Top 5 Time-Consuming Tasks in Clinical Data Analysis: How to Automate Them

Automating Clinical Data Analysis

Clinical researchers today are under more pressure than ever to generate insights quickly, accurately, and at scale. But behind every breakthrough finding is a long list of tedious, time-consuming tasks that can drain resources and delay progress.

At OpenSimplify, we’ve worked with researchers across hospitals, academic institutions, and CROs and the same five bottlenecks keep showing up. The good news? These can now be automated with no-code tools like OpenSimplify.

Let’s break down the top time-drainers in clinical data analysis and how automation can turn them into minutes instead of hours.

1. Cleaning and Preparing Messy Datasets

The problem: Clinical datasets are often riddled with missing values, duplicate entries, inconsistent coding, and misaligned formats. Data prep can take longer than the actual analysis and introduces human error.

How OpenSimplify helps:

• Automatically identifies and flags missing or inconsistent values

• One-click handling of outliers and duplicates

• Standardizes categorical variables (e.g., M/F vs Male/Female)

• Batch transforms columns without code

Time saved: Hours of manual Excel or R wrangling.

2. Merging Data from Multiple Sources

The problem: Researchers often work with multiple files: lab results, clinical forms, survey data, each with its own structure. Merging them can be tricky, especially when IDs don’t match cleanly.

How OpenSimplify helps:

• Upload multiple files and match on common variables (patient ID, visit date)

• Visual interface to preview merges and resolve conflicts

• Automatic deduplication and harmonization

Time saved: 1–2 hours per study phase, per dataset.

3. Generating Descriptive Statistics and Summaries

The problem: Every analysis starts with summary tables: mean, median, SD, counts by group. But manually coding these or formatting them for reports can be repetitive and error-prone.

How OpenSimplify helps:

• Instant summary tables grouped by treatment, age group, or outcome

• Export-ready tables in Excel, Word, or PDF formats

• Automatically updated when data changes

Time saved: Days of manual updates across study reports.

4. Calculating Risk Scores Across Large Datasets

The problem: Risk models like CHADS-VASc or custom scores are often applied at scale, but many tools require coding or row-by-row calculations in spreadsheets.

How OpenSimplify helps:

• Apply scores across thousands of patients in seconds

• Auto-stratify patients into low/moderate/high risk groups

Time saved: Hours to days, especially with large cohorts.

5. Generating Kaplan-Meier Curves and Survival Analyses

The problem: Survival analysis is core to clinical research, but generating Kaplan-Meier curves, log-rank tests, and hazard ratios usually requires coding in R or SAS.

How OpenSimplify helps:

• Point-and-click interface for survival analysis

• Upload time-to-event and censoring variables, and get instant plots

• Export publication-ready graphics with customizable options and manuscript-ready results

Time saved: Significant, especially for non-statisticians.

Ready to Simplify Your Research Workflow?

What used to take days, or a dedicated statistician, can now be done in minutes with OpenSimplify. Our no-code platform automates the most repetitive parts of clinical research analysis so you can focus on what matters: interpreting results and publishing impactful science.

Whether you’re running a single-center study or managing large-scale trial data, OpenSimplify can help you go from raw data to insight faster, cleaner, and more reproducibly.