| 来源类型 | Research Reports
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| 规范类型 | 报告
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| DOI | https://doi.org/10.7249/RR2934
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| 来源ID | RR-2934-ASPEC
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| Evaluation of Technology-Enabled Collaborative Learning and Capacity Building Models: Materials for a Report to Congress |
| Shira H. Fischer; Adam J. Rose; Ryan K. McBain; Laura J. Faherty; Jessica Sousa; Monique Martineau
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| 发表日期 | 2019
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| 出版年 | 2019
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| 页码 | 202
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| 语种 | 英语
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| 结论 |
Empirical evidence for the effects of ECHO and ECHO-like models (EELM) on patient and provider outcomes remains modest- Based on a literature review and a technical expert panel, the quality of evidence for the effectiveness of EELM is rated as "low" or "very low" based on generally accepted systems of measurement. However, it is important to note that many models of care delivery are supported only by low-quality evidence.
- In the areas that researchers have measured, EELM mostly show positive effects.
- The intention of EELM is to increase access to specialty care by educating generalist health providers, particularly those in locales with limited access such as rural and remote areas. However, there is a need for targeted funding to evaluate EELM effectively.
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| 摘要 |
- Implementers and evaluators can engage with policymakers, funders, and others to explore mechanisms for supporting rigorous evaluation. Such mechanisms would ideally address care delivery imperatives in the near term and enable rigorous evaluations that would expand the evidence base to support longer-term investments in EELM.
- An expanded focus on rigorous reporting of program characteristics of EELM would help evaluators assess how the model is put into practice and what "ingredients" might lead to better outcomes and are worth replicating.
- Building capacity to evaluate EELM is a third critical opportunity and is two-pronged. Building such capacity could help implementers design EELM to facilitate improved evaluations, and it could help researchers more effectively choose populations for study, outcomes, comparators, and study designs.
- Implementers and evaluators can engage with policymakers, funders, and others to explore mutually beneficial mechanisms for supporting rigorous evaluation. Such mechanisms would ideally address care delivery imperatives in the near term and enable rigorous evaluations that would expand the evidence base to support longer-term investments in EELM.
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| 主题 | Community-Based Health Care
; Cost-Effectiveness in Health Care
; Evidence Based Health Practice
; Health Care Access
; Health Care Quality Measurement
; Health Care Services Capacity
; Telemedicine
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| URL | https://www.rand.org/pubs/research_reports/RR2934.html
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| 来源智库 | RAND Corporation (United States)
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| 引用统计 |
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| 资源类型 | 智库出版物
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| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/108990
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推荐引用方式
GB/T 7714 |
Shira H. Fischer,Adam J. Rose,Ryan K. McBain,et al. Evaluation of Technology-Enabled Collaborative Learning and Capacity Building Models: Materials for a Report to Congress. 2019.
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