The use of narrative electronic prescribing instructions in pharmacoepidemiology: A scoping review for the International Society for Pharmacoepidemiology

Abstract

Narrative electronic prescribing instructions (NEPIs) are text that convey information on the administration or co‐administration of a drug as directed by a prescriber. For researchers, NEPIs have the potential to advance our understanding of the risks and benefits of medications in populations; however, due to their unstructured nature, they are not often utilized. The goal of this scoping review was to evaluate how NEPIs are currently employed in research, identify opportunities and challenges for their broader application, and provide recommendations on their future use. The scoping review comprised a comprehensive literature review and a survey of key stakeholders. From the literature review, we identified 33 primary articles that described the use of NEPIs. The majority of articles (n = 19) identified issues with the quality of information in NEPIs compared with structured prescribing information; nine articles described the development of novel algorithms that performed well in extracting information from NEPIs, and five described the used of manual or simpler algorithms to extract prescribing information from NEPIs. A survey of 19 stakeholders indicated concerns for the quality of information in NEPIs and called for standardization of NEPIs to reduce data variability/errors. Nevertheless, stakeholders believed NEPIs present an opportunity to identify prescriber's intent for the prescription and to study temporal treatment patterns. In summary, NEPIs hold much promise for advancing the field of pharmacoepidemiology. Researchers should take advantage of addressing important questions that can be uniquely answered with NEPIs, but exercise caution when using this information and carefully consider the quality of the data.

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“Take up to eight tablets per day”: Incorporating free‐text medication instructions into a transparent and reproducible process for preparing drug exposure data for pharmacoepidemiology

“…However, there are challenges in applying such algorithms due to different datasets, different analytical software, regulatory issues and lack of standardization of narrative electronic prescribing instructions. 12 We have recently updated our narrative prescribing text-mining algorithm as an R package "Doseminer" and also released an R package version of our prior STATA "DrugPrep" algorithm, with accompanying example datasets and short vignettes (doseminer and drugprepr are both freely available on CRAN).…”

“…We have performed prior work in text‐mining dosage instructions to allow pharmacoepidemiology studies to handle free‐text instructions, outputting a machine‐readable range of possible exposures for any single prescription 11 . This allows researcher‐led interpretation of possible ranges of exposure derived from instructions such as “one to two tablets up to four times a day.” However, there are challenges in applying such algorithms due to different datasets, different analytical software, regulatory issues and lack of standardization of narrative electronic prescribing instructions 12 . We have recently updated our narrative prescribing text‐mining algorithm as an R package “Doseminer” and also released an R package version of our prior STATA “DrugPrep” algorithm, with accompanying example datasets and short vignettes ( doseminer and drugprepr are both freely available on CRAN).…”

“… 11 This allows researcher‐led interpretation of possible ranges of exposure derived from instructions such as “one to two tablets up to four times a day.” However, there are challenges in applying such algorithms due to different datasets, different analytical software, regulatory issues and lack of standardization of narrative electronic prescribing instructions. 12 We have recently updated our narrative prescribing text‐mining algorithm as an R package “Doseminer” and also released an R package version of our prior STATA “DrugPrep” algorithm, with accompanying example datasets and short vignettes ( doseminer and drugprepr are both freely available on CRAN). Together, these two linked R packages have the potential to improve the efficiency and transparency of drug exposure data preparation, including the incorporation of free‐text prescribing instructions.…”