Implementation and comparison of two text mining methods with a standard pharmacovigilance method for signal detection of medication errors

Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers

Monitoring drug safety is a central concern throughout the drug life cycle. Information about toxicity and adverse events is generated at every stage of this life cycle, and stakeholders have a strong interest in applying text mining and artificial intelligence (AI) methods to manage the ever-increasing volume of this information. Recognizing the importance of these applications and the role of challenge evaluations to drive progress in text mining, the organizers of BioCreative VII (Critical Assessment of Information Extraction in Biology) convened a panel of experts to explore 'Challenges in Mining Drug Adverse Reactions'. This article is an outgrowth of the panel; each panelist has highlighted specific text mining application(s), based on their research and their experiences in organizing text mining challenge evaluations. While these highlighted applications only sample the complexity of this problem space, they reveal both opportunities and challenges for text mining to aid in the complex process of drug discovery, testing, marketing and post-market surveillance. Stakeholders are eager to embrace natural language processing and AI tools to help in this process, provided that these tools can be demonstrated to add value to stakeholder workflows. This creates an opportunity for the BioCreative community to work in partnership with regulatory agencies, pharma and the text mining community to identify next steps for future challenge evaluations.

Healthcare professionals' perceptions on medication communication challenges and solutions - text mining and manual content analysis - cross-sectional study

BACKGROUND

Communication challenges contribute to medication incidents in hospitals, but it is unclear how communication can be improved. The aims of this study were threefold: firstly, to describe the most common communication challenges related to medication incidents as perceived by healthcare professionals across specialized hospitals for adult patients; secondly, to consider suggestions from healthcare professionals with regard to improving medication communication; and thirdly, to explore how text mining compares to manual analysis when analyzing the free-text content of survey data.

METHODS

This was a cross-sectional, descriptive study. A digital survey was sent to professionals in two university hospital districts in Finland from November 1, 2019, to January 31, 2020. In total, 223 professionals answered the open-ended questions; respondents were primarily registered nurses (77.7 %), physicians (8.6 %), and pharmacists (7.3 %). Text mining and manual inductive content analysis were employed for qualitative data analysis.

RESULTS

The communication challenges were: (1) inconsistent documentation of prescribed and administered medication; (2) failure to document orally given prescriptions; (3) nurses' unawareness of prescriptions (given outside of ward rounds) due to a lack of oral communication from the prescribers; (4) breaks in communication during care transitions to non-communicable software; (5) incomplete home medication reconciliation at admission and discharge; (6) medication lists not being updated during the inpatient period due to a lack of clarity regarding the responsible professional; and (7) work/environmental factors during medication dispensation and the receipt of verbal prescriptions. Suggestions for communication enhancements included: (1) structured digital prescriptions; (2) guidelines and training on how to use documentation systems; (3) timely documentation of verbal prescriptions and digital documentation of administered medication; (4) communicable software within and between organizations; (5) standardized responsibilities for updating inpatients' medication lists; (6) nomination of a responsible person for home medication reconciliation at admission and discharge; and (7) distraction-free work environment for medication communication. Text mining and manual analysis extracted similar primary results.

CONCLUSIONS

Non-communicable software, non-standardized medication communication processes, lack of training on standardized documentation, and unclear responsibilities compromise medication safety in hospitals. Clarification is needed regarding interdisciplinary medication communication processes, techniques, and responsibilities. Text mining shows promise for free-text analysis.

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

Objectives: To analyze the content of publications within the medical NLP domain in 2020.

Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Results: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included.

Conclusion: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks

Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review

BACKGROUND

Real-world evidence research plays an increasingly important role in diabetes care. However, a large fraction of real-world data are "locked" in narrative format. Natural language processing (NLP) technology offers a solution for analysis of narrative electronic data.

METHODS

We conducted a systematic review of studies of NLP technology focused on diabetes. Articles published prior to June 2020 were included.

RESULTS

We included 38 studies in the analysis. The majority (24; 63.2%) described only development of NLP tools; the remainder used NLP tools to conduct clinical research. A large fraction (17; 44.7%) of studies focused on identification of patients with diabetes; the rest covered a broad range of subjects that included hypoglycemia, lifestyle counseling, diabetic kidney disease, insulin therapy and others. The mean F₁ score for all studies where it was available was 0.882. It tended to be lower (0.817) in studies of more linguistically complex concepts. Seven studies reported findings with potential implications for improving delivery of diabetes care.

CONCLUSION

Research in NLP technology to study diabetes is growing quickly, although challenges (e.g. in analysis of more linguistically complex concepts) remain. Its potential to deliver evidence on treatment and improving quality of diabetes care is demonstrated by a number of studies. Further growth in this area would be aided by deeper collaboration between developers and end-users of natural language processing tools as well as by broader sharing of the tools themselves and related resources.