Health Information Management: Changing with Time
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Objective: With the evolution of patient medical records from paper to electronic media and the changes to the way data is sourced, used, and managed, there is an opportunity for health information management (HIM) to learn and facilitate the increasing expanse of available patient data. Methods: This paper discusses the emerging trends and lessons learnt in relation with the following four areas: 1) data and information governance, 2) terminology standards certification, 3) International Classification of Diseases, 11th edition (ICD-11), and 4) data analytics and HIM. Results: The governance of patient data and information increasingly requires the HIM profession to incorporate the roles of data scientists and data stewards into its portfolio to ensure data analytics and digital transformation is appropriately managed. Not only are terminology standards required to facilitate the structure and primary use of this data, developments in Canada in relation with the standards, role descriptions, framework and curricula in the form of certification provide one prime example of ensuring the quality of the secondary use of patient data. The impending introduction of ICD-11 brings with it the need for the HIM profession to manage the transition between ICD versions and country modifications incorporating changes to standards and tools, and the availability and type of patient data available for secondary use. Conclusions: In summary, the health information management profession now requires abilities in leadership, data, and informatics in addition to health information science and coding skills to facilitate the expanding secondary use of patient data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it