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Design science research in quality improvement: Embedding rigour in digital health innovation
Robin E Dyers1, Hassan Mahomed, Darelle Van Greunen
1Division of Health Systems and Public Health, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; and, WHO-FIC Collaborating Centre, Burden of Disease Research Unit, Cape Town, South Africa; and, Department of Health and Wellness, Western Cape Government, Cape Town. rdyers@sun.ac.za.
Abstract:
Design science research (DSR) transforms how healthcare researchers create digital innovations by treating artefacts as knowledge repositories rather than mere technical solutions. It provides a problem-solving paradigm that creates artefacts embodying prescriptive knowledge about solving classes of problems, complementing quality improvement methodologies. Through its systematic approach, DSR equips healthcare researchers with methods for building digital health innovations, using quality improvement concepts as reference points to facilitate understanding and adoption. The methodology presents philosophical foundations distinguishing design sciences from natural sciences, five artefact types (constructs, models, methods, instantiations, design theories), and a six-phase framework (problem identification, objectives, design, demonstration, evaluation, communication). Systematic problem investigation transforms vague complaints into measurable problems amenable to designed solutions. This paradigm distinguishes itself as one where the artefact is the knowledge contributor. While quality improvement produces innovations solving problems, DSR produces artefacts embodying prescriptive knowledge about solving classes of problems. Both methodologies innovate; the distinction lies in knowledge representation. The DSR approach treats artefacts as knowledge repositories containing extractable design principles, while quality improvement focuses on demonstrating improved outcomes. Methodological synergies strengthen both approaches through complementary evaluation frameworks and iterative refinement. Practical considerations include maintaining methodological rigour through transparent documentation, addressing AI integration challenges, ensuring sustainability, and avoiding common pitfalls. African healthcare contexts particularly benefit from DSR's orientation, with resource constraints demanding solutions addressing complex socio-technical challenges while contributing to global design knowledge. Future research should establish DSR training programmes and develop artefact repositories for systematic knowledge transfer, positioning African researchers as contributors to healthcare's digital transformation.
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