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ObjectivesChallenges with manual methodologies to identify frailty, have led to enthusiasm for utilising large-scale administrative data, particularly standardised diagnostic codes. However, concerns have been raised regarding coding reliability and variability. We aimed to quantify variation in coding frailty syndromes within standardised diagnostic code fields of an international dataset.SettingPooled data from 37 hospitals in 10 countries from 2010 to 2014.ParticipantsPatients ≥75 years with admission of >24 hours (N=1 404 671 patient episodes).Primary and secondary outcome measuresFrailty syndrome groups were coded in all standardised diagnostic fields by creation of a binary flag if the relevant diagnosis was present in the 12 months leading to index admission. Volume and percentages of coded frailty syndrome groups by age, gender, year and country were tabulated, and trend analysis provided in line charts. Descriptive statistics including mean, range, and coefficient of variation (CV) were calculated. Relationship to in-hospital mortality, hospital readmission and length of stay were visualised as bar charts.ResultsThe top four contributors were UK, US, Norway and Australia, which accounted for 75.4% of the volume of admissions. There were 553 595 (39.4%) patient episodes with at least one frailty syndrome group coded. The two most frequently coded frailty syndrome groups were ‘Falls and Fractures’ (N=3 36 087; 23.9%) and ‘Delirium and Dementia’ (N=221 072; 15.7%), with the lowest CV. Trend analysis revealed some coding instability over the frailty syndrome groups from 2010 to 2014. The four countries with the lowest CV for coded frailty syndrome groups were Belgium, Australia, USA and UK. There was up to twofold, fourfold and twofold variation difference for outcomes of length of stay, 30-day readmission and inpatient mortality, respectively, across the countries.ConclusionsVariation in coding frequency for frailty syndromes in standardised diagnostic fields are quantified and described. Recommendations are made to account for this variation when producing risk prediction models.

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.

ObjectiveTo 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.DesignA retrospective quasi-experimental study without randomisation where patients who received post-discharge care interventions were matched 1:1 with unenrolled patients as controls.SettingThe 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.ParticipantsLinked 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.InterventionsFor both programmes, teams conducted follow-up home visits and phone calls to monitor and manage patients’ post-discharge.Primary outcome measuresOne-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.ResultsPatients 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.ConclusionsBoth 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.

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. Electronic supplementary material The online version of this article (10.1186/s12913-019-4248-1) contains supplementary material, which is available to authorized users.

ObjectiveWe 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 settingThis is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.ParticipantsPatients 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 measuresPHU 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.ResultsPHUs 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%).ConclusionsThe 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.

Increasing rates of Clostridium difficile infection (CDI) among those without traditional risk factors have been reported mainly in Europe and North America. Here we describe the epidemiology, clinical features and ribotypes of CDI at National University Hospital (NUH), a 1000-bed tertiary care hospital in Singapore, from December 2011 to May 2012. All laboratory-confirmed CDI cases ≥21 years old who gave informed consent were included. Clinical data were collected prospectively and participants underwent an interviewer-administered questionnaire. Cases were classified by healthcare facility exposure and severity according to the SHEA guidelines. Included cases were also subjected to PCR and were classified by ribotype. In total, 66 patients participated in the study, of which 33 (50.0%) were healthcare-facility-associated hospital onset (HCFA-HO). Of the 33 community-onset (CO) cases, 14 (42.4%) were HCFA-CO, 10 (30.3%) were indeterminate and 9 (27.3%) were community-associated (CA). Of the CA cases, a majority (90.9%) had prior exposure to a healthcare facility within the last 12 weeks. Clinical characteristics, exposures and outcomes were not different between HO-CDI and CO-CDI. Diagnosis was delayed in CO-CDI compared with HO-CDI (4 days vs. 1 day; P=0.014). There was no difference in distribution of ribotypes between CO-CDI and HO-CDI, with 053 being most prevalent in both groups. CO-CDI increasingly contributes to the burden of CDI in NUH. This may reflect a trend in other parts of Asia. Healthcare professionals should be aware of the possible role of outpatient healthcare environments to CDI risk and thus extend control measures to outpatient settings.

Background Early diagnosis of human immunodeficiency virus (HIV) allows for appropriately timed interventions with improved outcomes, but HIV screening among asymptomatic persons and the general population in Singapore remains low. In 2008, Singapore's Ministry of Health implemented HIV voluntary opt-out screening (VOS) for hospitalised adults. We evaluated the outcome of VOS and surveyed reasons for its low uptake in our institution. Methods We assessed the outcomes of the VOS programme from January 2010 to December 2013 at National University Hospital, a 1081-bed tertiary hospital in Singapore. We also examined reasons for opting-in and opting-out using an interviewer-administered structured questionnaire in a representative sample in January 2013. Results 107,523 patients fulfilled VOS criteria and were offered HIV screening, of which 5215 (4.9%) agreed to testing. 4850 (93.1%) of those who opted-in had an HIV test done. Three (0.06%) tested positive for HIV. 238 patients (14.2%) were surveyed regarding reasons for opting-in or out of VOS. 21 (8.8%) had opted-in. Patients who opted-in were likely to be younger, more educated and reported having more regular sexual partners. Type of housing, number of casual sexual partners, sexual orientation, intravenous drug use, condom use and previous sexually transmitted infection were not associated with deciding to opt-in/out. Patients' most common reasons for opting-out were: belief that they were at low risk (50.2%), belief that they were too old (26.8%), cost (6.9%) and aversion to venepuncture (6.5%). The most common reason for opting-in was desire to know their HIV status (47.6%). Conclusion The success of an HIV-VOS program is largely determined by test uptake. Our study showed that the majority of eligible VOS patients opted-out of HIV screening. Given the considerable cost and low yield of this programme, more needs to be done to better equip patients in self-risk assessment and opting in to testing.

Background Few studies have comprehensively described tropical respiratory disease surveillance in military populations. There is also a lack of studies comparing clinical characteristics of the non-influenza pathogens with influenza and amongst themselves. Methods From May 2009 through October 2012, 7733 consenting cases of febrile respiratory illness (FRI) (temperature [greater than or equal to]37.5degreesC with cough or sorethroat) and controls in the Singapore military had clinical data and nasal washes collected prospectively. Nasal washes underwent multiplex PCR, and the analysis was limited to viral mono-infections. Results 49% of cases tested positive for at least one virus, of whom 10% had multiple infections. 53% of the FRI cases fulfilled the definition of influenza-like illness (ILI), of whom 52% were positive for at least one virus. The most frequent etiologies for mono-infections among FRI cases were Influenza A(H1N1)pdm09 (13%), Influenza B (13%) and coxsackevirus (9%). The sensitivity, specificity, positive predictive value and negative predictive value of ILI for influenza among FRI cases were 72%, 48%, 40% and 69% respectively. On logistic regression, there were marked differences in the prevalence of different symptoms and signs between viruses with fever more prevalent amongst influenza and adenovirus infections than other viruses. Conclusion There are multiple viral etiologies for FRI and ILI with differing clinical symptoms in the Singapore military. Influenza and coxsackevirus were the most common etiology for FRI, while influenza and adenoviruses displayed the most febrile symptoms. Further studies should explore these differences and possible interventions.