Local Public Health Department Adoption and Use of Electronic Health Records
Bibliographic record
Abstract
CONTEXT: Electronic health records (EHRs) may help local health departments (LHDs) to improve services and thereby promote and protect population health. Yet, little is known about nationwide trends and correlates of EHR use by LHDs. OBJECTIVE: We examine relative contributions of LHD finances, leadership, and governance to EHR adoption and use from 2010 to 2013. The impact of LHD service provision and meaningful use factors on EHR use is explored in depth. DESIGN: Combining data from the National Association of County & City Health Officials Profile survey and the Area Health Resource File, logistic regression models were used to examine EHR use in 2013. Multinomial logistic models examined EHR adoption, use, or discontinuation from 2010 to 2013. PARTICIPANTS: EHR usage data were available for 514 and 488 LHDs in 2010 and 2013, respectively. A total of 117 LHDs had data for both 2010 and 2013. MAIN OUTCOME MEASURES: Outcomes included dichotomized measures of LHD self-reported use of EHRs in 2010 and 2013. For LHDs with 2 years of data, a 4-category variable measuring self-reported EHR use, nonuse, adoption, or discontinuation was analyzed. RESULTS: Overall LHD EHR use did not increase significantly between 2010 (19.3%) and 2013 (22.0%). While 15% of LHDs reported adopting EHRs from 2010 to 2013, another 8.5% reported discontinuing use of EHRs during this time. Likelihood of EHR use was strongly associated with LHD clinical service characteristics, per capita expenditures, and state governance structure. CONCLUSIONS: EHRs do not appear to be rapidly diffusing across LHDs, and retention of current systems may be a concern. Given trends away from clinical service provision and other pressing demands for LHD resources, the benefits of EHR adoption are unclear.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".