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Cost of Medical Care of Patients with Advanced Serious Illness in Singapore (COMPASS): prospective cohort study protocol.

ABSTRACT: BACKGROUND:Advanced cancer significantly impacts quality of life of patients and families as they cope with symptom burden, treatment decision-making, uncertainty and costs of treatment. In Singapore, information about the experiences of advanced cancer patients and families and the financial cost they incur for end-of-life care is lacking. Understanding of this information is needed to inform practice and policy to ensure continuity and affordability of care at the end of life. The primary objectives of the Cost of Medical Care of Patients with Advanced Serious Illness in Singapore (COMPASS) cohort study are to describe changes in quality of life and to quantify healthcare utilization and costs of patients with advanced cancer at the end of life. Secondary objectives are to investigate patient and caregiver preferences for diagnostic and prognostic information, preferences for end-of-life care, caregiver burden and perceived quality of care and to explore how these change as illness progresses and finally to measure bereavement adjustment. The purpose of this paper is to present the COMPASS protocol in order to promote scientific transparency. METHODS:This cohort study recruits advanced cancer patients (n?=?600) from outpatient medical oncology clinics at two public tertiary healthcare institutions in Singapore. Patients and their primary informal caregiver are surveyed every 3 months until patients' death; caregivers are followed until 6 months post patient death. Patient medical and billing records are obtained and merged with patient survey data. The treating medical oncologists of participating patients are surveyed to obtain their beliefs regarding care delivery for the patient. DISCUSSION:The study will allow combination of self-report, medical, and cost data from various sources to present a comprehensive picture of the end-of-life experience of advanced cancer patients in a unique Asian setting. This study is responsive to Singapore's National Strategy for Palliative Care which aims to identify opportunities to meet the growing need for high quality care for Singapore's aging population. Results will also be of interest to policy makers and researchers beyond Singapore who are interested to understand and improve the end-of-life experience of cancer patients. TRIAL REGISTRATION:NCT02850640 (Prospectively registered on June 9, 2016).

SUBMITTER: Teo I

PROVIDER: S-EPMC5913880 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Cost of Medical Care of Patients with Advanced Serious Illness in Singapore (COMPASS): prospective cohort study protocol.

Teo Irene I Singh Ratna R Malhotra Chetna C Ozdemir Semra S Dent Rebecca A RA Kumarakulasinghe Nesaretnam Barr NB Yeo Wee Lee WL Cheung Yin Bun YB Malhotra Rahul R Kanesvaran Ravindran R Yee Alethea Chung Pheng ACP Chan Noreen N Wu Huei Yaw HY Chin Soh Mun SM Allyn Hum Yin Mei HYM Yang Grace Meijuan GM Neo Patricia Soek Hui PSH Nadkarni Nivedita V NV Harding Richard R Finkelstein Eric A EA

BMC cancer 20180423 1

<h4>Background</h4>Advanced cancer significantly impacts quality of life of patients and families as they cope with symptom burden, treatment decision-making, uncertainty and costs of treatment. In Singapore, information about the experiences of advanced cancer patients and families and the financial cost they incur for end-of-life care is lacking. Understanding of this information is needed to inform practice and policy to ensure continuity and affordability of care at the end of life. The prim ...[more]

PMID: 29688843

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Project description:BackgroundKnee osteoarthritis (OA) is a leading cause of global disability. The understanding of the role of psychosocial factors in knee OA outcomes is still evolving particularly in an Asian context. The primary aim of this study is to explore psychosocial factors that prognosticate short and long-term clinical outcomes, productivity, and healthcare utilization in patients with knee OA. Secondary aims are to explore the mediation and directional relationships and the role it plays in predicting the discordance between self-reported measures (SRM), physical-performance measures (PPMs) and objective clinical parameters.MethodsA multi-centre prospective cohort study of community ambulant knee OA patients seeking treatment in the tertiary healthcare institutions in Singapore will be conducted. Patients with secondary arthritis, significant cognitive impairment, severe medical comorbidities or previous knee arthroplasty will be excluded. Primary clinical outcome measure is the Knee injury and OA Outcome Score-12 (KOOS-12). Baseline characteristics include sociodemographic status, arthritis status including symptom duration and radiographic severity, comorbidities and functional status through Charlson Comorbidities Index (CCI), Barthel Index (BI) and Parker Mobility Score (PMS). Psychosocial variables include social support, kinesiophobia, negative affect, self-efficacy, injustice, chronic illness shame and the built environment. Clinical outcomes include quality of life, physical performance, global assessment, satisfaction and physical activity levels. Productivity and healthcare utilization will be assessed by a modified OA Cost and Consequences Questionnaire (OCC-Q) and the Work Productivity and Activity Impairment Questionnaire (WPAI). Variables will be collected at baseline, 4, 12 months and yearly thereafter. Regression, mediation and structural equation modelling will be used for analysis.DiscussionResults will allow contextualization, identification, and phenotyping of the critical (and potentially modifiable) psychosocial parameters that predict positive clinical outcomes in the OA population to guide optimization and refinement of healthcare and community. This will facilitate: 1. identification of high-risk knee OA subpopulations that will likely experience poor outcomes and 2. formulation of targeted multidisciplinary comprehensive approaches to address these psychosocial factors to optimize non-surgical treatment care, maximize functional outcomes and create more value-based care model for knee OA.Ethics and disseminationThe study has been registered under clinicaltrials.gov registry (Identifier: NCT04942236).

Project description:BackgroundSerious mental illnesses (SMI) are common, disabling, and challenging to treat, requiring years of monitoring and treatment adjustments. Stress or reduced medication adherence can lead to rapid worsening of symptoms and behaviors. Illness exacerbations and relapses generally occur with little or no clinician awareness in real time, leaving limited opportunity to modify treatments. Previous research suggests that passive mobile sensing may be beneficial for individuals with SMI by helping them monitor mental health status and behaviors, and quickly detect worsening mental health for prompt assessment and intervention. However, there is too little research on its feasibility and acceptability and the extent to which passive data can predict changes in behaviors or symptoms.ObjectiveThe aim of this research is to study the feasibility, acceptability, and safety of passive mobile sensing for tracking behaviors and symptoms of patients in treatment for SMI, as well as developing analytics that use passive data to predict changes in behaviors and symptoms.MethodsA mobile app monitors and transmits passive mobile sensor and phone utilization data, which is used to track activity, sociability, and sleep in patients with SMI. The study consists of a user-centered design phase and a mobile sensing phase. In the design phase, focus groups, interviews, and usability testing inform further app development. In the mobile sensing phase, passive mobile sensing occurs with participants engaging in weekly assessments for 9 months. Three- and nine-month interviews study the perceptions of passive mobile sensing and ease of app use. Clinician interviews before and after the mobile sensing phase study the usefulness and feasibility of app utilization in clinical care. Predictive analytic models are built, trained, and selected, and make use of machine learning methods. Models use sensor and phone utilization data to predict behavioral changes and symptoms.ResultsThe study started in October 2020. It has received institutional review board approval. The user-centered design phase, consisting of focus groups, usability testing, and preintervention clinician interviews, was completed in June 2021. Recruitment and enrollment for the mobile sensing phase began in October 2021.ConclusionsFindings may inform the development of passive sensing apps and self-tracking in patients with SMI, and integration into care to improve assessment, treatment, and patient outcomes.Trial registrationClinicalTrials.gov NCT05023252; https://clinicaltrials.gov/ct2/show/NCT05023252.International registered report identifier (irrid)DERR1-10.2196/39010.

Dataset Information

Cost of Medical Care of Patients with Advanced Serious Illness in Singapore (COMPASS): prospective cohort study protocol.

Publications

Cost of Medical Care of Patients with Advanced Serious Illness in Singapore (COMPASS): prospective cohort study protocol.

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