Normalization of drug and therapeutic concepts with Thera-Py

Objective

The diversity of nomenclature and naming strategies makes therapeutic terminology difficult to manage and harmonize. As the number and complexity of available therapeutic ontologies continues to increase, the need for harmonized cross-resource mappings is becoming increasingly apparent. This study creates harmonized concept mappings that enable the linking together of like-concepts despite source-dependent differences in data structure or semantic representation.

Materials and Methods

For this study, we created Thera-Py, a Python package and web API that constructs searchable concepts for drugs and therapeutic terminologies using 9 public resources and thesauri. By using a directed graph approach, Thera-Py captures commonly used aliases, trade names, annotations, and associations for any given therapeutic and combines them under a single concept record.

Results

We highlight the creation of 16 069 unique merged therapeutic concepts from 9 distinct sources using Thera-Py and observe an increase in overlap of therapeutic concepts in 2 or more knowledge bases after harmonization using Thera-Py (9.8%-41.8%).

Conclusion

We observe that Thera-Py tends to normalize therapeutic concepts to their underlying active ingredients (excluding nondrug therapeutics, eg, radiation therapy, biologics), and unifies all available descriptors regardless of ontological origin.

Development of a Drug Management Performance Application: A Needs Assessment in Indonesia

OBJECTIVES

This study assessed the current state of pharmacy management information systems in Indonesia and systematically determined the improvements needed from the stakeholders' perspective.

METHODS

This descriptive study used focus group discussions and observations in 13 institutions, and 17 respondents were selected by purposive sampling. The PIECES (performance, information, economy, control, efficiency, service) framework was used to help identify needs. The research was conducted from September 2021 to November 2021 at primary health centers and health offices in Yogyakarta, Indonesia and involved pharmacists and information systems staff.

ESULTS

There was no standardized information system in place to support drug management and no format or rules for drug labeling (performance). Pharmacists were not able to provide non-prescription services outside the pharmacy warehouse (information). A new system needs to be developed, and budget availability needs to be determined (economy). System security decreases when users share accounts (control), and the existing systems have not been integrated as needed (efficiency). It is first necessary to plan and support regulations for system development (service). The authors formulated a recommended drug labeling format and a proposed system integration plan.

CONCLUSIONS

The development of an information system to support drug management is eagerly awaited by pharmacists in Indonesia to assist in their work. Further research on the development and implementation of an information system is needed to improve the quality of drug management at primary health centers.

Categorization of Medication Safety Errors in Ambulatory Electronic Health Records

Preventable medication errors continue to affect the quality and consistency in the delivery of care. While numerous studies on medication safety have been performed in the inpatient setting, a review of ambulatory patient safety by the American Medical Association found that medication safety errors were the most frequent safety problems in the outpatient arena. The leading cause of ambulatory safety problems, adverse drug events (ADEs), are common, with estimates of more than 2 million ADEs each year in the ambulatory Medicare population alone, and these events are frequently preventable. We conducted an environmental scan that allowed us to create our own categorization schema of medication safety errors in electronic healthcare records (EHRs) found in the outpatient setting and observed which of these were additionally supported in the literature. This study combines data from the California Hospital Patient Safety Organization (CHPSO), with several key articles in the area of medication errors in the EHR era. Method: To best utilize the various EHR ambulatory medication events submitted into CHPSO’s database, we chose to create a framework to bucket the near misses or adverse events (AEs) submitted to the database. This newly created categorization scheme was based on our own drafted categorization labels of events, after a high-level review, and from two leading articles on physician order entry. Additionally, we conducted a literature review of computerized provider order entry (CPOE) medication errors in the ambulatory setting. Within the newly created categorization scheme, we organized the articles based on issues addressed so we could see areas that were supported by the literature and what still needed to be researched. Results: We initially screened the CHPSO database for ambulatory safety events and found 25,417 events. Based on those events, an initial review was completed, and 19,242 events were found in the “Medication or Other Substance” and “Other” categories, in which the EHR appeared to have been a potential contributing factor. This review identified a subset of 2,236 events that were then reviewed. One hundred events were randomly selected for further review to identify common categories. The most common categories in which errors occurred were orders in order sets and plans (n=12) and orders crossing or not crossing encounters (n=12), incorrect order placed on correct patient (n=10), orders missing (n=8), standing orders (n=8), manual data entry errors (n=6), and future orders (n=6). Conclusion: There were several common themes seen in this analysis of ambulatory medication safety errors related to the EHR. Common among them were incorrect orders consisting of examples such as dose errors or ordering the wrong medication. The manual data entry errors consisted of height or weight being entered incorrectly or entering the wrong diagnostic codes. Lastly, different sources of medication safety information demonstrate a diversity of errors in ambulatory medication safety. This confirms the importance of considering more than one source when attempting to comprehensively describe ambulatory medication safety errors.

Combination of similarity measures based on symbolic regression for confusing drug names identification

Despite advances in medical safety, errors related to adverse drug reactions are still very common. The most common reason for a patient to develop an adverse reaction to a medication is confusion over the prescribed medication. The similarity of drug names (by their spelling or phonetic similarity) is recognized as the most critical factor causing medication confusion. Several studies have studied techniques for the identification of confusing medications pairs, the most important of which employ techniques based on similarity measures that indicate the degree of similarity that exists between two drugs names. Although it generates good results in the identification of confusing drug names, each of the similarity measures used detects to a greater or lesser degree of similarity that exists between a pair. Recent studies indicate that the optimized combination of several similarity measures can generate better results than the individual application of each one. This paper presents an optimized method of combining various similarity measures based on symbolic regression. The obtained results show an improvement in the identification of confusing drug names.

Computerized provider order entry-related medication errors among hospitalized patients: An integrative review

The Institute of Medicine estimates that 7,000 lives are lost yearly as a result of medication errors. Computerized physician and/or provider order entry was one of the proposed solutions to overcome this tragic issue. Despite some promising data about its effectiveness, it has been found that computerized provider order entry may facilitate medication errors.The purpose of this review is to summarize current evidence of computerized provider order entry -related medication errors and address the sociotechnical factors impacting the safe use of computerized provider order entry. By using PubMed and Google Scholar databases, a systematic search was conducted for articles published in English between 2007 and 2019 regarding the unintended consequences of computerized provider order entry and its related medication errors. A total of 288 articles were screened and categorized based on their use within the review. One hundred six articles met our pre-defined inclusion criteria and were read in full, in addition to another 27 articles obtained from references. All included articles were classified into the following categories: rates and statistics on computerized provider order entry -related medication errors, types of computerized provider order entry -related unintended consequences, factors contributing to computerized provider order entry failure, and recommendations based on addressing sociotechnical factors. Identifying major types of computerized provider order entry -related unintended consequences and addressing their causes can help in developing appropriate strategies for safe and effective computerized provider order entry. The interplay between social and technical factors can largely affect its safe implementation and use. This review discusses several factors associated with the unintended consequences of this technology in healthcare settings and presents recommendations for enhancing its effectiveness and safety within the context of sociotechnical factors.

Systematic review of computerized prescriber order entry and clinical decision support

PURPOSE

Results of a systematic review of published data on the effect of computerized prescriber order entry (CPOE) with clinical decision support on medication error (ME) and adverse drug event (ADE) rates are presented.

METHODS

Literature searches of MEDLINE, Embase, and other databases were conducted to identify English- and Spanish-language articles on selected CPOE outcomes published from 1995 through 2016; in addition, 5 specific journals were searched for pertinent articles published during the period 2010-16. Publications on controlled prospective studies and before-and-after studies that assessed MEs and/or ADEs as main outcomes were selected for inclusion in the review.

RESULTS

Nineteen studies met the inclusion criteria. Data on MEs and ADEs could not be pooled, mainly due to heterogeneity in outcome definitions and study methodologies. The reviewed evidence indicated that CPOE implementation led to an overall reduction in errors at the prescription stage of the medication-use process (relative risk reduction, 0.29 [95% confidence interval, 0.10-0.85]; I ² = 99%) and reductions in most types of prescription errors, but CPOE also resulted in the emergence of other types of errors.

CONCLUSION

CPOE reduces the overall ME rate in the prescription process, as well as specific types of errors, such as wrong dose or strength, wrong drug, frequency, administration route, and drug-drug interaction errors. The implementation of CPOE can lead to new errors, such as wrong drug selection from drop-down menus.

Using drug knowledgebase information to distinguish between look-alike-sound-alike drugs

Objective

To extract drug indications from a commercial drug knowledgebase and determine to what extent drug indications can discriminate between look-alike-sound-alike (LASA) drugs.

Methods

We extracted drug indications disease concepts from the MedKnowledge Indications module from First Databank Inc. (South San Francisco, CA) and associated them with drugs on the Institute for Safe Medication Practices (ISMP) list of commonly confused drug names. We used high-level concepts (rather than granular concepts) to represent the general indications for each drug. Two pharmacists reviewed each drug's association with its high-level indications concepts for accuracy and clinical relevance. We compared the high-level indications for each commonly confused drug pair and categorized each pair as having a complete overlap, partial overlap or no overlap in high-level indications.

Results

Of 278 LASA drug pairs, 165 (59%) had no overlap and 58 (21%) had partial overlap in high-level indications. Fifty-five pairs (20%) had complete overlap in high-level indications; nearly half of these were comprised of drugs with the same active ingredient and route of administration (e.g., Adderall, Adderall XR).

Conclusions

Drug indications data from a drug knowledgebase can discriminate between many LASA drugs.