Research time savings analysis
Illustrative planning model
Illustrative planning model
Research data pipeline · Illustrative model
Model how structured, de-identified capture can reduce manual data-preparation steps between routine care and downstream analysis. Spend less time reconstructing the dataset before analysis begins.
Data teams can spend a substantial share of their time preparing data before analysis begins.
The cited surveys put data preparation at roughly 45 to 80 percent of a data team's working time (Anaconda State of Data Science; CrowdFlower). The cited chart-review and de-identification sources describe additional preparation work in real-world clinical research. Those figures establish the manual baseline used in this model. The question is how much of that preparation can be reduced when routine clinical data starts in a structured form.
Across one hundred treatment episodes, modeled data-preparation time falls from 124 hours to about 3.
How to read this. This is an illustrative planning model, not a measured product result. The manual times are drawn from the sources cited in the model. The Arcametric estimates assume fields are captured in structured form, governed vocabulary mappings are used where verified, and exports do not require the same narrative reconstruction. Every study should validate the assumptions against its own protocol, governance requirements, and data.
Eight data-pipeline tasks between routine care and an analysis-ready dataset. Times are per one hundred episodes, in hours.
| # | Data-pipeline task | Manual (hrs) | Arcametric (hrs) | Saved (hrs) | Basis |
|---|---|---|---|---|---|
| 1 | Chart abstraction into a study dataset | 50 | 1 | 49 | 20 to 45 min per record, chart-review studies |
| 2 | Removing direct patient names from shared records | 12 | 0 | 12 | Patient names are not stored in the shared Arcametric clinical record; study-specific de-identification may still be required |
| 3 | Linking de-identified records across sessions | 6 | 0 | 6 | De-identified patient references preserve longitudinal linkage |
| 4 | Normalizing fields covered by verified external vocabulary mappings | 20 | 0 | 20 | Mapped at capture where verified; protocol-specific validation still applies |
| 5 | Cleaning and formatting for R, Python, or SAS | 16 | 1 | 15 | Data-prep surveys (Anaconda; CrowdFlower) |
| 6 | Building the export package (CSV or JSON) | 8 | 0.2 | 7.8 | One-click structured export |
| 7 | Verifying completeness and protocol fidelity | 6 | 1 | 5 | Structured collection and status fields support completeness review |
| 8 | Linking follow-up over time across the arc | 6 | 0 | 6 | Structured linkage over time |
| Per 100 treatment episodes | 124 | 3.2 | ~121 |
Where verified mappings exist, exports preserve governed codes and structured fields. Patient identity remains at the site of care; study-specific mapping, de-identification, and protocol validation may still be required.
For a 500-episode multi-site study, the model implies about 600 hours of data preparation removed.
The modeled gain comes from reducing reconstruction, normalization, and manual packaging before analysis. The data starts structured and de-identified, with governed vocabulary mappings where verified, so the research team has less cleanup to perform before applying its own protocol-specific methods.
| What this is | What this is not |
|---|---|
| A planning model of data-preparation time, built from published surveys, chart-review studies, and structured-capture design | Not a guarantee of realized time savings. Each study should validate against its own protocol and data. |
| A count of hours spent preparing data, not analyzing it | Not a claim about study findings or clinical outcomes. Arcametric structures and exports data; it does not interpret it. |
The fastest way to judge the model is to review a sample de-identified export and the data model behind it. Watch the on-demand demo or contact Arcametric for a deeper technical review.
Next, see the Clinical Data Taxonomy and Export Dictionary for the data model, the variable dictionary, and the export formats behind these datasets.
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