistic regression compared the groups’ persistence as a HU in any group into the subsequent year, adjusting for socio-demographic information and diagnosis history.

Results

A total of 388,162 patients above the age of 21 were included in the study. Cost-LOS-SOC HUs had the highest multi-morbidity and persistence into the second year. Common conditions among Cost-LOS and Cost-LOS-SOC HUs were cardiovascular disease, acute cerebrovascular disease and pneumonia, while most LOS and LOS-SOC HUs were diagnosed with at least one mental health condition. Regression analyses revealed that HUs across all groups were more likely to persist compared to Non-HUs, with stronger relationships seen in groups with high SOC utilization. Similar trends remained after further adjustment.

Conclusion

HUs of healthcare services are a diverse group and can be further segmented into different subgroups based on cost and utilization patterns. Segmentation by these metrics revealed differences in socio-demographic characteristics, disease profile and persistence. Most HUs did not persist in their high utilization, and high SOC users should be prioritized for further longitudinal analyses. Segmentation will enable policy makers to better identify the diverse needs of patients, detect gaps in current care and focus their efforts in delivering care relevant and tailored to each segment.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4239-2) contains supplementary material, which is available to authorized users.

Drivers of hospital expenditure and length of stay in an academic medical centre: a retrospective cross-sectional study

BACKGROUND

As healthcare expenditure and utilization continue to rise, understanding key drivers of hospital expenditure and utilization is crucial in policy development and service planning. This study aims to investigate micro drivers of hospital expenditure and length of stay (LOS) in an Academic Medical Centre.

METHODS

Data corresponding to 285,767 patients and 207,426 inpatient visits was extracted from electronic medical records of the National University of Hospital in Singapore between 2005 to 2013. Generalized linear models and generalized estimating equations were employed to build patient and inpatient visit models respectively. The patient models provide insight on the factors affecting overall expenditure and LOS, whereas the inpatient visit models provide insight on how expenditure and LOS accumulate longitudinally.

RESULTS

Although adjusted expenditure and LOS per inpatient visit were largely similar across socio-economic status (SES) groups, patients of lower SES groups accumulated greater expenditure and LOS over time due to more frequent visits. Admission to a ward class with greater government subsidies was associated with higher expenditure and LOS per inpatient visit. Inpatient death was also associated with higher expenditure per inpatient visit. Conditions that drove patient expenditure and LOS were largely similar, with mental illnesses affecting LOS to a larger extent. These observations on condition drivers largely held true at visit-level.

CONCLUSIONS

The findings highlight the importance of distinguishing the drivers of patient expenditure and inpatient utilization at the patient-level from those at the visit-level. This allows better understanding of the drivers of healthcare utilization and how utilization accumulates longitudinally, important for health policy and service planning.

Predictive ability of an expert-defined population segmentation framework for healthcare utilization and mortality - a retrospective cohort study

Background

Population segmentation of patients into parsimonious and relatively homogenous subgroups or segments based on healthcare requirements can aid healthcare resource planning and the development of targeted intervention programs. In this study, we evaluated the predictive ability of a previously described expert-defined segmentation approach on 3-year hospital utilization and mortality.

Methods

We segmented all adult patients who had a healthcare encounter with Singapore Health Services (SingHealth) in 2012 using the SingHealth Electronic Health Records (SingHealth EHRs). Patients were divided into non-overlapping segments defined as Mostly Healthy, Stable Chronic, Serious Acute, Complex Chronic without Frequent Hospital Admissions, Complex Chronic with Frequent Hospital Admissions, and End of Life, using a previously described expert-defined segmentation approach. Hospital admissions, emergency department attendances (ED), specialist outpatient clinic attendances (SOC) and mortality in different patient subgroups were analyzed from 2013 to 2015.

Results

819,993 patients were included in this study. Patients in Complex Chronic with Frequent Hospital Admissions segment were most likely to have a hospital admission (IRR 22.7; p < 0.001) and ED visit (IRR 14.5; p < 0.001) in the follow-on 3 years compared to other segments. Patients in the End of Life and Complex Chronic with Frequent Hospital Admissions segments had the lowest three-year survival rates of 58.2 and 62.6% respectively whereas other segments had survival rates of above 90% after 3 years.

Conclusion

In this study, we demonstrated the predictive ability of an expert-driven segmentation framework on longitudinal healthcare utilization and mortality.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4251-6) contains supplementary material, which is available to authorized users.

Retrospective evaluation of healthcare utilisation and mortality of two post-discharge care programmes in Singapore

OBJECTIVE

To evaluate the impact on healthcare utilisation frequencies and charges, and mortality of a programme for frequent hospital utilisers and a programme for patients requiring high acuity post-discharge care as part of an integrated healthcare model.

DESIGN

A retrospective quasi-experimental study without randomisation where patients who received post-discharge care interventions were matched 1:1 with unenrolled patients as controls.

SETTING

The National University Health System (NUHS) Regional Health System (RHS), which was one of six RHS in Singapore, implemented the NUHS RHS Integrated Interventions and Care Extension (NICE) programme for frequent hospital utilisers and the NUHS Transitional Care Programme (NUHS TCP) for high acuity post-discharge care. The programmes were supported by the Ministry of Health in Singapore, which is a city-state nation located in Southeast Asia with a 5.6 million population.

PARTICIPANTS

Linked healthcare administrative data, for the time period of January 2013 to December 2016, were extracted for patients enrolled in NICE (n=554) or NUHS TCP (n=270) from June 2014 to December 2015, and control patients.

INTERVENTIONS

For both programmes, teams conducted follow-up home visits and phone calls to monitor and manage patients' post-discharge.

PRIMARY OUTCOME MEASURES

One-year pre- and post-enrolment healthcare utilisation frequencies and charges of all-cause inpatient admissions, emergency admissions, emergency department attendances, specialist outpatient clinic (SOC) attendances, total inpatient length of stay and mortality rates were compared.

RESULTS

Patients in NICE had lower mortality rate, but higher all-cause inpatient admission, emergency admission and emergency department attendance charges. Patients in NUHS TCP did not have lower mortality rate, but had higher emergency admission and SOC attendance charges.

CONCLUSIONS

Both NICE and NUHS TCP had no improvements in 1 year healthcare utilisation across various setting and metrics. Singular interventions might not be as impactful in effecting utilisation without an overhauling transformation and restructuring of the hospital and healthcare system.

The Relationship Between Austrian Tax Auditors and Self-Employed Taxpayers: Evidence From a Qualitative Study

A constructive, highly professional relationship between tax authorities and taxpayers is essential for tax compliance. The aim of the present paper was to explore systematically the determinants of this relationship and related tax compliance behaviors based on the extended slippery slope framework. We used in-depth qualitative interviews with 33 self-employed taxpayers and 30 tax auditors. Interviewees described the relationship along the extended slippery slope framework concepts of power and trust. However, also novel sub-categories of power (e.g., setting deadlines) and trust (e.g., personal assistance) were mentioned. Furthermore, also little-studied categories of tax behavior emerged, such as accepting tax behavior, e.g., being available to the tax authorities, or stalling tax behavior, e.g., the intentional creation of complexity. The results comprehensively summarize the determinants of the tax relationship and tax compliance behaviors. Additionally, results highlight future research topics and provide insights for policy strategies.

Outpatient primary and tertiary healthcare utilisation among public rental housing residents in Singapore

Background

Globally, public housing is utilized to provide affordable housing for low-income households. Studies have shown an association between public housing and negative health outcomes. There is paucity of data pertaining to outpatient primary and tertiary healthcare resources utilization among public rental housing residents in Singapore.

Methods

A retrospective cohort study was performed, involving patients under the care of SingHealth Regional Health System (SHRS) in Year 2012. Healthcare utilization outcomes evaluated included number of outpatient primary and specialist care clinic visits, emergency department visits and hospitalization in Year 2011. Multivariate logistical analyses were used to examine the association between public rental housing and healthcare utilization.

Results

Of 147,105 patients, 10,400 (7.1%) patients stayed in public rental housing. There were more elderly (54.8 ± 18.0 vs 49.8 ± 17.1, p < 0.001) and male patients [5279 (50.8%) vs 56,892 (41.6%), p < 0.001] residing in public rental housing. Co-morbidities such as hypertension and hyperlipidemia were more prevalent among public rental housing patients. (p < 0.05).

After adjustment for covariates, public rental housing was not associated with frequent outpatient primary care clinic or specialist outpatient clinic attendances (p > 0.05). However, it was associated with increased number of emergency department visits (OR: 2.41, 95% CI: 2.12–2.74) and frequent hospitalization (OR: 1.56, 95% CI: 1.33–1.83).

Conclusion

Residing in public rental housing was not associated with increased utilization of outpatient healthcare resources despite patients’ higher disease burden and frequency of emergency department visits and hospitalizations. Further research is required to elucidate their health seeking behaviours.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4047-8) contains supplementary material, which is available to authorized users.

Implementation fidelity of a strategy to integrate service delivery: learnings from a transitional care program for individuals with complex needs in Singapore

BACKGROUND

To cope with rising demand for healthcare services in Singapore, Regional Health Systems (RHS) comprising of health and social care providers across care settings were set up to integrate service delivery. Tasked with providing care for the western region, in 2012, the National University Health System (NUHS) - RHS developed a transitional care program for elderly patients with complex healthcare needs who consumed high levels of hospital resources. Through needs assessment, development of personalized care plans and care coordination, the program aimed to: (i) improve quality of care, (ii) reduce hospital utilization, and (iii) reduce healthcare-related costs. In this study, recognizing the need for process evaluation in conjunction with outcome evaluation, we aim to evaluate the implementation fidelity of the NUHS-RHS transitional care program to explain the outcomes of the program and to inform further development of (similar) programs.

METHODS

Guided by the modified version of the Conceptual Framework for Implementation Fidelity (CFIF), adherence and moderating factors influencing implementation were assessed using non-participatory observations, reviews of medical records and program databases.

RESULTS

Most (10 out of 14) components of the program were found to be implemented with low or moderate level of fidelity. The frequency or duration of the program components were observed to vary based on the needs of users, availability of care coordinators (CC) and their confidence. Variation in fidelity was influenced predominantly by: (1) complexity of the program, (2) extent of facilitation through guiding protocols, (3) facilitation of program implementation through CCs' level of training and confidence, (4) evolving healthcare participant responsiveness, and (5) the context of suboptimal capability among community providers.

CONCLUSION

This is the first study to assess the context-specific implementation process of a transitional care program in the context of Southeast Asia. It provides important insights to facilitate further development and scaling up of transitional care programs within the NUHS-RHS and beyond. Our findings highlight the need for greater focus on engaging both healthcare providers and users, training CCs to equip them with the relevant skills required for their jobs, and building the capability of the community providers to implement such programs.

Defining a 'frequent admitter' phenotype among patients with repeat heart failure admissions

AIMS

We aimed to identify a 'frequent admitter' phenotype among patients admitted for acute decompensated heart failure (HF).

METHODS AND RESULTS

We studied 10 363 patients in a population-based prospective HF registry (2008-2012), segregated into clusters based on their 3-year HF readmission frequency trajectories. Using receiver-operating characteristic analysis, we identified the index year readmission frequency threshold that most accurately predicts HF admission frequency clusters. Two clusters of HF patients were identified: a high frequency cluster (90.9%, mean 2.35 ± 3.68 admissions/year) and a low frequency cluster (9.1%, mean 0.50 ± 0.81 admission/year). An index year threshold of two admissions was optimal for distinguishing between clusters. Based on this threshold, 'frequent admitters', defined as patients with ≥ 2 HF admissions in the index year (n = 2587), were of younger age (68 ± 13 vs 69 ± 13 years), more often male (58% vs. 54%), smokers (38.4% vs. 34.4%) and had lower left ventricular ejection fraction (37 ± 17 vs. 41 ± 17%) compared to 'non-frequent admitters' (< 2 HF admissions in the index year; n = 7776) (all P < 0.001). Despite similar rates of advanced care utilization, frequent admitters had longer length of stay (median 4.3 vs. 4.0 days), higher annual inpatient costs (€ 7015 vs. € 2967) and higher all-cause mortality at 3 years compared to the non-frequent admitters (adjusted odds ratio 2.33, 95% confidence interval 2.11-2.58; P < 0.001).

CONCLUSION

'Frequent admitters' have distinct clinical characteristics and worse outcomes compared to non-frequent admitters. This study may provide a means of anticipating the HF readmission burden and thereby aid in healthcare resource distribution relative to the HF admission frequency phenotype.

Effect of Asian BMI on risk of chronic disease progression: A Singapore perspective

Objectives:

High body mass index (BMI) has been associated with increased mortality, healthcare utilization and costs. This study investigates the one-year chronic disease progression and risk of developing diabetes with varying cardiovascular disease (CVD) risks based on the Asian BMI categories.

Methods:

Patients with BMI information from 2008 to 2014 were included in the analysis ( N=23,508). Patients were stratified into low, moderate, high and very high CVD risk categories. To study disease progression for patients with varying CVD risks, patients were further segmented into seven mutually exclusive disease states based on prevalence of chronic diseases and their complications. The categories were no known chronic disease, at-risk of developing chronic disease, one chronic condition, more than two chronic conditions, chronic conditions with complications, patients with cancer and death. Logistic regression was used to determine the association of CVD risk categories and risk of having diabetes.

Results:

High CVD risk patients had more chronic diseases in the following year as compared with low CVD risk patients. With reference to low CVD risk patients, patients in the moderate, high and very high risk categories had an odds ratio of 1.78 (95% confidence interval (CI): 1.60 to 1.98), 2.84 (95% CI: 2.51 to 3.21) and 3.99 (95% CI: 3.30 to 4.82) for having diabetes after adjusting for age, gender and ethnicity.

Conclusions:

Higher BMI is associated with greater chronic disease progression in the following year. Diet control and lifestyle modifications should be encouraged to prevent people from shifting to higher BMI strata as this can be detrimental in the long run.

Effect of multimorbidity on survival of patients diagnosed with heart failure: a retrospective cohort study in Singapore

OBJECTIVE

Multimorbidity in patients with heart failure (HF) results in poor prognosis and is an increasing public health concern. We aim to examine the effect of multimorbidity focusing on type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) on all-cause and cardiovascular disease (CVD)-specific mortality among patients diagnosed with HF in Singapore.

DESIGN

Retrospective cohort study.

SETTING

Primary and tertiary care in three (out of six) Regional Health Systems in Singapore.

PARTICIPANTS

Patients diagnosed with HF between 2003 and 2016 from three restructured hospitals and nine primary care polyclinics were included in this retrospective cohort study.

PRIMARY OUTCOMES

All-cause mortality and CVD-specific mortality.

RESULTS

A total of 34 460 patients diagnosed with HF from 2003 to 2016 were included in this study and were followed up until 31 December 2016. The median follow-up time was 2.1 years. Comorbidities prior to HF diagnosis were considered. Patients were categorised as (1) HF only, (2) T2DM+HF, (3) CKD+HF and (4) T2DM+CKD+HF. Cox regression model was used to determine the effect of multimorbidity on (1) all-cause mortality and (2) CVD-specific mortality. Adjusting for demographics, other comorbidities, baseline treatment and duration of T2DM prior to HF diagnosis, 'T2DM+CKD+HF' patients had a 56% higher risk of all-cause mortality (HR: 1.56, 95% CI 1.48 to 1.63) and a 44% higher risk of CVD-specific mortality (HR: 1.44, 95% CI 1.32 to 1.56) compared with patients diagnosed with HF only.

CONCLUSION

All-cause and CVD-specific mortality risks increased with increasing multimorbidity. This study highlights the need for a new model of care that focuses on holistic patient management rather than disease management alone to improve survival among patients with HF with multimorbidity.

Evaluation of a practical expert defined approach to patient population segmentation: a case study in Singapore

Background

Segmenting the population into groups that are relatively homogeneous in healthcare characteristics or needs is crucial to facilitate integrated care and resource planning. We aimed to evaluate the feasibility of segmenting the population into discrete, non-overlapping groups using a practical expert and literature driven approach. We hypothesized that this approach is feasible utilizing the electronic health record (EHR) in SingHealth.

Methods

In addition to well-defined segments of “Mostly healthy”, “Serious acute illness but curable” and “End of life” segments that are also present in the Ministry of Health Singapore framework, patients with chronic diseases were segmented into “Stable chronic disease”, “Complex chronic diseases without frequent hospital admissions”, and “Complex chronic diseases with frequent hospital admissions”. Using the electronic health record (EHR), we applied this framework to all adult patients who had a healthcare encounter in the Singapore Health Services Regional Health System in 2012. ICD-9, 10 and polyclinic codes were used to define chronic diseases with a comprehensive look-back period of 5 years. Outcomes (hospital admissions, emergency attendances, specialist outpatient clinic attendances and mortality) were analyzed for years 2012 to 2015.

Results

Eight hundred twenty five thousand eight hundred seventy four patients were included in this study with the majority being healthy without chronic diseases. The most common chronic disease was hypertension. Patients with “complex chronic disease” with frequent hospital admissions segment represented 0.6% of the eligible population, but accounted for the highest hospital admissions (4.33 ± 2.12 admissions; p < 0.001) and emergency attendances (ED) (3.21 ± 3.16 ED visits; p < 0.001) per patient, and a high mortality rate (16%). Patients with metastatic disease accounted for the highest specialist outpatient clinic attendances (27.48 ± 23.68 visits; p < 0.001) per patient despite their relatively shorter course of illness and high one-year mortality rate (33%).

Conclusion

This practical segmentation framework can potentially distinguish among groups of patients, and highlighted the high disease burden of patients with chronic diseases. Further research to validate this approach of population segmentation is needed.

Electronic supplementary material

The online version of this article (doi: 10.1186/s12913-017-2736-8) contains supplementary material, which is available to authorized users.

Performance of the LACE index to identify elderly patients at high risk for hospital readmission in Singapore

Unplanned readmissions may be avoided by accurate risk prediction and appropriate resources could be allocated to high risk patients. The Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past six months (LACE) index was developed to predict hospital readmissions in Canada. In this study, we assessed the performance of the LACE index in a Singaporean cohort by identifying elderly patients at high risk of 30-day readmissions. We further investigated the use of additional risk factors in improving readmission prediction performance.Data were extracted from the hospital's electronic health records (EHR) for all elderly patients ≥ 65 years, with alive-discharge episodes from Singapore General Hospital in 2014. In addition to LACE, we also collected patients' data during the index admission, including demographics, medical history, laboratory results, and previous medical utilization.Among the 17,006 patients analyzed, 2051 or 12.1% of them were observed 30-day readmissions. The final predictive model was better than the LACE index in terms of discriminative ability; c-statistic of LACE index and final logistic regression model was 0.595 and 0.628, respectively.The LACE index had poor discriminative ability in identifying elderly patients at high risk of 30-day readmission, even if it was augmented with additional risk factors. Further studies should be conducted to discover additional factors that may enable more accurate and timely identification of patients at elevated risk of readmissions, so that necessary preventive actions can be taken.

FAM-FACE-SG: a score for risk stratification of frequent hospital admitters

Background

An accurate risk stratification tool is critical in identifying patients who are at high risk of frequent hospital readmissions. While 30-day hospital readmissions have been widely studied, there is increasing interest in identifying potential high-cost users or frequent hospital admitters. In this study, we aimed to derive and validate a risk stratification tool to predict frequent hospital admitters.

Methods

We conducted a retrospective cohort study using the readily available clinical and administrative data from the electronic health records of a tertiary hospital in Singapore. The primary outcome was chosen as three or more inpatient readmissions within 12 months of index discharge. We used univariable and multivariable logistic regression models to build a frequent hospital admission risk score (FAM-FACE-SG) by incorporating demographics, indicators of socioeconomic status, prior healthcare utilization, markers of acute illness burden and markers of chronic illness burden. We further validated the risk score on a separate dataset and compared its performance with the LACE index using the receiver operating characteristic analysis.

Results

Our study included 25,244 patients, with 70% randomly selected patients for risk score derivation and the remaining 30% for validation. Overall, 4,322 patients (17.1%) met the outcome. The final FAM-FACE-SG score consisted of nine components: Furosemide (Intravenous 40 mg and above during index admission); Admissions in past one year; Medifund (Required financial assistance); Frequent emergency department (ED) use (≥3 ED visits in 6 month before index admission); Anti-depressants in past one year; Charlson comorbidity index; End Stage Renal Failure on Dialysis; Subsidized ward stay; and Geriatric patient or not. In the experiments, the FAM-FACE-SG score had good discriminative ability with an area under the curve (AUC) of 0.839 (95% confidence interval [CI]: 0.825–0.853) for risk prediction of frequent hospital admission. In comparison, the LACE index only achieved an AUC of 0.761 (0.745–0.777).

Conclusions

The FAM-FACE-SG score shows strong potential for implementation to provide near real-time prediction of frequent admissions. It may serve as the first step to identify high risk patients to receive resource intensive interventions.

Predicting frequent hospital admission risk in Singapore: a retrospective cohort study to investigate the impact of comorbidities, acute illness burden and social determinants of health

OBJECTIVES

To evaluate the impact of comorbidities, acute illness burden and social determinants of health on predicting the risk of frequent hospital admissions.

DESIGN

Multivariable logistic regression was used to associate the predictive variables extracted from electronic health records and frequent hospital admission risk. The model's performance of our predictive model was evaluated using a 10-fold cross-validation.

SETTING

A single tertiary hospital in Singapore.

PARTICIPANTS

All adult patients admitted to the hospital between 1 January 2013 and 31 May 2014 (n=25 244).

MAIN OUTCOME MEASURE

Frequent hospital admissions, defined as 3 or more inpatient admissions within 12 months of discharge. Area under the receiver operating characteristic curve (AUC) of the predictive model, and the sensitivity, specificity and positive predictive values for various cut-offs.

RESULTS

4322 patients (17.1%) met the primary outcome. 11 variables were observed as significant predictors and included in the final regression model. The strongest independent predictor was treatment with antidepressants in the past 1 year (adjusted OR 2.51, 95% CI 2.26 to 2.78). Other notable predictors include requiring dialysis and treatment with intravenous furosemide during the index admission. The predictive model achieved an AUC of 0.84 (95% CI 0.83 to 0.85) for predicting frequent hospital admission risk, with a sensitivity of 73.9% (95% CI 72.6% to 75.2%), specificity of 79.1% (78.5% to 79.6%) and positive predictive value of 42.2% (95% CI 41.1% to 43.3%) at the cut-off of 0.235.

CONCLUSIONS

We have identified several predictors for assessing the risk of frequent hospital admissions that achieved high discriminative model performance. Further research is necessary using an external validation cohort.

Diabetes-related lower extremity amputations in Singapore

Background:

Lower extremity amputation (LEA) is defined as the complete loss in the transverse anatomical plane of any part of the lower limb. The objective of this study is to look at the trend and mortality trend of LEA performed in diabetes patients from 2008 to 2013.

Methods:

All patients age 15 and above with diabetes mellitus who had undergone LEAs (both major and minor) in Tan Tock Seng Hospital, Singapore from 1 January 2008 to 31 December 2013 were included. The outcomes of interest were deaths from all causes within 30 days and within 1 year.

Results:

Major LEA rate has increased from 11.0 per 100,000 population in 2008 to 13.3 per 100,000 population in 2013. The 30-day mortality rate ranges from 6.0% to 11.1% and the 1-year mortality rate ranges from 24.3% to 30.6%. Minor LEA rate has increased from 10.8 per 100,000 population in 2008 to 13.9 per 100,000 population in 2013. The 30-day mortality rate ranges from 1.5% to 3.7% and the 1-year mortality rate ranges from 9.7% to 18.3%.

Conclusions:

The trends in major and minor LEA are increasing. The 30-day and 1-year mortality for both major and minor LEA are comparable to figures reported worldwide. There is a need to re-look at preventive strategies to reduce LEA in diabetes patients in Singapore.

Frequent hospital admissions in Singapore: clinical risk factors and impact of socioeconomic status

INTRODUCTION

Frequent admitters to hospitals are high-cost patients who strain finite healthcare resources. However, the exact risk factors for frequent admissions, which can be used to guide risk stratification and design effective interventions locally, remain unknown. Our study aimed to identify the clinical and sociodemographic risk factors associated with frequent hospital admissions in Singapore.

METHODS

An observational study was conducted using retrospective 2014 data from the administrative database at Singapore General Hospital, Singapore. Variables were identified a priori and included patient demographics, comorbidities, prior healthcare utilisation, and clinical and laboratory variables during the index admission. Multivariate logistic regression analysis was used to identify independent risk factors for frequent admissions.

RESULTS

A total of 16,306 unique patients were analysed and 1,640 (10.1%) patients were classified as frequent admitters. On multivariate logistic regression, 16 variables were independently associated with frequent hospital admissions, including age, cerebrovascular disease, history of malignancy, haemoglobin, serum creatinine, serum albumin, and number of specialist outpatient clinic visits, emergency department visits, admissions preceding index admission and medications dispensed at discharge. Patients staying in public rental housing had a 30% higher risk of being a frequent admitter after adjusting for demographics and clinical conditions.

CONCLUSION

Our study, the first in our knowledge to examine the clinical risk factors for frequent admissions in Singapore, validated the use of public rental housing as a sensitive indicator of area-level socioeconomic status in Singapore. These risk factors can be used to identify high-risk patients in the hospital so that they can receive interventions that reduce readmission risk.

Housing as a Social Determinant of Health in Singapore and Its Association with Readmission Risk and Increased Utilization of Hospital Services

BACKGROUND

Residence in public rental housing is an area-level measure of socioeconomic status, but its impact as a social determinant of health in Singapore has not been studied. We therefore aimed to examine the association of public rental housing with readmission risk and increased utilization of hospital services in Singapore.

METHODS

We conducted a retrospective cohort study using retrospective 2014 data from Singapore General Hospital's electronic health records. Variables known to affect readmission risk and health-care utilization were identified a priori and include patient demographics, comorbidities, health-care utilization in the preceding 1 year and clinical variables from the index admission in 2014. Multivariate logistic regression was used to evaluate public rental housing as an independent risk factor for admission risk, emergency department (ED), and specialist outpatient clinic attendances.

RESULTS

A total of 14,457 unique patients were analyzed, and 2,163 patients (15.0%) were rental housing residents. Rental housing patients were significantly more likely to be male; required financial assistance; have chronic obstructive pulmonary disease; usage of anti-depressant and anti-psychotic medications; longer length of hospital stay during the index admission; and higher Charlson Comorbidity Index scores. After adjusting for demographics and clinical variables, staying in public rental housing remained an independent risk factor for readmission within 15 and 30 days, frequent hospital admissions and ED attendances in Singapore.

CONCLUSION

Our study showed an association between public rental housing with readmission risk and increased utilization of hospital services in Singapore. A deeper understanding of the residents' social circumstances and health seeking behavior would be insightful.

Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore

The LACE index (length of stay, acuity of admission, Charlson comorbidity index, CCI, and number of emergency department visits in preceding 6 months) derived in Canada is simple and may have clinical utility in Singapore to predict readmission risk. We compared the performance of the LACE index with a derived model in identifying 30-day readmissions from a population of general medicine patients in Singapore. Additional variables include patient demographics, comorbidities, clinical and laboratory variables during the index admission, and prior healthcare utilization in the preceding year. 5,862 patients were analysed and 572 patients (9.8%) were readmitted in the 30 days following discharge. Age, CCI, count of surgical procedures during index admission, white cell count, serum albumin, and number of emergency department visits in previous 6 months were significantly associated with 30-day readmission risk. The final logistic regression model had fair discriminative ability c-statistic of 0.650 while the LACE index achieved c-statistic of 0.628 in predicting 30-day readmissions. Our derived model has the advantage of being available early in the admission to identify patients at high risk of readmission for interventions. Additional factors predicting readmission risk and machine learning techniques should be considered to improve model performance.