PMID:18226244: Difference between revisions

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<span style = 'font-size: large'> '''Barrett, JS, Mondick, JT, Narayan, M, Vijayakumar, K and Vijayakumar, S'''  (2008) Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy.''BMC Med Inform Decis Mak'' '''8''':6</span>
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|{{TableHeadingColor}} |'''Abstract''' || <protect>Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.</protect>
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'''Barrett, JS, Mondick, JT, Narayan, M, Vijayakumar, K and Vijayakumar, S''' (2008) Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy. ''BMC Med Inform Decis Mak'' '''8''':6
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!align=left  |Abstract
*  [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=18226244 PubMed]
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Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.
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[http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=18226244 PubMed] [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2254609 PMC2254609]
Online version:[http://dx.doi.org/10.1186/1472-6947-8-6 10.1186/1472-6947-8-6]
Online version:[http://dx.doi.org/10.1186/1472-6947-8-6 10.1186/1472-6947-8-6]
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!align=left  |Keywords
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Algorithms; Antimetabolites, Antineoplastic/administration & dosage; Antimetabolites, Antineoplastic/adverse effects; Antimetabolites, Antineoplastic/pharmacokinetics; Antimetabolites, Antineoplastic/therapeutic use; Bayes Theorem; Child; Clinical Pharmacy Information Systems; Computer Simulation; Decision Support Systems, Clinical; Hospitals, Pediatric; Humans; Medical Records Systems, Computerized; Methotrexate/administration & dosage; Methotrexate/adverse effects; Methotrexate/pharmacokinetics; Methotrexate/therapeutic use; Neoplasms/drug therapy; Pharmacy Service, Hospital/organization & administration; Philadelphia; Systems Integration
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Latest revision as of 04:33, 15 September 2013

Citation

Barrett, JS, Mondick, JT, Narayan, M, Vijayakumar, K and Vijayakumar, S (2008) Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy. BMC Med Inform Decis Mak 8:6

Abstract

Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.

Links

PubMed PMC2254609 Online version:10.1186/1472-6947-8-6

Keywords

Algorithms; Antimetabolites, Antineoplastic/administration & dosage; Antimetabolites, Antineoplastic/adverse effects; Antimetabolites, Antineoplastic/pharmacokinetics; Antimetabolites, Antineoplastic/therapeutic use; Bayes Theorem; Child; Clinical Pharmacy Information Systems; Computer Simulation; Decision Support Systems, Clinical; Hospitals, Pediatric; Humans; Medical Records Systems, Computerized; Methotrexate/administration & dosage; Methotrexate/adverse effects; Methotrexate/pharmacokinetics; Methotrexate/therapeutic use; Neoplasms/drug therapy; Pharmacy Service, Hospital/organization & administration; Philadelphia; Systems Integration

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