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Dual Master of Science (2 Degrees) — Conferred jointly with Mltio University (USA) & Northeastern University Alliance (USA)
Northeastern University (United States)
Online or on campus
Bridging healthcare systems, biomedical AI, HIPAA-compliant cloud architecture, and medical telemetry for the future of clinical leadership. Students earn 2 independent accredited diplomas with a 60% / 40% international credit split. MSc Digital Health Informatics & AI (Dual Award) is taught as one award, not a list of unrelated modules. The published length is 18 months, and the credit total is 36. Students move from Health systems through Health systems; Quality and safety; Information in care; Health project; Health systems (MSC-605); Quality and safety (MSC-606), and finish on Quality and safety (MSC-606). Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Two master's diplomas recognized across global healthcare and biotech sectors Applied clinical informatics capstone with hospital and medtech partners Expertise in healthcare cybersecurity, EHR interoperability, and medical AI ethics Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.
Study online
Online tuition is free. Online examinations are free. There is no exam fee for online study.
On-campus study
Prefer to learn at our Minneapolis campus? On-campus delivery includes tuition fees, in-person seminars, and access to university facilities.
| Code | Course | Credits · hours |
|---|---|---|
| MSC-601 | Health systems Health systems (MSC-601) is a 6-credit course on MSc Digital Health Informatics & AI (Dual Award), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Health systems as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| MSC-602 | Quality and safety Quality and safety (MSC-602) is a 6-credit course on MSc Digital Health Informatics & AI (Dual Award), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Quality and safety as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| MSC-603 | Information in care Information in care (MSC-603) is a 6-credit course on MSc Digital Health Informatics & AI (Dual Award), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Information in care as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| Code | Course | Credits · hours |
|---|---|---|
| MSC-604 | Health project Health project (MSC-604) is a 6-credit course on MSc Digital Health Informatics & AI (Dual Award), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Health project as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| MSC-605 | Health systems (MSC-605) Health systems (MSC-605) (MSC-605) is a 6-credit course on MSc Digital Health Informatics & AI (Dual Award), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Health systems (MSC-605) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| Code | Course | Credits · hours |
|---|---|---|
| MSC-606 | Quality and safety (MSC-606) Quality and safety (MSC-606) (MSC-606) is a 6-credit course on MSc Digital Health Informatics & AI (Dual Award), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Quality and safety (MSC-606) as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
Modules are assessed through a published mix of coursework, applied projects, and examinations. Exam windows are announced in advance so students in other time zones are not forced into overnight sittings. Alternative arrangements are available where documented.
The published duration is 18 months. Teaching language: English. Actual time-to-complete depends on mode and any recognised prior learning.
You may study this award fully online from your country, or — where published — on campus at Mltio. Online study does not require a student visa. Campus study may.
Degree tuition for this award is published as £0 / tuition-free on the online pathway. Examination or administrative fees may apply at checkout — never an annual tuition invoice. Check the Fees page for any extras.
Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.
Assessment is typically a mix of coursework, projects, and examinations. Doctoral awards include a thesis or dissertation and an oral examination. Details sit in the programme specification and module outlines.
Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. Mltio University publishes verification pages for certificates. We do not claim automatic equivalence in every country.
Start an application on this website. Progress is saved from the first step. Admissions: admissions@mltio.com.
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