Postgraduate Diploma in Monitoring, ePRO and Patient Data
800-hour online Postgraduate Diploma in Monitoring, ePRO and Patient Data. 8 modules covering Regulation (EU) 2017/745 (MDR), IEC 60601-1-11 (home-use equipment) and REDCap. Dedicated tutor and open enrolment.
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What you will be able to do
Design remote monitoring programmes for chronic patients.
Select devices and sensors according to clinical and regulatory requirements.
Implement the capture of patient-reported outcomes.
Clean and process physiological data from wearable sensors.
Configure alert rules using clinical criteria.
Integrate patient data with the electronic health record using standards.
Apply privacy and governance principles to health data.
Who it's for
- Remote monitoring programme manager looking to specialise in Monitoring, ePRO and Patient Data.
- Clinical data scientist looking to specialise in Monitoring, ePRO and Patient Data.
- Connected device engineer looking to specialise in Monitoring, ePRO and Patient Data.
- ePRO specialist looking to specialise in Monitoring, ePRO and Patient Data.
- Health data protection officer looking to specialise in Monitoring, ePRO and Patient Data.
- Telemedicine coordinator looking to specialise in Monitoring, ePRO and Patient Data.
- Engineering or science graduates looking to enter digital health.
What you'll work with
- Regulation (EU) 2017/745 (MDR)
- IEC 60601-1-11 (home-use equipment)
- ISO/IEEE 11073 (personal health device communication)
- HL7 FHIR
- IEC 62304 (medical device software lifecycle)
- Regulation (EU) 2016/679 (GDPR)
8 modules · 800 h
1.1Telemonitoring and telemedicine models1.2Candidate chronic conditions: HF, COPD and diabetes1.3Hospital at home and early discharge1.4Evidence of telemonitoring effectiveness1.5Care pathways and clinical roles1.6Alert management and alarm fatigue1.7Connectivity and logistics requirements1.8Patient and carer experience1.9Funding and reimbursement models1.10Case study: telemonitoring programme for heart failure
2.1Connected blood pressure monitors, pulse oximeters and scales2.2Continuous glucose monitors2.3ECG patches and Holter monitors2.4Activity and sleep wearables2.5Implantable sensors and cardiac devices2.6Clinical validation of devices2.7Accuracy versus consumer grade and battery life2.8Connectivity: Bluetooth LE, NB-IoT and gateways2.9MDR classification of devices2.10Laboratory: reading data from a BLE pulse oximeter
3.1PROMs in routine clinical practice3.2Electronic symptom diaries3.3Computerised adaptive questionnaires3.4Frequency and timing of capture3.5Integrating PROMs into the consultation3.6Symptom-based alerts3.7Compliance and reminder strategies3.8Accessibility and the digital divide3.9Validity of the electronic version3.10Practice: designing an ePRO workflow in oncology with alerts
4.1Preprocessing of physiological signals4.2Detection of artefacts and outliers4.3Missing data and intermittent use4.4Resampling and temporal aggregation4.5Features derived from time series4.6Data quality according to ISO 80004.7Clinical labelling of events4.8Metadata and provenance4.9Time-series storage4.10Laboratory: cleaning a wearable heart rate series
5.1Clinical threshold rules5.2Individualised anomaly detection5.3Predictive models of decompensation5.4NEWS2 early warning scores5.5Validation of predictive algorithms5.6Bias and fairness in algorithms5.7Explainability for clinicians5.8AI software as a medical device5.9Assessment of clinical impact5.10Practice: readmission prediction model using monitoring data
6.1HL7 FHIR standards for observations6.2IHE profiles for personal devices6.3IEEE 11073 standard6.4LOINC and SNOMED CT terminologies6.5Integration with the electronic health record6.6Commercial telemonitoring platforms6.7Apple Health and Google Health Connect APIs6.8European Health Data Space6.9Secondary use of data for research6.10Workshop: mapping home measurements to FHIR Observation
7.1GDPR and special category data7.2Data protection impact assessment7.3Consent and legal bases7.4Pseudonymisation and anonymisation7.5Cybersecurity of medical devices IEC 81001-5-17.6Access control and auditing7.7Clinical data governance7.8Clinical responsibility in monitoring7.9Communicating risks to patients7.10Case study: DPIA of a home monitoring programme
8.1Selection of population and condition8.2Design of the care pathway8.3Selection of devices and PROMs8.4Data architecture and integration8.5Design of alert rules8.6Privacy and security plan8.7Programme evaluation plan8.8Testing with simulated data8.9Report and presentation8.10Final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and a dedicated technical tutor. No fixed timetable: students progress at their own pace within the intake, making it possible to combine with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is REDCap, Apple ResearchKit, Python (pandas), Grafana, Power BI, in free, academic or evaluation versions. Trials are studied using real procedures from home remote monitoring pilots, validation of wearable sensors against clinical reference, and adherence analysis of ePRO questionnaires. Assessment is continuous by module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Design remote monitoring programmes for chronic patients.
Select devices and sensors according to clinical and regulatory requirements.
Implement the capture of patient-reported outcomes.
Clean and process physiological data from wearable sensors.
Configure alert rules using clinical criteria.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme software.
- Sufficient Spanish to follow technical regulatory texts.
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 comprises 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.
The programme works with REDCap, Apple ResearchKit, Python (pandas), Grafana and Power BI. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
An SEIUM own diploma on passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry-recognised qualification.
Yes. Modules passed in a course or Postgraduate Diploma in the same area are credited towards the advanced programme. Consult the credit transfer policy when you enrol.
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