How Data Analytics Improves Health Outcomes
A growing number of healthcare organizations are leveraging big data analytics to address social injustice.
Many organizations recognize the importance of big data analytics for finding opportunities to improve the quality of life for various at-risk groups. By analyzing massive volumes of information, decision-makers can make data-informed decisions to improve the quality of multiple services and benefits rather than relying on guesswork. These data analytics tools can provide lawmakers, policy changers, and social organizations to make decisions with positive impacts.
Provider organizations are finding innovative ways to deliver improved health outcomes for a wide range of issues. By analyzing the patient data of at-risk groups, organizations are improving service deployment and quality.
Using Data Analysis to Help the Nation’s Heroes
As of 2018, there are nearly 5 million current veterans records that officials can analyze to help improve the quality of life for the group, according to the United States veteran administration. In partnership with tech companies, the Veterans Administration has developed tools and practices to better serve the needs of the nation’s heroes.
For instance, the National Health Study for a New Generation of U.S. Veterans helps the VA to explore the overall health of new veterans. The goal of the initiative is to understand the needs of the group to offer them the best possible quality of treatment.
Approximately 20 veterans commit suicide every day. Another data-informed initiative led by the VA – REACH VET – helps to identify former soldiers with a high probability of attempting suicide. The program uses predictive analytics to pinpoint veterans who are highly likely to commit the act.
REACH VET coordinators use the resulting analyses to connect with and identify at-risk individuals. To date, the initiative has served 30,000 veterans.
Among those contacted, nearly 7,000 veterans use the program every month. It has resulted in fewer instances of mental health hospitalization and increased use of available mental health services.
Leveraging Data to Improve Public Health Outcomes
Each year, public health advocates gather to discuss how information technology influences medicine in the United States during National Health IT Week. Healthcare Information and Management Systems Society (HIMSS) representatives express that the nation’s healthcare system must address several issues to improve treatment coordination, including:
-Increasing patient satisfaction
-Increasing provider satisfaction
-Reducing testing redundancy
To accomplish these goals, express officials, nonclinical and clinical healthcare providers must somehow facilitate information sharing and data matching.
Today, the social determinants of health (SDOH) are the largest determinate of public health outcomes. As a result, care providers have a strong desire to understand patient backgrounds so that they can deploy appropriate interventions and support.
By analyzing SDOH, HIMSS trusts that healthcare providers can make data-informed decisions that will improve the quality of services in the United States and minimize the threats faced by at-risk groups. Such information might include:
-Food insecurity reporting
With this information, care provider organizations can work with municipalities to mitigate threats before they result in the need for care. Resultantly, a growing number of healthcare professionals recognize the importance of analyzing SDOH data to improve public health outcomes.
According to Dr. Kislaya Prasad, research professor and academic director of the Center for Global Business at the University of Maryland Robert H. Smith School of Business, digital literacy and wider broadband access become critically important for health.
“We need a conscious effort on the part of government, foundations, and corporations to promote the development of easy to use and low-cost devices for the management of health. Funding agencies and medical researchers need to be alert to data gaps (and the growing disparity in user-generated data) and their effects on population health.”
Data Analysis Can Eliminate Healthcare Gender Bias
Around the world, there’s a 32% gender gap in the ratio of male to female talent, according to the World Economic Forum. Logic dictates that all researchers and medical professionals would intuitively leverage the scientific method to eliminate gender disparity in the healthcare field.
Unfortunately, this is not the case. Studies show that 40% of United States medical schools fail to stimulate equality in the healthcare field by recruiting, promoting and training women.
Gender bias places institutions at higher risk of failure, employee churn, and burnout. It also decreases the quality of care and increases the likelihood of litigation due to perceived malpractice. Ultimately, gender bias in the medical field reduces the quality of care for patients.
Some organizational leaders have tried to address the issue of gender equality in the healthcare field. However, their methods so far have mainly been instinctual. Resultantly, efforts to deal with gender bias have targeted specific and local issues, rather than addressing the systemic roots of the problem.
The Be Ethical Campaign is an organization that strives to compel healthcare leaders to use data-informed decision-making to promote equity, diversity, and inclusion. The campaign urges decision-makers to learn more about workplace gender equality research. Such knowledge will enable executive leaders to avoid bias decision-making and ensure that female professionals receive fair representation in the workplace.
Healthcare organizations hope that big data analyses can help providers head off threats in the community. With a wealth of information, care providers hope to deliver interventions outside of the clinical setting.
By using data analysis to understand the social determinants that place individuals at risk, care providers hope to overcome the challenges of addressing the needs of at-risk groups with effective interventions.