Rebuilding the Standing Prescription Renewal Orders.


Journal

Applied clinical informatics
ISSN: 1869-0327
Titre abrégé: Appl Clin Inform
Pays: Germany
ID NLM: 101537732

Informations de publication

Date de publication:
01 2019
Historique:
entrez: 31 1 2019
pubmed: 31 1 2019
medline: 31 3 2020
Statut: ppublish

Résumé

Managing prescription renewal requests is a labor-intensive challenge in ambulatory care. In 2009, Vanderbilt University Medical Center developed clinic-specific standing prescription renewal orders that allowed nurses, under specific conditions, to authorize renewal requests. Formulary and authorization changes made maintaining these documents very challenging. This article aims to review, standardize, and restructure legacy standing prescription renewal orders into a modular, scalable, and easier to manage format for conversion and use in a new electronic health record (EHR). We created an enterprise-wide renewal domain model using modular subgroups within the main institutional standing renewal order policy by extracting metadata, medication group names, medication ingredient names, and renewal criteria from approved legacy standing renewal orders. Instance-based matching compared medication groups in a pairwise manner to calculate a similarity score between medication groups. We grouped and standardized medication groups with high similarity by mapping them to medication classes from a medication terminology vendor and filtering them by intended route (e.g., oral, subcutaneous, inhalation). After standardizing the renewal criteria to a short list of reusable criteria, the Pharmacy and Therapeutics (P&T) committee reviewed and approved candidate medication groups and corresponding renewal criteria. Seventy-eight legacy standing prescription renewal orders covered 135 clinics (some applied to multiple clinics). Several standing orders were perfectly congruent, listing identical medications for renewal. We consolidated 870 distinct medication classes to 164 subgroups and assigned renewal criteria. We consolidated 379 distinct legacy renewal criteria to 21 criteria. After approval by the P&T committee, we built subgroups in a structured and consistent format in the new EHR, where they facilitated chart review and standing order adherence by nurses. Additionally, clinicians could search an autogenerated document of the standing order content from the EHR data warehouse. We describe a methodology for standardizing and scaling standing prescription renewal orders at an enterprise level while transitioning to a new EHR.

Sections du résumé

BACKGROUND
Managing prescription renewal requests is a labor-intensive challenge in ambulatory care. In 2009, Vanderbilt University Medical Center developed clinic-specific standing prescription renewal orders that allowed nurses, under specific conditions, to authorize renewal requests. Formulary and authorization changes made maintaining these documents very challenging.
OBJECTIVE
This article aims to review, standardize, and restructure legacy standing prescription renewal orders into a modular, scalable, and easier to manage format for conversion and use in a new electronic health record (EHR).
METHODS
We created an enterprise-wide renewal domain model using modular subgroups within the main institutional standing renewal order policy by extracting metadata, medication group names, medication ingredient names, and renewal criteria from approved legacy standing renewal orders. Instance-based matching compared medication groups in a pairwise manner to calculate a similarity score between medication groups. We grouped and standardized medication groups with high similarity by mapping them to medication classes from a medication terminology vendor and filtering them by intended route (e.g., oral, subcutaneous, inhalation). After standardizing the renewal criteria to a short list of reusable criteria, the Pharmacy and Therapeutics (P&T) committee reviewed and approved candidate medication groups and corresponding renewal criteria.
RESULTS
Seventy-eight legacy standing prescription renewal orders covered 135 clinics (some applied to multiple clinics). Several standing orders were perfectly congruent, listing identical medications for renewal. We consolidated 870 distinct medication classes to 164 subgroups and assigned renewal criteria. We consolidated 379 distinct legacy renewal criteria to 21 criteria. After approval by the P&T committee, we built subgroups in a structured and consistent format in the new EHR, where they facilitated chart review and standing order adherence by nurses. Additionally, clinicians could search an autogenerated document of the standing order content from the EHR data warehouse.
CONCLUSION
We describe a methodology for standardizing and scaling standing prescription renewal orders at an enterprise level while transitioning to a new EHR.

Identifiants

pubmed: 30699459
doi: 10.1055/s-0038-1675813
pmc: PMC6353649
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

77-86

Informations de copyright

Georg Thieme Verlag KG Stuttgart · New York.

Déclaration de conflit d'intérêts

None.

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Auteurs

Scott D Nelson (SD)

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
HealthIT, Vanderbilt University Medical Center, Nashville, Tennessee, United States.

Hayley H Rector (HH)

Pharmacy Department, Vanderbilt University Medical Center, Nashville, Tennessee, United States.

Daniel Brashear (D)

College of Pharmacy and Health Sciences, Lipscomb University, Nashville, Tennessee, United States.

Janos L Mathe (JL)

HealthIT, Vanderbilt University Medical Center, Nashville, Tennessee, United States.

Haomin Wen (H)

HealthIT, Vanderbilt University Medical Center, Nashville, Tennessee, United States.

Stacey Lynn English (SL)

College of Pharmacy and Health Sciences, Lipscomb University, Nashville, Tennessee, United States.

William Hedges (W)

College of Pharmacy and Health Sciences, Lipscomb University, Nashville, Tennessee, United States.

Christoph U Lehmann (CU)

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
HealthIT, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Department of Pediatrics, Vanderbilt University, Nashville, Tennessee, United States.

Asli Ozdas-Weitkamp (A)

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
HealthIT, Vanderbilt University Medical Center, Nashville, Tennessee, United States.

Shane P Stenner (SP)

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
HealthIT, Vanderbilt University Medical Center, Nashville, Tennessee, United States.

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