Text Mining Method for Studying Medication Administration Incidents and Nurse-Staffing Contributing Factors: A Pilot Study

Factors Considered Important by Healthcare Professionals for the Management of Using Complementary Therapy in Diabetes: A Text-Mining Analysis

Text-mining algorithms can identify the most prevalent factors of risk-benefit assessment on the use of complementary and integrative health approaches that are found in healthcare professionals' written notes. The aims of this study were to discover the key factors of decision-making on patients' complementary and integrative health use by healthcare professionals and to build a consensus-derived decision algorithm on the benefit-risk assessment of complementary and integrative health use in diabetes. The retrospective study of an archival dataset used a text-mining method designed to extract and analyze unstructured textual data from healthcare professionals' responses. The techniques of classification, clustering, and extraction were performed with 1398 unstructured clinical notes made by healthcare professionals between 2019 and 2020. The most important factor for decision-making by healthcare professionals about complementary and integrative health use in patients with diabetes was the ingredients of the product. Other important factors were the patient's diabetes control, the undesirable effects from complementary and integrative health, evidence-based complementary and integrative health, medical laboratory data, and the product's affordability. This exploratory text-mining study provides insight into how healthcare professionals decide complementary and integrative health use for patients with diabetes after a risk-benefit assessment from clinical narrative notes.

Natural Language Processing of Nursing Notes: An Integrative Review

Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings.

[The analysis of CIRSmedical.de using Natural Language Processing]

BACKGROUND

CIRSmedical.de is a publicly accessible, cross-institutional reporting and learning system, which is organized by the German Agency for Quality in Medicine (ÄZQ). CIRSmedical.de has existed since 2005 and has published more than 6,000 event reports. Up to now it has been common practice to analyse these reports in detail or carry out systematic evaluations focusing on specific topics. A systematic evaluation of all case reports has not yet been conducted. Natural Language Processing (NLP) is an analysis strategy from the field of Artificial Intelligence for indexing texts. The examination of case reports using NLP was carried out to describe the characteristics of event reports and comments.

MATERIALS AND METHODS

For this analysis 6,480 case reports from CIRSmedical.de (as of December 10, 2019) were provided by the ÄZQ as Excel files. Several free text fields were included in the analysis as well as the feedback of the CIRS team (expert commentary). Text lengths, reporting behaviour, sentiment values and keywords were examined. The algorithms for the analysis were developed with the programming language Python and the corresponding libraries NLTK and SpaCy.

RESULTS

The comparison of report lengths depending on the different subject groups presented a heterogeneous picture, in terms of both the number of reports and the number of words. There are more than 4,000 reports from the field of anaesthesiology, whereby text lengths vary particularly strongly with a right-skewed distribution. There are only a few reports from the field of psychotherapy, and these are also very short. The different professional groups (nurses, doctors, other staff) write reports of about the same length. Reports and expert commentaries also differ in terms of sentiment values. Due to the length of the comments, they are more negative in terms of sentiment. Keywords can be identified but show a high heterogeneity.

DISCUSSION

Systematic analysis using NLP allows for the description of text properties in event reports and comments. It is now possible to draw a conclusion about the reporters' intention, focus and mood when they report in CIRS. The sentiment analysis is an indication of the mood which the texts convey, both as a report and as a commentary. Text length analysis draws attention to different problems and tendencies: event reports are usually much shorter. Texts that are too short, however, run the risk that the information will not be readily usable for analysis. Comments are often longer, but here one faces the opposite problem: texts that are too long may not be read. The examination of texts by means of NLP helps to rethink the reason for and the form of input, both when reporting and when commenting. It is a first step in the automatic, supportive classification of texts and an improvement of the interaction between reporters and the system.

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.

Medication administration in Australian residential aged care: A time-and-motion study

RATIONALE/AIM

Medication administration is a complex and time-consuming task in residential aged care facilities (RACFs). Understanding the time associated with each administration step may help identify opportunities to optimize medication management in RACFs. This study aimed to investigate the time taken to administer medications to residents, including those with complex care needs such as cognitive impairment and swallowing difficulties.

METHOD

A time-and-motion study was conducted in three South Australian RACFs. A representative sample of 57 scheduled medication administration rounds in 14 units were observed by a single investigator. The rounds were sampled to include different times of day, memory support units for residents living with dementia and standard units, and medication administration by registered and enrolled nurses. Medications were administered from pre-prepared medication strip packaging. The validated Work Observation Method By Activity Timing (WOMBAT) software was used to record observations.

RESULTS

Thirty nurses were observed. The average time spent on scheduled medication administration rounds was 5.2 h/unit of average 22 residents/day. The breakfast medication round had the longest duration (1.92 h/unit). Resident preparation, medication preparation and provision, documentation, transit, communication, and cleaning took an average of 5 minutes per resident per round. Medication preparation and provision comprised 60% of overall medication round time and took significantly longer in memory support than in standard units (66 vs 49 seconds per resident per round for preparation, 79 vs 58 for provision; P < .001 for both). Almost half (42%) of tablets/capsules were crushed in memory support units. The time taken for medication administration was not significantly different among registered and enrolled nurses.

CONCLUSIONS

Nurses took an average of 5 minutes to administer medications per resident per medication round. Medication administration in memory support units took an additional minute per resident per round, with almost half of tablets and capsules needing to be crushed.

Factors contributing to reported medication administration incidents in patients' homes - A text mining analysis

AIMS

To describe the characteristics of medication administration (MA) incidents reported to have occurred in patients' own homes (reporters' profession, incident types, contributing factors, patient consequence, and most common medications involved) and to identify the connection terms related to the most common contributing factors based on free text descriptions.

DESIGN

A retrospective study using descriptive statistical analysis and text mining.

METHODS

Medication administration incidents (N = 19,725) reported to have occurred in patients' homes between 2013-2018 in one district in Finland were analysed, describing the data by the reporters' occupation, incident type, contributing factors, and patient consequence. SAS® Text Miner was used to analyse free text descriptions of the MA incidents to understand contributing factors, using concept linking.

RESULTS

Most MA incidents were reported by practical (lower level) nurses (77.8%, N = 15,349). The most common category of harm was 'mild harm' (40.1%, N = 7,915) and the most common error type was omissions of drug doses (47.4%, N = 9,343). The medications most commonly described were Marevan [warfarin] (N = 2,668), insulin (N = 811), Furesis [furosemide] (N = 590), antibiotic (N = 446), and Panadol [paracetamol] (N = 416). The contributing factors most commonly reported were 'communication and flow of information' (25.5%, N = 5,038), 'patient and relatives' (22.6%, N = 4,451), 'practices' (9.9%, N = 1,959), 'education and training' (4.8%, N = 949), and 'work environment and resources' (3.0%, N = 598).

CONCLUSION

There is need for effective communication and clear responsibilities between home care patients and their relatives and health providers, about MA and its challenges in home environments. Knowledge and skills relating to safe MA are also essential.

IMPACT

These findings about MA incidents that have occurred in patients' homes and have been reported by home care professionals demonstrate the need for medication safety improvement in home care.

Reducing the Burden of Complex Medication Regimens: SImplification of Medications Prescribed to Long-tErm care Residents (SIMPLER) Cluster Randomized Controlled Trial

OBJECTIVE

To assess the application of a structured process to consolidate the number of medication administration times for residents of aged care facilities.

DESIGN

A nonblinded, matched-pair, cluster randomized controlled trial.

SETTING AND PARTICIPANTS

Permanent residents who were English-speaking and taking at least 1 regular medication, recruited from 8 South Australian residential aged care facilities (RACFs).

METHODS

The intervention involved a clinical pharmacist applying a validated 5-step tool to identify opportunities to reduce medication complexity (eg, by administering medications at the same time or through use of longer-acting or combination formulations). Residents in the comparison group received routine care. The primary outcome at 4-month follow-up was the number of administration times per day for medications charted regularly. Resident satisfaction and quality of life were secondary outcomes. Harms included falls, medication incidents, hospitalizations, and mortality. The association between the intervention and primary outcome was estimated using linear mixed models.

RESULTS

Overall, 99 residents participated in the intervention arm and 143 in the comparison arm. At baseline, the mean resident age was 86 years, 74% were female, and medications were taken an average of 4 times daily. Medication simplification was possible for 62 (65%) residents in the intervention arm, with 57 (62%) of 92 simplification recommendations implemented at follow-up. The mean number of administration times at follow-up was reduced in the intervention arm in comparison to usual care (-0.36, 95% confidence interval -0.63 to -0.09, P = .01). No significant changes in secondary outcomes or harms were observed.

CONCLUSIONS AND IMPLICATIONS

One-off application of a structured tool to reduce regimen complexity is a low-risk intervention to reduce the burden of medication administration in RACFs and may enable staff to shift time to other resident care activities.

The Contribution of Staffing to Medication Administration Errors: A Text Mining Analysis of Incident Report Data

PURPOSE

(a) To describe trigger terms that can be used to identify reports of inadequate staffing contributing to medication administration errors, (b) to identify such reports, (c) to compare the degree of harm within incidents with and without those triggers, and (d) to examine the association between the most commonly reported inadequate staffing trigger terms and the incidence of omission errors and "no harm" terms.

DESIGN AND SETTING

This was a retrospective study using descriptive statistical analysis, text mining, and manual analysis of free text descriptions of medication administration-related incident reports (N = 72,390) reported to the National Reporting and Learning System for England and Wales in 2016.

METHODS

Analysis included identifying terms indicating inadequate staffing (manual analysis), followed by text parsing, filtering, and concept linking (SAS Text Miner tool). IBM SPSS was used to describe the data, compare degree of harm for incidents with and without triggers, and to compare incidence of "omission errors" and "no harm" among the inadequate staffing trigger terms.

FINDINGS

The most effective trigger terms for identifying inadequate staffing were "short staffing" (n = 81), "workload" (n = 80), and "extremely busy" (n = 51). There was significant variation in omission errors across inadequate staffing trigger terms (Fisher's exact test = 44.11, p < .001), with those related to "workload" most likely to accompany a report of an omission, followed by terms that mention "staffing" and being "busy." Prevalence of "no harm" did not vary statistically between the trigger terms (Fisher's exact test = 11.45, p = 0.49), but the triggers "workload," "staffing level," "busy night," and "busy unit" identified incidents with lower levels of "no harm" than for incidents overall.

CONCLUSIONS

Inadequate staffing levels, workload, and working in haste may increase the risk for omissions and other types of error, as well as for patient harm.

CLINICAL RELEVANCE

This work lays the groundwork for creating automated text-analytical systems that could analyze incident reports in real time and flag or monitor staffing levels and related medication administration errors.

Identifying risks areas related to medication administrations - text mining analysis using free-text descriptions of incident reports

BACKGROUND

Some medications carry increased risk of patient harm when they are given in error. In incident reports, names of the medications that are involved in errors could be found written both in a specific medication field and/or within the free text description of the incident. Analysing only the names of the medications implicated in a specific unstructured medication field does not give information of the associated factors and risk areas, but when analysing unstructured free text descriptions, the information about the medication involved and associated risk factors may be buried within other non-relevant text. Thus, the aim of this study was to extract medication names most commonly used in free text descriptions of medication administration incident reports to identify terms most frequently associated with risk for each of these medications using text mining.

METHOD

Free text descriptions of medication administration incidents (n = 72,390) reported in 2016 to the National Reporting and Learning System for England and Wales were analysed using SAS® Text miner. Analysis included text parsing and filtering free text to identify most commonly mentioned medications, followed by concept linking, and clustering to identify terms associated with commonly mentioned medications and the associated risk areas.

RESULTS

The following risk areas related to medications were identified: 1. Allergic reactions to antibacterial drugs, 2. Intravenous administration of antibacterial drugs, 3. Fentanyl patches, 4. Checking and documenting of analgesic doses, 5. Checking doses of anticoagulants, 6. Insulin doses and blood glucose, 7. Administration of intravenous infusions.

CONCLUSIONS

Interventions to increase medication administration safety should focus on checking patient allergies and medication doses, especially for intravenous and transdermal medications. High-risk medications include insulin, analgesics, antibacterial drugs, anticoagulants, and potassium chloride. Text mining may be useful for analysing large free text datasets and should be developed further.