Health Technology Disinvestment in Singapore.

Healthcare decision-makers are constantly challenged by growing healthcare needs in tandem with rising healthcare costs. Disinvesting in technologies and practices that are "low in value" is one strategy to re-allocate limited resources to the most effective, safe and cost-effective technologies. We put forward a health technology reassessment framework and examined the opportunities and challenges on technology disinvestment in Singapore and deliberated on possible solutions. We coordinated and supported a disinvestment programme in 2 hospitals, 1 specialist centre and 9 primary care institutions in the public healthcare sector. The key processes were identifying, prioritising and assessing low-value health technologies and practices, disseminating and implementing disinvestment recommendations, and post-implementation evaluation. Through case studies, we explored the barriers and enablers to the success of the programme. One of the barriers to disinvestment included difficulty in demonstrating a lack of benefit of in-use technologies from published studies. Differing viewpoint and priority might preclude a healthcare leader's support in such initiatives and that posed an unsurmountable hurdle. On the other hand, engaging the stakeholder throughout the evidence review process and striking a balance between rigour and timeliness of review were likely to assure success. Lastly, monitoring the impact on resources and patient outcomes can be diverse and methods need to be developed. Understanding barriers and enablers in health technology disinvestment can translate into improved opportunities for eliminating and minimising resource wastage.

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

INTRODUCTION: With population health management being a priority in the Singapore, this paper aims to provide a data-driven perspective of the population health management initiatives to aid program planning and serves as a baseline for evaluation of future implemented programs. METHODS: A database with information on patient demographics, health services utilization, cost, diagnoses and chronic disease information from 2008 to 2013 for three regional health systems in Singapore was used for analysis. Patients with three or more inpatient admissions were considered as "Frequent Admitters." Health service utilization was quantified, and cross utilization of services was studied. One-year readmission rate for inpatients was studied, and a predictive model for readmission or death was developed. RESULTS: There were a total of 2.8 M patients in the database. Frequent admitters accounted for 0.9% of all patients with an average cost per patient of S$29 547. Of these, 89% had chronic diseases. Cross utilization of health services showed that 8.2% of the patients utilized services from more than one hospital with 19.6% utilizing hospital and polyclinic services in 2013. The highest risk of readmission or death was for those patients who had five or more inpatient episodes in each of the preceding 2 years. CONCLUSION: By understanding the profile of the patients and their utilization patterns in the three regional health systems, our study will help clinicians and decision makers design appropriate integrated care programs for patients with the aim of covering the healthcare needs for the enitre population across the healthcare spectrum in Singapore. Copyright © 2015 John Wiley & Sons, Ltd.

Projection of prediabetes and diabetes population size in Singapore using a dynamic Markov model.

BACKGROUND: The aim of the present study was to forecast the prevalence and number of adult Singapore residents with prediabetes and diabetes in 2035. METHODS: A dynamic Markov model with nine mutually exclusive states was developed based on the clinical course of diabetes using time-dependent rates and probabilities. A 1-year cycle over a 25-year time horizon from 2010 to 2035 was used in the model. With publicly available data and a chronic disease register, the model forecast annual disease burden by simulating transition of cohorts across different health states using prevalence rates, incidence rates, mortality rates, disease transition, disease detection, and complication rates. An aging index was used in the model in anticipation of population aging to minimize risks of underestimating disease burden. RESULTS: From 2010 to 2035, the number of Singapore residents with prediabetes and diabetes is projected to more than double, from 434 685 to 903 596 and from 373 104 to 823 802, respectively. The prevalence of prediabetes and diabetes will rise steadily from 15.5 % to 24.9 % and from 13.3 % to 22.7 %, respectively. By 2035, a further estimate of 733 174 and 100 250 patients with prediabetes and uncomplicated diabetes, respectively, will remain undiagnosed. The prevalence of detected and undetected complications is forecast to rise from 60.0 % in 2010 to 70.2 % by 2035. CONCLUSION: By 2035, the prevalence of prediabetes and diabetes among Singapore residents aged 21+ years is expected to be one in four and one in five, respectively. There is an impetus to adopt more aggressive interventions to contain disease progression.