Taking an in-depth look at data science in healthcare

The healthcare services market size has grown significantly in recent years.
Also, it’s expected to grow to $10,908.99 billion in 2028 at a compound annual growth rate (CAGR) of 5.0%.

In this article, we’ll explore how data science (DS) contributes to the development of the healthcare industry by enhancing patient care, improving patient outcomes, and reducing costs.

What’s more, we’ll take a closer look at DS benefits, challenges, and trends.

FYI. Data science (DS) involves using various techniques and tools to analyze and interpret complex datasets.

Its goal is to extract knowledge from data to help make informed decisions.

Not all AI is DS, but there’s a huge overlap. To understand the difference between AI, ML, and data science terms, read this guide first.

Better patient care

Hospital readmissions are considered to be one of the most important issues that influence the quality of patient care.

Research shows that 15.3% of 491 patients were readmitted within a month of their discharge. The most common cause was persistence of symptoms (43%).

To deal with this issue, DS assists in identifying at-risk patients and providing specialized follow-ups to ensure that nobody is discharged until they’ve been completely cured (and nobody stays in the hospital longer than needed).

Also, using predictive analytics, it’s possible to forecast and prepare for patient readmissions based on their historical data, patient profiles, and treatment outcomes.

Therefore, data science leads to reductions in readmission rates, hospital stays, and healthcare costs.

Early disease prevention & diagnosis

Logistic regression and neural networks are used for early disease detection, which results in enhanced diagnostic accuracy, early intervention rates, and better patient outcomes.

Also, these machine learning (ML) techniques analyze patient data to identify patterns and predict the likelihood of disease development, allowing for timely and targeted medical interventions.

Providers and payers for care also use ‘population health’ ML models to even predict populations at risk of particular diseases or accidents.

For example, Cancer Epidemiology, Biomarkers & Prevention research showed that a predictive analytics model was able to accurately identify patients at higher than normal risk for pancreatic cancer.

By identifying the high-risk population, doctors can improve prevention efforts and early screenings.

Personalized medicine & treatment plans

According to research, machine learning algorithms can read clinical charts quicker and more correctly than human review methods, enabling plans and providers to detect hidden risk factors and healthcare gaps, increase risk score accuracy, and make informed choices for better patient care.

That’s why using ML algorithms, such as decision trees and random forests, to tailor treatment based on patient data leads to enhanced treatment efficacy, reduced adverse reactions, and patient satisfaction.

Enhanced new drugs & therapies development

Data science presents numerous opportunities to increase the efficiency and speed of the discovery process.

For example, ML algorithms are used to find and predict the effectiveness of potential medication candidates.

This is achieved by analyzing large amounts of data, such as genetic information, clinical trial data, and molecular structures.

Optimized healthcare workflows & resource allocation

Through the analysis of patient flow, use of resources, and wait times data, DS approaches can boost healthcare operations.

By optimizing scheduling, staffing, and resource allocation, this data eventually improves patient care and lowers expenses.

As a result, organizations reduce operational costs, improve patient throughput, and increase resource utilization.

Create a privacy policy

Create a clear privacy policy, which outlines the procedures of data collection, processing, storage, and sharing within the organization.

Organize staff training on best practices, security protocols, and compliance requirements to foster a data security culture.

These are the first steps you should take to avoid data leaks. To learn more, check out this article.

Data accuracy

How to ensure the results are accurate and you can trust those insights?

The answer is pretty simple — choose true professionals. This is because data science is a real science.

To be good at it, strong statistical and math knowledge is needed.

Lots of applicants are underqualified. They simply don’t have relevant data backgrounds. No STEM degree, just a 3-month bootcamp certification.

That’s why organizations face a talent shortage in data science, trying to find the right team with in-depth knowledge and domain expertise.

To find top professionals, reach out to data science companies with technical expertise and business understanding, do some background research on the data scientists who’ll work with you, and select a vendor you can rely on.