Master’s in Decentralised Trials and Remote Monitoring Engineering
Online master's programme of 1,900 hours in decentralised trials and remote monitoring engineering. 12 modules covering decentralised clinical trials, GCP regulation, clinical wearables, digital biomarkers, monitoring architecture, data integrity, signal analysis and statistics, privacy and cybersecurity, remote trial operations, telemonitoring and health software, with an assigned technical tutor.
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What you will be able to do
Design clinical trial protocols with decentralised elements in line with ICH E6(R3).
Select and validate wearable sensors according to the V3 framework.
Define robust digital biomarkers and endpoints.
Build interoperable data architectures from the device to the EDC.
Ensure the integrity of remote data by applying ALCOA+ principles.
Assess the ethical, privacy and cybersecurity risks of the trial.
Plan the logistics and operations of a hybrid trial.
Who it's for
- Decentralised clinical trial managers in pharma, CROs and research centres.
- Digital device validation engineers working with wearables and sensors.
- Clinical data scientists handling dense longitudinal and sensor data.
- Remote clinical monitors applying risk-based and centralised monitoring.
- Digital biomarker specialists.
- Clinical research quality managers.
- Engineering or science graduates seeking to enter the pharmaceutical industry.
What you'll work with
- ICH E6(R3) (Good Clinical Practice)
- Regulation (EU) 536/2014 (EU Clinical Trials Regulation)
- Regulation (EU) 2017/745 (EU Medical Device Regulation, MDR)
- Royal Decree 1090/2015 (Spanish regulation on clinical trials with medicines)
- 21 CFR Part 11 (FDA electronic records and signatures)
- ICH E9(R1) (estimands and sensitivity analysis)
- IEC 62304 (medical device software life cycle)
- ISO 14971 (risk management)
- Medidata Rave
- REDCap
- R
- Python
- ActiGraph CentrePoint
12 modules · 1900 h
1.1Evolution of clinical trials towards hybrid models1.2Decentralised elements: home, telemedicine and direct-to-patient shipping1.3Protocol design with remote visits1.4Actors: sponsor, CRO, investigator and technology providers1.52022 European recommendation paper on decentralised elements1.6FDA guidance on decentralised trials1.7Advantages in recruitment, diversity and retention1.8Methodological risks and introduced biases1.9Patient selection and remote eligibility1.10Case study: hybrid redesign of a phase III trial
2.1ICH E6(R3) and risk-based oversight2.2Regulation (EU) 536/2014 and the CTIS portal2.3Royal Decree 1090/2015 in Spain2.4Electronic informed consent and identity verification2.5Investigator responsibility in home visits2.6Direct-to-participant shipment of medication2.721 CFR Part 11 and electronic records2.8GCP inspections in remote settings2.9Safety reporting and remote pharmacovigilance2.10Workshop: compliance matrix for a hybrid protocol
3.1Accelerometry, photoplethysmography and single-lead ECG3.2Temperature, SpO2 and blood pressure sensors3.3Biometric patches for continuous monitoring3.4Precision, accuracy and sensor drift3.5Sampling rate, battery and wear adherence3.6Verification and analytical and clinical validation: the V3 framework3.7Device selection according to the endpoint3.8Wearables as medical devices under Regulation 2017/7453.9Participant use and adherence protocols3.10Laboratory: validation of an actigraph against a reference
4.1Definition of a digital biomarker and BEST categories4.2Mobility: gait speed, steps and SV95C4.3Biomarker qualification with the EMA and FDA4.4Electronic patient-reported outcomes4.5Equivalence between paper and electronic questionnaires4.6Minimal clinically important difference4.7Within-subject variability and test-retest reliability4.8Design of validation studies for digital endpoints4.9Respiratory and sleep digital biomarkers4.10Case study: digital endpoint for Parkinson's disease
5.1From sensor to cloud: gateways and mobile apps5.2BLE protocol, synchronisation and loss handling5.3Interoperability with HL7 FHIR and CDISC standards5.4Integrated eCOA, eConsent and telemedicine platforms5.5Device management, firmware and provisioning5.6Infrastructure scalability and availability5.7Computerised system validation according to GAMP 55.8Audit trail and time-stamping5.9Identity management and access control5.10Laboratory: data flow from wearable to EDC
6.1ALCOA+ principles applied to sensor data6.2Detection of missing data and non-wear periods6.3Motion artefacts, cleaning and filtering6.4Metadata and data provenance6.5Reconciliation between remote and on-site sources6.6Statistical centralised monitoring6.7Key risk indicators and quality tolerance limits6.8Management of remote protocol deviations6.9Remote source data verification6.10Practical: data management plan for a DCT
7.1Preprocessing of physiological signals7.2Time- and frequency-domain features7.3Event and activity detection algorithms7.4Machine learning to classify physical activity7.5Mixed models for dense longitudinal data7.6Missing data and estimands according to ICH E9(R1)7.7Sample size with digital endpoints7.8Freezing and validating algorithms before analysis7.9Data visualisation for the monitoring committee7.10Laboratory: actigraphy analysis pipeline in Python
8.1GDPR and Organic Law 3/2018 in clinical research8.2Data protection impact assessment8.3Pseudonymisation and international transfers8.4Health software cybersecurity: IEC 81001-5-18.5Vulnerability and patch management in the field8.6Ethics committees and participant burden8.7Digital divide, equity and inclusion8.8Communication of incidental findings8.9Data processing agreements with providers8.10Case study: impact assessment of a trial app
9.1Home nursing and mobile units9.2Biological sample logistics and cold chain9.3Distribution of kits and devices to the participant9.4Patient training and technical support9.5Provider management and quality agreements9.6Budget and cost model of a DCT9.7Recruitment, retention and adherence metrics9.8Contingency plan for technology failures9.9Digital recruitment and social media campaigns9.10Workshop: operational plan for home visits
10.1Telemonitoring of heart failure and COPD10.2Hospital at home and remote care10.3Alert thresholds and clinical alarm management10.4Integration with the electronic health record10.5Cost-effectiveness evidence10.6Real-world data and post-authorisation studies10.7Patient safety and clinical liability10.8Reimbursement models and healthcare adoption10.9Patient-centred design and user experience10.10Case study: cardiac telemonitoring programme
11.1Software classification under MDR Rule 1111.2Software life cycle according to IEC 6230411.3Risk management with ISO 1497111.4Usability engineering according to IEC 62366-111.5The European AI Act in healthcare11.6Adaptive algorithms and predetermined change control plans11.7Clinical evaluation and post-market follow-up11.8Technical documentation and regulatory file11.9Semantic interoperability with SNOMED CT and LOINC11.10Practical: regulatory classification of an alert algorithm
12.1Problem selection and scope of the work12.2Evidence review and state of the art12.3Design of the proposed decentralised protocol12.4Technology selection and data architecture12.5Validation and statistical analysis plan12.6Assessment of regulatory and ethical risks12.7Cost estimate and timeline12.8Critical discussion of results12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Medidata Rave, REDCap, R, Python and ActiGraph CentrePoint, in free, academic or evaluation versions. Practical work is based on real procedures: validation of a wearable sensor against a reference method, design of a hybrid protocol with home visits and risk-based centralised monitoring. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ICH E6(R3) Good Clinical Practice, Regulation (EU) 536/2014 on clinical trials, Regulation (EU) 2017/745 on medical devices, Royal Decree 1090/2015, 21 CFR Part 11, ICH E9(R1), IEC 62304, ISO 14971.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Medidata Rave, REDCap, R, Python and ActiGraph CentrePoint, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software, and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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