Creating a Culture of Safety Around Bar-Code Medication Administration: An Evidence-Based Evaluation Framework.

Bar-code medication administration (BCMA) effectiveness is contingent upon compliance with best-practice protocols. We developed a 4-phased BCMA evaluation program to evaluate the degree of integration of current evidence into BCMA policies, procedures, and practices; identify barriers to best-practice BCMA use; and modify BCMA practice in concert with changes to the practice environment. This program provides an infrastructure for frontline nurses to partner with hospital leaders to continually evaluate and improve BCMA using a systematic process.

Evaluation of the use of bar-code medication administration in nursing practice using an evidence-based checklist.

The goal of this project was to determine whether the use of bar-code medication administration complied with current evidence as to how it should be used. Using an evidence-based checklist, we performed a gap analysis on bar-code medication administration (BCMA) in an acute care setting. Compliance with current evidence was identified through observation and unstructured nurse interviews. Based on findings from the initial gap analysis, quality improvement initiatives were implemented followed by a reassessment to identify possible improvement of the identified gaps. Initially, there was 72% compliance with current evidence-based practices using BCMA. After implementation of initial quality improvement initiatives targeting 3 of 9 areas with deficits in compliance, compliance was found to be 81%. The evidence-based checklist was helpful in identifying gaps in current performance and opportunities for improvement with BCMA.

Clinical intelligence.

Clinical intelligence is an emerging field in healthcare that will change nursing practice, driving clinical outcomes and operational efficiencies. Clinical intelligence is essential to healthcare organizations in fully realizing the value of the growing amount of data being generated through electronic health records and other clinical information systems. This article discusses the asset of clinical data, the multiple roles of nurses in maximizing the value of this asset, and a vision for nursing's future in regards to clinical intelligence.

Realizing electronic medical record benefits: an easy-to-do usability study.

Heuristic evaluation is a type of study that is useful for uncovering usability issues in a human-computer interface such as the electronic medical record (EMR). Findings can be very useful in overcoming usability problems to better realize the benefits of the EMR. Correction of the usability violations will improve the efficiency and effectiveness of EMRs. The authors discuss their use of heuristic evaluation to assess usability issues found in their nursing electronic documentation system.

Safety issues related to the electronic medical record (EMR): synthesis of the literature from the last decade, 2000-2009.

Healthcare is a complex industry burdened by numerous and complicated clinical and administrative transactions that require many behavioral changes by patients, clinicians, and provider organizations. While healthcare information technology (HIT) is intended to relieve some of the burden by reducing errors, several aspects of systems such as the electronic medical record (EMR) may actually increase the incidence of certain types of errors or produce new safety risks that result in harm. Healthcare leaders must appreciate the complexity surrounding EMRs and understand the safety issues in order to mandate sound EMR design, development, implementation, and use. This article seeks to inform executives, clinicians, and technology professionals what has been learned through published research on the safety of HIT systems during the last decade, focusing on computerized physician order entry (CPOE), clinical decision support systems (CDSS), and bar-coded medication administration (BCMA).

Heuristic Evaluation of a Top-Rated Diabetes Self-Management App.

OBJECTIVE: The purpose of this study was to evaluate the usability of a top-rated diabetes app. Such apps are intended to markedly support the achievement of optimal health and financial outcomes by providing patients with substantive and continual support for self-management of their disease between periodic clinician visits. Poor usability can deter use which is especially concerning in patients with diabetes due to prevalence of the disease and impact of self-management on long-term prognosis. METHODS: A diabetes app was selected due to the prevalence and seriousness of the disease. A heuristic evaluation was then performed to collect and analyze data on the usability of the app based on Nielsen's heuristics. Pareto analysis was used to illustrate the contribution of each type of heuristic violation, augmented by a stacked bar chart illuminating associated severity. RESULTS: There were 51 heuristic violations on the opening screen, violating 6 of Nielsen's 10 heuristics. Pareto analysis revealed 29 (57%) of the heuristic violations involved a match between system and real world and 8 (16%) aesthetic and minimalist design. Severity ratings ranged from 1.0 to 4.0 (mean: 3.01) with 80% comprising a major usability problem and 6% a usability catastrophe. CONCLUSION: Studies show that people with diabetes are more likely to receive greater benefit from a diabetes app if they are easy to use. The number and severity of heuristic violations in this study suggest that the commercialization of mobile health apps may play a factor in bypassing experts in clinical informatics during the design phase of development. Usability and associated benefits received from mobile health apps can be enhanced by debugging the user interface of identified heuristic violations during design. Waiting to correct ongoing usability issues while apps are in production can result in patients disengaging from use of digital health tools engendering poorer outcomes.

Headache in the neurology clinic: a 2-year audit.

BACKGROUND: Headache is the most common reason for referral to neurology outpatient clinics in Ireland and represents approximately 30% of all new appointments. AIM: To evaluate diagnoses and outcomes of all patients newly referred with headache to a consultant neurologist at an Irish university teaching hospital over a 2-year period. DESIGN: A retrospective analysis and audit. METHODS: Data including patient demographics, red flags, investigations, diagnosis, and outcome were collected on consecutive patients newly referred with headache to a consultant neurologist's outpatient clinic over a 2-year period. RESULTS: Two hundred and seventy patients with headache were seen; 75% were women with mean age of 39 years. 89.26% (241/270) were diagnosed with a primary headache disorder alone; 4.44% (12/270) with a secondary headache disorder alone; 3.33% (9/270) with both a primary and secondary headache disorders; and 2.96% (8/270) with a painful cranial neuropathy. Migraine was the most common diagnosis, frequently associated with medication overuse. Non-attendance rates were high. Although imaging abnormalities were frequently found, on no occasion was it thought to be the cause of the headache. CONCLUSIONS: Most patients referred to a neurology outpatient clinic with headache have a primary headache disorder. Alternate pathways should be considered to reduce the burden on Ireland's limited neurology resources without compromising patient safety.

Meta-analysis of fall-risk tools in hospitalized adults.

OBJECTIVE: The aim of the study was to identify which fall-risk tool is most accurate for assessing adults in the hospital setting. BACKGROUND: Falls can have physical, emotional, social, and financial consequences. Risk assessment affords the first opportunity in prevention. METHODS: To standardize the use of a fall-risk tool across the Baylor Health Care System, nurse executives undertook a meta-analysis of published research on fall-risk assessment tools used with adult inpatients. RESULTS: Both random-effects and fixed-effects models showed that Morse Fall Scale had significantly higher sensitivity than St Thomas's Risk Assessment Tool (STRATIFY). Specificity of Morse Fall Scale was significantly lower than that of STRATIFY with the fixed-effects model, but the random-effects model showed the opposite. Morse Fall Scale had a significantly higher Youden index than STRATIFY with the fixed-effects model (P = .001), but the result from random-effects model indicated no significant difference (P = .117). The sensitivity, specificity, and Youden index fell within the 95% confidence intervals. CONCLUSIONS: Meta-analysis is a useful methodology for evaluating current evidence when variation exists in the literature.