Using computational modeling to transform nursing data into actionable information.
J Biomed Inform
; 36(4-5): 351-61, 2003.
Article
em En
| MEDLINE
| ID: mdl-14643731
ABSTRACT
Transforming organizational research data into actionable information nurses can use to improve patient outcomes remains a challenge. Available data are numerous, at multiple levels of analysis, and snapshots in time, which makes application difficult in a dynamically changing healthcare system. One potential solution is computational modeling. We describe our use of OrgAhead, a theoretically based computational modeling program developed at Carnegie Mellon University, to transform data into actionable nursing information. We calibrated the model by using data from 16 actual patient care units to adjust model parameters until performance of simulated units ordered in the same way as observed performance of the actual units 80% of the time. In future research, we will use OrgAhead to generate hypotheses about changes nurses might make to improve patient outcomes, help nurses use these hypotheses to identify and implement changes on their units, and then measure the impact of those changes on patient outcomes.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos de Enfermagem
/
Biologia Computacional
Tipo de estudo:
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
J Biomed Inform
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2003
Tipo de documento:
Article
País de afiliação:
Estados Unidos