Duke Global Health Institute, Duke University, Durham, North Carolina, USA.
IntraHealth International, Chapel Hill, North Carolina, USA.
Glob Health Action. 2022 Dec 31;15(1):2056312. doi: 10.1080/16549716.2022.2056312.
Donor financing is increasingly relying on performance-based measures that demonstrate impact. As new technologies and interventions enter the innovation space to address global health challenges, innovators often need to model their potential impact prior to obtaining solid effectiveness data. Diverse stakeholders rely on impact modeling data to make key funding and scaling decisions. With a lack of standardized methodology to model impact and various stakeholders using different modeling strategies, we propose that a universal innovation impact checklist be used to aid in transparent and aligned modeling efforts. This article describes a new Health Innovation Impact Checklist (HIIC) - a tool developed while evaluating the impact of health innovations funded under the Saving Lives at Birth (SL@B) program. SL@B, a global health Grand Challenge initiative, funded 116 unique maternal and newborn health innovations, four of which were selected for cost-effectiveness analyses (CEAs) within our evaluation. A key data source needed to complete a CEA was the lives saved estimate. HIIC was developed to help validate draft impact models from the SL@B donors and our own team's additional modeling efforts, to ensure the inclusion of standardized elements and to pressure test assumptions for modeling impact. This article describes the core components of HIIC including its strengths and limitations. It also serves as an open call for further reviewing and tailoring of this checklist for applicability across global efforts to model the impact of health innovations.
捐赠融资越来越依赖基于绩效的措施，以展示影响力。随着新技术和干预措施进入创新领域，以应对全球健康挑战，创新者通常需要在获得可靠的有效性数据之前，对其潜在影响进行建模。不同的利益相关者依赖于影响建模数据来做出关键的资金和扩展决策。由于缺乏标准化的方法来进行影响建模，并且不同的利益相关者使用不同的建模策略，我们建议使用通用的创新影响清单来帮助进行透明和一致的建模工作。本文描述了一种新的健康创新影响清单（HIIC）- 这是在评估在拯救生命出生（SL@B）计划下资助的健康创新的影响时开发的工具。SL@B 是一个全球健康大挑战倡议，资助了 116 种独特的母婴健康创新，其中 4 种在我们的评估中被选为成本效益分析（CEA）。完成 CEA 需要的关键数据源是估计的拯救生命数。HIIC 是为了帮助验证 SL@B 捐赠者的草案影响模型和我们自己团队的额外建模工作，以确保纳入标准化要素并对建模影响的假设进行压力测试。本文描述了 HIIC 的核心组成部分，包括其优势和局限性。它还呼吁进一步审查和调整这份清单，以使其适用于全球范围内建模健康创新影响的努力。
