Convergent evolution of health information management and health informatics
Bibliographic record
Abstract
Summary Clearly defined boundaries are disappearing among the activities, sources, and uses of health care data and information managed by health information management (HIM) and health informatics (HI) professionals. Definitions of the professional domains and scopes of practice for HIM and HI are converging with the proliferation of information and communication technologies in health care settings. Convergence is changing both the roles that HIM and HI professionals serve in their organizations as well as the competencies necessary for training future professionals. Many of these changes suggest a blurring of roles and responsibilities with increasingly overlapping curricula, job descriptions, and research agendas. Blurred lines in a highly competitive market create confusion for students and employers. In this essay, we provide some perspective on the changing landscape and suggest a course for the future. First we review the evolving definitions of HIM and HI. We next compare the current domains and competencies, review the characteristics as well as the education and credentialing of both disciplines, and examine areas of convergence. Given the current state, we suggest a path forward to strengthen the contributions HIM and HI professionals and educators make to the evolving health care environment. Citation: Gibson CJ, Dixon BE, Abrams K. Convergent evolution of health information management and health informatics – a perspective on the future of information professionals in health care. Appl Clin Inf 2015; 6: 163–184 http://dx.doi.org/10.4338/ACI-2014-09-RA-0077
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".