Sustaining "Meaningful Use" of Health Information Technology in Low-Resource Practices
Bibliographic record
Abstract
PURPOSE: The implementation of electronic health records (EHRs) has been extensively studied, but their maintenance once implemented has not. The Regional Extension Center (REC) program provides implementation assistance to priority practices-those with limited financial, technical, and organizational resources-but the assistance is time limited. Our objective was to identify potential barriers to maintenance of meaningful use of EHRs in priority primary care practices using a qualitative observational study for federally qualified health centers (FQHCs) and priority practices in Michigan. METHODS: We conducted cognitive task analysis (CTA) interviews and direct observations of health information technology implementation in FQHCs. In addition, we conducted semistructured interviews with implementation specialists serving priority practices to detect emergent themes relevant to maintenance. RESULTS: Maintaining EHR technology will require ongoing expert technical support indefinitely beyond implementation to address upgrades and security needs. Maintaining meaningful use for quality improvement will require ongoing support for leadership and change management. Priority practices not associated with larger systems lack access to the necessary technical expertise, financial resources, and leverage with vendors to continue alone. Rural priority practices are particularly challenged, because expertise is often not available locally. CONCLUSIONS: Priority practices, especially in rural areas, are at high risk for falling on the wrong side of a "digital divide" as payers and regulators enact increasing expectations for EHR use and information management. For those without affiliation to maintain the necessary expert staff, ongoing support will be needed for those practices to remain viable.
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.
How this classification was reachedexpand
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".