HEALING WITH DATA: HOW DATA
SCIENCE IS REVOLUTIONIZING
HEALTHCARE

The healthcare industry is facing numerous
challenges, including rising costs, an aging population,
and a shortage of medical professionals. To address
these issues, many healthcare organizations are
turning to data science.
Data science can help healthcare providers make
better decisions, improve patient outcomes, and
reduce costs. In this presentation, we will explore the
problems in healthcare that lead to the use of data
science.
INTRODUCTION

Patients and providers face a great amount of uncertainty
before, during, and after hospital encounters. Without
data science, healthcare providers may have difficulty
predicting outcomes for patients. They may not have
access to historical data on similar cases or be able to
identify risk factors that could impact a patient's
recovery. This can lead to uncertainty in treatment
decisions and suboptimal outcomes.
Data science can help healthcare providers predict
outcomes by analyzing historical data on similar cases
and identifying risk factors. This allows them to make
informed decisions about treatment options and improve
patient outcomes.
DIFFICULTY IN
PREDICTING OUTCOMES

RISING COSTS
One of the biggest problems in healthcare is the rising cost of
medical treatment. Healthcare costs can increase due to
inefficiencies and suboptimal treatment decisions. Providers
may not have access to data on cost-effective treatments or
be able to identify areas where cost savings can be achieved.
With more people requiring medical care, the cost of
providing that care has increased significantly.
Data science can help reduce healthcare costs by identifying
areas where cost savings can be achieved and analyzing data
on cost-effective treatments. This allows healthcare providers
to make informed decisions about treatment options and
reduce unnecessary spending. It can help healthcare
providers identify ways to reduce costs by analyzing large
amounts of data. By identifying patterns and trends,
providers can optimize their operations and reduce waste.

As the baby boomer generation ages, the demand for
healthcare services is increasing. There are certain health
conditions that are expected to be a challenge to our
healthcare system with the increasing aging population.
As people age, they are more likely to experience several
conditions at the same time. This presents a significant
challenge for healthcare providers, as they must find ways
to provide quality care to a growing population.
Data science can help healthcare providers manage this
challenge by analyzing patient data to identify the most
effective treatments and interventions. By using data to
personalize care plans, providers can improve patient
outcomes and reduce costs.
AGING POPULATION

Another problem facing the healthcare industry is a
shortage of medical professionals. Workforce shortage may
be defined as not having the right number of people with
the right skills in the right place at the right time, to provide
the right services to the right people As the demand for
healthcare services increases, there are not enough doctors
and nurses to provide care. In short, there is an imbalance
between need and supply.
Data science can help address this problem by automating
routine tasks and freeing up medical professionals to focus
on more complex cases. For example, data science can be
used to analyze patient data and identify potential health
issues before they become serious, allowing medical
professionals to intervene early.
SHORTAGE OF MEDICAL
PROFESSIONALS

Medical errors are a significant problem in healthcare, with
studies suggesting that they may be responsible for as many as
250,000 deaths per year in the United States alone. This might
include an inaccurate or incomplete diagnosis or treatment of
a disease, injury, syndrome, behavior, infection, or other
ailments. It is challenging to uncover a consistent cause of
errors and, even if found, to provide a consistent viable
solution that minimizes the chances of a recurrent event.
Data science can help reduce the incidence of medical errors
by analyzing patient data to identify patterns and trends that
may indicate a potential issue. By using data to predict and
prevent errors, healthcare providers can improve patient
outcomes and reduce costs associated with medical
malpractice.
MEDICAL ERRORS

Low-quality healthcare services are holding back progress on
improving health in countries at all income levels, according
to a joint report by the OECD, World Health Organization
(WHO), and the World Bank. It leads to sicker patients, more
disabilities, higher costs, and lower confidence in the
healthcare industry. Without data science, the quality of care
provided to patients can suffer. Providers may not have
access to data on best practices or be able to identify areas
where improvements can be made. This can lead to poor
patient outcomes and decreased satisfaction.
Data science can help improve the quality of care by
analyzing data on best practices and identifying areas where
improvements can be made. This allows healthcare providers
to make informed decisions about patient care and improve
patient outcomes and satisfaction.
POOR QUALITY OF CARE

Without data science, healthcare providers may not be
able to offer personalized medicine to patients. They may
not have access to information on a patient's genetic
makeup, lifestyle, or environmental factors that could
impact their health. This can lead to generalized
treatments that may not be effective for all patients.
Data science can help healthcare providers offer
personalized medicine by analyzing patient data and
identifying patterns and correlations. This allows them to
tailor treatments to individual patients based on their
unique characteristics and needs.
LACK OF PERSONALISED
MEDICINE

Health resources allocation refers to how the government or
the market allocates health resources in different fields,
regions, departments, projects, and populations fairly and
efficiently, so as to maximize the social and economic benefits
of health resources. Healthcare organizations often struggle
with inefficient resource allocation without data science. They
may not have accurate data on patient demand, staff
availability, or equipment utilization. This can lead to long
wait times, overcrowding, and wasted resources.
Data science can help healthcare organizations optimize
resource allocation by analyzing data on patient demand, staff
availability, and equipment utilization. This allows them to
make informed decisions about staffing levels, scheduling,
and equipment purchases.
INEFFICIENT RESOURCE
ALLOCATION

Without data science, healthcare providers have a limited
understanding of patient needs. Patient data is often scattered
across different departments and systems, making it difficult
to obtain a comprehensive view of their health status. This can
lead to misdiagnosis, improper treatment, and even medical
errors. When a patient cannot access her clinician, it is
impossible to receive medical care, build relationships with
her providers, and achieve overall patient wellness. 66% of
patients expect medical practices to understand their needs and
expectations, yet most of them are treated like numbers.
Data science can help consolidate patient data from various
sources and provide a complete picture of their health status.
This allows healthcare providers to make informed decisions
about patient care and tailor treatments to individual needs.
Limited Understanding
of Patient Needs

CONCLUSION
In conclusion, data science has the potential to
revolutionize healthcare by improving diagnosis
and treatment, delivering personalized medicine,
and improving efficiency and cost savings.
However, there are also significant challenges
and limitations that must be addressed. By
working together, data scientists and healthcare
professionals can overcome these challenges and
unlock the full potential of data science in
healthcare.