Developing consensus on hospital prescribing indicators of potential harms amenable to decision support

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Developing consensus on hospital prescribing indicators of potential harms amenable to decision support. / Thomas, Sarah; McDowell, Sarah; Hodson, James; Nwulu, Ugochi; Howard, Rachel; Avery, Anthony; Slee, Ann; Coleman, Jamie.

In: British Journal of Clinical Pharmacology, Vol. 76, No. 5, 12.2013, p. 797-809.

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Thomas, Sarah ; McDowell, Sarah ; Hodson, James ; Nwulu, Ugochi ; Howard, Rachel ; Avery, Anthony ; Slee, Ann ; Coleman, Jamie. / Developing consensus on hospital prescribing indicators of potential harms amenable to decision support. In: British Journal of Clinical Pharmacology. 2013 ; Vol. 76, No. 5. pp. 797-809.

Bibtex

@article{86e7388e6d2d43a4932397f1fd19362b,
title = "Developing consensus on hospital prescribing indicators of potential harms amenable to decision support",
abstract = "AimsTo develop a list of prescribing indicators specific for the hospital setting that would facilitate the prospective collection of high-severity and/or high-frequency prescribing errors, which are also amenable to electronic clinical decision support.MethodsA two-stage consensus technique (electronic Delphi) was carried out with 20 experts across England. Participants were asked to score prescribing errors using a five-point Likert scale for their likelihood of occurrence and the severity of the most likely outcome. These were combined to produce risk scores, from which median scores were calculated for each indicator across the participants in the study. The degree of consensus between the participants was defined as the proportion that gave a risk score in the same category as the median. Indicators were included if a consensus of 80% or more was achieved.ResultsA total of 80 prescribing errors were identified by consensus as being high or extreme risk. The most common drug classes named within the indicators were antibiotics (n = 13), antidepressants (n = 8), nonsteroidal anti-inflammatory drugs (n = 6) and opioid analgesics (n = 6). The most frequent error type identified as high or extreme risk were those classified as clinical contraindications (n = 29 of 80).ConclusionsEighty high-risk prescribing errors in the hospital setting have been identified by an expert panel. These indicators can serve as a standardized, validated tool for the collection of prescribing data in both paper-based and electronic prescribing processes. This can assess the impact of safety improvement initiatives, such as the implementation of electronic clinical decision support.",
keywords = "Delphi Technique, consensus, Electronic prescribing, Medication Errors",
author = "Sarah Thomas and Sarah McDowell and James Hodson and Ugochi Nwulu and Rachel Howard and Anthony Avery and Ann Slee and Jamie Coleman",
note = "{\textcopyright} 2013 The Authors. British Journal of Clinical Pharmacology {\textcopyright} 2013 The British Pharmacological Society.",
year = "2013",
month = dec,
doi = "10.1111/bcp.12087",
language = "English",
volume = "76",
pages = "797--809",
journal = "British Journal of Clinical Pharmacology",
issn = "0306-5251",
publisher = "Wiley",
number = "5",

}

RIS

TY - JOUR

T1 - Developing consensus on hospital prescribing indicators of potential harms amenable to decision support

AU - Thomas, Sarah

AU - McDowell, Sarah

AU - Hodson, James

AU - Nwulu, Ugochi

AU - Howard, Rachel

AU - Avery, Anthony

AU - Slee, Ann

AU - Coleman, Jamie

N1 - © 2013 The Authors. British Journal of Clinical Pharmacology © 2013 The British Pharmacological Society.

PY - 2013/12

Y1 - 2013/12

N2 - AimsTo develop a list of prescribing indicators specific for the hospital setting that would facilitate the prospective collection of high-severity and/or high-frequency prescribing errors, which are also amenable to electronic clinical decision support.MethodsA two-stage consensus technique (electronic Delphi) was carried out with 20 experts across England. Participants were asked to score prescribing errors using a five-point Likert scale for their likelihood of occurrence and the severity of the most likely outcome. These were combined to produce risk scores, from which median scores were calculated for each indicator across the participants in the study. The degree of consensus between the participants was defined as the proportion that gave a risk score in the same category as the median. Indicators were included if a consensus of 80% or more was achieved.ResultsA total of 80 prescribing errors were identified by consensus as being high or extreme risk. The most common drug classes named within the indicators were antibiotics (n = 13), antidepressants (n = 8), nonsteroidal anti-inflammatory drugs (n = 6) and opioid analgesics (n = 6). The most frequent error type identified as high or extreme risk were those classified as clinical contraindications (n = 29 of 80).ConclusionsEighty high-risk prescribing errors in the hospital setting have been identified by an expert panel. These indicators can serve as a standardized, validated tool for the collection of prescribing data in both paper-based and electronic prescribing processes. This can assess the impact of safety improvement initiatives, such as the implementation of electronic clinical decision support.

AB - AimsTo develop a list of prescribing indicators specific for the hospital setting that would facilitate the prospective collection of high-severity and/or high-frequency prescribing errors, which are also amenable to electronic clinical decision support.MethodsA two-stage consensus technique (electronic Delphi) was carried out with 20 experts across England. Participants were asked to score prescribing errors using a five-point Likert scale for their likelihood of occurrence and the severity of the most likely outcome. These were combined to produce risk scores, from which median scores were calculated for each indicator across the participants in the study. The degree of consensus between the participants was defined as the proportion that gave a risk score in the same category as the median. Indicators were included if a consensus of 80% or more was achieved.ResultsA total of 80 prescribing errors were identified by consensus as being high or extreme risk. The most common drug classes named within the indicators were antibiotics (n = 13), antidepressants (n = 8), nonsteroidal anti-inflammatory drugs (n = 6) and opioid analgesics (n = 6). The most frequent error type identified as high or extreme risk were those classified as clinical contraindications (n = 29 of 80).ConclusionsEighty high-risk prescribing errors in the hospital setting have been identified by an expert panel. These indicators can serve as a standardized, validated tool for the collection of prescribing data in both paper-based and electronic prescribing processes. This can assess the impact of safety improvement initiatives, such as the implementation of electronic clinical decision support.

KW - Delphi Technique

KW - consensus

KW - Electronic prescribing

KW - Medication Errors

U2 - 10.1111/bcp.12087

DO - 10.1111/bcp.12087

M3 - Article

C2 - 23362926

VL - 76

SP - 797

EP - 809

JO - British Journal of Clinical Pharmacology

JF - British Journal of Clinical Pharmacology

SN - 0306-5251

IS - 5

ER -