ArticleBMC medical informatics and decision making2014
The clinical adoption meta-model: a temporal meta-model describing the clinical adoption of health information systems.
Article in BMC medical informatics and decision making, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Factors affecting the mature use of electronic medical records by primary care physicians: a systematic review.BMC medical informatics and decision making · 2021Pooled it
- The State of Evidence in Patient Portals: Umbrella Review.Journal of medical Internet research · 2020Pooled it
- Primary care physicians' attitudes to the adoption of electronic medical records: a systematic review and evidence synthesis using the clinical adoption framework.BMC medical informatics and decision making · 2018Pooled it
- Complexity of healthcare improvement - a comparative analysis of frameworks for improvement science.Frontiers in health services · 2026Article
- The Evolution of Medical Student Competencies and Attitudes in Digital Health Between 2016 and 2022: Comparative Cross-Sectional Study.JMIR medical education · 2025Article
- How theories, models, and frameworks have been used to implement digital health interventions in services for people with severe mental health problems: a scoping review.BMC public health · 2025Article
- Differences in changes of data completeness after the implementation of an electronic medical record in three surgical departments of a German hospital-a longitudinal comparative document analysis.BMC medical informatics and decision making · 2024Article
- The challenge of adopting a collaborative information system for independent healthcare workers in France: a comprehensive study.Scientific reports · 2024Article
- Shifting Mindsets: The Impact of a Patient Portal on Functioning and Recovery in a Mental Health Setting.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2024Article
- Access to automated comparative feedback reports in primary care - a study of intensity of use and relationship with clinical performance among Swedish primary care practices.BMC health services research · 2024Article
- A dual analysis of bougie and stylet development trend and impact of Chinese regulations on medical devices innovation.American journal of translational research · 2024Review
- Improving medication safety in a paediatric hospital: a mixed-methods evaluation of a newly implemented computerised provider order entry system.BMJ health & care informatics · 2023Observational
- Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research.Frontiers in health services · 2023Review
- How a point-of-care dashboard facilitates co-production of health care and health for and with individuals with psychotic disorders: a mixed-methods case study.BMC health services research · 2022Article
- Is primary health care ready for artificial intelligence? What do primary health care stakeholders say?BMC medical informatics and decision making · 2022Article
- End-users feedback and perceptions associated with the implementation of a clinical-rule based Check of Medication Appropriateness service.BMC medical informatics and decision making · 2022Article
- Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation.Journal of biomedical informatics · 2022Review
- Team-based care Evaluation and Adoption Model (TEAM) Framework: Supporting the comprehensive evaluation of primary care transformation over time.Canadian family physician Medecin de famille canadien · 2021Review
- Personal Electronic Records of Medications (PERMs) for medication reconciliation at care transitions: a rapid realist review.BMC medical informatics and decision making · 2021Review
- Electronic patient record and its effects on social aspects of interprofessional collaboration and clinical workflows in hospitals (eCoCo): a mixed methods study protocol.BMC health services research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Health information systems (HISs) hold the promise to transform health care; however, their adoption is challenged. We have developed the Clinical Adoption Meta-Model (CAMM) to help describe processes and possible challenges with clinical adoption. The CAMM, developed through an action research study to evaluate a provincial HIS, is a temporal model with four dimensions: availability, use, behaviour changes, and outcome changes. Seven CAMM archetypes are described, illustrating classic trajectories of adoption of HISs over time. Each archetype includes an example from the literature. The CAMM and its archetypes can support HIS implementers, evaluators, learners, and researchers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.