Can routine treatment records answer a research question?

Begin with the question, then assess the data. Arcametric preserves structured treatment events, timing, case relationships and eligible outcomes so researchers can examine whether routine clinical information fits a defined analysis.

Illustrative question: Among coded cases with a documented treatment episode, how do PHQ-9 scores differ between a baseline measurement and a later recorded assessment? How many cases have no eligible follow-up score?

What the analysis would need

Required fact	Why it matters to the analysis
Case reference	Link repeated observations within an eligible coded case without relying on name-based joins.
Treatment occurrence and date	Place assessments in relation to treatment, not simply in calendar order.
Instrument and assessment date	Identify what was measured and when; do not substitute an undated score.
Recorded score	Use only supported, actually recorded measurements and preserve missing values.
Source and eligibility	Distinguish native, imported or otherwise qualifying records under the particular output's rules.

Example: a small source-to-analysis extract

Synthetic values solely to illustrate the research question. These are conceptual columns, not a promise of an exact export schema or approved study dataset.

Coded example	Treatment	Baseline PHQ-9	Later PHQ-9	Interpretation
Example 01	Jan 11	16 on Jan 07	11 on Feb 11	Both timepoints recorded
Example 02	Jan 15	18 on Jan 12	Not recorded	Follow-up missing; not zero
Example 03	Jan 20	Not recorded	9 on Feb 17	Cannot calculate change from baseline

What this proves: a usable analysis depends on observed data, temporal definitions, eligibility and missingness. A structured value does not establish treatment effect.

Evaluate data fitness

Structure helps. Methods determine what it means.

An analytically useful record preserves the clinical concept, when it occurred, the coded case and the relationship to other events. Research quality still depends on completeness, valid measurement, selection and study design.

Data to examine in a feasibility review

Clinical domain	Representative structured information	Key research question
Exposure	Substance, dose event, route where captured, occurrence and event timing	At what granularity can treatment exposure be defined?
Physiologic context	Time-linked vital signs and treatment-session observations	Which measurements are complete enough to compare?
Longitudinal measures	Assessment instrument, recorded score, date and case relationship	Are the same measures captured at comparable intervals?
Continuity and safety	Integration work, follow-up activities and documented safety events	Which events are linked to a treatment rather than only to a case?
Data governance	Coded reference, record source and applicable export rules	What can an approved research dataset lawfully include?

What the research team should verify

Data fit

Definitions, denominator, inclusion rules, observed timepoints, completeness, and treatment-to-outcome linkage.

Study fit

Selection bias, confounding, instrument consistency, missing follow-up, consent, permissions and protocol requirements.

Real-world data is not automatically real-world evidence. Structured care records can support research when relevant data are available and appropriately governed. Valid findings require a defensible question, dataset, design and analysis. A coded reference alone does not establish legal de-identification or cross-organization research authorization.

Why the connection matters

Clinicians record meaningful facts in ordinary care. Researchers specify study questions and measurement needs. Program sponsors may support consistent follow-up collection. These complementary roles may reduce later chart abstraction, but data access and research arrangements must be established for each use.

Assess your variable fit

Review the clinical data taxonomy and bring a specific protocol question for a field-by-field discussion of granularity, eligibility and export formats.

Review the data taxonomy Discuss a study's data needs