AI Can Advance Health Equity

Health Equity Is Core to Quality and AI can Help

Because of COVID-19’s disproportionate infection and morbidity rates for communities of color, payers and providers are reprioritizing equity as a top strategic priority.^2,3尽管20多年来，平等一直是质量的核心维度，但由于种族、民族、主要语言、地理位置、性取向和社会经济地位等个人特征，护理提供的质量仍经常存在差异。

人工智能增强的卫生技术改善了基于数据的决策，为显著改善卫生公平结果提供了潜力。寻求使用人工智能改善医疗的组织必须严格评估数据源，发现数据中的偏差，并测试算法偏差。如果被滥用，算法可能会加剧差距，加剧卫生不平等。有了适当的目标、数据和技术，人工智能可以作为一种工具，系统地理解和加强卫生公平。

Improving Reliability of Data is Necessary to Increase Health Equity

ChristianaCare致力于实现医疗公平，并确立了减少医疗差距的目标。该组织面临着一些挑战，包括在注册点不一致地收集个人特征数据，以不同方式分类和存储特征的多源系统，以及随时间变化的分类方案。不可靠的数据造成了混乱，限制了评估不同群体结果的能力，并使评估结果在群体内的分布变得困难。ChristianaCare需要一种能够使用其数据来理解和改善医疗公平的解决方案。

Leveraging Predictive Analytics Improves Health Equity

为了提高数据的一致性和实用性，ChristianaCare对其数百个注册点的个人特征数据的收集进行标准化，并将历史数据映射到当前标准。The organization used the Health Catalyst^®Data Operating System (DOS™) platform and a robust suite of analytics applications as the organization’s single “source of truth” for personal characteristic data.

该组织利用其数据平台18个月的历史数据、符合企业数据标准的master person数据和AI进行股权分析。世界杯厄瓜多尔vs塞内加尔波胆预测ChristianaCare创建了在6个平等维度上的7种情况下评估9种不同指标的能力，包括年龄、种族、民族、性别、语言和邮政编码。本组织进行了单变量和多变量分析，对每个结果和公平性维度进行了全面分析，在仪表板中将数据可视化，并创建了用于测量、比较和跟踪卫生公平性的独特统计数据。

Results

By leveraging its data platform and AI, ChristianaCare can now evaluate its health equity focus and provide valuable insight into its patients’ care and outcomes. ChristianaCare has identified:

• Specific opportunities to improve health equity.
  • Age:readmissions for chronic obstructive pulmonary disease and heart failure (HF).
  • Race and gender:HF readmission.
  • Race and geography:COVID-19 testing in Black/African American patients in parts of Wilmington.
• Opportunities that should be investigated further.
  • Ethnicity:脓毒症的死亡率。
• Areas of current health equity that can be monitored.
  • Hemoglobin A1c control among diabetic patients.
  • Blood pressure greater than 130/80 or 140/90 among patients being treated for hypertension.

ChristianaCare is working to overcome disparities in COVID-19 testing, increasing access to care through innovative clinics that combine virtual primary care and COVID-19 testing in some of Wilmington’s underserved communities.

“卫生公平和人工智能是相互关联的。技术和人工智能需要帮助缩小健康差距，而不是加剧这种差距。与Health Catalyst世界杯葡萄牙vs加纳即时走地合作使我们能够开发一个健康公平分析框架，支持我们努力减少个人特征(如性别、种族、民族、地理、语言、性取向、支付人或社会经济地位)对我们社区健康结果的影响。”

– Ed Ewen, MD, Director, Clinical Data and Analytics, Center for Strategic Information Management

What’s Next

ChristianaCare plans to perform analyses on various healthcare processes and outcomes, enabling the organization to further identify opportunities to improve health equity. By integrating the analyses into the work of its improvement team, it is refining the framework, expectations, and communication plans for health equity goals at the organizational, practice, and provider level.

References

1. Obermeyer, Z. et al. (2019). Algorithmic bias in health care: A path forward.Health Affairs Blog. Retrieved fromhttps://www.healthaffairs.org/do/10.1377/hblog20191031.373615/full/
2. APM Research Lab. (2021).The color of coronavirus: COVID-19 deaths by race and ethnicity in the U.S.Retrieved fromhttps://www.apmresearchlab.org/covid/deaths-by-race
3. Centers for Disease Control and Prevention. (2020).COVID-19 hospitalization and death by race/ethnicity.Retrieved fromhttps://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-race-ethnicity.html