Automated system for risk determinantion, facilitation and community support to mitigate social determinants of health in minority populations

用于风险确定、促进和社区支持的自动化系统，以减轻少数群体健康的社会决定因素

• 批准号: 10325657
• 负责人: Deboleena Dutta
• 金额: $ 25.66万
• 依托单位: VIORA HEALTH INC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10325657
• 关键词: Algorithms; Automation; Behavioral; Biological; Body Weight decreased; Centers for Disease Control and Prevention (U.S.); Characteristics; Collection; Communication; Communities; Community Services; Computer software; Confidential Information; Cost Savings; Current Procedural Terminology Codes; Data; Data Set; Databases; Development; Development Plans; Diabetes prevention; Disease Management; Disease Progression; Drops; Emergency department visit; Ensure; Environment; Face; Family; Feasibility Studies; Feedback; Felis catus; Focus Groups; General Population; Goals; Health; Health Care Costs; Health Insurance; Health Personnel; Health Promotion; Health Services; Health system; Healthcare; Healthcare Systems; Home; Human; Individual; Institution; Institutional Review Boards; Intellectual functioning disability; Intervention; Lesbian Gay Bisexual Transgender Queer; Life Style; Measures; Medical; Minority; Minority Groups; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Output; Participant; Patient Monitoring; Patients; Performance; Phase; Philadelphia; Population; Prediabetes syndrome; Prevention; Probability; Process; Program Effectiveness; Protocols documentation; Provider; Public Health; Recording of previous events; Research; Resources; Risk; Risk Assessment; Risk Factors; Risk Reduction; Side; Socioeconomic Factors; Socioeconomic Status; Study Subject; Support System; Surveys; System; Technology; Testing; Text; Text Messaging; Transportation; Underinsured; Underserved Population; Vulnerable Populations; Woman; Work; barrier to care; base; clinical risk; cloud based; commercialization; comorbidity; cost; data de-identification; design; diabetes prevention program; diabetic patient; disorder prevention; ethnic minority population; evidence base; experience; follow-up; health care service; hospital readmission; human subject; improved; innovation; insurance plan; literacy; machine learning method; meetings; member; personalized intervention; physically handicapped; preference; programs; prototype; racial and ethnic; racial minority; recruit; risk stratification; service gap; social; social culture; social health determinants; socioeconomics; software systems; success; usability

Abstract Vulnerable and underserved populations such as ethnic and racial minorities, the poor, the LGBTQ community and those with physical or intellectual disabilities, often suffer the greatest health risks while facing significant barriers to receiving healthcare. While biological and clinical risks influence outcomes, sociocultural and socioeconomic factors also create major barriers to receiving healthcare services, at an individual, community and population level for these populations. These social determinants of health (SDOH), such as transportation, literacy, and finances can severely limit participation in health programs designed for the general population. Diabetes Prevention Programs are a good example of a lifestyle change protocol that is proven to reduce the risk of type 2 diabetes by half for pre-diabetics patients at risk by encouraging weight-loss. But, despite the effectiveness of the program, more than 50% of participants drop out before completing this year-long lifestyle change program, severely limiting the program’s success. This is in part due to the fact that programs like DPP take little account of the limitations faced by underserved and vulnerable populations that it is meant to help. This lack of support at the individual and community level combined with the fact that healthcare institutions are not designed for, nor economical, in supporting patient health outside the medical facility where it is most needed. This creates a gap in healthcare service that current systems cannot overcome. Viora is closing this health services gap with a system that interfaces between the health care system, the underserved patient, and underutilized community-based resources and providers, to facilitate better outcomes in programs like DPP. By using risk stratification based on SDOH factors, each DPP participant can have tailored interventions that consider their socioeconomic and sociocultural characteristics. The Viora Health system connects patients to local resources using simple communication between the patient, health providers, and community services, facilitating patient completion of the DPP program, thereby improving health outcomes. In Phase I, Viora will evaluate a larger set of pre-diabetes and SDOH risk factors across 5000 minority and underserved subjects to select the most relevant variables for risk assessment and personal prevention plan development. The system will automatically screen pre-diabetic participants who are eligible, ready and able to adhere to DPP, and will create a personalized intervention plan that is the most impactful in reducing their risk but is also doable. The system’s communication network will be expanded to provide direct communication between the patient, DPP coach, health providers and community resources, to support the participant toward maintaining their DPP goals. Finally, the project will assess the basic function of the system, preferences and human interaction in a human subject study with underserved subjects and community-based partners. Completion of Phase I will demonstrate feasibility of our software system incorporating major components of the VIORA SYSTEM, which will inform the development of a minimum viable product to be tested in Phase 2.

摘要