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Treating asthma by the guidelines: developing a medication management information system for use in primary care
Joan E Twiggs1, J Fifield, E Jackson
1Department of Sociology, Anthropology and Social Work, Kansas State University, Manhattan, Kansas, USA. jetwiggs@netplex.net
An automated asthma medication management information system (MMIS) provides guidance based on clinical guidelines. The system successfully generated valid reports for 83% of visits, aiding in appropriate asthma medication management.
Area of Science:
- Clinical Informatics
- Pediatric Pulmonology
- Health Information Systems
Background:
- Asthma medication management requires adherence to clinical guidelines for optimal patient outcomes.
- Existing systems may lack automated, patient-specific feedback for practitioners.
- The 1997 National Asthma Education and Prevention Program (NAEPP) guidelines provide a framework for asthma care.
Purpose of the Study:
- To develop and assess an automated asthma medication management information system (MMIS).
- To provide patient-specific guidance based on 1997 NAEPP guidelines.
- To evaluate the system's impact on asthma medication prescribing practices in primary care.
Main Methods:
- Developed and implemented an internet-accessible, centralized MMIS in pediatric primary care.
- Collected detailed patient asthma medication data and practitioner-reported severity.
- Utilized a system algorithm to compare actual pharmacotherapy to guideline-recommended, severity-appropriate medications.
Main Results:
- The MMIS produced a valid feedback report for 83% of patient visits.
- Missing data constituted 60% of system error cases.
- Practitioners prescribed severity-appropriate medications in 60% of cases, with a tendency towards over-prescribing (22%) rather than under-prescribing (5%).
Conclusions:
- The developed MMIS is a viable tool for supporting guideline-adherent asthma medication management.
- The system identified opportunities for improving medication appropriateness in pediatric asthma care.
- Further refinement of data collection may reduce error rates and enhance system utility.
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