Postgraduate Diploma in Outcome Validation and Patient Follow-Up
Online 800-hour Postgraduate Diploma in Outcome Validation and Patient Follow-Up. 8 modules covering ISO 14155 (clinical investigation of medical devices), Regulation (EU) 2017/745 (medical devices), and a dedicated tutor, with enrolment open.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Define clinical, functional and patient-perceived outcomes relevant to each intervention.
Select measurement instruments that are valid, reliable and sensitive to change.
Design interoperable and sustainable patient follow-up systems.
Manage real-world data with quality and completeness controls.
Analyse outcomes adjusting for risk and confounding factors.
Validate and audit outcomes before their clinical or regulatory use.
Develop an outcome follow-up project for a service or product.
Who it's for
- Health outcomes analyst who wants to specialise in Outcome Validation and Patient Follow-Up.
- Clinical records manager who wants to specialise in Outcome Validation and Patient Follow-Up.
- Researcher in patient-reported outcomes who wants to specialise in Outcome Validation and Patient Follow-Up.
- Real-world evidence specialist who wants to specialise in Outcome Validation and Patient Follow-Up.
- Clinical quality manager who wants to specialise in Outcome Validation and Patient Follow-Up.
- Health technology assessment consultant who wants to specialise in Outcome Validation and Patient Follow-Up.
- Graduates in engineering or sciences seeking to enter public and private healthcare.
What you'll work with
- ISO 14155 (clinical investigation of medical devices)
- Regulation (EU) 2017/745 (medical devices)
- COSMIN standard (quality of measurement instruments)
- ICHOM Standard Sets
- HL7 FHIR
- Regulation (EU) 2016/679 (GDPR)
- ISO 13485
8 modules · 800 h
1.1Clinical, PROM and PREM outcomes1.2Value-based care1.3ICHOM standard sets1.4Donabedian structure, process and outcome1.5Intermediate and final outcomes1.6The patient perspective1.7Variation in clinical practice1.8Quality of care indicators1.9Using outcomes in management1.10Case study: selecting outcomes for hip arthroplasty
2.1Psychometric properties according to COSMIN2.2Content and construct validity2.3Reliability and measurement error2.4Responsiveness to change2.5Cross-cultural adaptation of questionnaires2.6PROMIS and adaptive tests2.7Minimal clinically important difference2.8Generic instruments: EQ-5D and SF-362.9Disease-specific instruments2.10Practice: psychometric evaluation of a questionnaire
3.1Designing follow-up cohorts3.2Patient clinical registries3.3Follow-up schedule3.4Electronic capture of PROMs3.5Integration with the clinical record3.6Retention and loss to follow-up3.7Reminders and contact channels3.8Monitoring complications and readmissions3.9Quality of collected data3.10Workshop: designing a postoperative follow-up pathway
4.1Sources: EHR, registries and insurers4.2OMOP common data model4.3Data quality and completeness4.4Record linkage4.5Biases in observational studies4.6Target trial emulation4.7Real-world evidence in regulatory decisions4.8Data protection in secondary use4.9OHDSI community tools4.10Practice: querying a sample OMOP database
5.1Longitudinal analysis of repeated measures5.2Survival analysis5.3Risk adjustment5.4Propensity scores5.5Missing data in PROMs5.6Hierarchical models by centre5.7Individual interpretation of change5.8Visualisation of trajectories5.9Responder analysis5.10Laboratory: PROM analysis with mixed models
6.1Clinical audit and the improvement cycle6.2Comparison between centres and benchmarking6.3Funnel plots6.4CUSUM and VLAD charts6.5Detection of outlier centres6.6Source data verification6.7Transparency and publication of results6.8Risks of unadjusted comparison6.9Peer review of outcomes6.10Case study: auditing cardiac surgery outcomes
7.1Feedback to professionals7.2Shared decision-making7.3PROMs in the consultation7.4Pay for performance7.5Value-based contracts7.6Continuous process improvement7.7Patient involvement7.8Outcome dashboards7.9Implementation barriers7.10Workshop: outcome dashboard for a service
8.1Clinical condition and population8.2Outcome set8.3Selection of instruments8.4Follow-up pathway8.5Analysis plan8.6Data validation8.7Plan for using outcomes8.8Success indicators8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 100% online training on the SEIUM Institute campus, with downloadable material, recorded classes and a dedicated technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is R, SPSS, REDCap, Python (pandas), Power BI, in free, academic or evaluation versions. Trials are studied using real procedures for the psychometric validation of outcome questionnaires, analysis of real-world data records, and auditing the quality of follow-up data. 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.
Define clinical, functional and patient-perceived outcomes relevant to each intervention.
Select measurement instruments that are valid, reliable and sensitive to change.
Design interoperable and sustainable patient follow-up systems.
Manage real-world data with quality and completeness controls.
Analyse outcomes adjusting for risk and confounding factors.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme’s software.
- Sufficient Spanish to follow technical regulatory reading.
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 total hours spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You will work with R, SPSS, REDCap, Python (pandas) and Power BI. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM Institute own diploma upon passing the continuous assessment and the integrating project. This is in-house training, not an official Ministry of Education qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the advanced programme. Check the credit transfer policy when you enrol.
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