Using Time-Referenced Data to Assess Medication Administration Performance and Quality

Describing Medication Administration and Alert Patterns Experienced by New Graduate Nurses During the First Year of Practice

The aim of this study was to describe medication administration and alert patterns among a cohort of new graduate nurses over the first year of practice. Medical errors related to clinical decision-making, including medication administration errors, may occur more frequently among new graduate nurses. To better understand nursing workflow and documentation workload in today's clinical environment, it is important to understand patterns of medication administration and alert generation during barcode-assisted medication administration. Study objectives were addressed through a descriptive, longitudinal, observational cohort design using secondary data analysis. Set in a large, urban medical center in the United States, the study sample included 132 new graduate nurses who worked on adult, inpatient units and administered medication using barcode-assisted medication administration. Data were collected through electronic health record and administration sources. New graduate nurses in the sample experienced a total of 587 879 alert and medication administration encounters, administering 772 unique medications to 17 388 unique patients. Nurses experienced an average medication workload of 28.09 medications per shift, 3.98% of which were associated with alerts, over their first year of practice. In addition to high volume of medication administration, new graduate nurses administer many different types of medications and are exposed to numerous alerts while using barcode-assisted medication administration.

Frequency of Medication Administration Timing Error in Hospitals: A Systematic Review

BACKGROUND

Medication administration timing error (MATE) leads to poor medication efficacy, harm, and death. Frequency of MATE is understudied.

PURPOSE

To determine MATE frequency, and characteristics and quality of reporting studies.

METHODS

A systematic review of articles between 1999 and 2021 was conducted using the Cumulative Index of Nursing and Allied Health Literature, ProQuest, and PubMed databases. Articles were scored for quality using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist.

RESULTS

Initially, 494 articles were screened; 23 were included in this review. MATE was defined as administration beyond 60 minutes before or after the scheduled time in 13 (57%) of the included studies. Measurement procedures included data abstraction, self-report, and observation. Frequency of MATE was 1% to 72.6%. Moderate study quality was found in 78% of articles.

CONCLUSION

Research on MATE is characterized by inconsistent definitions, measurements procedures, and calculation techniques. High-quality studies are lacking. Many research improvement opportunities exist.

A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload

OBJECTIVE

The aim of the study is to introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for the examination of nurse staffing and workload using data generated during regular nursing workflow.

METHODS

Using 1 year (October 1, 2014-September 30, 2015) of BCMA data for 11 acute care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within 2 hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start time.

RESULTS

As the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional R2  = 0.237; marginal R2  = 0.199; intraclass correlation = 0.05). On average, an RN and a licensed practical nurse (LPN) with four patients, each with six medications, would be expected to take 70 and 74 minutes, respectively, to complete the medication pass. On a unit with median 10 patients per LPN, the median duration (127 minutes) represents untimely medication administration on more than half of staff days. With each additional patient assigned to a nurse, average start time was earlier by 4.2 minutes for RNs and 1.4 minutes for LPNs.

CONCLUSION

Medication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.

Coincidence Analysis: A Novel Approach to Modeling Nurses' Workplace Experience

OBJECTIVES

The purpose of this study is to identify combinations of workplace conditions that uniquely differentiate high, medium, and low registered nurse (RN) ratings of appropriateness of patient assignment during daytime intensive care unit (ICU) work shifts.

METHODS

A collective case study design and coincidence analysis were employed to identify combinations of workplace conditions that link directly to high, medium, and low RN perception of appropriateness of patient assignment at a mid-shift time point. RN members of the study team hypothesized a set of 55 workplace conditions as potential difference makers through the application of theoretical and empirical knowledge. Conditions were derived from data exported from electronic systems commonly used in nursing care.

RESULTS

Analysis of 64 cases (25 high, 24 medium, and 15 low) produced three models, one for each level of the outcome. Each model contained multiple pathways to the same outcome. The model for "high" appropriateness was the simplest model with two paths to the outcome and a shared condition across pathways. The first path comprised of the absence of overtime and a before-noon patient discharge or transfer, and the second path comprised of the absence of overtime and RN assignment to a single ICU patient.

CONCLUSION

Specific combinations of workplace conditions uniquely distinguish RN perception of appropriateness of patient assignment at a mid-shift time point, and these difference-making conditions provide a foundation for enhanced observability of nurses' work experience during hospital work shifts. This study illuminates the complexity of assessing nursing work system status by revealing that multiple paths, comprised of multiple conditions, can lead to the same outcome. Operational decision support tools may best reflect the complex adaptive nature of the work systems they intend to support by utilizing methods that accommodate both causal complexity and equifinality.

Reducing Near Miss Medication Events Using an Evidence-Based Approach

BACKGROUND

Near miss medication events are pervasive without patient harm, mostly because of coincidence. Dynamic clinical environments challenge nurse compliance with medication administration rights and proper use of electronic technology.

PROBLEM

All nurses are educated in appropriate medication management, but our unit's barcoded medication administration scanning and electronic patient identification practices fell below the 97% benchmarks, representing hundreds of near miss medication events each month.

APPROACH

Transformative leadership guided frontline staff to identify gaps in care processes and determined root causes for unsanctioned medication administration practices using a FOCUS (Find-Organize-Clarify-Understand-Select)-PDSA (Plan-Do-Study-Act) methodology.

OUTCOMES

An interdisciplinary team committed to zero events of preventable harm overcame challenges to improve care delivery. Medication management scores exceeded organizational benchmarks, with sustainable gains over 2 years.

CONCLUSIONS

A rapid-cycle, evidence-based approach engaged staff to reduce near miss medication events. Workable solutions driven by transparent communication and interpersonal collaboration influenced positive safety behaviors.

The Impact of Delayed Symptomatic Treatment Implementation in the Intensive Care Unit

We estimated the harm related to medication delivery delays across 12,474 medication administration instances in an intensive care unit using retrospective data in a large urban academic medical center between 2012 and 2015. We leveraged an instrumental variables (IV) approach that addresses unobserved confounds in this setting. We focused on nurse shift changes as disruptors of timely medication (vasodilators, antipyretics, and bronchodilators) delivery to estimate the impact of delay. The average delay around a nurse shift change was 60.8 min (p < 0.001) for antipyretics, 39.5 min (p < 0.001) for bronchodilators, and 57.1 min (p < 0.001) for vasodilators. This delay can increase the odds of developing a fever by 32.94%, tachypnea by 79.5%, and hypertension by 134%, respectively. Compared to estimates generated by a naïve regression approach, our IV estimates tend to be higher, suggesting the existence of a bias from providers prioritizing more critical patients.

Evaluation of medication administration timing variance using information from a large health system's clinical data warehouse

Disclaimer

In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Purpose

An analysis to determine the frequency of medication administration timing variances for specific therapeutic classes of high-risk medications using data extracted from a health-system clinical data warehouse (CDW) is presented.

Methods

This multicenter retrospective, observational analysis of 1 year of medication administration data from 14 hospitals was conducted using a large enterprise health-system CDW. The primary objective was to assess medication administration timing variance for focused therapeutic classes using medication orders and electronic medication administration records data extracted from the electronic health record (EHR). Administration timing variance patterns between standard hospital staffing shifts, within therapeutic drug classes, and for as-needed (PRN) medications were also studied. Calculated variables for delayed medication administration (ie, administration time variance) were created for documented administration time intervals of 30-59, 60-120, and more than 120 minutes before or after medication orders.

Results

A total of 5,690,770 medication administrations (3,418,275 scheduled and 2,272,495 PRN) were included in the normalized data set. Scheduled medications were frequently subject to delays of ≥60 minutes (15% of administrations, n = 275,257) when scheduled for administration between 9-10 AM and between 9-10 PM. By therapeutic drug class, scheduled administrations of insulins, heparin products, and platelet aggregation inhibitors (most commonly heparin flushes and line-management preparations) were the most commonly delayed. For PRN medications, medications in the anticoagulant and antiplatelet agent class were most likely to be administered early (<60 minutes from the scheduled time of first administration).

Conclusion

The findings of this study assist in understanding patterns of delayed medication administration. Medication class, time of day of scheduled administration, and frequency were factors that influenced medication administration timing variance.

Nurse Cognition, Decision Support, and Barcode Medication Administration: A Conceptual Framework for Research, Practice, and Education

This article synthesizes theoretical perspectives related to nurse cognition. We present a conceptual model that can be used by multiple stakeholders to study and contemplate how nurses use clinical decision support systems, and specifically, Barcode-Assisted Medication Administration, to make decisions during the delivery of care. Theoretical perspectives integrated into the model include dual process theory, the Cognitive Continuum Theory, human factors engineering, and the Recognition-Primed Decision model. The resulting framework illustrates the process of nurse cognition during Barcode-Assisted Medication Administration. Additionally, the model includes individual or human and environmental factors that may influence nurse cognition and decision making. It is important to consider the influence of individual, human, and environmental factors on the process of nurse cognition and decision making. Specifically, it is necessary to explore the impact of heuristics and biases on clinician decision making, particularly related to the development of alarm and alert fatigue. Aided by the proposed framework, stakeholders may begin to identify heuristics and cognitive biases that influence the decision of clinicians to accept or override a clinical decision support system alert and whether heuristics and biases are associated with inappropriate alert override.

Impact of nurse's worked hours on medication administration near-miss error alerts

Long working hours have been shown to negatively impact adverse events in health care. In this study, a retrospective correlational design was used to evaluate the relationship between working hours and near-miss medication error alerts. During a two-year period, 5372 nurses triggered 420,706 near-miss alerts on 9, 285, 786 medication administrations. Nurses who worked 60 h or more in a week yielded an average near-miss rate of 4.0% compared to 3.0% (p <.001) for nurses who did not. Nurses working extended hours had a significantly increased risk of triggering a near-miss alert compared to those not working extended hours.

Alert Types and Frequencies During Bar Code-Assisted Medication Administration: A Systematic Review

BACKGROUND

Existing literature explores the effectiveness of bar code-assisted medication administration (BCMA) on the reduction of medication administration error as well as on nurse workarounds during BCMA. However, there is no review that comprehensively explores types and frequencies of alerts generated by nurses during BCMA.

PURPOSE

The purpose was to describe alert generation type and frequency during BCMA.

METHODS

A systematic review of the literature using PRISMA guidelines was conducted using CINAHL, PubMed, EMBASE, and Ovid Medline databases.

RESULTS

After screening for inclusion and exclusion criteria, a total of 8 articles were identified and included in the review. Alert types included patient mismatch, wrong medication, and wrong dose, though other alert types were also reported. The frequency of alert generation varied across studies, from 0.18% to 42%, and not all alerts were clinically meaningful.

CONCLUSIONS

This systematic review synthesized literature related to alert type and frequency during BCMA. However, further studies are needed to better describe alert generation patterns as well as factors that influence alert generation.

Exploring Inpatient Medication Patterns: A Big Data and Multilevel Approach

PURPOSE

Exploratory study to examine inpatient medication administration patterns.

METHODS

Data from multiple sources were utilized for this study. The outcome was time difference between medication schedule and administration. A 3-level hierarchical linear regression approach, both unadjusted and adjusted, was considered for this study where medication administration events are nested within patients nested within nurses or units. Intraclass correlation coefficients (ICCs) were calculated and compared.

RESULTS

On average, medications were delayed by 12 (SD, 48.8) minutes. From the full model, patient ICCs decreased when "unit" replaced "nurse" as the 3rd level (0.541 vs 0.444). Patients who spoke Spanish had a significant 2.3- to 4.2-minute delay in medication administration. Certified nurses significantly give medications earlier compared with noncertified nurses by 1.6 minutes.

DISCUSSION

Optimal medication administration is a multifactorial concern with nurses playing a role. Nursing leaders should also consider patient demographics and unit conditions, such as culture, for medication administration optimization.