How cross-sector collaboration can optimize healthcare data and advance equity
The COVID-19 pandemic highlighted pervasive health disparities throughout the country. To address the complexities of this issue, health equity has rightfully b...
The COVID-19 pandemic highlighted pervasive health disparities throughout the country. To address the complexities of this issue, health equity has rightfully become a focal point for the current administration. With the resurgence of COVID-19 cases, it’s important to put the lessons of the not-so-distant past into practice.
To achieve this goal, the Centers for Medicare and Medicaid Services recently released an updated Framework for Health Equity 2022-2033. The strategic framework consists of five priorities — Priority 1 is to “expand the collection, reporting and analysis of standardized data.” Specifically, CMS aims to improve its “collection and use of comprehensive, interoperable, standardized individual-level demographic and social determinants of health (SDOH) data.”
The CMS framework is consistent with the objectives in the Executive Order (13985) on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government and aligns with the Department of Health and Human Services’ Healthy People 2030 Framework. HHS’ framework groups SDOH into five domains: economic stability, educational access and quality, health care access and quality, neighborhood and built environment, and social and community context. To adequately understand and address these factors, it’s paramount to collect, standardize, analyze and operationalize health data.
Considerations for data collection and standardization
Healthcare data management and analysis can unlock a plethora of tangible benefits for patients and providers alike. However, if information is not collected, or shared among disparate stakeholders, the true value of that information cannot be realized. As such, it’s imperative that data collection practices be standardized so information is easily transferable and interoperable.
This is a significant hurdle that a range of healthcare agencies, providers and industry experts are currently exploring. While the ideal methods for data collection and standardization are still up for debate, the objective is universal: situational awareness. Situational awareness is the ability to synthesize a variety of data, determine what is relevant, and act on it. At the very least, healthcare providers should have enough data available to them to make informed decisions about patient care and wellbeing.
While the benefits of data collection and standardization are extensive, valid concerns related to data privacy and security remain. As underscored by CMS in the Health Equity Framework, data collection should always remain voluntary to ensure individual consent, privacy and accurate self-identification.
High quality data on patient demographics, social drivers of health and medical history can empower healthcare providers to rapidly address patients’ needs and optimal health. For example, advanced data algorithms can use patient data to identify and respond to emerging population and geographic health trends. Moreover, this information can be used to track and measure health trends over time, which would enable federal agencies to evaluate the effects of any policy changes on health outcomes and disparities.
Unfortunately, the data available for socially marginalized and underserved communities is often disaggregated and inaccurate. This makes it extremely challenging, if not impossible, to project or determine the best possible care and needed resources. Therefore, the healthcare industry, providers and federal agencies should prioritize inclusivity as they strive to transform policy and mitigate health disparities.
For socially marginalized and underserved communities it is critical to incorporate the feedback of community stakeholders. Community stakeholders often hold the key to necessary collaborations, resources and processes that advance optimal health outcomes.
While the type of data that is collected should be standardized, and the IT systems used to share that data across the healthcare sector must be interoperable, in certain cases it’s crucial to customize care and ensure it suits the population’s needs. Increasing both the quantitative and qualitative data available can help providers determine how to deliver quality care that addresses whole-person health. These outcomes will require extensive and consistent collaboration throughout the entire healthcare sector.
Collaboration is key to securely maximize the benefits of data management
As noted by CMS and HHS alike, with thorough communication, coordination and a shared agenda of advancing health equity the healthcare industry can capitalize on available health data for optimal health outcomes for all. Artificial intelligence and automation are prime examples of innovative data solutions. This technology can predict health trends, project the outcome of specific treatments, promote collaboration of a multidisciplinary team of health care providers and community health workers, and identify otherwise imperceptible healthcare gaps.
Historically, a community’s health needs assessment was required to determine a population’s needs and address disparities. These in-depth assessments can take upwards of 18 months to publish and present, meaning countless individuals do not receive appropriate treatment and a lack of resources persists. Moreover, given the speed at which health trends can shift, the results of the assessment are outdated and no longer relevant or actionable. With sufficient data, AI would be able to generate the same results in a fraction of the time.
However, it’s critical that the data these algorithms utilize is representative of the population in question. The White House’s Blueprint for an AI Bill of Rights also stresses the importance of human oversight for automated healthcare systems. Given the sensitivity of health data, well-trained IT professionals should continually assess AI-generated results, monitor the cultural competency of provider data, and directly review any automated high-risk decisions.
There is a long road ahead, but an equitable healthcare future can be reached if we all continue to work together and engage in open discussions about how to securely, ethically and strategically leverage data to improve patient outcomes for all Americans.
Kamala Green is the social drivers of health program manager at National Government Services.
How cross-sector collaboration can optimize healthcare data and advance equity
The COVID-19 pandemic highlighted pervasive health disparities throughout the country. To address the complexities of this issue, health equity has rightfully b...
The COVID-19 pandemic highlighted pervasive health disparities throughout the country. To address the complexities of this issue, health equity has rightfully become a focal point for the current administration. With the resurgence of COVID-19 cases, it’s important to put the lessons of the not-so-distant past into practice.
To achieve this goal, the Centers for Medicare and Medicaid Services recently released an updated Framework for Health Equity 2022-2033. The strategic framework consists of five priorities — Priority 1 is to “expand the collection, reporting and analysis of standardized data.” Specifically, CMS aims to improve its “collection and use of comprehensive, interoperable, standardized individual-level demographic and social determinants of health (SDOH) data.”
The CMS framework is consistent with the objectives in the Executive Order (13985) on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government and aligns with the Department of Health and Human Services’ Healthy People 2030 Framework. HHS’ framework groups SDOH into five domains: economic stability, educational access and quality, health care access and quality, neighborhood and built environment, and social and community context. To adequately understand and address these factors, it’s paramount to collect, standardize, analyze and operationalize health data.
Considerations for data collection and standardization
Healthcare data management and analysis can unlock a plethora of tangible benefits for patients and providers alike. However, if information is not collected, or shared among disparate stakeholders, the true value of that information cannot be realized. As such, it’s imperative that data collection practices be standardized so information is easily transferable and interoperable.
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This is a significant hurdle that a range of healthcare agencies, providers and industry experts are currently exploring. While the ideal methods for data collection and standardization are still up for debate, the objective is universal: situational awareness. Situational awareness is the ability to synthesize a variety of data, determine what is relevant, and act on it. At the very least, healthcare providers should have enough data available to them to make informed decisions about patient care and wellbeing.
While the benefits of data collection and standardization are extensive, valid concerns related to data privacy and security remain. As underscored by CMS in the Health Equity Framework, data collection should always remain voluntary to ensure individual consent, privacy and accurate self-identification.
High quality data on patient demographics, social drivers of health and medical history can empower healthcare providers to rapidly address patients’ needs and optimal health. For example, advanced data algorithms can use patient data to identify and respond to emerging population and geographic health trends. Moreover, this information can be used to track and measure health trends over time, which would enable federal agencies to evaluate the effects of any policy changes on health outcomes and disparities.
Unfortunately, the data available for socially marginalized and underserved communities is often disaggregated and inaccurate. This makes it extremely challenging, if not impossible, to project or determine the best possible care and needed resources. Therefore, the healthcare industry, providers and federal agencies should prioritize inclusivity as they strive to transform policy and mitigate health disparities.
For socially marginalized and underserved communities it is critical to incorporate the feedback of community stakeholders. Community stakeholders often hold the key to necessary collaborations, resources and processes that advance optimal health outcomes.
While the type of data that is collected should be standardized, and the IT systems used to share that data across the healthcare sector must be interoperable, in certain cases it’s crucial to customize care and ensure it suits the population’s needs. Increasing both the quantitative and qualitative data available can help providers determine how to deliver quality care that addresses whole-person health. These outcomes will require extensive and consistent collaboration throughout the entire healthcare sector.
Collaboration is key to securely maximize the benefits of data management
As noted by CMS and HHS alike, with thorough communication, coordination and a shared agenda of advancing health equity the healthcare industry can capitalize on available health data for optimal health outcomes for all. Artificial intelligence and automation are prime examples of innovative data solutions. This technology can predict health trends, project the outcome of specific treatments, promote collaboration of a multidisciplinary team of health care providers and community health workers, and identify otherwise imperceptible healthcare gaps.
Historically, a community’s health needs assessment was required to determine a population’s needs and address disparities. These in-depth assessments can take upwards of 18 months to publish and present, meaning countless individuals do not receive appropriate treatment and a lack of resources persists. Moreover, given the speed at which health trends can shift, the results of the assessment are outdated and no longer relevant or actionable. With sufficient data, AI would be able to generate the same results in a fraction of the time.
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However, it’s critical that the data these algorithms utilize is representative of the population in question. The White House’s Blueprint for an AI Bill of Rights also stresses the importance of human oversight for automated healthcare systems. Given the sensitivity of health data, well-trained IT professionals should continually assess AI-generated results, monitor the cultural competency of provider data, and directly review any automated high-risk decisions.
There is a long road ahead, but an equitable healthcare future can be reached if we all continue to work together and engage in open discussions about how to securely, ethically and strategically leverage data to improve patient outcomes for all Americans.
Kamala Green is the social drivers of health program manager at National Government Services.
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