Exploring Lean Healthcare Transformation using The Theory of Planned Behavior

Abstract The literature suggests that lean transformation efforts in manufacturing and healthcare industries fail approximately in 90% of applications. As such, it is critical to understand how lean implementation efforts affect human behavior. In this research study, the physician author was embedded in an academic specialty out-patient department for 12 months providing training on lean methodology to supervisors and staff, and facilitating Kaizen events. Direct observations, informal interviews and journal notes were kept to capture event outcomes, change in behaviors, and staff comments following the events. The behaviors were examined using the Theory of Planned Behavior. Data analysis suggests that supervisors and staff rapidly grasped the knowledge about lean tools for improving processes and creating new services, yet failed at committing to lean thinking and taking responsibility for implemented improvements. Through an understanding of subjective norms, perceived behavioral control and attitudes, the author offers insights into successes and failures of lean efforts at behavioral change in healthcare

Quality Improvement in Hospitals: Identifying and Understanding Behaviors

Improving operational performance in hospitals is complicated, particularly if process improvement requires complex behavioral changes. Using single-loop and double-loop learning theory as a foundation, the purpose of this research is to empirically uncover key improvement behaviors and the factors that may be associated with such behaviors in hospitals. A two-phased approach was taken to collect data regarding improvement behaviors and associated factors, and data analysis was conducted using methods proposed by grounded theorists. The contributions of this research are twofold. First, five key behaviors related to process improvement are identified, namely Quick Fixing, Initiating, Conforming, Expediting, and Enhancing. Second, based on these observed behaviors, a set of force field diagrams is developed to structure and organize possible factors that are important to consider when attempting to change improvement behaviors. This begins to fill the gap in the knowledge about what factors drive effective improvement efforts in hospital settings

Quality Improvement in Hospitals: Identifying and Understanding Behaviors

ABSTRACT Improving operational performance in hospitals is complicated, particularly if process improvement requires complex behavioral changes. Using single-loop and double-loop learning theory as a foundation, the purpose of this research is to empirically uncover key improvement behaviors and the factors that may be associated with such behaviors in hospitals. A two-phased approach was taken to collect data regarding improvement behaviors and associated factors, and data analysis was conducted using methods proposed by grounded theorists. The contributions of this research are twofold. First, five key behaviors related to process improvement are identified, namely Quick Fixing, Initiating, Conforming, Expediting, and Enhancing. Second, based on these observed behaviors, a set of force field diagrams is developed to structure and organize possible factors that are important to consider when attempting to change improvement behaviors. This begins to fill the gap in the knowledge about what factors drive effective improvement efforts in hospital settings

Successful outcome of phytostabilization in Cr(VI) contaminated soils amended with alkalizing additives

This study analysed the effect of three alkalizing soil amendments (limestone, dolomite chalcedonite) on aided phytostabilization with Festuca rubra L. depending on the hexavalent chromium (Cr(VI)) level in contaminated soil. Four different levels of Cr(VI) were added to the soil (0, 50, 100 and 150 mg/kg). The Cr contents in the plant roots and above-ground parts and the soil (total and extracted Cr by 0.01 M CaCl2) were determined with flame atomic absorption spectrometry. The phytotoxicity of the soil was also determined. Soil amended with chalcedonite significantly increased F. rubra biomass. Chalcedonite and limestone favored a considerable accumulation of Cr in the roots. The application of dolomite and limestone to soil contaminated with Cr(VI) contributed to a significant increase in pH values and was found to be the most effective in reducing total Cr and CaCl2-extracted Cr contents from the soil. F. rubra in combination with a chalcedonite amendment appears to be a promising solution for phytostabilization of Cr(VI)-contaminated areas. The use of this model can contribute to reducing human exposure to Cr(VI) and its associated health risks. © 2020 by the authors.Ministerstwo Nauki i Szkolnictwa Wyższego: MNiS

Toward a better understanding of task demands, workload, and performance during physician-computer interactions

OBJECTIVE: To assess the relationship between (1) task demands and workload, (2) task demands and performance, and (3) workload and performance, all during physician-computer interactions in a simulated environment. METHODS: Two experiments were performed in 2 different electronic medical record (EMR) environments: WebCIS (n = 12) and Epic (n = 17). Each participant was instructed to complete a set of prespecified tasks on 3 routine clinical EMR-based scenarios: urinary tract infection (UTI), pneumonia (PN), and heart failure (HF). Task demands were quantified using behavioral responses (click and time analysis). At the end of each scenario, subjective workload was measured using the NASA-Task-Load Index (NASA-TLX). Physiological workload was measured using pupillary dilation and electroencephalography (EEG) data collected throughout the scenarios. Performance was quantified based on the maximum severity of omission errors. RESULTS: Data analysis indicated that the PN and HF scenarios were significantly more demanding than the UTI scenario for participants using WebCIS (P < .01), and that the PN scenario was significantly more demanding than the UTI and HF scenarios for participants using Epic (P < .01). In both experiments, the regression analysis indicated a significant relationship only between task demands and performance (P < .01). DISCUSSION: Results suggest that task demands as experienced by participants are related to participants' performance. Future work may support the notion that task demands could be used as a quality metric that is likely representative of performance, and perhaps patient outcomes. CONCLUSION: The present study is a reasonable next step in a systematic assessment of how task demands and workload are related to performance in EMR-evolving environments

The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars. I. Identifying and Characterizing the Protostellar Content of the OMC-2 FIR4 and OMC-2 FIR3 Regions

We present ALMA (0.87~mm) and VLA (9~mm) observations toward OMC2-FIR4 and OMC2-FIR3 within the Orion integral-shaped filament that are thought to be the nearest regions of intermediate mass star formation. We characterize the continuum sources within these regions on $\sim$40~AU (0\farcs1) scales and associated molecular line emission at a factor of $\sim$30 better resolution than previous observations at similar wavelengths. We identify six compact continuum sources within OMC2-FIR4, four in OMC2-FIR3, and one additional source just outside OMC2-FIR4. This continuum emission is tracing the inner envelope and/or disk emission on less than 100~AU scales. HOPS-108 is the only protostar in OMC2-FIR4 that exhibits emission from high-excitation transitions of complex organic molecules (e.g., methanol and other lines) coincident with the continuum emission. HOPS-370 in OMC2-FIR3 with L~$\sim$~360~\lsun, also exhibits emission from high-excitation methanol and other lines. The methanol emission toward these two protostars is indicative of temperatures high enough to thermally evaporate methanol from icy dust grains; overall these protostars have characteristics similar to hot corinos. We do not identify a clear outflow from HOPS-108 in \twco, but find evidence of interaction between the outflow/jet from HOPS-370 and the OMC2-FIR4 region. The multitude of observational constraints indicate that HOPS-108 is likely a low to intermediate-mass protostar in its main mass accretion phase and it is the most luminous protostar in OMC2-FIR4. The high resolution data presented here are essential for disentangling the embedded protostars from their surrounding dusty environments and characterizing them

Elevated NSD3 histone methylation activity drives squamous cell lung cancer

Amplification of chromosomal region 8p11-12 is a common genetic alteration that has been implicated in the aetiology of lung squamous cell carcinoma (LUSC)(1-3). The FGFR1 gene is the main candidate driver of tumorigenesis within this region(4). However, clinical trials evaluating FGFR1 inhibition as a targeted therapy have been unsuccessful(5). Here we identify the histone H3 lysine 36 (H3K36) methyltransferase NSD3, the gene for which is located in the 8p11-12 amplicon, as a key regulator of LUSC tumorigenesis. In contrast to other 8p11-12 candidate LUSC drivers, increased expression of NSD3 correlated strongly with its gene amplification. Ablation of NSD3, but not of FGFR1, attenuated tumour growth and extended survival in a mouse model of LUSC. We identify an LUSC-associated variant NSD3(T1232A) that shows increased catalytic activity for dimethylation of H3K36 (H3K36me2) in vitro and in vivo. Structural dynamic analyses revealed that the T1232A substitution elicited localized mobility changes throughout the catalytic domain of NSD3 to relieve auto-inhibition and to increase accessibility of the H3 substrate. Expression of NSD3(T1232A) in vivo accelerated tumorigenesis and decreased overall survival in mouse models of LUSC. Pathological generation of H3K36me2 by NSD3(T1232A) reprograms the chromatin landscape to promote oncogenic gene expression signatures. Furthermore, NSD3, in a manner dependent on its catalytic activity, promoted transformation in human tracheobronchial cells and growth of xenografted human LUSC cell lines with amplification of 8p11-12. Depletion of NSD3 in patient-derived xenografts from primary LUSCs containing NSD3 amplification or the NSD3(T1232A)-encoding variant attenuated neoplastic growth in mice. Finally, NSD3-regulated LUSC-derived xenografts were hypersensitive to bromodomain inhibition. Thus, our work identifies NSD3 as a principal 8p11-12 amplicon-associated oncogenic driver in LUSC, and suggests that NSD3-dependency renders LUSC therapeutically vulnerable to bromodomain inhibition

Promoting Safety Mindfulness: Recommendations For The Design And Use Of Simulation-Based Training In Radiation Therapy

There is a need to better prepare radiation therapy (RT) providers to safely operate within the health information technology (IT) sociotechnical system. Simulation-based training has been preemptively used to yield meaningful improvements during providers\u27 interactions with health IT, including RT settings. Therefore, on the basis of the available literature and our experience, we propose principles for the effective design and use of simulated scenarios and describe a conceptual framework for a debriefing approach to foster successful training that is focused on safety mindfulness during RT professionals\u27 interactions with health IT

Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology

Purpose: Pretreatment quality assurance (QA) of treatment plans often requires a high cognitive workload and considerable time expenditure. This study explores the use of machine learning to classify pretreatment chart check QA for a given radiation plan as difficult or less difficult, thereby alerting the physicists to increase scrutiny on difficult plans. Methods and Materials: Pretreatment QA data were collected for 973 cases between July 2018 and October 2020. The outcome variable, a degree of difficulty, was collected as a subjective rating by physicists who performed the pretreatment chart checks. Potential features were identified based on clinical relevance, contribution to plan complexity, and QA metrics. Five machine learning models were developed: support vector machine, random forest classifier, adaboost classifier, decision tree classifier, and neural network. These were incorporated into a voting classifier, where at least 2 algorithms needed to predict a case as difficult for it to be classified as such. Sensitivity analyses were conducted to evaluate feature importance. Results: The voting classifier achieved an overall accuracy of 77.4% on the test set, with 76.5% accuracy on difficult cases and 78.4% accuracy on less difficult cases. Sensitivity analysis showed features associated with plan complexity (number of fractions, dose per monitor unit, number of planning structures, and number of image sets) and clinical relevance (patient age) were sensitive across at least 3 algorithms. Conclusions: This approach can be used to equitably allocate plans to physicists rather than randomly allocate them, potentially improving pretreatment chart check effectiveness by reducing errors propagating downstream

Promoting safety mindfulness: Recommendations for the design and use of simulation-based training in radiation therapy

There is a need to better prepare radiation therapy (RT) providers to safely operate within the health information technology (IT) sociotechnical system. Simulation-based training has been preemptively used to yield meaningful improvements during providers' interactions with health IT, including RT settings. Therefore, on the basis of the available literature and our experience, we propose principles for the effective design and use of simulated scenarios and describe a conceptual framework for a debriefing approach to foster successful training that is focused on safety mindfulness during RT professionals' interactions with health IT