Medication SafetyUsing Pharmacy Data to Screen for Look-Alike, Sound-Alike Substitution Errors in Pediatric Prescriptions
Section snippets
Data Source
This study used 2000–2006 South Carolina Medicaid paid claims data for patients aged less than 20 years, obtained from the South Carolina Office of Research and Statistics.12 The dataset contained unique encrypted patient identifiers used to link enrollees to pharmacy and diagnostic data. We began with outpatient dispensed prescriptions, then matched patient data (from enrollee files) to each prescription. We obtained subjects' encounter diagnoses from outpatient, inpatient, and emergency
Results
Among the 22 test drugs, there were 1 420 091 prescriptions to 173 005 subjects. We identified 395 screening alerts, for a screening alert rate of 0.28 screening alerts per 1000 prescriptions. The Table shows patients prescribed each of the 22 drugs (11 LASA pairs), number of prescriptions of each drug, number (and frequencies) of prescriptions that triggered a screening alert, the PPVs of the alerts, and the corresponding estimate of true LASA error rate. The total number of prescriptions
Discussion
We believe that this study demonstrates the feasibility of real-time pharmacy screening for LASA errors, and the frequency of those errors appears to be generally low in the 22 drugs studied. We believe these analyses to be the first to attempt to determine frequencies of LASA errors in pediatric care. Indeed, little published data of any type exist on LASA errors in children. One other group of authors has evaluated LASA errors in children, also utilizing a Medicaid database. Although their
Acknowledgments
This project was supported by grant K08HS015679 from the Agency for Healthcare Research and Quality (William T. Basco, Jr, principal investigator). The project was also supported by grant D54HP05448 from the Health Resources and Services Administration (William T. Basco, Jr, principal investigator). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. The authors thank James R. Roberts, MD, MPH, for his assistance
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2016, Academic PediatricsCitation Excerpt :We used a broad definition of potential LASA error such that any patient who received both drugs of a LASA pair within any 6-month period represented a potential LASA error.2 Such an event could be detected at the point of prescribing or dispensing utilizing automated systems that would trigger an electronic alert when 2 drugs in a LASA pair were prescribed or dispensed within 6 months of one another.2 This approach produces a very inclusive error estimate, likely representing the upper bound of the true frequency of LASA substitution error.
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2012, Journal of Biomedical InformaticsCitation Excerpt :Senger et al. [71] write about drug misspellings as an information retrieval problem in Heidelberg University’s drug information system, describing error types as cognitive, phonetic and typographic, with typographic the most problematic. “Look-alike, sound-alike” (LASA) errors are defined by Basco et al. [72] as “the erroneous prescription or delivery of a drug because the name of the drug (generic or brand) is similar in appearance to or sounds like another drug”. Not surprisingly, lay people have difficulty with aspects of medication names that are challenging to health professionals, and need help distinguishing among similarly-sounding medications and dealing with spelling of medication names.
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