The maturity assessment of hospital information systems based on Electronic Medical Record Adoption Model – A comparison between private and governmental hospitals
Bibliographic record
Abstract
Today, Information Technology (IT) is considered as one of the major national development principles in each country which is applied in different fields. One of the most important fields in which IT is applied is health care and hospitals are similarly considered as most substantial organizations that use IT vastly. Although, different benchmarks and frameworks were developed to assess different aspects of Hospital Information Systems (HIS), still there was no reference model to benchmark HIS in the world until very recently. Eventually, Electronic Medical Record Adoption Model (EMRAM) which is globally a well-known model to benchmark the rate of HIS utilization in the hospitals, were emerged. Nevertheless, this model has not been introduced in majority of developing and even some developed countries in the world yet. In this study, EMRAM is applied to benchmark both governmental and private hospitals in Iran. This research is based on an applied descriptive method to assess five governmental and three private hospitals in Isfahan in 2015. This province is one of the most important provinces of Iran. The results reveal that HIS is not at the center of concern in these hospitals and are in the first and second maturity stages in accordance with EMRAM. Therefore, these types of hospitals are far away from desirable conditions and stages. Yet, the immaturity of HISs in private hospitals is more observable. This situation including the pressure of different beneficiaries such as insurance companies, has forced hospital managers to develop and enhance their HISs, especially in governmental hospitals.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".