Real-world examples of descriptive statistics in healthcare examples

Bedside and hospital-level examples of descriptive statistics in healthcare examples

The most familiar examples of descriptive statistics in healthcare are often the ones right in front of clinicians all day.

On a single patient’s vital signs monitor, you see:

• Current heart rate (e.g., 88 beats per minute)
• Average heart rate over the last 24 hours
• Minimum and maximum heart rate during the shift

Those are descriptive statistics. They summarize all the heartbeat-by-heartbeat data into a few clear numbers. Nurses and physicians instantly see whether the patient is stable, trending up, or trending down.

Scale this up to the unit or hospital, and you get more examples of descriptive statistics in healthcare examples:

• Average length of stay in the ICU this month
• Median time from emergency department (ED) arrival to being seen by a provider
• Percentage of ED patients who leave without being seen
• Distribution of patient ages on a pediatric floor

These descriptive summaries drive staffing decisions, bed management, and quality improvement. Hospitals track these numbers over time and compare them to national benchmarks from sources like the Agency for Healthcare Research and Quality (AHRQ) or the Centers for Medicare & Medicaid Services (CMS).

Infection control: examples include rates, averages, and trends

One of the best examples of descriptive statistics in healthcare examples is hospital infection surveillance. Infection prevention teams don’t start with complex predictive models; they start with clean descriptive summaries.

Common descriptive statistics in infection control include:

• Monthly rate of central line–associated bloodstream infections (CLABSI) per 1,000 central line days
• Average number of catheter-associated urinary tract infections (CAUTI) per unit per quarter
• Percentage of surgical cases with a post-operative infection in the last year
• Trend lines showing infection rates before and after a new protocol

A hospital might report that its CLABSI rate fell from 2.5 to 1.3 infections per 1,000 line days over 12 months after implementing a new checklist. That’s a descriptive summary, not a causal proof—but it’s powerful. It gives leadership a clear before-and-after snapshot.

Public-facing dashboards from the CDC’s National Healthcare Safety Network (NHSN) also rely heavily on descriptive statistics. They publish summary infection rates by hospital type, region, and year, allowing facilities to compare themselves to national averages.

For a concrete, up-to-date example, the CDC’s Healthcare-Associated Infections (HAI) data reports show:

• Percent change in standardized infection ratios over time
• Median infection rates by state
• Distributions by hospital size and teaching status

These are all descriptive tools that make complex surveillance data understandable.

Chronic disease management: diabetes and hypertension as real examples

Chronic disease care is another rich source of examples of descriptive statistics in healthcare. Primary care practices, accountable care organizations (ACOs), and health plans track simple metrics that summarize how well they’re managing conditions like diabetes and hypertension.

For diabetes, an example of descriptive statistics in healthcare might look like this:

• Mean HbA1c level for all adult patients with diabetes in the practice
• Percentage of those patients with HbA1c under 7%, between 7% and 9%, and over 9%
• Median time since last HbA1c test

A quality dashboard could show that 62% of diabetic patients have HbA1c under 8%, 25% are between 8% and 9%, and 13% are over 9%. Those percentages are descriptive statistics that immediately flag where to focus outreach.

For hypertension, similar descriptive metrics apply:

• Average systolic and diastolic blood pressure at the last visit
• Proportion of patients with blood pressure under 130/80, between 130/80 and 140/90, and above 140/90
• Standard deviation of blood pressure measurements, showing how variable readings are across the population

The CDC’s National Health and Nutrition Examination Survey (NHANES) uses descriptive statistics to summarize the prevalence of hypertension and diabetes in the U.S. population—reporting percentages by age group, sex, and race/ethnicity. These summaries guide national prevention strategies and funding.

Public health surveillance: COVID-19 and respiratory illness

The COVID-19 pandemic put descriptive statistics on the front page. Every day, agencies like the CDC and the World Health Organization (WHO) published:

• New cases reported in the last 24 hours
• 7-day moving averages of cases, hospitalizations, and deaths
• Percent of tests that were positive
• Hospital bed occupancy rates and ICU capacity

Those daily and weekly summaries are textbook examples of descriptive statistics in healthcare examples. They didn’t predict exactly who would get sick, but they described the current situation and recent trends.

As we move into 2024–2025, public health surveillance has shifted to broader respiratory disease tracking. Descriptive statistics now summarize:

• Percentage of emergency department visits due to COVID-19, influenza, or RSV
• Age distribution of hospitalized respiratory patients
• Regional differences in hospitalization rates

The CDC’s COVID Data Tracker and FluView reports are built almost entirely on descriptive statistics—counts, rates, percentages, and trend lines—organized so policymakers and the public can quickly see what’s happening.

Patient satisfaction and experience: averages, medians, and distributions

Patient experience surveys, such as HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems), are another strong example of descriptive statistics in healthcare.

Hospitals summarize survey responses using:

• Mean scores on questions like “communication with nurses” or “pain management”
• Percent of patients who rate the hospital 9 or 10 out of 10
• Distribution of responses across categories (e.g., “always,” “usually,” “sometimes,” “never”)

An example of descriptive statistics in healthcare here would be a report stating:

78% of patients rated their hospital stay as 9 or 10, with a median rating of 9. The interquartile range of overall ratings was 8–10.

These descriptive numbers help leadership understand where patient experience is strong and where it needs improvement. Comparing current averages to last year’s, or to national benchmarks published by CMS, gives a quick view of progress.

Readmissions and quality metrics: real examples from hospital analytics

Hospital readmissions are expensive and closely watched. Analytics teams use descriptive statistics to keep a constant pulse on performance.

Common metrics include:

• 30-day all-cause readmission rate for heart failure, pneumonia, or myocardial infarction
• Mean number of days between discharge and readmission
• Percentage of readmissions that occur within 7 days vs. 8–30 days
• Comparison of readmission rates across hospital units or physician groups

Imagine a hospital quality report that says:

In 2024, the 30-day readmission rate for heart failure patients was 18%, down from 21% in 2022. The median time to readmission was 11 days, with 40% of readmissions occurring within the first week.

This is another of the best examples of descriptive statistics in healthcare examples: a clear summary that supports targeted interventions, such as earlier follow-up visits or better discharge education.

CMS’s Hospital Readmissions Reduction Program (HRRP) uses descriptive summaries of readmission rates to determine financial penalties and incentives. Hospitals scrutinize these statistics because they directly affect reimbursement.

Telehealth and digital health: 2024–2025 trend examples

Post-pandemic, telehealth has stabilized at a higher baseline than before 2020. Health systems and insurers rely on descriptive statistics to understand how virtual care is being used.

Examples include:

• Percentage of all outpatient visits conducted via telehealth each month
• Average telehealth visit length compared with in-person visits
• Distribution of telehealth use by specialty (behavioral health vs. primary care vs. dermatology)
• Patient satisfaction scores for telehealth vs. in-person care

A health system might report:

In 2025, 22% of all behavioral health visits were conducted via telehealth, with an average visit length of 48 minutes and a 4.7/5 patient satisfaction rating.

These descriptive statistics help executives decide where to invest in infrastructure, how to schedule clinicians, and whether to expand virtual programs.

Reports from organizations like the Office of the National Coordinator for Health Information Technology (ONC) and leading academic centers summarize national telehealth trends using straightforward descriptive statistics—percentages, averages, and year-over-year changes.

Clinical trials and research: baseline tables as classic examples

If you’ve ever looked at a clinical trial paper, you’ve seen one of the purest examples of descriptive statistics in healthcare: the baseline characteristics table.

Before any inferential statistics or hypothesis testing, researchers describe who was in the study using:

• Mean and standard deviation of age
• Median and interquartile range for lab values like creatinine
• Percentages of participants by sex, race/ethnicity, and comorbidities
• Proportions receiving different concomitant medications

For instance, a randomized trial of a new blood pressure medication might report:

Mean age 63.4 ± 8.2 years; 46% female; 28% with diabetes; median baseline systolic blood pressure 152 mmHg (IQR 146–160).

These descriptive statistics tell readers whether the study population looks like their own patients and whether the treatment and control groups were similar at baseline.

The National Institutes of Health (NIH) strongly encourages transparent descriptive reporting so that trial results can be interpreted and reused. Without these foundational summaries, later inferential claims would be much harder to judge.

Operational planning: staffing, bed capacity, and ED crowding

Hospital operations teams live on descriptive statistics. To keep a hospital running, they monitor:

• Average daily census (number of occupied beds) by unit
• Peak census times by hour and day of week
• Mean and median ED wait times
• Percentage of time the hospital is at or above capacity

An operations dashboard might show that ED arrivals spike between 4 p.m. and 10 p.m., with an average wait time of 38 minutes and a median of 27 minutes. The difference between mean and median hints at a skewed distribution, with a subset of patients waiting much longer.

These are practical examples of descriptive statistics in healthcare examples that directly inform staffing levels, shift schedules, and surge planning. During respiratory virus season, descriptive stats on bed occupancy and ventilator use help leaders decide when to open additional units or postpone elective procedures.

Why descriptive statistics matter before anything else

Behind every predictive model, AI triage tool, or risk score, there is a layer of descriptive statistics. Analysts start by cleaning data and summarizing it before they attempt anything more complex.

Some of the best examples of descriptive statistics in healthcare examples are actually internal QA checks:

• Comparing mean lab values across sites to spot data entry issues
• Looking at distributions of ages or diagnoses to catch missing categories
• Summarizing missingness rates (e.g., “12% of patients have no recorded blood pressure in the last year”)

If those descriptive numbers look wrong, analysts know to fix the data before they start modeling. In that sense, descriptive statistics are not just a reporting tool; they are a quality control step.

From bedside monitors to national dashboards, the examples of descriptive statistics in healthcare above show one pattern: simple, well-chosen summaries turn overwhelming data into usable information. You don’t need advanced math to interpret a mean, median, or percentage—but you do need those numbers to run a safe, efficient, and patient-centered health system.

FAQ: examples of descriptive statistics in healthcare

Q1. What are common examples of descriptive statistics in healthcare settings?
Common examples include average length of stay, median wait time in the emergency department, percentage of patients with controlled blood pressure, infection rates per 1,000 device days, 30-day readmission rates, and patient satisfaction scores summarized as means and percentages.

Q2. Can you give an example of descriptive statistics in a clinical trial?
Yes. A typical example of descriptive statistics in a clinical trial is the baseline characteristics table: mean age of participants, percentage of women, median lab values with interquartile ranges, and proportions with comorbid conditions like diabetes or chronic kidney disease. These summaries describe who was studied before any treatment effects are analyzed.

Q3. How are descriptive statistics different from predictive models in healthcare?
Descriptive statistics summarize what has already happened: averages, counts, percentages, and distributions. Predictive models try to estimate what is likely to happen in the future, such as a patient’s risk of readmission. In practice, predictive modeling always starts with descriptive statistics to understand and validate the data.

Q4. What are some real examples of descriptive statistics used in public health reports?
Public health agencies like the CDC routinely publish real examples of descriptive statistics: weekly case counts, 7-day moving averages of hospitalizations, test positivity percentages, age-stratified hospitalization rates, and state-level vaccination coverage. These summaries guide policy decisions and public messaging.

Q5. Why do hospitals rely so heavily on examples of descriptive statistics in healthcare examples for quality improvement?
Hospitals need fast, understandable feedback. Descriptive statistics provide that: a clear picture of current performance and trends over time. Whether it’s infection rates, readmissions, or telehealth adoption, these statistics highlight where care is improving and where targeted interventions are needed, without requiring advanced statistical training to interpret.