Advanced Clinical Decision Support for Vaccine Adverse Event Detection and Reporting
| Autor: | Michael Klompas, Crystal Garcia, Pedro L. Moro, Bob Zambarano, David Bar-Shain, Meghan A Baker, Richard Platt, Adam Douglas Henry, Megan Mazza, David C. Kaelber |
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| Rok vydání: | 2015 |
| Předmět: |
Adult
Male Microbiology (medical) Vaccine safety Adolescent Drug-Related Side Effects and Adverse Reactions Clinical decision support system Article Odds Young Adult Adverse Event Reporting System medicine Adverse Drug Reaction Reporting Systems Humans Medical diagnosis Child Adverse effect Aged Ohio Aged 80 and over Risk Management Vaccines business.industry Infant Newborn Health services research Infant Middle Aged Decision Support Systems Clinical medicine.disease Confidence interval Infectious Diseases Child Preschool Female Health Services Research Medical emergency business |
| Zdroj: | Clinical Infectious Diseases. 61:864-870 |
| ISSN: | 1537-6591 1058-4838 |
| Popis: | IMPORTANCE: Reporting of adverse events (AEs) following vaccination can help identify rare or unexpected complications of immunizations and aid in characterizing potential vaccine safety signals. OBJECTIVE: To create an electronic health record (EHR) module to assist clinicians with AE detection and reporting. DESIGN: We developed an open-source, generalizable clinical decision system called Electronic Support for Public Health–Vaccine Adverse Event Reporting System (ESP-VAERS) to facilitate automated AE detection and reporting using EHRs. ESP-VAERS prospectively monitors patients’ electronic records for new diagnoses, changes in laboratory values and new allergies for up to 6 weeks following vaccinations. When suggestive events are found, ESP-VAERS sends a secure electronic message to the patient’s clinician. The clinician is invited to affirm or refute the event, add comments, and if they wish, submit an automated, pre-populated electronic case report to the national VAERS. High probability AEs following vaccination are reported automatically even if the clinician does not respond. SETTING: We implemented ESP-VAERS in December 2012 at the MetroHealth System, an inpatient and outpatient integrated healthcare system in Ohio with nearly 1 million encounters per year. We queried the VAERS database to determine MetroHealth’s baseline reporting rates from 1/2009–3/2012 and then assessed changes in reporting rates with ESP-VAERS. PARTICIPANTS: All patients receiving vaccinations between 12/04/2012 and 08/03/2013 and their clinicians. EXPOSURE: ESP-VAERS MAIN OUTCOME AND MEASURE: The odds ratio of a VAERS report submission during the intervention period compared to the comparable pre-intervention period. RESULTS: In the 8 months following implementation, 91,622 vaccinations were given. ESP-VAERS sent 1,385 messages to responsible clinicians describing potential AEs (15 per 1000 vaccinations, mean 0.4 alerts per clinician per month (range 0–8)). Clinicians reviewed 1,304 (94%) messages, responded to 209 (15%), and confirmed 16 for transmission to VAERS. An additional 16 high probability AEs were sent automatically. Reported events included seizure, pleural effusion, and lymphocytopenia. The odds of a VAERS report submission during the pilot period were 30.2 (95% CI, 9.52–95.5) times greater than the odds during the comparable pre-pilot period. CONCLUSION AND RELEVANCE: An open-source EHR-based clinical decision support system can increase AE detection and reporting rates in VAERS. |
| Databáze: | OpenAIRE |
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