Using Computerized Data to Identify Adverse Drug Events in Outpatients

Honigman, Benjamin; Lee, Joshua; Rothschild, Jeffrey M.; Light, Patrice; Pulling, Russel M.; Yu, Tony Fu‐Lai; Bates, David W.

doi:10.1136/jamia.2001.0080254

Cited by 197 publications

(182 citation statements)

References 47 publications

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“…Therefore, 15 studies with a total of 278,212 patients and 126,197 incidents of positive triggers were included in this review. 17,20,31,[33][34][35][36][37][38][39][40][41][42][43][44][45] Of the 15 included studies, 9 were conducted in the United States, 3 in Scotland, and 1 each in Brazil, France, and Australia (Table 2). Eight studies were published since 2010 and 2 were published before 2000.…”

Section: Resultsmentioning

confidence: 99%

“…38 Therefore, the quan- tification of the accuracy of the triggers is limited to the positive predictive value (PPV). The value ranged widely from 0.05% for drug laboratory rules 39 to 92% for hospitalization for new rectal cancer. 42 The PPV of polypharmacy ranged from 19.5% to 29.3% (depending on the number of medications used to define polypharmacy), for laboratory abnormalities ranged from 0.05% 39 to 30.6% 36 , for hospital admissions ranged from 1.8% 33 to 9% 37 , and for combined tools ranged from 1.8% 33 to 24%.…”

Section: Adverse Eventsmentioning

confidence: 99%

“…20,31,34,35,37 The most commonly used method to identify pAEs was chart review. 20,31,[33][34][35][36][37][38][39][40][41][42][43][44] Four studies used physicians only as reviewers 35,37,38,40 , 3 used a combination of physicians and pharmacists. 33,36,41 1 used nurses 34 , 1 used pharmacists 44 , and 1 used a combination of physicians and nurses 20 , 5 studies used more than 1 reviewer and all these used at least 2 independent reviewers.…”

Section: Adverse Eventsmentioning

confidence: 99%

“…33,36,41 1 used nurses 34 , 1 used pharmacists 44 , and 1 used a combination of physicians and nurses 20 , 5 studies used more than 1 reviewer and all these used at least 2 independent reviewers. 20,33,[37][38][39] Interrater reliability of chart review was assessed in 4 studies 33,37,40,44 and ranged from a Cohen's kappa of 0.33 37 to 0.89. 44 Bigby et al 37 showed an intrarater Trigger 9: complaint letter Look to see if the complaint related to an event (i.e., request for the waiver of co-payment, payment or concern about quality of care).…”

Section: Adverse Eventsmentioning

confidence: 99%

“…Several studies cited the same patients having multiple triggers within a given period of review. Honigman et al 39 noted that "The relationship between computer-identified incidents and ADEs was often 'many to 1Ј." Across all studies, most of the triggers were identified by either computerized notification (8/14; 57.1%) or manual record review (6/14; 42.9%).…”

Section: Triggersmentioning

confidence: 99%

See 4 more Smart Citations

The Accuracy of Trigger Tools to Detect Preventable Adverse Events in Primary Care: A Systematic Review

et al. 2018

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Purpose: To understand the ability of trigger tools to detect preventable adverse events (pAEs) in the primary care outpatient setting using the Institute for Healthcare Improvement's (IHI) Outpatient Adverse Event Trigger Tool (IHI Tool).Methods: The OVID MEDLINE and OVID MEDLINE In-process and non-Indexed citations databases were queried using controlled vocabulary and Medical Subject Headings related to the concepts "primary care" and "adverse events." Included articles were conducted in the outpatient setting, used at least 1 of the triggers identified in the IHI Tool, and identified pAEs of any type. Articles were selected for inclusion based first on assessment of titles then abstracts by 2 trained reviewers independently, followed by full text review by 2 authors.Results: Our search identified 6435 unique articles, and we included 15 in our review. The most common studied trigger was laboratory abnormalities. The most common pAEs were medication errors followed by unplanned hospitalizations. The effectiveness of triggers in identifying AEs varied widely.

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Section: Resultsmentioning

confidence: 99%

Section: Adverse Eventsmentioning

confidence: 99%

Section: Adverse Eventsmentioning

confidence: 99%

Section: Adverse Eventsmentioning

confidence: 99%

Section: Triggersmentioning

confidence: 99%

See 3 more Smart Citations

The Accuracy of Trigger Tools to Detect Preventable Adverse Events in Primary Care: A Systematic Review

et al. 2018

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Linking Laboratory and Pharmacy

Schiff¹,

Klass²,

Peterson³

et al. 2003

Arch Intern Med

122

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A myriad of errors and lost improvement opportunities result from failure of clinical laboratory and pharmacy information systems to effectively communicate. Pharmacotherapy could benefit from enhanced laboratory-pharmacy linkage with respect to (1) drug choice (laboratory-based indications and contraindications), (2) drug dosing (renal or hepatic, blood level-guided adjustments), (3) laboratory monitoring (laboratory signals of toxicity, baseline and ongoing monitoring), (4) laboratory result interpretation (drug interfering with test), and (5) broader quality improvement (surveillance for unrecognized toxicity, monitoring clinician response delays). Linkages can be retrospective or real-time. Many organizations could benefit now by linking existing pharmacy and laboratory data. Greater improvement is possible through implementation of electronic order entry with real-time decision support incorporating linked laboratory and pharmacy data. While many guidelines, admonitions, and rules exist regarding drugs and the laboratory, substantial new knowledge and evidence in this area are needed. Focusing on these unmet needs and accompanying logistical challenges is a priority.

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Identifying Diagnostic Errors in Primary Care Using an Electronic Screening Algorithm

et al. 2007

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Background: Diagnostic errors are the leading basis for malpractice claims in primary care, yet these errors are underidentified and understudied. Computerized methods used to screen for other types of errors (eg, medication related) have not been applied to diagnostic errors. Our objectives were to assess the feasibility of computerized screening to identify diagnostic errors in primary care and to categorize diagnostic breakdowns using a recently published taxonomy. Methods: We used an algorithm to screen the electronic medical records of patients at a single hospital that is part of a closed health care system. A Structured Query Language-based program detected the presence of 1 of 2 mutually exclusive electronic screening criteria: screen 1, a primary care visit (index visit) followed by a hospitalization in the next 10 days; or screen 2, an index visit followed by 1 or more primary care, urgent care, or emergency department visits within 10 days. Two independent, blinded reviewers determined the presence or absence of diagnostic error through medical record review of visits with positive and negative screening results. Results: Among screen 1 and 2 positive visits, 16.1% and 9.4%, respectively, were associated with a diagnostic error. The error rate was 4% in control cases that met neither screening criterion. The most common primary errors in the diagnostic process were failure or delay in eliciting information and misinterpretation or suboptimal weighing of critical pieces of data from the history and physical examination. The most common secondary errors were suboptimal weighing or prioritizing of diagnostic probabilities and failure to recognize urgency of illness or its complications. Conclusions: Electronic screening has potential to identify records that may contain diagnostic errors in primary care, and its performance is comparable to screening tools for other types of errors. Future studies that validate these findings in other settings could provide improvement initiatives in this area.

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Using Computerized Data to Identify Adverse Drug Events in Outpatients

Cited by 197 publications

References 47 publications

The Accuracy of Trigger Tools to Detect Preventable Adverse Events in Primary Care: A Systematic Review

The Accuracy of Trigger Tools to Detect Preventable Adverse Events in Primary Care: A Systematic Review

Linking Laboratory and Pharmacy

Identifying Diagnostic Errors in Primary Care Using an Electronic Screening Algorithm

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