Performance of Community Pharmacy Drug Interaction Software

Hazlet, Thomas K.; Lee, Todd A.; Hansten, Philip D.; Horn, John R.

doi:10.1016/s1086-5802(16)31230-x

Cited by 111 publications

(99 citation statements)

References 7 publications

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“…Then again, such systems are not entirely reliable; in one evaluation, they failed to detect up to a third of drug-drug interactions but frequently alerted users to trivial issues. 55 …”

Section: Improved Identification and Management Of Drug Interactionsmentioning

confidence: 99%

Review article: Improving drug safety for patients undergoing anesthesia and surgery

Orser

Hyland²,

David³

et al. 2012

Can J Anesth/J Can Anesth

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Purpose This article presents a summary of recent advances, including tools and interventions, that are designed to improve drug safety for patients in critical care settings, particularly those undergoing anesthesia and surgery. Principal findings Medication error remains a leading cause of adverse events among patients undergoing anesthesia. Misidentification of ampoules, vials, and syringes is a common source of error. Systems are now being engineered to reduce the likelihood of medication misidentification through approaches such as revision of standards for labelling of drug ampoules and vials and the development of bar code systems that allow ''double checking'' or drug verification in the operating room. Also, efforts are being made to improve medication reconciliation, a process for accurately communicating a patient's medication information during transitions from one healthcare setting to another. Finally, the opportunity exists for anesthesiologists to increase awareness about the rising problem of opioid addiction in patients for whom typical doses are initially prescribed for appropriate indications such as postoperative pain. Conclusions There is a need to improve drug delivery systems in complex critical care environments, particularly the operating room. Anesthesiologists must continue to play a leading role in promoting drug safety in these environments.

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“…Then again, such systems are not entirely reliable; in one evaluation, they failed to detect up to a third of drug-drug interactions but frequently alerted users to trivial issues. 55 …”

Section: Improved Identification and Management Of Drug Interactionsmentioning

confidence: 99%

Review article: Improving drug safety for patients undergoing anesthesia and surgery

Orser

Hyland²,

David³

et al. 2012

Can J Anesth/J Can Anesth

View full text Add to dashboard Cite

show abstract

“…Hazlett et al 20 assessed the ability of pharmacy computer systems used in chain and HMO pharmacies in Washington state to accurately detect clinically important DDIs. Pharmacy chains and HMOs with at least 7 practice sites were selected for study.…”

Section: Quality Of Computer-generated Prospective Dur Alertsmentioning

confidence: 99%

“…In many instances, the pharmacist never sees or heeds the alert for a variety of reasons. [18][19][20][21] Safe and effective pharmacotherapy occurs in the context of a medication use system with predictable results. 30 The hallmarks of such a system include a high degree of coordination and cooperation among the persons involved: patient, caregiver, prescriber, pharmacist, and nurse.…”

Section: Ss Next Steps For Quality Improvement In Prospective Durmentioning

confidence: 99%

Current Status of Prospective Drug Utilization Review

Fulda¹,

Lyles²,

Pugh³

et al. 2004

JMCP

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“…[6][7][8][9] Typically, their design focuses on high sensitivity to detect interacting drugs. [10][11][12][13] However, the unavoidable trade-off is that highly sensitive CDSS based on comprehensive interaction databases impose an overwhelming alert burden on the prescribing physician. This may lead to "alert fatigue", meaning that CDSS-triggered alerts are indiscriminately given little attention or even completely disregarded.…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance of Two Drug Interaction Screening Programmes Analysing a Cross-Sectional Prescription Dataset of 84,625 Psychiatric Inpatients

et al. 2013

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BACKGROUND: Clinical decision support software (CDSS) solutions can automatically identify drug interactions and thereby aim to improve drug safety. However, data on the comparative performance of different CDSS to detect and appropriately classify interactions in real-life prescription datasets is limited. OBJECTIVE: The aim of this study was to compare the results from two different CDSS analysing the pharmacotherapy of a large population of psychiatric inpatients for drug interactions. METHODS: We performed mass analyses of cross-sectional patient-level prescriptions from 84,625 psychiatric inpatients using two CDSS -MediQ and ID PHARMA CHECK(®). Interactions with the highest risk ratings and the most frequent ratings were reclassified according to the Zurich Interaction System (ZHIAS), a multidimensional classification that incorporates the OpeRational ClassificAtion of Drug Interactions (ORCA) and served as a reference standard. RESULTS: MediQ reported 6,133 unique interacting combinations responsible for 270,617 alerts affecting 63,454 patients. ID PHARMA CHECK(®) issued 5,400 interactions and 157,489 alerts in 48,302 patients. Only 2,154 unique interactions were identified by both programmes, but overlap increased with higher risk rating. MediQ reported high-risk interactions in 2.5 % of all patients, compared with 5 % according to ID PHARMA CHECK(®). The positive predictive value for unique major alerts to be (provisionally) contraindicated according to ORCA was higher for MediQ (0.63) than for either of the two ID PHARMA CHECK(®) components (0.42 for hospINDEX and 0.30 for ID MACS). MediQ reported more interactions, and ID PHARMA CHECK(®) tended to classify interactions into a higher risk class, but overall both programmes identified a similar number of (provisionally) contraindicated interactions according to ORCA criteria. Both programmes identified arrhythmia as the most frequent specific risk associated with interactions in psychiatric patients. CONCLUSIONS: CDSS can be used for mass-analysis of prescription data and thereby support quality management. However, in clinical practice CDSS impose an overwhelming alert burden on the prescriber, and prediction of clinical relevance remains a major challenge. Only a small subset of yet to be determined alerts appears suitable for automated display in clinical routine. DOI: https://doi.org/10.1007/s40264-013-0027-9Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-76756 Accepted Version Originally published at: Zorina, Olesya I; Haueis, Patrick; Greil, Waldemar; Grohmann, Renate; Kullak-Ublick, Gerd A; Russmann, Stefan (2013). Comparative performance of two drug interaction screening programmes analysing a cross-sectional prescription dataset of 84,625 psychiatric inpatients. Drug Safety, 36(4):247-258.

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Performance of Community Pharmacy Drug Interaction Software

Cited by 111 publications

References 7 publications

Review article: Improving drug safety for patients undergoing anesthesia and surgery

Review article: Improving drug safety for patients undergoing anesthesia and surgery

Current Status of Prospective Drug Utilization Review

Comparative Performance of Two Drug Interaction Screening Programmes Analysing a Cross-Sectional Prescription Dataset of 84,625 Psychiatric Inpatients

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