Postgraduate Diploma in Clinical Metrics and Progress Assessment
Online 800-hour Postgraduate Diploma in Clinical Metrics and Progress Assessment. 8 modules covering the International Classification of Functioning (ICF, WHO), COSMIN Guidelines (measurement properties) and R. Assigned tutor and open enrolment.
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What you will be able to do
Select clinical measurement instruments suited to the assessment objective.
Analyse the reliability, validity and responsiveness to change of a scale.
Calculate the minimal clinically important difference.
Integrate functional and biomechanical metrics into patient assessment.
Model progress trajectories using longitudinal data.
Design clinical outcome dashboards within clinical information systems.
Evaluate the effectiveness of a care programme using outcome indicators.
Who it's for
- Clinical results analyst looking to specialise in Clinical Metrics and Progress Assessment.
- Clinical biostatistician looking to specialise in Clinical Metrics and Progress Assessment.
- Quality of care coordinator looking to specialise in Clinical Metrics and Progress Assessment.
- Rehabilitation researcher looking to specialise in Clinical Metrics and Progress Assessment.
- Clinical data manager looking to specialise in Clinical Metrics and Progress Assessment.
- Health programme evaluation specialist looking to specialise in Clinical Metrics and Progress Assessment.
- Engineering or science graduates looking to enter hospitals and clinics.
What you'll work with
- International Classification of Functioning (ICF, WHO)
- COSMIN Guidelines (measurement properties)
- ICH E9 (statistical principles for clinical trials)
- ISO 14155 (clinical investigation of medical devices)
- HL7 FHIR (clinical data exchange)
- Regulation (EU) 2016/679 (GDPR)
8 modules · 800 h
1.1Classical test theory and error1.2Levels of measurement and scales1.3Outcome measures: clinical, functional and perceived1.4ICF framework of functioning and disability1.5Instrument selection with COSMIN1.6Measurement banks: PROMIS and COMET1.7Performance-based versus self-report measurement1.8Surrogate variables and their validity1.9Context of use of a measure1.10Case study: selecting measures for cardiac rehabilitation
2.1Test-retest and inter-rater reliability2.2Intraclass correlation coefficient2.3Standard error of measurement and minimal detectable change2.4Content, construct and criterion validity2.5Confirmatory factor analysis2.6Item response theory and Rasch2.7Ceiling and floor effects2.8Responsiveness to change2.9Interpretability of scores2.10Lab: calculating ICC and MDC with real data
3.1Minimal clinically important difference3.2Anchor-based methods3.3Distribution-based methods3.4Jacobson-Truax reliable change index3.5Responders and threshold analysis3.6Effect size and its interpretation3.7Goal Attainment Scaling (GAS)3.8The patient perspective on relevance3.9Communicating change to the patient3.10Practice: classifying responders in a physiotherapy programme
4.1Gait tests: 6MWT and Timed Up and Go4.2Balance scales: Berg and Mini-BESTest4.3Dynamometry and muscle strength4.4Kinematic analysis with inertial sensors4.5Force platforms and load analysis4.6Physical activity metrics using accelerometry4.7Independence scales: Barthel and FIM4.8Frailty and the SPPB battery4.9Validating wearables against laboratory measures4.10Lab: gait measurement with IMUs and comparison with a reference
5.1Repeated-measures designs5.2Linear mixed-effects models5.3Growth curves and trajectories5.4Missing data and attrition5.5Single-case N-of-1 designs5.6Progress charts and statistical process control5.7Predicting individual progression5.8Continuous remote monitoring5.9Early detection of deterioration5.10Practice: mixed model of functional recovery after hip replacement
6.1Structured recording of measures in the clinical record6.2SNOMED CT and LOINC terminologies6.3FHIR Observation and Questionnaire resources6.4Electronic capture of PROMs6.5Data quality and validation6.6Progress dashboards for clinicians6.7Feedback to the patient6.8Clinical registries and benchmarking6.9Data privacy and security6.10Workshop: FHIR modelling of a functional scale
7.1Effectiveness evaluation in real-world practice7.2Outcome indicators by service7.3Risk adjustment in comparisons7.4Value-based care and ICHOM7.5Cost measurement per episode7.6Quality of care assessment7.7Clinical outcomes audit7.8Communicating results to managers7.9Use of metrics in decision-making7.10Case study: outcomes report for a rehabilitation unit
8.1Defining the population and objectives8.2Justified selection of instruments8.3Defining thresholds for relevant change8.4Measurement plan and schedule8.5Data model and interoperability8.6Progress monitoring dashboard8.7Statistical analysis plan8.8Pilot test 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 an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is R, IBM SPSS, REDCap, Python (pandas), Power BI, in free, academic or evaluation versions. Studies are examined using real procedures from test-retest reliability studies in clinics, longitudinal follow-up of patient cohorts and outcome audits of rehabilitation programmes. Assessment is continuous per module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Select clinical measurement instruments suited to the assessment objective.
Analyse the reliability, validity and responsiveness to change of a scale.
Calculate the minimal clinically important difference.
Integrate functional and biomechanical metrics into patient assessment.
Model progress trajectories using longitudinal data.
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'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 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The programme uses R, IBM SPSS, REDCap, Python (pandas) and Power BI. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the final integrative project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Check the recognition policy when you enrol.
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