Fiscal Year 2023
Released March, 2022
Topics on this page: Objective 4.1: Improve the design, delivery, and outcomes of HHS programs by prioritizing science, evidence, and inclusion | Objective 4.1 Table of Related Performance Measures
Objective 4.1: Improve the design, delivery, and outcomes of HHS programs by prioritizing science, evidence, and inclusion
HHS works on strategies to improve the design, delivery, and outcomes of HHS programs by prioritizing science, evidence, and inclusion. The Department leverages stakeholder engagement, communication, and collaboration to build and implement evidence-based interventions and approaches for stronger health, public health, and human services outcomes.
The Office of the Secretary leads this objective. All divisions are responsible for implementing programs under this strategic objective. The narrative below provides a brief summary of any past work towards these objectives and strategies planned to improve or maintain performance on these objectives.
Objective 4.1 Table of Related Performance Measures
| FY 2016 | FY 2017 | FY 2018 | FY 2019 | FY 2020 | FY 2021 | FY 2022 | FY 2023 | |
|---|---|---|---|---|---|---|---|---|
| Target | Develop an adaptive smoking cessation intervention targeting adolescents of health disparity populations using the QuitStart mobile application. | Determine if a mobile phone app is effective in promoting physical activity or reducing weight among racial and ethnic minority populations. | Investigate the utility of a natural language processing (NLP) algorithm to identify patients from health disparity populations who are experiencing social isolation or other social stressors using clinical narratives in electronic health record (EHR) systems. | |||||
| Result | NIH investigators developed a new smoking cessation mobile application, QuitJourney, based on QuitGuide (not QuitSTART, which is for adolescents) and conducted acceptability and usability testing with 48 young adults. | Dec. 2022 | Dec 2023 | |||||
| Status | Target Met | In Progress | In Progress |
Health information technology (health IT) refers to a variety of electronic methods that can be used to manage information about people’s health and health care. Although health IT holds much promise for reducing disparities in populations that are medically underserved by facilitating behavior change and improving quality of health care services and health outcomes, few studies have examined the impact of health IT adoption on improving health outcomes and reducing health disparities among racial and ethnic minority individuals, people of less privileged socioeconomic status, underserved rural populations, and sexual and gender minority populations. Thus, NIH is investing in research to explore the potential of health IT for improving the health of underserved populations and reducing health disparities using technologies such as decision support tools, mobile apps, and new technologies such as artificial intelligence and natural language processing.
In FY 2021, NIH investigators developed QuitJourney, a mobile health (mHealth) smoking cessation intervention for low socioeconomic young adult smokers using the mobile app QuitGuide. The investigators also recruited 48 low socioeconomic young adult smokers, ages 18 to 29, to ensure the features of QuitJourney would be acceptable to and usable by low socioeconomic young adult smokers. Now the investigators plan to conduct a pilot study that will help them develop an algorithm to accurately predict post-quit cravings in real time and deliver personalized messages to help low socioeconomic young adult smokers cope with their cravings. They also plan to conduct a proof-of-concept study to obtain preliminary evidence on QuitJourney’s effectiveness in real-world settings. If QuitJourney is proven to be effective, it will provide a tool to help low socioeconomic young adult smokers quit smoking and stay smokefree.
In FY 2022, NIH is supporting research to determine if a mobile phone app is effective in promoting physical activity or reducing weight among racial and ethnic minority populations. In FY 2023, NIH will investigate the utility of a natural language processing (NLP) algorithm to identify patients from health disparity populations who are experiencing social isolation or other social stressors using clinical narratives in electronic health record (EHR) systems.
| FY 2016 | FY 2017 | FY 2018 | FY 2019 | FY 2020 | FY 2021 | FY 2022 | FY 2023 | |
|---|---|---|---|---|---|---|---|---|
| Target | 62.4 % | 57.3 % | 56.4 % | 64.5 % | 65.8 % | Prior Result +3PP | Prior Result +3PP | Prior Result +3PP |
| Result | 54.3 % | 53.4 % | 61.5 % | 62.8 % | 66.3% | Oct 30, 2022 | Oct 30, 2023 | Oct 30, 2024 |
| Status | Target Not Met | Target Not Met | Target Exceeded | Target Not Met but Improved | Target Exceeded | Pending | Pending | Pending |
The most efficient and effective programs often use evidence-based and evidence-informed practices. ACF developed an efficiency measure to gauge progress towards programs’ use of these types of practices. ACF is working closely with the states to promote more rigorous evaluations of their funded programs. Over time, ACF expects to increase the number of effective programs and practices that are implemented, thereby maximizing the impact and efficiency of Community-Based Child Abuse Prevention (CBCAP) funds. For the purposes of this efficiency measure, ACF defines evidence-based and evidence-informed programs and practices along a continuum, which includes the following four categories of programs or practices: Emerging and Evidence Informed; Promising; Supported; and Well-Supported. Programs determined to fall within specified program parameters will be considered to be implementing “evidence-informed” or “evidence-based” practices (collective referred to as “EBPs”), as opposed to programs that have not been evaluated using any set criteria. The funding directed towards these types of programs (weighted by EBP level) will be calculated over the total amount of CBCAP funding used for direct service programs to determine the percentage of total funding that supports evidence-based and evidence-informed programs and practices. A baseline of 27 percent was established for this measure in FY 2006. The target of a three percentage point annual increase in the amount of funds devoted to evidence-based practice was selected as a meaningful increment of improvement that takes into account the fact that this is the first time that the program has required grantees to target their funding towards evidence-based and evidence-informed programs, and it will take time for states to adjust their funding priorities to meet these new requirements.
In general, the majority of CBCAP funding is directed toward EBPs. In FY 2016, 54.3 percent of funds went to EBPs. The FY 2017 result represented a slight decrease in performance, at 53.4 percent, yet still with a majority of funds being used for EBPs. ACF is committed to continuing to work with CBCAP grantees to invest in known EBPs and focusing efforts to provide one-on-one and peer learning technical assistance to increase accuracy of data reporting for this measure. Fiscal year 2018 represented an increase with grantees reporting 61.5 percent of funds being directed at EBPs. Fiscal year 2019 also saw an increase with grantees reporting 62.8 percent of funds directed toward EBPs. Despite this increase, it did not meet the target of 64.5 percent. In FY 2020, however, the percentage spent on EBPs increased to 66.3 percent, exceeding the target of 65.8 percent. ACF will continue to promote evaluation and innovation, so as to expand the availability and use of evidence-informed and evidence-based practice over time and continue to set the target of an annual three percentage point increase over the prior year.
