Today business intelligence and data analytics are the primary
technologies that enable Population Health Management. Population
healthcare management (PHM) is a healthcare methodology that
describes and enables care delivery across a population or a group
of individuals. To achieve the goals of Population Health
Management, the clinical, financial, and operational data from the
overall organization must be brought together and actionable steps
for providers via analytics, including population health data
analytics and Predictive analytics. Population health analytics
technologies and effective programs will provide real-time
insights, allowing providers to identify and address any care gaps
within the patient population. This enables a healthcare
organization to improve patient outcomes while also saving money by
predicting individual healthcare journeys to deliver
person-centered care and the costs involved. The data gathered
during the Population Health Management (PHM) analytics phase
informs 'what' interventions are implemented to 'who' in the
population and 'how' this will be measured. Aside from the variety
of data required for this method, big population health care data
are distinguished by high volume, high velocity, and inconsistent
data flows. Therefore big data continues to grow in size. The
specialized tools and analytical methods required to extract
valuable insights from Big Data sources revolutionize big data's
use in public health. These specialized technologies are referred
to as "big data analytics." Using big data analytics to improve
public health can help with research, surveillance, and
intervention. As a result, it can help to design and implement more
effective, evidence-based public health policies. RESEARCH: Allows
precise identification of at-risk populations in a better way of
understanding of human health and disease through population health
solutions, including the interaction of genetic, lifestyle, and
environmental determinants of health; SURVEILLANCE: Allows for
improved surveillance of communicable and non-communicable
diseases; and INTERVENTION: Improves health promotion and disease
prevention through more targeted strategies and interventions.
Implementing big data to help, manage and evaluate population
health analytics has resulted in several healthcare improvements.
The population health management, reporting, and evaluation
processes generated additional data that, when analyzed, helps to
improve program implementation and quality. This population health
management framework, which incorporates Big Data, determines which
focus areas best fit the new managerial and population
requirements. Such development methodologies generally use data to
find ways to improve the quality, efficiency, or equity of care
provided. It usually consists of: Identifying unjustified variation
in the system Analyze duplication Bridge the Gaps Quadruple Error
analysis where events in the population are high cost, low quality,
represent a poor patient experience and contribute to increased
inequalities. Therefore, the professionals of population health,
fraud, and errors can be easily detected and prevented when using
big data and predictive analytics, saving healthcare organizations
a lot of money. There are already several big data solutions and
analytics solutions available to assist providers in preventing
such frauds and human errors, particularly when it comes to dosage.
CONCLUSION: Big data has the potential to entirely transform
population Health management. To improve patient outcomes, reduce
costs, and increase efficiency across all departments, the
incorporation of Big Data is the need of the hour. Perhaps more
importantly, big data will assist clinicians and hospitals in
providing more targeted healthcare and achieving better results.
Big data is a driving force for pharma companies, allowing them to
design and build more innovative drugs and products. Thus entire
healthcare stakeholders can rely on population health management,
big data, and predictive analytics to address significant
readmission rates, high-risk patient care, staffing issues, dosage
errors, and other issues. Source:
https://www.osplabs.com/population-health-analytics/
The Healthcare Industry is increasing when it comes to the
development of their patient's health and safety. Population Health
Analytics collects the local population's data across multiple
information technology resources combined into a single patient's
record, improving their clinical and financial outcomes. It assists
with the proper patient identification and seamlessly collects
healthcare data from multiple healthcare sources. Population Health
Solutions is an approach to identify and resolve healthcare issues
of the whole population. It segments the needs of the patients and
provides them with the best services to meet their needs. The local
population is divided into several groups depending on their health
risk identification and various aspects such as health behaviors,
health status and use, and quality of available services.
Population Healthcare Data Analytics and risk stratification
contribute to improved overall health outcomes, increasing patient
satisfaction and loyalty towards this digital technology. It
instantly discovers high-risk patients from a clinical perspective,
resulting in providing them with the best possible services. The
performance and feedback are measured and recorded in the patient's
data, and the benefits are tailoring accordingly. The ultimate goal
of population health data analytics is to improve patients'
healthcare quality while reducing cost. The Healthcare providers
are incentivizing to treat and provide as many patients as
possible; the payment for such is highly dependent on the service
and the following health outcomes. Source:
https://www.osplabs.com/population-health-analytics/
Healthcare organizations worldwide with a large amount of patient
population are fast-growing. It depends on the tried and tested or
evidence-based theory to safeguard the patients' health and safety.
Back when the traditional principle was following called
"Fee-for-service," which only aimed at dealing with the patients'
sickness and not with health outcomes, leading to less attention
and engagement towards the patient's health. Many healthcare
providers highly use population healthcare management to improve
patients' health by tracking and monitoring the clinical and health
outcomes while estimating costs. The need for population health
analytics arises due to various challenges faced, such as
pharmaceutical costs, risk stratification, proper patient
identification, etc. The communication gap between doctors and the
patients was the central issue where the patients hesitated or did
not centralize an exact conversation with their healthcare
providers. It overcame all the challenges due to population health
solutions. It engaged more and more patients beyond the shorter
timeframes and helped identify high-risk patients via an easy
patient identification strategy. The healthcare industry has
initiated various population health management strategies that
provide positive health outcomes. The local population's data is
collected as a whole and later transformed into a single patient's
record depending on the patient's health status and the best
quality of services available for him/her. Proper patient
identification makes it easier to ensure the adequate delivery of
the patients' benefits, hence improving their health and safety.
After collecting data from the local population, they identify and
evaluate improvement opportunities based on care variation. Due to
this digital revolution, healthcare organizations have switched
from a fee-for-service model to a value-based model, which brings
about positive health outcomes. It optimizes care management
processes and products to support individuals across the continuum
of care. Conclusion: Population Health Analytics rewards healthcare
providers for promoting wellness and offering additional funding to
improve workflow and technology. These changes make it easier for
them to balance evidence-based practices and focus on preventative
care, improving care quality. Source:
https://www.osplabs.com/population-health-analytics
