End-user support for a primary care electronic medical record: aqualitative case study of a vendor's perspective
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
BACKGROUND: In primary care settings, users often rely on vendors to provide support for health information technology (HIT). Yet, little is known about the vendors' perspectives on the support they provide, how support personnel perceive their roles, the challenges they face and the ways they deal with them. OBJECTIVE: To provide in-depth insight into an electronic medical record (EMR) vendor's perspective on end-user support. METHODS: As part of a larger case study research, we conducted nine semi-structured interviews with help desk staff, trainers and service managers of an EMR vendor, and observed two training sessions of a new client. RESULTS: With a growing client base, the vendor faced challenges of support staff shortage and high variance in users' technical knowledge. Additionally, users sometimes needed assistance with infrastructure, and not just software problems. These challenges sometimes hindered the provision of timely support and required supporters to possess good interpersonal skills and adapt to diverse client population. CONCLUSION: This study highlights the complexity of providing end-user support for HIT. With increased adoption, other vendors are likely to face similar challenges. To deal with these issues, supporters need not only strong technical knowledge of the systems, but also good interpersonal communication skills. Some responsibilities may be delegated to super-users. Users may find it useful to hire local IT staff, at least on an on-call basis, to provide assistance with infrastructure problems, which are not supported by the software vendor. Vendors may consider expanding their service packages to cover these elements.
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 | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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