Prospective or Retrospective Research? Choose the Right Clinical Data Management Setup

The choice between prospective and retrospective research determines more than just your study design. It directly impacts your clinical data management setup, data quality, and regulatory compliance. Each research type requires a different technical infrastructure. Organizations that select the right setup from the start avoid costly mid-study adjustments.

In this article, we explain what prospective and retrospective research are and which systems you need for each.

What Is the Difference Between Prospective and Retrospective Research?

Let’s start at the beginning. What defines prospective and retrospective research? Below you’ll find a concise summary, including three examples for each type.

Prospective Research

Prospective studies collect real-time patient data from the moment they are exposed to a treatment or risk factor until the outcome becomes known. This design enables researchers to determine cause and effect relationships.

Examples of prospective studies:

• Drug efficacy study
  A pharmaceutical company wants to test whether a new diabetes drug is more effective than existing treatments. They recruit 500 diabetes patients, randomly assign them to two groups (new drug vs. standard therapy), and monitor blood sugar levels over 12 months.

• Lifestyle and heart disease study
  Researchers want to investigate whether daily walking reduces the risk of heart disease. They recruit 2,000 healthy adults, record baseline data, and follow them for 5 years, measuring fitness, blood pressure, and cardiovascular events every 6 months.

• Medical device trial
  A medtech company tests a new pacemaker. The device is implanted in 300 cardiac patients across 15 hospitals. Over 2 years, performance, battery life, and side effects are monitored via scheduled visits and patient diaries.

Retrospective Research

Retrospective studies analyze existing data to examine previous exposure to suspected risk factors. This approach allows for large datasets to be analyzed quickly and cost-effectively, without long follow-up periods.

Examples of retrospective studies:

• COVID-19 risk factor analysis
  Hospitals analyze medical records from 10,000 COVID-19 patients (2020–2022) to identify factors (age, obesity, diabetes) associated with severe illness and hospitalization.

• Drug side effect investigation
  A regulator suspects a link between a blood pressure drug and liver problems. They analyze medical data from 50,000 patients who used the drug over the past 5 years to assess whether there is an increased risk.

• Surgical technique comparison
  A hospital compares outcomes of knee replacement surgeries performed between 2018–2023. They evaluate 1,500 patient records to compare recovery time, pain, and mobility between different surgical methods.

Checklist: Which Study Design Fits Your Research?

Which systems do you need for prospective and retrospective research?

After determining the type of study, it is time to select the right systems, modules and technical requirements for clinical data management. It is good to know that the Clinical Research Suite from ResearchManager contains all the necessary modules for both prospective and retrospective research. You can therefore use it for both types of research, only the modules we activate for the study differ.

Technical requirements for a prospective study

In prospective studies, each participant goes through a structured journey. Therefore, a Clinical Research Suite is needed in which you can manage everything from start to finish. From patient recruitment to collecting real-time data during the clinical trial and exporting it to any desired statistical program for data analysis.

Step 1: Recruitment of participants (patient recruitment)

Our patient recruitment module offers a registration form accessible via QR code or unique URL. This registration link can be shared via social media, websites or targeted digital campaigns. Thanks to automatic pre-screening of applicants, only potentially eligible candidates are forwarded to the study coordinator.
» More information about digital patient recruitment

Step 2: Consent (eConsent)

This system allows potential participants in the clinical study to review information about the study at their own pace and ask questions before they finally give their digital signature for participation. This digital consent process is fully traceable with audit trails that meet regulatory compliance.
» More information about eConsent

Step 3: Randomization (RTSM)

For controlled studies, RTSM provides randomization and medication management. This module in our Clinical Research Suite ensures that treatments are correctly assigned according to the study protocol.
» More information about RTSM

Step 4: Visit planning (Visit Planning)

The Visit Planning module coordinates all scheduled patient visits, both at the study site(s) and remotely via an online meeting. Each visit is automatically linked to the patient record, and by assigning a financial code per visit type, you can easily manage reimbursement for participants.
» More information about the Visit Planning module

Step 5: Data collection (EDC + ePRO)

EDC collects all study data in real time and contains built-in validation rules that immediately flag errors. At the same time, study participants can keep a diary and complete questionnaires on their own smartphone via ePRO. This combination ensures complete datasets without manual data entry afterwards.
» More information about the EDC
» More information about ePRO

Step 6: Data export

After completion, all data is exported to Excel or SPSS. The export includes both EDC data and patient-reported outcomes in a structured dataset. This allows biostatisticians to immediately start statistical analysis without time-consuming data cleaning.

Do you have questions about using ResearchManager’s Clinical Research Suite for a prospective study? Feel free to contact us.

Technical requirements for a retrospective study

In retrospective studies, data has already been collected, so the modules Patient Recruitment, ePRO, RTSM, eConsent and Visit Planning are not needed. The focus shifts entirely to linking existing data from multiple sources to the EDC. Both methods of data import are supported and, if desired, performed by ResearchManager.

Two ways for error-free data import into an EDC

Option 1: Manual data import via component mapping

Component mapping ensures that existing data automatically ends up in the correct place in the EDC.
Each question (component) in the study template receives a unique tag.
This tag matches the tag of a column in the Excel/CSV file.
The data is automatically placed in the correct location in the EDC and protocol.
During data import, the EDC automatically generates anonymous patient numbers to comply with GDPR/AVG requirements.

Practical example: component mapping for a drug study
A CRO wants to perform a retrospective analysis of the side effects of a blood pressure medication. They have Excel files from 3 hospitals with different column names:
Hospital A: “Leeftijd_patient”, “Systolische_BP”, “Bijwerking_ja_nee”
Hospital B: “Age”, “SBP_mmHg”, “Adverse_Event”
Hospital C: “Leeftijd”, “Bloeddruk_sys”, “SE_opgetreden”
Through component mapping, these different column names are linked to the standard EDC fields:
All age variants → EDC field “patient_age”
All blood pressure variants → EDC field “systolic_bp”
All adverse event variants → EDC field “adverse_event”

Result: 2,500 patient records from 3 different sources are automatically merged into one standardized database and are ready for analysis.

Option 2: Automatic data import via an API integration

Save time by automating data import from existing systems. An API is ideal for large datasets and continuous data updates.

• Data is automatically imported at fixed times from source systems such as EDC systems.

• Only new data is added, without overwriting existing data to maintain the audit trail.

• Real-time synchronization is possible between multiple systems.

• During data import, the EDC automatically generates anonymous patient numbers in compliance with GDPR/AVG.

Practical example: data import via API for a COVID-19 study
A research institute wants to retrospectively analyze risk factors for severe COVID-19 infections. They have access to anonymized EHR data from 4 hospitals from 2020–2022.
Via the API integration, ResearchManager automatically imports:

• Patient characteristics: age, BMI, comorbidities (diabetes, hypertension)

• COVID-19 data: test results, symptoms, hospital admissions

• Outcomes: ICU admission, ventilation duration, death
  The API processes a total of 25,000 patient records from different EHR systems (such as Epic, ChipSoft HiX, Nexus) and automatically converts the data formats into one standardized EDC structure.

Result: Within one week, a complete database is available that would normally take six months of manual work. The research team can immediately perform statistical analyses on the factors associated with severe COVID-19.

Do you have questions about importing data into an EDC system? Feel free to contact us.

ResearchManager offers one platform suitable for both study designs

Whether it is a complex prospective study or a retrospective analysis of existing data, our Clinical Research Suite offers the right modules and technical requirements for both types of studies.

Competitive project-based pricing
We apply a transparent project price, making both small pilot studies and large projects with thousands of patients financially feasible.

100% compliance guaranteed
All workflows comply with GDPR/AVG requirements and audit trails. The platform is fully audit ready.

Modular approach
The Clinical Research Suite is tailored to the study’s needs. Prospective studies use all modules required for the full patient journey. Retrospective studies focus on the EDC with data integration via APIs and Component Mapping. Whatever your current or next study may be, with our Clinical Research Suite you are always ready.

Schedule a demo and see within 30 minutes how your study benefits from the right setup.