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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. Continuous glucose monitoring (CGM) offers an opportunity for patients with diabetes to modify their lifestyle to better manage their condition and for clinicians to provide personalized healthcare and lifestyle advice. However, analytic tools are needed to standardize and analyze the rich data that emerge from CGM devices. This would allow glucotypes of patients to be identified to aid clinical decision-making. Methods. In this paper, we develop an analysis pipeline for CGM data and apply it to 148 diabetic patients with a total of 8632 days of follow up. The pipeline projects CGM data to a lower-dimensional space of features representing centrality, spread, size, and duration of glycemic excursions and the circadian cycle. We then use principal components analysis and k -means to cluster patients’ records into one of four glucotypes and analyze cluster membership using multinomial logistic regression. Results. Glucotypes differ in the degree of control, amount of time spent in range, and on the presence and timing of hyper- and hypoglycemia. Patients on the program had statistically significant improvements in their glucose levels. Conclusions. This pipeline provides a fast automatic function to label raw CGM data without manual input.

Background With increasing type 2 diabetes prevalence, there is a need for effective programs that support diabetes management and improve type 2 diabetes outcomes. Mobile health (mHealth) interventions have shown promising results. With advances in wearable sensors and improved integration, mHealth programs could become more accessible and personalized. Objective The study aimed to evaluate the feasibility, acceptability, and effectiveness of a personalized mHealth-anchored intervention program in improving glycemic control and enhancing care experience in diabetes management. The program was coincidentally implemented during the national-level lockdown for COVID-19 in Singapore, allowing for a timely study of the use of mHealth for chronic disease management. Methods Patients with type 2 diabetes or prediabetes were enrolled from the Singapore Armed Forces and offered a 3-month intervention program in addition to the usual care they received. The program was standardized to include (1) in-person initial consultation with a clinical dietitian; (2) in-person review with a diabetes specialist doctor; (3) 1 continuous glucose monitoring device; (4) access to the mobile app for dietary intake and physical activity tracking, and communication via messaging with the dietitian and doctor; and (5) context-sensitive digital health coaching over the mobile app. Medical support was rendered to the patients on an as-needed basis when they required advice on adjustment of medications. Measurements of weight, height, and glycated hemoglobin A1c (HbA1c) were conducted at 2 in-person visits at the start and end of the program. At the end of the program, patients were asked to complete a short acceptability feedback survey to understand the motivation for joining the program, their satisfaction, and suggestions for improvement. Results Over a 4-week recruitment period, 130 individuals were screened, the enrollment target of 30 patients was met, and 21 patients completed the program and were included in the final analyses; 9 patients were lost to follow-up (full data were not available for the final analyses). There were no differences in the baseline characteristics between patients who were included and excluded from the final analyses (age category: P=.23; gender: P=.21; ethnicity: P>.99; diabetes status category: P=.52, medication adjustment category: P=.65; HbA1c category: P=.69; BMI: P>.99). The 21 patients who completed the study rated a mean of 9.0 out of 10 on the Likert scale for both satisfaction questions. For the Yes-No question on benefit of the program, all of the patients selected “Yes.” Mean HbA1c decreased from 7.6% to 7.0% (P=.004). There were no severe hypoglycemia events (glucose level Conclusions The personalized mHealth program was feasible, acceptable, and produced significant reductions in HbA1c (P=.004) and body weight (P

BACKGROUND With increasing type 2 diabetes prevalence, there is a need for effective programs that support diabetes management and improve type 2 diabetes outcomes. Mobile health (mHealth) interventions have shown promising results. With advances in wearable sensors and improved integration, mHealth programs could become more accessible and personalized. OBJECTIVE The study aimed to evaluate the feasibility, acceptability, and effectiveness of a personalized mHealth-anchored intervention program in improving glycemic control and enhancing care experience in diabetes management. The program was coincidentally implemented during the national-level lockdown for COVID-19 in Singapore, allowing for a timely study of the use of mHealth for chronic disease management. METHODS Patients with type 2 diabetes or prediabetes were enrolled from the Singapore Armed Forces and offered a 3-month intervention program in addition to the usual care they received. The program was standardized to include (1) in-person initial consultation with a clinical dietitian; (2) in-person review with a diabetes specialist doctor; (3) 1 continuous glucose monitoring device; (4) access to the mobile app for dietary intake and physical activity tracking, and communication via messaging with the dietitian and doctor; and (5) context-sensitive digital health coaching over the mobile app. Medical support was rendered to the patients on an as-needed basis when they required advice on adjustment of medications. Measurements of weight, height, and glycated hemoglobin A1c (HbA1c) were conducted at 2 in-person visits at the start and end of the program. At the end of the program, patients were asked to complete a short acceptability feedback survey to understand the motivation for joining the program, their satisfaction, and suggestions for improvement. RESULTS Over a 4-week recruitment period, 130 individuals were screened, the enrollment target of 30 patients was met, and 21 patients completed the program and were included in the final analyses; 9 patients were lost to follow-up (full data were not available for the final analyses). There were no differences in the baseline characteristics between patients who were included and excluded from the final analyses (age category: P=.23; gender: P=.21; ethnicity: P>.99; diabetes status category: P=.52, medication adjustment category: P=.65; HbA1c category: P=.69; BMI: P>.99). The 21 patients who completed the study rated a mean of 9.0 out of 10 on the Likert scale for both satisfaction questions. For the Yes-No question on benefit of the program, all of the patients selected “Yes.” Mean HbA1c decreased from 7.6% to 7.0% (P=.004). There were no severe hypoglycemia events (glucose level P2 to 26.7 kg/m2 (P CONCLUSIONS The personalized mHealth program was feasible, acceptable, and produced significant reductions in HbA1c (P=.004) and body weight (P CLINICALTRIAL

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: Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress. Objective: The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas. Methods: On the basis of a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multilevel views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), whereas socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated. Results: Visit-level (N=7,778,761) and patient-level records (n=549,109) were generated. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. Diagnosis codes that underwent modification (truncation or zero addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other and positively correlated with health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger. Conclusions: The framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.

Introduction : Frequent admitters FA, defined as patients with three or more inpatient acute hospital admissions within one year, account for about 27% of episodes and incur high healthcare costs. National University Health System Regional Health System NUHS-RHS Integrated Interventions and Care Extension NICE, launched in 2014, targeted FA with the aim of reducing avoidable hospital utilisation. NICE patients were assigned a case manager who customised their care plan based on holistic needs assessment. NICE provided post-discharge home visits and/or phone calls to monitor patients’ progress, appropriate referrals to community health and social services, and consolidated care under one team. We retrospectively evaluated NICE’s effect on reducing utilisation over 1-year post-enrolment: all-cause admissions ADM, emergency admissions EM, emergency department visits ED, specialist outpatient clinic visits SOC, and total inpatient length of stay LOS. Methods : NICE patients enrolled between June 2014 to December 2015 were grouped as cases n=668. Unenrolled patients who were FA during the same period were designated as potential controls n=5,714, taking their third admission date during this period as proxy date of enrolment. Propensity score matching was conducted with the following: gender, race, age, residential housing type, number of comorbidities at enrolment, date of enrolment and pre-enrolment utilisation 1-year prior. This resulted in 604 matched case-control pairs. As the variance of each measure of utilisation is greater than its mean, negative binomial regression was used to model post-enrolment utilisation, adjusting for log-transformed pre-enrolment levels and matching propensity score, with an offset term to account for potential mortality during follow-up. We report incidence rate ratios IRR of cases to controls, with 95% confidence intervals 95%CI. Results : Cases unadjusted mean=6.7 had significantly fewer post-enrolment SOC than controls unadjusted mean=8.6, IRR=0.86 95%CI: 0.77-0.97. No statistically significant differences were observed for post-enrolment ADM, EM, ED, and LOS. Discussion : The results suggest that NICE provided adequate post-discharge follow-up care that patients would otherwise seek from SOC, thus potentially decreasing patients’ time and cost burden. For a significant proportion of patients in both groups, post-enrolment ADM, EM, ED, and LOS may have regressed to a low mean following an acute period of high admissions, resulting in floor effects which limit any further reductions in a 1-year period. Conclusion : NICE had an impact in reducing patients’ SOC post-discharge. Lessons Learned : The higher than expected variability in the utilisation patterns suggest FA are a heterogeneous group, and stratification could allow for more targeted interventions that better cater to care needs. Limitations : Patients were not randomised into cases and controls, but inherent bias from confounding factors was mitigated with matching. Different patient profiles and needs possibly also led to implementation inconsistencies. Suggestions for Future Research : Future research could stratify FA into subgroups and explore programme effects in reducing readmissions within patient subsets over a longer follow-up period. Targeted interventions for various subsets could be tested and inform future iterations of such programmes. Cost-benefit analysis should be conducted to determine if the savings from reduced SOC that are attributable to NICE outweighs the cost to run the programme.

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.

BACKGROUND Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress. OBJECTIVE The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas. METHODS On the basis of a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multilevel views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), whereas socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated. RESULTS Visit-level (N=7,778,761) and patient-level records (n=549,109) were generated. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. Diagnosis codes that underwent modification (truncation or zero addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other and positively correlated with health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger. CONCLUSIONS The framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.

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.