The healthcare industry has learned an unwelcome lesson because of the COVID-19 outbreak. In addition to putting a significant load on the healthcare system, it has helped us understand how crucial it is to update data to lessen healthcare inequity. Therefore, selecting the ideal healthcare analytics consulting partner is essential if we want to advance long-term equity in healthcare and eliminate bias from the data.

1. HOW CAN A REVAMPED
DATA ANALYTICS APPROACH
MITIGATE HEALTHCARE
DISPARITIES?

2. B
ig data and analytics have greatly aided in enhancing patient outcomes and allowing val-
ue-based care for many years till now. Healthcare professionals (HCPs) were able to estab-
lish good defenses and plan effective reactions to the Covid-19 outbreak because of data.
The ability to leverage data comes with a warning in healthcare, even though it is ultimately the
key to closing the disparities gaps. In downstream analysis, the bias present in the originating
data is typically maintained, considerably enhancing the disparity already there.
Using Data Analytics to Tackle the Healthcare Disparities Gap:
• Ensuring Data equality: Data equity at the source can make healthcare decisions fairer.
The machine learning models represent the diversity of backgrounds and eliminate bias in
algorithms by strengthening data equality at the retrieval and collecting stages. Utilizing
nationwide surveys that oversample vulnerable groups, merging related clinical surveys from
different periods, and targeted periodic surveys can help overcome sample size limits. For
underprivileged communities, these strategies can aid in closing profile gaps. In addition,
data collection on race, ethnicity, and income may be encouraged by offering data suppli-
ers to include diversity markers.
• Exterior Sources: The target population’s patient profiles need to be created in detail by
healthcare professionals. As a crucial step to expanding the scope of data beyond EHRs,
HCPs should develop a large ecosystem including specialists, healthcare organizations,
pharmaceutical firms, communities, affiliates, technological partners, and wearable makers.
Race, Ethnicity, Language, demographic information, medical information, and information
from public health organizations are also self-reported and patient-generated remote infor-
mation.
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3. • Machine Learning Algorithms to Identify Patterns Of Disparity: Health Care Providers must
actively work together to construct the predictive models that inform decisions with their an-
alytics partners. To reduce the healthcare gap, the establishment of data processes and their
improvement through objective data gathering, analytics, and insight-driven action must be
the focus of healthcare providers.
Data issues that enhance healthcare disparity:
• Source and Management of Atomistic Data: Critical patient data factors like poverty, color,
and ethnicity are frequently omitted from Electronic Health Records (EHR), creating a data
gap that exacerbates bias in downstream analytics and decision-making and may lead to
an unequal distribution of care. It is essential to have a thorough and consistent data collec-
tion process that considers patients’ ethnic and racial features. Large sample sizes of data
with self-reported racial and ethnicity information are required to identify data discrepancies
within the population, which increases accuracy.
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4. • Lack of standards for data exchange: Healthcare participants have hastily embraced various
standards for gathering, organizing, and encoding clinical data to digitize data. The Social
Determinants of Health are collected using instruments, formats, and methodologies (SDOH).
Even using popular technologies, health care providers still use individual forms to do busi-
ness. Health Care Providers will be able to interact more effectively and rapidly by using a
common data vocabulary, resulting in a healthcare ecosystem that supports patients. Stan-
dardizing the SDOH collection would help encourage the interoperability needed to include
all demographic segments in the availability of healthcare at the appropriate time and from
the provider.
• Coordination and Connectivity Gaps: Health Care Providers, care management teams, and
follow-up teams sometimes struggle to work well together and provide their data teams with
a multidisciplinary training. Participants in the healthcare industry must establish interdisci-
plinary teams that communicate using a common data vocabulary and the authority to share
clinical data regardless of the instruments they employ.
Conclusion
The healthcare industry has learned an unwelcome lesson because of the COVID-19 outbreak.
In addition to putting a significant load on the healthcare system, it has helped us understand
how crucial it is to update data to lessen healthcare inequity. Therefore, selecting the ideal
healthcare analytics consulting partner is essential if we want to advance long-term equity in
healthcare and eliminate bias from the data.
anumak.ai

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