Implementation of social prescribing: lessons learnt from contextualising an intervention in a community hospital in Singapore.

The need to develop holistic public health approaches that go beyond treating the biological causes of ill health, to addressing the social determinants of health, have been highlighted in the global health agenda. Social prescribing, where care professionals link individuals to community resources that tackle social needs have gained increasing traction worldwide. In Singapore, SingHealth Community Hospitals introduced social prescribing in July 2019 to manage the complex health and social needs of the aging populace. Faced with the paucity of evidence on the effectiveness of social prescribing and its implementation, implementers had to contextualise the theory of social prescribing to patients' needs and setting of practice. Using an iterative approach, the implementation team constantly reviewed and adapted practices, work processes and outcome measurement tools based on data and stakeholder feedback to address implementation challenges. As social prescribing continues to scale in Singapore and take root in the Western Pacific region, agile implementation and continued evaluation of programmes to build an evidence pool will help to guide best practices. The aim of this paper is to review the implementation of a social prescribing programme from the exploratory phase to full implementation, and draw lessons learned in the process.

Lessons learned from the hospital to home community care program in Singapore and the supporting AI multiple readmissions prediction model.

In a prior practice and policy article published in Healthcare Science, we introduced the deployed application of an artificial intelligence (AI) model to predict longer-term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home (H2H) program that has been operating since 2017. In this follow on practice and policy article, we further elaborate on Singapore's H2H program and care model, and its supporting AI model for multiple readmission prediction, in the following ways: (1) by providing updates on the AI and supporting information systems, (2) by reporting on customer engagement and related service delivery outcomes including staff-related time savings and patient benefits in terms of bed days saved, (3) by sharing lessons learned with respect to (i) analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants, (ii) balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables, and (iii) the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems, (4) by highlighting how this H2H effort supported broader Covid-19 response efforts across Singapore's public healthcare system, and finally (5) by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards. For the convenience of the reader, some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.

Global developments in social prescribing.

Social prescribing is an approach that aims to improve health and well-being. It connects individuals to non-clinical services and supports that address social needs, such as those related to loneliness, housing instability and mental health. At the person level, social prescribing can give individuals the knowledge, skills, motivation and confidence to manage their own health and well-being. At the society level, it can facilitate greater collaboration across health, social, and community sectors to promote integrated care and move beyond the traditional biomedical model of health. While the term social prescribing was first popularised in the UK, this practice has become more prevalent and widely publicised internationally over the last decade. This paper aims to illuminate the ways social prescribing has been conceptualised and implemented across 17 countries in Europe, Asia, Australia and North America. We draw from the 'Beyond the Building Blocks' framework to describe the essential inputs for adopting social prescribing into policy and practice, related to service delivery; social determinants and household production of health; workforce; leadership and governance; financing, community organisations and societal partnerships; health technology; and information, learning and accountability. Cross-cutting lessons can inform country and regional efforts to tailor social prescribing models to best support local needs.

A qualitative study of challenges and enablers faced by private general practitioners providing primary care to patients with complex needs in Singapore.

BACKGROUND: Singapore faces an ageing population with increasingly complex healthcare needs, a problem which could be addressed by high quality primary care. Many patients with complex needs are not managed by private general practitioners (GPs) who form the majority of the primary care workforce. Currently, there is paucity of literature describing the needs of these private GPs in providing such care. AIM: Understand the challenges, enablers and possible solutions from the perspective of private GPs in providing primary care of patients with complex needs. METHOD: We conducted a qualitative study using an inductive approach. Private GPs were interviewed using a semi-structured question guide with convenience sampling until thematic saturation was reached. These 12 interviewees were part of a network of clinics that provide primary care for complex patients who were recently discharged from a community hospital providing post-acute care. Data was transcribed prior to a process of familiarisation, coded and analysed using thematic analysis by three independent investigators. RESULTS: Three themes emerged in the analysis. From a micro-organizational standpoint, private GPs and patients with complex needs must be willing to accept each other to have a therapeutic encounter (e.g., patients' multidimensional needs, GP clinic set-up is simple yet busy). Next, from a meso-organizational view, trust and good communication channels between the referring doctors and private GPs must exist for effective collaboration in managing complex care. Lastly, macro-organizationally, external stakeholders (e.g., policy-makers) should fund care models, which are financially viable to both patients, and private GPs (e.g., via adequate subsidies and renumeration respectively) as such complex care require many resources. CONCLUSION: Multiple factors exist which influence the ability of private GPs in Singapore to care for patients with complex needs. Addressing these factors may reduce the over dependence on high-cost hospitals for care delivery in similar healthcare systems.

Do healthcare needs-based population segments predict outcomes among the elderly? Findings from a prospective cohort study in an urbanized low-income community.

BACKGROUND: A rapidly ageing population with increasing prevalence of chronic disease presents policymakers the urgent task of tailoring healthcare services to optimally meet changing needs. While healthcare needs-based segmentation is a promising approach to efficiently assessing and responding to healthcare needs at the population level, it is not clear how available schemes perform in the context of community-based surveys administered by non-medically trained personnel. The aim of this prospective cohort, community setting study is to evaluate 4 segmentation schemes in terms of practicality and predictive validity for future health outcomes and service utilization. METHODS: A cohort was identified from a cross-sectional health and social characteristics survey of Singapore public rental housing residents aged 60 years and above. Baseline survey data was used to assign individuals into segments as defined by 4 predefined population segmentation schemes developed in Singapore, Delaware, Lombardy and North-West London. From electronic data records, mortality, hospital admissions, emergency department visits, and specialist outpatient clinic visits were assessed for 180 days after baseline segment assignment and compared to segment membership for each segmentation scheme. RESULTS: Of 1324 residents contacted, 928 agreed to participate in the survey (70% response). All subjects could be assigned an exclusive segment for each segmentation scheme. Individuals in more severe segments tended to have lower quality of life as assessed by the EQ-5D Index for health utility. All population segmentation schemes were observed to exhibit an ability to differentiate different levels of mortality and healthcare utilization. CONCLUSIONS: It is practical to assign individuals to healthcare needs-based population segments through community surveys by non-medically trained personnel. The resulting segments for all 4 schemes evaluated in this way have an ability to predict health outcomes and utilization over the medium term (180 days), with significant overlap for some segments. Healthcare needs-based segmentation schemes which are designed to guide action hold particular promise for promoting efficient allocation of services to meet the needs of salient population groups. Further evaluation is needed to determine if these schemes also predict responsiveness to interventions to meet needs implied by segment membership.

Health-seeking behaviour of the elderly living alone in an urbanised low-income community in Singapore.

INTRODUCTION: Elderly persons who live alone are more likely to be socially isolated and at increased risk of adverse health outcomes, unnecessary hospital re-admissions and premature mortality. We aimed to understand the health-seeking behaviour of elderly persons living alone in public rental housing in Singapore. METHODS: In-depth interviews were conducted using a semi-structured question guide. Participants were selected using a purposive sampling approach. Interviews were conducted until theme saturation was reached. Qualitative data collected was analysed using manual thematic coding methods. RESULTS: Data analysis revealed five major themes: accessibility of healthcare services and financial assistance schemes; perceived high cost of care; self-management; self-reliance; and mismatch between perceived needs and services. CONCLUSION: Elderly persons living in one-room rental flats are a resilient and resourceful group that values self-reliance and independence. Most of the elderly who live alone develop self-coping mechanisms to meet their healthcare needs rather than seek formal medical consultation. The insightful findings from this study should be taken into consideration when models of healthcare delivery are being reviewed and designed so as to support the disadvantaged elderly living alone.

Factors associated with emergency room visits and hospitalisation amongst low-income public rental flat dwellers in Singapore.

BACKGROUND: In Singapore, a densely urbanised Asian society, more than 80% of the population stays in public housing estates and the majority (90%) own their own homes. For the needy who cannot afford home ownership, public rental flats are available. Staying in a public rental flat is associated with higher hospital readmission rates and poorer access to health services. We sought to examine sociodemographic factors associated with hospital admissions and emergency room visits amongst public rental flat residents. METHODS: We surveyed all residents aged ≥60 years in a public rental housing precinct in central Singapore in 2016. Residents self-reported their number of emergency room visits, as well as hospitalisations, in the past 6 months. We obtained information on residents' sociodemographic characteristics, medical, functional and social status via standardised questionnaires. We used chi-square to identify associations between emergency room visits/hospitalisations and sociodemographic characteristics, on univariate analysis; and logistic regression for multivariate analysis. RESULTS: Of 1324 contactable residents, 928 participated in the survey, with a response rate of 70.1%. A total of 928 residents participated in our study, of which 59.5% were male (553/928) and 51.2% (476/928) were ≥ 70 years old. Around 9% (83/928) of residents had visited the emergency room in the last 6 months; while 10.5% (100/928) had been admitted to hospital in the past 6 months. On multivariable analysis, being religious (aOR = 0.43, 95%CI = 0.24-0.76) and having seen a primary care practitioner in the last 6 months (aOR = 0.46, 95%CI = 0.27-0.80) were independently associated with lower odds of emergency room visits, whereas loneliness (aOR = 1.96, 95%CI = 1.13-3.43), poorer coping (aOR = 1.72, 95%CI = 1.01-3.03) and better adherence (aOR = 2.23, 95%CI = 1.29-3.83) were independently associated with higher odds of emergency room visits. For hospitalisations, similarly poorer coping (aOR = 1.85, 95%CI = 1.12-3.07), better adherence (aOR = 1.69, 95%CI = 1.04-2.75) and poorer functional status (aOR = 1.85, 95%CI = 1.15-2.98) were all independently associated with higher odds of hospitalisations, whereas those who were religious (aOR = 0.62, 95%CI = 0.37-0.99) and those who were currently employed (aOR = 0.46, 95%CI = 0.37-0.99) had lower odds of being hospitalised. CONCLUSION: In this public rental flat population, functional status, coping and adherence, and having a religion were independently associated with emergency room visits and hospitalisation. Residents who had seen a primary care practitioner in the last 6 months had lower odds of visiting the emergency room.

Anti-hypertensive medications and injurious falls in an older population of low socioeconomic status: a nested case-control study.

BACKGROUND: This study aimed to determine whether the number of anti-hypertensive medication classes or any change in anti-hypertensive medication were associated with injurious fall among the community-dwelling older population of low socioeconomic status. METHODS: Using data from electronic medical records, we performed a nested case-control study among older Singapore residents (≥60) of low socioeconomic status (N = 210). Controls (n = 162) were matched to each case (n = 48) by age and gender. Variables with p < 0.10 in univariate analysis were included in multivariate analysis. We used conditional logistic regression to assess the associations of the number of anti-hypertensive medication classes and change in anti-hypertensive medication with injurious falls. We also performed stepwise regressions as sensitivity analyses. p < 0.05 was considered statistically significant. RESULTS: The mean (±SD) age of participants was 78.1 (± 8.33) years; 127 (60.4%) were female, 189 (90.0%) were Chinese. Those on ≥2 anti-hypertensive medication classes had an increased risk of experiencing an injurious fall compared to those not on any anti-hypertensive medication (OR = 5.45; CI:1.49-19.93; p = 0.01). Among those who were taking anti-hypertensive medication, those who had a change in the medication 180-day prior to injurious fall had a significantly increased risk of experiencing an injurious fall compared to those that did not report any change in anti-hypertensive medication (OR = 3.88; CI:1.23-12.19; p = 0.02). Sensitivity analyses generated consistent findings. CONCLUSION: Both ≥2 anti-hypertensive medication classes and change in anti-hypertensive medication were associated with an increased risk of experiencing an injurious fall among the older population of low socioeconomic status. Our findings could guide prescribers to exercise caution in the initiation of anti-hypertensive medications or in making medication changes, especially among the older population of low socioeconomic status.

A systematic review of health status, health seeking behaviour and healthcare utilisation of low socioeconomic status populations in urban Singapore.

INTRODUCTION: It is well-established that low socioeconomic status (SES) influences one's health status, morbidity and mortality. Housing type has been used as an indicator of SES and social determinant of health in some studies. In Singapore, home ownership is among the highest in the world. Citizens who have no other housing options are offered heavily subsidised rental housings. Residents staying in such rental housings are characterised by low socioeconomic status. Our aim is to review studies on the association between staying in public rental housing in Singapore and health status. METHODS: A PubMed and Scopus search was conducted in January 2017 to identify suitable articles published from 1 January 2000 to 31 January 2017. Only studies that were done on Singapore public rental housing communities were included for review. A total of 14 articles including 4 prospective studies, 8 cross-sectional studies and 2 retrospective cohort studies were obtained for the review. Topics addressed by these studies included: (1) Health status; (2) Health seeking behaviour; (3) Healthcare utilisation. RESULTS: Staying in public rental housing was found to be associated with poorer health status and outcomes. They had lower participation in health screening, preferred alternative medicine practitioners to western-trained doctors for primary care, and had increased hospital utilisation. Several studies performed qualitative interviews to explore the causes of disparity and concern about cost was one of the common cited reason. CONCLUSION: Staying in public rental housing appears to be a risk marker of poorer health and this may have important public health implications. Understanding the causes of disparity will require more qualitative studies which in turn will guide interventions and the evaluation of their effectiveness in improving health outcome of this sub-population of patients.

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.

Transitional Home Care Program Utilizing the Integrated Practice Unit Concept (THC-IPU): Effectiveness in Improving Acute Hospital Utilization.

BACKGROUND: Organizing care into integrated practice units (IPUs) around conditions and patient segments has been proposed to increase value. We organized transitional care into an IPU (THC-IPU) for a patient segment of functionally dependent patients with limited community ambulation. METHODS: 1,166 eligible patients were approached for enrolment into THC-IPU. THC-IPU patients received a comprehensive assessment within two weeks of discharge; medication reconciliation; education using standardized action plans and a dedicated nurse case manager for up to 90 days after discharge. Patients who rejected enrolment into THC-IPU received usual post-discharge care planned by their attending hospital physician, and formed the control group. The primary outcome was the proportion of patients with at least one unscheduled readmission within 30 days after discharge. RESULTS: We found a statistically significant reduction in 30-day readmissions and emergency department visits in patients on THC-IPU care compared to usual care, even after adjusting for confounders. CONCLUSION: Delivering transitional care to patients with functional dependence in the form of home visits and organized into an IPU reduced acute hospital utilization in this patient segment. Extending the program into the pre-hospital discharge phase to include discharge planning can have incremental effectiveness in reducing avoidable hospital readmissions.

Evaluating a novel Integrated Community of Care (ICoC) for patients from an urbanised low-income community in Singapore using the participatory action research (PAR) methodology: a study protocol.

INTRODUCTION: Poorer health outcomes and disproportionate healthcare use in socioeconomically disadvantaged patients is well established. However, there is sparse literature on effective integrated care interventions that specifically target these high-risk individuals. The Integrated Community of Care (ICoC) is a novel care model that integrates hospital-based transitional care with health and social care in the community for high-risk individuals living in socially deprived communities. This study aims to evaluate the effectiveness of the ICoC in reducing acute hospital use and investigate the implementation process and its effects on clinical outcomes using a mixed-methods participatory action research (PAR) approach. METHODS AND ANALYSIS: This is a single-centre prospective, controlled, observational study performed in the SingHealth Regional Health System. A total of 250 eligible patients from an urbanised low-income community in Singapore will be enrolled during their index hospitalisation. Our PAR model combines two research components: quantitative and qualitative, at different phases of the intervention. Outcomes of acute hospital use and health-related quality of life are compared with controls, at 30 days and 1 year. The qualitative study aims at developing a more context-specific social ecological model of health behaviour. This model will identify how influences within one's social environment: individual, interpersonal, organisational, community and policy factors affect people's experiences and behaviours during care transitions from hospital to home. Knowledge on the operational aspects of ICoC will enrich our evidence-based strategies to understand the impact of the ICoC. The blending of qualitative and quantitative mixed methods recognises the dynamic implementation processes as well as the complex and evolving needs of community stakeholders in shaping outcomes. ETHICS AND DISSEMINATION: Ethics approval was granted by the SingHealth Centralised Institutional Review Board (CIRB 2015/2277). The findings from this study will be disseminated by publications in peer-reviewed journals, scientific meetings and presentations to government policy-makers. TRIAL REGISTRATION NUMBER: NCT02678273.

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.

Applying the Integrated Practice Unit Concept to a Modified Virtual Ward Model of Care for Patients at Highest Risk of Readmission: A Randomized Controlled Trial.

BACKGROUND: Emerging evidence from the virtual ward care model showed that multidisciplinary case management are inadequate to reduce readmissions or death for high risk patients. There is consensus that interventions should encompass both pre-hospital discharge and post-discharge transitional care to be effective. Integrated practice units (IPU) had been proposed as an approach of restructuring the organization and work processes of multidisciplinary teams to achieve value in healthcare. Our primary objective is to evaluate if the novel application of the IPU concept to organize a modified virtual ward model incorporating pre-hospital discharge transitional care can reduce readmissions of patients at highest risk for readmission. METHODS: We conducted an open label, assessor blinded randomized controlled trial on patients with one or more unscheduled readmissions in the prior 90 days and LACE score ≥ 10. 840 patients were randomized in 1:1 ratio and blocks of 6 to the intervention program (n = 420) or control (n = 420). Allocation concealment was effected via an off-site telephone service maintained by a hospital administrator. Intervention patients received discharge planning, medication reconciliation, coaching on self-management of chronic diseases using standardized action plans and an individualized care plan complete with written discharge instructions, appointments schedule, medication changes and the contact information of the outpatient VW nurse before discharge. At discharge, care is handed over to the outpatient VW team. Patients were closely monitored in the VW for three months that included a telephone review within 72 hours of discharge, home assessment, regular telephone reviews to identify early complications and early review clinics for patients who destabilize. The VW meet daily to discuss new patients and review care plans for patients. Control patients received standard hospital care that included a standardized patient copy of the hospital discharge summary listing their medical diagnoses and medications; and follow up is arranged with a primary care provider or specialist as considered necessary. The primary outcome was the unplanned readmission rate to any hospital within 30 days of discharge. Secondary outcomes included the unplanned readmission rate, emergency department (ED) attendance rate to any hospital and the probability without readmission or death up to 180 days of discharge. Length of stay and mortality rate at 90-day were compared between the two groups. Outcome data were objectively retrieved from the hospital and National Electronic Health Records by a blinded outcome assessor. FINDINGS: All patients' outcomes were included in an intention-to-treat analysis. The characteristics of both study groups were similar. Patients in the intervention group had a significant reduction in the number of 30-day readmissions, IRR 0.67 (95% CI, 0.52 to 0.86, p = 0.001) and the number of 30-day emergency department attendances, IRR 0.60 (95% CI, 0.46 to 0.79, p<0.001) compared to those receiving standard hospital care. The effectiveness was sustained at 90 and 180 days. The intervention group utilized 1164 fewer hospital bed days at 90-day post discharge. No adverse events were reported. CONCLUSION: Applying the integrated practice unit concept to the virtual ward program resulted in reduced readmissions in patients who are at highest risk of readmission.

An approach to managing patients with complex Co-morbidites for family physicians: the SBAR4 model

Providing comprehensive and continuing care to patients is the forte of family physicians. The burden of providing such care to patients with complicated co-morbidities is increasing rapidly in ageing populations. Primary care systems around the world are ill equipped to face such a challenge. Family physicians need to hone their skills in this area of care. In this article, we introduce the SBAR4 model and propose it as a framework for managing patients with complex co-morbidities. This model is easy to learn and use by family physicians as it is based on the familiar SBAR model of clinical communication and Pendleton’s 7 Tasks of consultation. We believe that the SBAR4 will assist the clinician to assess patients with complex co-morbidities and map out a comprehensive care plan that can be easily understood by a multidisciplinary team caring for such patients.

Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity.

BACKGROUND: To reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity, and compare the model with the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past 6 months), an established risk stratification tool. METHOD: This was a retrospective cohort study of all patients ≥ 21 years of age, who were admitted to a tertiary hospital in Singapore from January 1, 2013 through May 31, 2015. Data were extracted from the hospital's electronic health records. The outcome was defined as unplanned readmissions within 30 days of discharge from the index hospitalization. Candidate predictive variables were broadly grouped into five categories: Patient demographics, social determinants of health, past healthcare utilization, medical comorbidities, and markers of hospitalization severity. Multivariable logistic regression was used to predict the outcome, and receiver operating characteristic analysis was performed to compare our model with the LACE index. RESULTS: 74,102 cases were enrolled for analysis. Of these, 11,492 patient cases (15.5%) were readmitted within 30 days of discharge. A total of fifteen predictive variables were strongly associated with the risk of 30-day readmissions, including number of emergency department visits in the past 6 months, Charlson Comorbidity Index, markers of hospitalization severity such as 'requiring inpatient dialysis during index admission, and 'treatment with intravenous furosemide 40 milligrams or more' during index admission. Our predictive model outperformed the LACE index by achieving larger area under the curve values: 0.78 (95% confidence interval [CI]: 0.77-0.79) versus 0.70 (95% CI: 0.69-0.71). CONCLUSION: Several factors are important for the risk of 30-day readmissions, including proxy markers of hospitalization severity.

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.