dc.contributor.author |
Ng, J |
en |
dc.contributor.author |
Andrew, P |
en |
dc.contributor.author |
Muir, P |
en |
dc.contributor.author |
Greene, M |
en |
dc.contributor.author |
Mohan, S |
en |
dc.contributor.author |
Knight, J |
en |
dc.contributor.author |
Hider, P |
en |
dc.contributor.author |
Davis, Peter |
en |
dc.contributor.author |
Seddon, M |
en |
dc.contributor.author |
Scahill, Shane |
en |
dc.contributor.author |
Harrison, Jeffrey |
en |
dc.contributor.author |
Zhou, L |
en |
dc.contributor.author |
Selak, Vanessa |
en |
dc.contributor.author |
Lawes, C |
en |
dc.contributor.author |
Galgali, G |
en |
dc.contributor.author |
Broad, Joanna |
en |
dc.contributor.author |
Crawley, M |
en |
dc.contributor.author |
Pevreal, W |
en |
dc.contributor.author |
Houston, N |
en |
dc.contributor.author |
Brott, T |
en |
dc.contributor.author |
Ryan, D |
en |
dc.contributor.author |
Peach, J |
en |
dc.contributor.author |
Brant, A |
en |
dc.contributor.author |
Bramley, D |
en |
dc.date.accessioned |
2019-03-21T01:56:33Z |
en |
dc.date.issued |
2018-10-26 |
en |
dc.identifier.citation |
New Zealand Medical Journal 131(1484):46-60 26 Oct 2018 |
en |
dc.identifier.issn |
0028-8446 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/46250 |
en |
dc.description.abstract |
AIM:To explore the feasibility and reliability of Clinical Coding Surveillance (CCS) for the routine monitoring of Adverse Drug Events (ADE) and describe the characteristics of harm identified through this approach in a large district health board (DHB). METHOD:All hospital admissions at Waitemata DHB from 2015 to 2016 with an ADE-related ICD10-AM code of Y40-Y59, X40-X49 or T36-T50 were extracted from clinical coded data. The data was analysed using descriptive statistics, statistical process control and Pareto charts. Two clinicians assessed a random sample of 140 ADEs for their accuracy against what was clinically documented in medical records. RESULTS:A total of 11,999 ADEs were identified in 244,992 admissions (4.9 ADEs per 100 admissions). ADEs were more prevalent in older adults and associated with longer average length of stays and medicines such as analgesics, antibiotics, anticoagulants and diuretics. Only 2,164 (18%) of ADEs were classified as originating within hospital. Of ADEs originating outside of the hospital, the main causes were poisoning by psychotropics, anti-epileptics and anti-parkinsonism agents and non-opioid analgesics. Clinicians agreed that 91% of ADE positive admissions were accurately classified as per clinical documentation. CONCLUSION:CCS is a feasible and reliable approach for the routine monitoring of ADEs in hospitals. |
en |
dc.format.medium |
Electronic |
en |
dc.language |
eng |
en |
dc.publisher |
New Zealand Medical Association |
en |
dc.relation.ispartofseries |
New Zealand Medical Journal |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.rights.uri |
http://www.nzma.org.nz/journal/contribute/articles |
en |
dc.subject |
Humans |
en |
dc.subject |
Hospitalization |
en |
dc.subject |
Feasibility Studies |
en |
dc.subject |
Reproducibility of Results |
en |
dc.subject |
Adverse Drug Reaction Reporting Systems |
en |
dc.subject |
Adolescent |
en |
dc.subject |
Adult |
en |
dc.subject |
Aged |
en |
dc.subject |
Aged, 80 and over |
en |
dc.subject |
Middle Aged |
en |
dc.subject |
Child |
en |
dc.subject |
Child, Preschool |
en |
dc.subject |
Infant |
en |
dc.subject |
Infant, Newborn |
en |
dc.subject |
Hospitals |
en |
dc.subject |
New Zealand |
en |
dc.subject |
Female |
en |
dc.subject |
Male |
en |
dc.subject |
Young Adult |
en |
dc.subject |
Clinical Coding |
en |
dc.title |
Feasibility and reliability of clinical coding surveillance for the routine monitoring of adverse drug events in New Zealand hospitals |
en |
dc.type |
Journal Article |
en |
pubs.issue |
1484 |
en |
pubs.begin-page |
46 |
en |
pubs.volume |
131 |
en |
dc.rights.holder |
Copyright: NZMA |
en |
pubs.author-url |
http://www.nzma.org.nz/journal/read-the-journal/all-issues/2010-2019/2018/vol-131-no-1484-26-october-2018/7724 |
en |
pubs.end-page |
60 |
en |
pubs.publication-status |
Published |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Article |
en |
pubs.elements-id |
755799 |
en |
pubs.org-id |
Medical and Health Sciences |
en |
pubs.org-id |
Pharmacy |
en |
pubs.org-id |
Population Health |
en |
pubs.org-id |
Epidemiology & Biostatistics |
en |
pubs.org-id |
School of Medicine |
en |
pubs.org-id |
Medicine Department |
en |
dc.identifier.eissn |
1175-8716 |
en |
pubs.record-created-at-source-date |
2019-08-16 |
en |
pubs.dimensions-id |
30359356 |
en |