The Master of Science is the analytical degree of Middle West University. Where the MBA trains managers and the LL.M trains lawyers, the MSc trains the people who produce the numbers both of them rely on: cost models, outcome studies, reimbursement analyses and evaluations of new interventions.
The program combines health economics with applied data analysis. Students work with anonymised claims, registry and operational datasets in every semester, and each course ends with a deliverable that could be presented to a management board or a payer.
Teaching is fully online. Analytics work is done in spreadsheet and open-source statistical environments, so no licensed commercial software is required.
Mode of study
Fully online, asynchronous study with scheduled tutorial consultations and analytics labs
Admission requirements
Bachelor's degree in economics, business, health sciences, law or a related field; basic numeracy is assumed
Learning outcomes
What graduates are able to do
01Build and defend a cost-effectiveness model including sensitivity and uncertainty analysis
02Clean, link and analyse administrative health data while respecting data-protection constraints
03Interpret and critique published economic evaluations and health technology assessments
04Design an outcome measurement framework for a clinical or operational service line
05Communicate quantitative findings to non-technical decision makers in writing and on slides
Full syllabus
Curriculum, semester by semester
Semester 1 — Economic and statistical foundations
The analytical toolkit
30 credits
MSC-101
Health Economics
10 credits
250 hours total workload
Demand and supply for health services, insurance and moral hazard, provider payment systems, market failure and the economics of regulation.
Course learning outcomes
Explain why health care markets deviate from competitive assumptions
Compare fee-for-service, capitation and DRG payment and their incentive effects
Assess the welfare consequences of a proposed reimbursement change
Assessment: Policy analysis paper (60%) and examination (40%)
MSC-102
Applied Statistics and Regression
10 credits
250 hours total workload
Estimation and inference, linear and logistic regression, model diagnostics, confounding and the interpretation of effect sizes.
Course learning outcomes
Specify and estimate a regression model appropriate to a research question
Diagnose violations of model assumptions and respond to them
Report results with confidence intervals and honest interpretation
Assessment: Three analytical problem sets (50%) and examination (50%)
MSC-103
Health Data Management and Governance
10 credits
250 hours total workload
Data structures in claims, EHR and registry systems, linkage and de-identification, quality assessment, and the HIPAA and GDPR constraints on analytical work.
Course learning outcomes
Assess the fitness for purpose of an administrative dataset
Design a de-identification and access plan for a secondary-use project
Document a reproducible data-preparation pipeline
Assessment: Data governance dossier (100%)
Semester 2 — Evaluation methods
Measuring value and outcome
30 credits
MSC-201
Economic Evaluation and Cost-Effectiveness Modelling
12 credits
300 hours total workload
Cost-minimisation, cost-effectiveness, cost-utility and cost-benefit analysis; decision trees, Markov models, discounting and probabilistic sensitivity analysis.
Course learning outcomes
Construct a Markov cost-utility model from published inputs
Run and present a probabilistic sensitivity analysis
Judge whether an evaluation supports its stated conclusion
Assessment: Full model with technical report (70%) and defence (30%)
MSC-202
Outcomes Research and Quality Measurement
9 credits
225 hours total workload
Clinical and patient-reported outcome measures, risk adjustment, case-mix, indicator design and the pitfalls of public performance reporting.
Course learning outcomes
Select and justify outcome measures for a defined service line
Apply risk adjustment and explain its limits
Design an indicator set that resists gaming
Assessment: Indicator framework (60%) and critique paper (40%)
MSC-203
Causal Inference with Observational Data
9 credits
225 hours total workload
Matching, difference-in-differences, instrumental variables and interrupted time series applied to policy and programme evaluation.
Course learning outcomes
Choose an identification strategy suited to the available data
Implement a difference-in-differences analysis and test its assumptions
State the causal claim a design does and does not support
Assessment: Empirical replication project (100%)
Semester 3 — Applied analytics
From analysis to decision
30 credits
MSC-301
Predictive Modelling in Health Care
10 credits
250 hours total workload
Prediction versus explanation, validation and calibration, class imbalance, fairness auditing and the governance of algorithmic tools in clinical settings.
Course learning outcomes
Develop and validate a risk-prediction model
Audit a model for calibration drift and subgroup performance
Document a model card suitable for a governance committee
Assessment: Model build with validation report (100%)
MSC-302
Pricing, Reimbursement and Market Access
10 credits
250 hours total workload
Value-based pricing, HTA submissions, managed-entry agreements and negotiation with payers in the United States and Europe.
Course learning outcomes
Assemble the economic core of an HTA submission
Design a managed-entry agreement addressing payer uncertainty
Anticipate the objections a reimbursement committee will raise
Assessment: Submission dossier (70%) and negotiation simulation (30%)
MSC-303
Research Design and Dissertation Preparation
10 credits
250 hours total workload
Question formulation, protocol writing, feasibility assessment, ethics review and pre-registration of the master's research project.
Course learning outcomes
Write a complete, feasible research protocol
Justify sample, method and analysis plan in advance
Pass internal ethics and data-access review
Assessment: Approved protocol (100%)
Semester 4 — Master's dissertation
Independent quantitative research
30 credits
MSC-401
Master's Dissertation and Defence
30 credits
750 hours total workload
An independent 15,000–18,000 word empirical study with reproducible analysis code, supervised by a member of faculty.
Course learning outcomes
Execute a pre-registered analysis and report deviations transparently
Deliver a reproducible analysis package alongside the written work
Defend method and interpretation before an examination panel
Assessment: Dissertation (80%) and oral defence (20%)
PDF downloads
Three English documents for this program
PDF document
MSc Program Handbook
4 pages · 71 KB
Full program description, mode of study and admission requirements
Software setup, dataset access, deadlines and the dissertation timeline.
Slide deck
Presenting Numbers to a Board
38 slides · Communication workshop
Turning a technical result into three defensible slides without distorting it.
Slide deck
Regression Diagnostics You Cannot Skip
52 slides · Annotated deck with code
Residual analysis, influence, multicollinearity and the reporting consequences of each.
Reading
Reporting Guidelines Compendium
Released in the study portal
CHEERS, STROBE and TRIPOD checklists annotated with worked examples from student work.
Video lecture
Reproducible Analysis for Your Dissertation
40 min · Academic Skills Unit
Project structure, versioning, seeds and the code package examiners expect.
Statute
Secondary-Use Data Rules
Released in the study portal
Curated HIPAA and GDPR provisions governing research use of health data, with commentary.
Slide deck
Why Health Care Markets Fail
46 slides · Annotated deck
Information asymmetry, insurance and induced demand, illustrated with market data.
PDF documents are free to download in English. Presentations, recorded lectures, readings, cases, datasets and templates are released inside the study portal at the start of each course window and remain available to enrolled students for the duration of the program.
How students are graded
Assessment structure
50–70%
Analytical projects
Models, datasets and code submitted with a written technical report.
20–40%
Examinations
Open-book examinations testing method selection and interpretation.
10–30%
Defences and simulations
Recorded defence of models and a live payer negotiation simulation.
30 credits
Dissertation
Empirical study with reproducible analysis package and oral defence.
Final independent work
Master's dissertation (30 credits)
The MSc dissertation is an empirical study of 15,000–18,000 words accompanied by a reproducible analysis package. Topics must address a health economic, outcome or data-governance question and are approved at the end of semester three.
Semester 3: protocol, data-access approval and pre-registration
Weeks 1–4: data preparation and cohort construction
Weeks 5–10: primary analysis and robustness checks
Weeks 11–16: full draft, supervisor feedback and code review
Weeks 17–18: submission, similarity check and oral defence
Fees
Tuition and payment
Total tuition
USD USD 4,999
Total tuition for the complete Master of Science, including datasets, analytical templates, tutorial consultations, examinations and the dissertation defence.
Deposit on enrolment: USD 999
Four semester instalments of USD 1,000
No separate examination, material or graduation fees
Tuition is quoted in United States dollars. Payment schedules are agreed in writing with the Student Affairs Office before enrolment.
Questions
Frequently asked questions
Do I need programming experience?
No. The program starts with spreadsheet-based analysis and introduces open-source statistical tools step by step from the first semester.
Is the MSc more technical than the MBA?
Yes. The MSc is built around quantitative methods and evaluation; the MBA is built around management and strategy.
Which software do I need to buy?
None. All required tools are free or open source and installation guides are provided.
Can I use my employer's data for the dissertation?
Yes, subject to a data-access agreement and internal ethics approval, which the supervisor helps you obtain.
Interested in the MSc?
The Student Affairs Office answers curriculum, admission and enrolment questions in English.