Top 5 Time-Consuming Tasks in Clinical Data Analysis: How to Automate Them
- Christina Steinberg
- Jun 19
- 2 min read
Updated: Jun 26

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.
Was interested in the risk scores calculation. The current ones online, mdcalc and the likes were not built for datasets, just individual ones. The one in OpenSimplify allowed me to compute ASCVD scores for the dataset cohort I was working on. Impressive
Few stat tools have integrated survey analysis, they mainly focus on others. Interesting to see this was included in the tool in addition to survival models. Although I have a request, add summary sections to it as was done for the multivariable model and linear regression.
I have found the deidentification, table one, and regression models extremely useful for my studies. Currently, we are migrating our data processing/cleaning and analysis to OpenSimplify. Whooooo! Excited