A Conceptual Analytics Model for an Outcome-Driven Quality Management Framework as Part of Professional Healthcare Education
Open accessAbstract
BACKGROUND: Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. OBJECTIVE: The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators' decision making. METHODS: A deductive case study approach was applied to develop the conceptual model. RESULTS: The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. CONCLUSIONS: The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach.
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The application of analytics is an emerging approach to study and interpret engagement with mobile health interventions.
“The application of analytics, defined as the use of data to generate new insights [18], is an emerging approach to study and interpret engagement with mHealth interventions [19].”
From A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review (2019) · cites this paper as [18]
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Cites
(1996) · Cited by 11,584 · see citing works
Penetrating the Fog: Analytics in Learning and Education.
Siemens, G., & Long, P. (2011) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · Cited by 1,761 · see citing works
Maxwell, J. (2009) · The SAGE Handbook of Applied Social Research Methods · doi:10.4135/9781483348858.n7 · Cited by 1,734 · see citing works
Learning analytics: drivers, developments and challenges
Ferguson, R. (2012) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2012.051816 · Cited by 1,357 · see citing works
Using case studies in research
Rowley, J. (2002) · Management Research News · doi:10.1108/01409170210782990 · Cited by 1,235 · see citing works
Learning analytics and educational data mining
Siemens, G., & Baker, R. S. (2012) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2330601.2330661 · Cited by 1,091 · see citing works
A reference model for learning analytics
Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2012.051815 · Cited by 702 · see citing works
Academic Analytics: A New Tool for a New Era.
Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007) · Educational Review · Cited by 505 · see citing works
Computer assisted learning in undergraduate medical education
Greenhalgh, T. (2001) · BMJ · doi:10.1136/bmj.322.7277.40 · Cited by 427 · see citing works
Rocco, T. S., & Plakhotnik, M. S. (2009) · Human Resource Development Review · doi:10.1177/1534484309332617 · Cited by 391 · see citing works
Ammenwerth, E., Demiris, G., Hasman, A., Haux, R., Hersh, W., Hovenga, E. J. S., Lun, K. C., Marín, H. S. V., Martin-Sanchez, F. J., Wright, G., et al. (2010) · Methods of Information in Medicine · doi:10.3414/me5119 · Cited by 319 · see citing works
(2012) · Cited by 311 · see citing works
Medical Education for the Future: Identity, Power and Location
Bleakley, A., Bligh, J. G., & Browne, J. B. (2011) · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · doi:10.1007/978-90-481-9692-0 · Cited by 182 · see citing works
New Pathways in General Medical Education
Tosteson, D. C. (1990) · New England Journal of Medicine · doi:10.1056/nejm199001253220405 · Cited by 177 · see citing works
Curricular Design Analysis: A Data-Driven Perspective
Méndez, G. G., Ochôa, X., Chiluiza, K., & Wever, B. D. (2014) · Journal of Learning Analytics · doi:10.18608/jla.2014.13.6 · Cited by 50 · see citing works
Big Data in Medical Informatics: Improving Education Through Visual Analytics
Vaitsis, C., Nilsson, G. H., & Zary, N. (2014) · Studies in health technology and informatics · doi:10.3233/978-1-61499-432-9-1163 · Cited by 27 · see citing works
Self-citationGraduate surgical education redesign: Reflections on curriculum theory and practice
DaRosa, D. A., & Bell, R. H. (2004) · Surgery · doi:10.1016/j.surg.2004.09.003 · Cited by 16 · see citing works
iKlassroom: Real-Time, Real-Place Teaching Analytics
Vatrapu, R., Kocherla, K., & Pantazos, K. (2013) · Cited by 10 · see citing works
Identities, Powers and Locations: What Does the Future Hold for Medical Education?
Bleakley, A., Bligh, J. G., & Browne, J. (2011) · Advances in medical education · doi:10.1007/978-90-481-9692-0_17 · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 20 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- [1]Crossing the quality chasm: a new health system for the 21st century 2001 Washington, D.C National Academy Press 25057539
- [3]Bleakley A Bligh J Browne J Medical Education for the Future Medical Education for the Future 2011 Dordrecht Springer Netherlands 252
- [8]Ferguson R The State of Learning Analytics in 2012: A Review and Future Challenges 2012 Milton Keynes Knowledge Media Institute
Cited by
Sorted by citations
Pham, Q., Graham, G., Carrion, C., Morita, P. P., Seto, E., Stinson, J. N., & Cafazzo, J. A. (2019) · JMIR mHealth and uHealth · doi:10.2196/11941 · Cited by 131 · see citing works
Big Data Analytics in Higher Education: A Review
Murumba, J., & Micheni, E. M. (2017) · The International Journal of Engineering and Science · doi:10.9790/1813-0606021421 · Cited by 56 · see citing works
Introduction to Big Data in Education and Its Contribution to the Quality Improvement Processes
Vaitsis, C., Hervatis, V., & Zary, N. (2016) · InTech eBooks · doi:10.5772/63896 · Cited by 33 · see citing works
Self-citationDion, M., Diouf, N. T., Robitaille, H., Turcotte, S., Adekpedjou, R., Labrecque, M., Cauchon, M., & Légaré, F. (2016) · JMIR Medical Education · doi:10.2196/mededu.6442 · Cited by 9 · see citing works
Trends of Educational Informatics in Latin America
Gómez, A. A., Caro, M. F., Solano, A. M., & Calao, Y. M. V. (2018) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.2018010106 · Cited by 6 · see citing works
Big Data on Real-World Applications
Soto, S. V., Luna, J. M., & Cano, A. (2016) · InTech eBooks · doi:10.5772/61396 · Cited by 3 · see citing works
Fattorini, R., & Paoletti, G. (2021) · Qwerty · doi:10.30557/qw000038
Trends of Educational Informatics in Latin America
Gómez, A. A., Manuel, F., Solano, A. M., & Calao, Y. M. V. (2019) · Natural Language Processing · doi:10.4018/978-1-7998-0951-7.ch047
COVID-19 Conceptual Modeling: Single-Center Cross-Sectional Study
Abuauf, M., Raboe, E., Alshareef, M. A. U., Rabie, N., Zailaie, R., Alharbi, A. A., Felemban, W. A., Alnasser, I. M., & Shalaby, H. (2023) · JMIR Formative Research · doi:10.2196/41376
Sugimura, N., & Ogasawara, K. (2025) · doi:10.2196/preprints.87436
Sugimura, N., & Ogasawara, K. (2026) · JMIR Nursing · doi:10.2196/87436
- Authors
- Hervatis, V., Loe, A., Barman, L., O'Donoghue, J., & Zary, N.
- Affiliations
- Karolinska Institutet (SE)
- Nanyang Technological University (SG)
- Imperial College London (GB)
- Corresponding author
- Hervatis, Vasilis
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2015-10-06
- Volume
- 1
- Issue
- 2
- Pages
- e11
- ISSN
- 2369-3762 (ISSN-L)
- PMC
- PMC5041364
- OpenAlex
- W2190266637
- Cited by
- 11
- References
- 20
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 1 · 2021: 1 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 3
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion, other-oa)
- Greater South Information System
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- data analytics, health professions education, quality management, conceptual model, data-driven analysis, quality indicators, decision support, educators, data to information
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