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What Is Quantitative Research in Healthcare, and How Does It Improve Patient Outcomes?

Quantitative research in healthcare provides the foundation for evidence-based practice by translating numerical data into meaningful insights. Through methods such as clinical trials, surveys and epidemiological studies, researchers can measure outcomes, identify trends and evaluate the effectiveness of medical interventions.

Involving the public in quantitative research can make samples more representative and ensure the results focus on the outcomes that matter most, according to research. This makes the research more accurate to advance medical knowledge. At UCF, our online healthcare systems engineering program teaches students to apply quantitative analysis in healthcare. Through the master’s and certificate tracks, students develop skills in systems modeling, simulation and quality management to improve patient experience, optimize operations and lead evidence-based improvements across healthcare organizations.

Keep reading to see how quantitative research in healthcare is improving patient outcomes.

Explore UCF’s Healthcare Systems Engineering Program

Key Takeaways

  • Collecting and analyzing numerical health data can identify trends, measure outcomes and evaluate treatment effectiveness.
  • Engaging patients and the public in research helps make study samples more representative and ensures outcomes reflect what truly matters.
  • Applying methods like epidemiological studies and surveys can track disease patterns, resource use and patient behaviors.
  • Clinical trials, cohort studies and health outcomes research can compare treatment effects, reduce readmissions and improve patient care strategies.
  • Translating quantitative findings into actionable insights helps optimize healthcare delivery, inform policy and enhance population health.

What Is Quantitative Research in Healthcare?

Quantitative research in healthcare focuses on collecting and analyzing numerical data to uncover measurable patterns, test hypotheses and identify relationships between variables. Unlike qualitative research, which explores experiences and perceptions, quantitative research relies on numbers, statistics and routine data collection to draw conclusions that can be generalized to larger populations.

Dr. Richard Biehl, the former education coordinator of our online healthcare systems engineering program, notes, “We used to get the data to support our research, now we’re getting the data to suggest our research. That’s very different.” This highlights how quantitative analysis can point researchers toward unexpected and promising insights.

How Is Quantitative Research in Healthcare Evolving?

Today, the scope of quantitative research is expanding even further thanks to wearable devices and connected health technologies. As Dr. Biehl explains, personal health data collection from devices like fitness trackers and smartwatches allows researchers to access hundreds of data points per user per day. This information helps detect early warning signs, monitor treatment effects, better understand population health trends and help determine appropriate public health interventions.

What Methods Are Used in Quantitative Research?

Quantitative research uses a variety of methods depending on the research question, desired outcomes and type of data needed. Some of the most common approaches include:

Clinical Trials

Clinical trials are controlled experiments designed to test the safety and effectiveness of new medications, treatments or medical devices. Participants are randomly assigned to groups to compare outcomes, ensuring reliable, statistically valid results.

Epidemiological Studies

These large-scale studies investigate disease prevalence, patterns and causes within specific populations. By tracking data such as infection rates or risk factors, researchers can identify trends and develop prevention strategies.

Surveys and Questionnaires

Structured surveys with closed-ended questions provide numerical data collection from patients, healthcare providers or the public. They are often used to measure patient satisfaction, adherence to treatments or public health behaviors.

Cohort Studies

Cohort studies follow a group of people over time to determine how various factors — like lifestyle, genetics or environment — affect the likelihood of developing certain conditions.

Health Outcomes Research

This approach evaluates how medical treatments or healthcare systems impact patient health, quality of life and cost-effectiveness. It helps healthcare providers and organizations identify the most efficient and beneficial care strategies.

What Are Examples of Quantitative Research in Healthcare?

These methods are actively shaping healthcare outcomes around the world. Here are examples of how quantitative research in healthcare is being applied in real-world settings:

Evaluating Vaccine Effectiveness

Large-scale clinical trials rely on quantitative approaches to measure how well vaccines prevent infection across different populations. Researchers collect numerical data on infection rates, side effects and antibody levels to determine vaccine efficacy and guide public health interventions. For example, updated COVID-19 vaccines led to 39% fewer hospitalizations and 64% fewer deaths among U.S. military veterans, according to a 2025 study.

Measuring Hospital Readmission Rates

Hospitals often analyze quantitative data to identify patterns in patient readmission. By examining factors such as age, diagnosis and length of stay, healthcare providers can design interventions that reduce readmissions and improve quality of care. For example, the Hospital Readmissions Reduction Program (HRRP), which used this data-driven approach, helped lower 30-day readmission rates for acute myocardial infarction among Medicare patients. Quantitative research made it possible to track trends, identify high-risk patients and implement targeted strategies that measurably improved hospital care outcomes.

Tracking Disease Trends Through Epidemiology

Epidemiologic data helps monitor and predict the spread of diseases such as diabetes, heart disease or influenza. This data collection helps public health officials allocate resources, plan prevention programs and evaluate the success of community health initiatives.

Assessing the Impact of Telehealth

As virtual care becomes more common, researchers use quantitative surveys and outcome measures to evaluate telehealth’s effectiveness. Metrics such as appointment completion rates, patient satisfaction scores and cost savings provide insights into how digital healthcare is improving accessibility and efficiency. As of early 2024, approximately 54% of Americans have participated in at least one telehealth visit, according to Telehealth Statistics. This reflects a substantial rise in virtual care utilization.

Analyzing Treatment Outcomes in Chronic Disease Management

Quantitative data analysis techniques are essential for comparing the results of different treatment plans for chronic conditions like hypertension or asthma. By analyzing blood pressure readings, medication adherence rates and symptom frequency, clinicians can identify the most effective strategies for long-term health management.

What Are the Characteristics of Quantitative Research?

Quantitative studies share several defining features that make their results reliable:

  • Data-Driven: Relies on measurable data such as patient vital signs, lab results or the number of cases within a population.
  • Statistical Analysis: Uses mathematical tools to identify trends, test hypotheses and find relationships between variables.
  • Objective Results: Findings are presented through charts, graphs and numerical summaries that minimize personal bias.
  • Large Sample Sizes: Often includes many participants to ensure results accurately reflect patterns across a wider population.

Why Does Quantitative Research Matter in Healthcare?

The ultimate goal of quantitative research is to strengthen evidence-based decision-making. By analyzing measurable outcomes, healthcare professionals can:

  • Compare treatment or intervention effectiveness
  • Predict future health trends and potential risks
  • Assess the quality, safety and efficiency of healthcare services
  • Identify and measure the prevalence and incidence of diseases
  • Support healthcare policies and resource allocation with reliable evidence

What Are the Challenges in Conducting Quantitative Research in Healthcare

While quantitative approaches provide clear, objective data, they are not without challenges. Maintaining data quality is critical, as incomplete or inaccurate information can skew results. For example, large-scale surveys can overestimate outcomes. Vaccine uptake was overreported by 14 to 17 percentage points compared with benchmark CDC data, according to one study. This highlights the importance of representative sampling.

Researchers must also ensure representative sampling to avoid bias and adhere to ethical standards, including patient confidentiality and informed consent. Additionally, large-scale studies often require significant time, funding and specialized expertise.


Learn Quantitative Research in Healthcare at UCF

At UCF, you can earn a master’s or graduate certificate in Healthcare Systems Engineering, teaching students the skills to improve healthcare systems. Through classes in quantitative analysis, systems modeling, simulation and quality management, you can develop the tools to optimize operations, manage risks and implement system-wide improvements that enhance patient experience. These programs prepare graduates to lead evidence-based initiatives, redesign complex healthcare systems and drive innovation across the industry.

How Is Quantitative Research Shaping the Future of Healthcare?

Quantitative research is transforming healthcare by turning data into actionable insights that improve patient outcomes, optimize operations and guide evidence-based decisions. By measuring trends and analyzing results, healthcare professionals can make informed choices that address quality of care and emerging challenges.

At the University of Central Florida, the online Master of Science in Healthcare Systems Engineering program prepares students to lead in this field. It equips students with the skills to apply quantitative analysis, systems thinking and process improvement techniques. As a result, this program can enable them to design smarter, more efficient and patient-centered healthcare systems that shape the future of medicine.

Explore UCF’s Healthcare Systems Engineering Programs

Frequently Asked Questions About Quantitative Research in Healthcare

What is an example of quantitative research in healthcare?

xamples of quantitative research in healthcare include clinical trials testing the effectiveness of a new medication, epidemiological studies tracking disease trends and surveys measuring patient satisfaction.

What are the four types of quantitative research?

The four common types are:

  1. Descriptive Research – Measures and describes characteristics of a population (e.g., patient demographics)
  2. Correlational Research – Examines relationships between variables (e.g., smoking and lung disease rates)
  3. Experimental Research – Tests interventions under controlled conditions (e.g., controlled trials)
  4. Causal-Comparative Research – Compares outcomes between groups to determine cause-and-effect relationships

What is quantitative data in healthcare?

Quantitative data refers to numerical information used to analyze health outcomes, such as patient vital signs, lab results, number of disease cases or survey ratings. It enables objective, statistically supported conclusions that can be applied to larger populations.

What advantages does quantitative research offer in healthcare?

Quantitative research offers healthcare professionals objective, data-driven insights that reduce bias and support evidence-based decision-making. By analyzing measurable trends and outcomes, quantitative data allows for accurate predictions, evaluation of treatment effects and improvements in healthcare processes. Large, representative samples ensure that findings can be generalized to broader populations, maximizing their impact on patient care and policy.

Source References

  1. Hannigan, Ailish. “Public and Patient Involvement in Quantitative Health Research: A Statistical Perspective.” Health Expectations, 2018. https://pmc.ncbi.nlm.nih.gov/articles/PMC6250860/
  2. Reuters. “Updated COVID Shot Led to Less Severe Illness, Fewer Hospitalizations Last Year in U.S.” Reuters, Oct. 8, 2025. https://www.reuters.com/business/healthcare-pharmaceuticals/updated-covid-shot-led-less-severe-illness-fewer-hospitalizations-last-year-us-2025-10-08/
  3. Wadhera, Rishi K., Karen E. Joynt Maddox, Jason H. Wasfy, Sebastien Haneuse, Changyu Shen, and Robert W. Yeh. “Association of the Hospital Readmissions Reduction Program With Mortality Among Medicare Beneficiaries Hospitalized for Heart Failure, Acute Myocardial Infarction, and Pneumonia.” JAMA, 2018. https://pubmed.ncbi.nlm.nih.gov/30575880
  4. Mastermind Behavior. “Telehealth Statistics.” Mastermind Behavior, 2025. https://www.mastermindbehavior.com/post/telehealth-statistics
  5. Bradley, Valerie C., Shiro Kuriwaki, Michael Isakov, Dino Sejdinovic, Xiao-Li Meng, and Seth Flaxman. 2021. “Unrepresentative Big Surveys Significantly Overestimate US Vaccine Uptake.” arXiv. https://arxiv.org/abs/2106.05818

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