# Journal of the American Medical Informatics Association

**Type:** Journals  
**Canonical URL:** https://scholariq.org/journals/journal-of-the-american-medical-informatics-association/

## Facts

| Field | Value |
| --- | --- |
| APC (USD) | 3,967 |
| Citations | 313,551 |
| h-index | 237 |
| Homepage | http://www.jamia.org/ |
| Open Access | false |
| ISSN-L | 1067-5027 |
| ISSNs | 1067-5027,1527-974X |
| OpenAlex ID | https://openalex.org/S129839026 |
| Publisher | Oxford University Press |
| Works | 5,246 |

## Journal papers

Showing 12 of 42.

- [Overriding of Drug Safety Alerts in Computerized Physician Order Entry](https://scholariq.org/papers/overriding-of-drug-safety-alerts-in-computerized-physician-order-entry/)
- [Launching PCORnet, a national patient-centered clinical research network](https://scholariq.org/papers/launching-pcornet-a-national-patient-centered-clinical-research-network/)
- [The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment](https://scholariq.org/papers/the-national-covid-cohort-collaborative-n3c-rationale-design-infrastructure-and/)
- [Comparing Computer-interpretable Guideline Models: A Case-study Approach](https://scholariq.org/papers/comparing-computer-interpretable-guideline-models-a-case-study-approach/)
- [The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: a systematic review of the literature](https://scholariq.org/papers/the-effectiveness-of-interventions-using-electronic-reminders-to-improve/)
- [MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care](https://scholariq.org/papers/minimar-minimum-information-for-medical-ai-reporting-developing-reporting/)
- [Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review](https://scholariq.org/papers/explainable-artificial-intelligence-models-using-real-world-electronic-health/)
- [Electronic medical records and the transgender patient: recommendations from the World Professional Association for Transgender Health EMR Working Group](https://scholariq.org/papers/electronic-medical-records-and-the-transgender-patient-recommendations-from-the/)
- [Calibration drift in regression and machine learning models for acute kidney injury](https://scholariq.org/papers/calibration-drift-in-regression-and-machine-learning-models-for-acute-kidney/)
- [Virtual care expansion in the Veterans Health Administration during the COVID-19 pandemic: clinical services and patient characteristics associated with utilization](https://scholariq.org/papers/virtual-care-expansion-in-the-veterans-health-administration-during-the-covid-19/)
- [Tiering Drug-Drug Interaction Alerts by Severity Increases Compliance Rates](https://scholariq.org/papers/tiering-drug-drug-interaction-alerts-by-severity-increases-compliance-rates/)
- [Online disease management of diabetes: Engaging and Motivating Patients Online With Enhanced Resources-Diabetes (EMPOWER-D), a randomized controlled trial](https://scholariq.org/papers/online-disease-management-of-diabetes-engaging-and-motivating-patients-online/)

## Journal top topics

Showing 8 of 25.

- [Electronic Health Records Systems](https://scholariq.org/topics/electronic-health-records-systems/)
- [Biomedical Text Mining and Ontologies](https://scholariq.org/topics/biomedical-text-mining-and-ontologies/)
- [Machine Learning in Healthcare](https://scholariq.org/topics/machine-learning-in-healthcare/)
- [Mobile Health and mHealth Applications](https://scholariq.org/topics/mobile-health-and-mhealth-applications/)
- [Artificial Intelligence in Healthcare and Education](https://scholariq.org/topics/artificial-intelligence-in-healthcare-and-education/)
- [Topic Modeling](https://scholariq.org/topics/topic-modeling/)
- [Healthcare Systems and Technology](https://scholariq.org/topics/healthcare-systems-and-technology/)
- [Patient Safety and Medication Errors](https://scholariq.org/topics/patient-safety-and-medication-errors/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.