Burnout and network centrality as proxies for assessing the human cost of resilient performance.

The extra effort of healthcare professionals to provide care is a manifestation of resilient performance (RP), usually going unnoticed due to successful outcomes. However, it is not clear how the human cost of RP can be assessed. This study addresses this gap by investigating the relationships between proxies of RP and its human cost. The proposed approach was tested in a 29-bed intensive care unit (ICU). The centrality of each professional in the advice-seeking social network was considered as the proxy of their contribution to system resilience. A resilience score was calculated for each professional as the product of three network centrality metrics (in-degree, closeness, and betweenness) and two non-network attributes, namely their availability and reliability. Professionals' burnout was the proxy of the human cost of RP, assessed through the Maslach Burnout Inventory, composed of 22 items divided into a triad of emotional exhaustion, depersonalization, and personal accomplishment. Both questionnaires, for social network analysis and burnout, included socio-demographic questions and were answered by 99.0% of the professionals. Results indicated a weak correlation between emotional exhaustion and the resilience score (p = 0.008). This score was also weakly correlated with working overtime (p = 0.005). Overall, findings provided initial evidence that RP as measured in our study matters to burnout, and that the two proxies are exemplars of applying a more general reasoning that might be valid for other proxies.

Modelling the no-show of patients to exam appointments of computed tomography.

BACKGROUND: Patients' no-shows negatively impact healthcare systems, leading to resources' underutilisation, efficiency loss, and cost increase. Predicting no-shows is key to developing strategies that counteract their effects. In this paper, we propose a model to predict the no-show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital. METHODS: We carried out a retrospective study on 8382 appointments to computed tomography (CT) exams between January and December 2017. Penalised logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients' no-shows. The predictive capabilities of the models were evaluated by analysing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). RESULTS: The no-show rate in computerised tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalised logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analysed appearing as significant. One of the variables included in the model (number of exams scheduled in the previous year) had not been previously reported in the related literature. CONCLUSIONS: Our findings may be used to guide the development of strategies to reduce the no-show of patients to exam appointments.

Playful interventions to promote the subjective wellbeing of pediatric cancer inpatients during laboratory and imaging exams: A qualitative study.

PURPOSE: We investigate the experience of pediatric oncology patients with objects and equipment involved in laboratory and image examinations during hospitalization for cancer treatment while generating guidelines for playful interventions to improve their subjective wellbeing. METHOD: The study was carried out at a public tertiary referral teaching hospital in Southern Brazil. Data collection was based on participatory observations with six children aged 4-8 years. Their experiences with exams were observed through pretend play and recorded in field diaries, audio, and video. Data were analyzed using Thematic Analysis and discussed according to the PERMA-V model, a theoretical framework from positive psychology. RESULTS: Several objects and equipment that seem to affect the wellbeing of children during exams were identified. Four playful interventions were proposed as supportive care initiatives: use of technology to allow immersive experiences in learning about treatment and medical condition; design for personalization; gamifying experiences to allow positive reinforcement; and design for focus redirection. CONCLUSIONS: Guidelines for playful interventions to foster the subjective wellbeing of hospitalized children during image and laboratory exams were proposed. The PERMA-V model provided a solid base for the analysis of the interventions, which will be implemented and tested in future studies in clinical settings.

Combining wavelength importance ranking to the random forest classifier to analyze multiclass spectral data.

Near Infrared (NIR) is a type of vibrational spectroscopy widely used in different areas to characterize substances. NIR datasets are comprised of absorbance measures on a range of wavelengths (λ). Typically noisy and correlated, the use of such datasets tend to compromise the performance of several statistical techniques; one way to overcome that is to select portions of the spectra in which wavelengths are more informative. In this paper we investigate the performance of the Random Forest (RF) classifier associated with several wavelength importance ranking approaches on the task of classifying product samples into categories, such as quality levels or authenticity. Our propositions are tested using six NIR datasets comprised of two or more classes of food and pharmaceutical products, as well as illegal drugs. Our proposed classification model, an integration of the χ2 ranking score and the RF classifier, substantially reduced the number of wavelengths in the dataset, while increasing the classification accuracy when compared to the use of complete datasets. Our propositions also presented good performance when compared to competing methods available in the literature.

Dynamic capacity allocation in a radiology service considering different types of patients, individual no-show probabilities, and overbooking.

BACKGROUND: We propose a mathematical model formulated as a finite-horizon Markov Decision Process (MDP) to allocate capacity in a radiology department that serves different types of patients. To the best of our knowledge, this is the first attempt at considering radiology resources with different capacities and individual no-show probabilities of ambulatory patients in an MDP model. To mitigate the negative impacts of no-show, overbooking rules are also investigated. METHODS: The model's main objective is to identify an optimal policy for allocating the available capacity such that waiting, overtime, and penalty costs are minimized. Optimization is carried out using traditional dynamic programming (DP). The model was applied to real data from a radiology department of a large Brazilian public hospital. The optimal policy is compared with five alternative policies, one of which resembles the one currently used by the department. We identify among alternative policies the one that performs closest to the optimal. RESULTS: The optimal policy presented the best performance (smallest total daily cost) in the majority of analyzed scenarios (212 out of 216). Numerical analyses allowed us to recommend the use of the optimal policy for capacity allocation with a double overbooking rule and two resources available in overtime periods. An alternative policy in which outpatients are prioritized for service (rather than inpatients) displayed results closest to the optimal policy, being also recommended due to its easy implementation. CONCLUSIONS: Based on such recommendation and observing the state of the system at any given period (representing the number of patients waiting for service), radiology department managers should be able to make a decision (i.e., define number and type of patients) that should be selected for service such that the system's cost is minimized.

Hospital Investment Decisions in Healthcare 4.0 Technologies: Scoping Review and Framework for Exploring Challenges, Trends, and Research Directions.

BACKGROUND: Alternative approaches to analyzing and evaluating health care investments in state-of-the-art technologies are being increasingly discussed in the literature, especially with the advent of Healthcare 4.0 (H4.0) technologies or eHealth. Such investments generally involve computer hardware and software that deal with the storage, retrieval, sharing, and use of health care information, data, and knowledge for communication and decision-making. Besides, the use of these technologies significantly increases when addressed in bundles. However, a structured and holistic approach to analyzing investments in H4.0 technologies is not available in the literature. OBJECTIVE: This study aims to analyze previous research related to the evaluation of H4.0 technologies in hospitals and characterize the most common investment approaches used. We propose a framework that organizes the research associated with hospitals' H4.0 technology investment decisions and suggest five main research directions on the topic. METHODS: To achieve our goal, we followed the standard procedure for scoping reviews. We performed a search in the Crossref, PubMed, Scopus, and Web of Science databases with the keywords investment, health, industry 4.0, investment, health technology assessment, healthcare 4.0, and smart in the title, abstract, and keywords of research papers. We retrieved 5701 publications from all the databases. After removing papers published before 2011 as well as duplicates and performing further screening, we were left with 244 articles, from which 33 were selected after in-depth analysis to compose the final publication portfolio. RESULTS: Our findings show the multidisciplinary nature of the research related to evaluating hospital investments in H4.0 technologies. We found that the most common investment approaches focused on cost analysis, single technology, and single decision-maker involvement, which dominate bundle analysis, H4.0 technology value considerations, and multiple decision-maker involvement. CONCLUSIONS: Some of our findings were unexpected, given the interrelated nature of H4.0 technologies and their multidimensional impact. Owing to the absence of a more holistic approach to H4.0 technology investment decisions, we identified five promising research directions for the topic: development of economic valuation methodologies tailored for H4.0 technologies; accounting for technology interrelations in the form of bundles; accounting for uncertainties in the process of evaluating such technologies; integration of administrative, medical, and patient perspectives into the evaluation process; and balancing and handling complexity in the decision-making process.

Approaches to the rationalization of surgical instrument trays: scoping review and research agenda.

BACKGROUND: Surgical Tray Rationalization (STR) consists of a systematic reduction in the number of surgical instruments to perform specific procedures without compromising patient safety while reducing losses in the sterilization and assembly of trays. STR is one example of initiatives to improve process performance that have been widely reported in industrial settings but only recently have gained popularity in healthcare organizations. METHODS: We conduct a scoping review of the literature to identify and map available evidence on surgical tray management. Five methodological stages are implemented and reported; they are: identifying research questions, identifying relevant studies, study selection, charting the data, and collating, summarizing and reporting the results. RESULTS: We reviewed forty-eight articles on STR, which were grouped according to their main proposed approaches: expert analysis, lean practices, and mathematical programming. We identify the most frequently used techniques within each approach and point to their potential contributions to operational and economic dimensions of STR. We also consolidate our findings, proposing a roadmap to STR with four generic steps (prepare, rationalize, implement, and consolidate) and recommended associated techniques. CONCLUSIONS: To the best of our knowledge, ours is the first study that reviews and systematizes the existing literature on the subject of STR. Our study closes with the proposition of future research directions, which are presented as nine research questions associated with the four generic steps proposed in the STR roadmap.

Man vs. machine: Predicting hospital bed demand from an emergency department.

BACKGROUND: The recent literature reports promising results from using intelligent systems to support decision making in healthcare operations. Using these systems may lead to improved diagnostic and treatment protocols and to predict hospital bed demand. Predicting hospital bed demand in emergency department (ED) attendances could help resource allocation and reduce pressure on busy hospitals. However, there is still limited knowledge on whether intelligent systems can operate as fully autonomous, user-independent systems. OBJECTIVE: Compare the performance of a computer-based algorithm and humans in predicting hospital bed demand (admissions and discharges) based on the initial SOAP (Subjective, Objective, Assessment, Plan) records of the ED. METHODS: This was a retrospective cohort study that compared the performance of humans and machines in predicting hospital bed demand from an ED. It considered electronic medical records (EMR) of 9030 patients (230 used as a testing set, and hence evaluated both by humans and by an algorithm, and 8800 used as a training set exclusively by the algorithm) who visited the ED of a tertiary care and teaching public hospital located in Porto Alegre, Brazil between January and December 2014. The machine role was played by Support Vector Machine Classifier and the human prediction was performed by four ED physicians. Predictions were compared in terms of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC). RESULTS: All graders achieved similar accuracies. The accuracy by AUROC for the testing set was 0.82 [95% confidence interval (CI) of 0.77-0.87], 0.80 (95% CI: 0.75-0.85), 0.76 (95% CI: 0.71-0.81) for novice physicians, machine, experienced physicians, respectively. Processing time per test EMR was 0.00812±0.0009 seconds. In contrast, novice physicians took on average 156.80 seconds per test EMR, while experienced physicians took on average 56.40 seconds per test EMR. CONCLUSIONS: Our data indicated that the system could predict patient admission or discharge states with 80% accuracy, which was similar the performance of novice and experienced physicians. These results suggested that the algorithm could operate as an autonomous and independent system to complete this task.

Strategies for synchronizing chocolate conching batch process data using dynamic time warping.

In batch processing, process control is typically carried out comparing trajectories of process variables with those in an in-control set of batches that yielded products within specifications. However, one strong assumption of these schemes is that all batches have equal duration and are synchronized, which is often not satisfied in practice. To overcome that, dynamic time warping (DTW) methods may be used to synchronize stages and align the duration of batches. In this paper, three DTW methods are compared using supervised classification through the k-nearest neighbor technique to determine the in-control set in a milk chocolate conching process. Four variables were monitored over time and a set of 62 batches with durations between 495 and 1170 min was considered; 53% of the batches were known to be conforming based on lab test results and experts' evaluations. All three DTW methods were able to promote the alignment and synchronization of batches; however, the KMT method (Kassidas et al. in AIChE J 44(4):864-875, 1998) outperformed the others, presenting 93.7% accuracy, 97.2% sensitivity, and 90.3% specificity in batch classification as conforming and non-conforming. The drive current of the main motor was the most consistent variable from batch to batch, being deemed the most important to promote alignment and synchronization of the chocolate conching dataset.

Sistemática para racionalização de instrumentais de bandejas cirúrgicas / A systematic approach for the rationalization of surgical tray instruments / Sistemática para racionalización de instrumentales de bandejas quirúrgicas

Objetivo: Relatar a experiência de desenvolver uma sistemática para racionalização de instrumentais em bandejas cirúrgicas. Método: Estudo de desenvolvimento de sistemática para racionalização de instrumentais, realizado em 2015, a partir do método qualitativo, em um centro de materiais e esterilização (CME) de um hospital universitário federal de Porto Alegre, Brasil. Resultados: Houve redução média do quantitativo de instrumentais em bandejas institucionais em 10,92%; diminuição de bandejas de propriedade das equipes médicas, sendo 84,06% pertencentes à equipe da otorrinolaringologia; e inativação definitiva de 369 instrumentais da cirurgia ortopédica, o que significou 72,84% do total dos instrumentais inativados. Além disso, houve condução de melhorias no gerenciamento de instrumentais, otimização do tempo de preparo e redução da esterilização por expiração do prazo de utilização. Conclusão: A realocação de instrumentais e o acréscimo de peças em bandejas específicas permitiu a reavaliação das solicitações de compras de instrumentais e a melhoria das relações entre as equipes. Essa sistemática contribuiu significativamente para o gerenciamento de instrumentais, otimizando processos e envolvendo as equipes cirúrgicas no trabalho do CME e evidenciou que pode ser aplicada em outras instituições.

Objective: To report the experience of developing a systematic approach for the rationalization of instruments in surgical trays. Method: Study of the development of a systematic approach for the rationalization of instruments, carried out in 2015, using a qualitative method, in the Central Sterile Supply Department (CSSD) of a federal university hospital in Porto Alegre, Brazil. Results: There was a 10.92% average reduction in the number of instruments in institutional trays, a reduction in the number of trays owned by medical teams ­ 84.06% belonged to the otorhinolaryngology team ­ and a definitive inactivation of 369 orthopedic surgery instruments, which represented 72.84% of the total number of inactivated instruments. In addition, improvements were made to the management of instruments, the optimization of preparation time and the reduction of sterilization by expiration date. Conclusion: The relocation of instruments and the addition of items in specific trays allowed for the reappraisal of requests for purchase of instruments and the improvement of relationships between the teams. This systematic approach contributed significantly to the management of instruments, the optimizing processes and the involvement of the surgical teams in the work of the CSSD, thus demonstrating that it can be applied in other institutions.

Objetivo: Relatar la experiencia de desarrollar una sistemática para racionalización de instrumentales en bandejas quirúrgicas. Método: Estudio de desarrollo de sistemática para racionalización de instrumentales, realizado en 2015, desde el método cualitativo, en un centro de materiales y esterilización (CSSD) de un hospital universitario federal de Porto Alegre, Brasil. Resultados: Hubo reducción media del cuantitativo de instrumentales en bandejas institucionales en el 10,92%; disminución de bandejas de propiedad de los equipos médicos, siendo el 84,06% pertenecientes al equipo de la otorrinolaringología; e inactivación definitiva de 369 instrumentales de la cirugía ortopédica, lo que significó el 72,84% del total de los instrumentales inactivados. Además, hubo conducción de mejoras en el gerenciamiento de instrumentales, optimización del tiempo de preparo y reducción de la esterilización por expiración del plazo de utilización. Conclusión: La reubicación de instrumentales y el incremento de piezas en bandejas específicas permitió la reevaluación de las solicitaciones de compras de instrumentales y la mejora de las relaciones entre los equipos. Esa sistemática contribuyó significativamente para el gerenciamiento de instrumentales, perfeccionando procesos e involucrando a los equipos quirúrgicos en el trabajo de CSSD y evidenció que puede aplicarse en otras instituciones.

Systematic Layout Planning of a Radiology Reporting Area to Optimize Radiologists' Performance.

Optimizing radiologists' performance is a major priority for managers of health services/systems, since the radiologists' reporting activity imposes a severe constraint on radiology productivity. Despite that, methods to optimize radiologists' reporting workplace layout are scarce in the literature. This study was performed in the Radiology Division (RD) of an 850-bed University-based general hospital. The analysis of the reporting workplace layout was carried out using the systematic layout planning (SLP) method, in association with cluster analysis as a complementary tool in early stages of SLP. Radiologists, architects, and hospital managers were the stakeholders consulted for the completion of different stages of the layout planning process. A step-by-step description of the proposed methodology to plan an RD reporting layout is presented. Clusters of radiologists were defined using types of exams reported and their frequency of occurrence as clustering variables. Sectors with high degree of interaction were placed in proximity in the new RD layout, with separation of noisy and quiet areas. Four reporting cells were positioned in the quiet area, grouping radiologists by subspecialty, as follows: cluster 1-abdomen; cluster 2-musculoskeletal; cluster 3-neurological, vascular and head & neck; cluster 4-thoracic and cardiac. The creation of reporting cells has the potential to limit unplanned interruptions and enhance the exchange of knowledge and information within cells, joining radiologists with the same expertise. That should lead to improvements in productivity, allowing managers to more easily monitor radiologists' performance.

[A data mining method for breast cancer identification based on a selection of variables]. / Método de mineração de dados para identificação de câncer de mama baseado na seleção de variáveis.

In the majority of countries, breast cancer among women is highly prevalent. If diagnosed in the early stages, there is a high probability of a cure. Several statistical-based approaches have been developed to assist in early breast cancer detection. This paper presents a method for selection of variables for the classification of cases into two classes, benign or malignant, based on cytopathological analysis of breast cell samples of patients. The variables are ranked according to a new index of importance of variables that combines the weighting importance of Principal Component Analysis and the explained variance based on each retained component. Observations from the test sample are categorized into two classes using the k-Nearest Neighbor algorithm and Discriminant Analysis, followed by elimination of the variable with the index of lowest importance. The subset with the highest accuracy is used to classify observations in the test sample. When applied to the Wisconsin Breast Cancer Database, the proposed method led to average of 97.77% in classification accuracy while retaining an average of 5.8 variables.

Método de mineração de dados para identificação de câncer de mama baseado na seleção de variáveis / A data mining method for breast cancer identification based on a selection of variables

Na maioria dos países, o câncer de mama entre as mulheres é predominante. Se diagnosticado precocemente, apresenta alta probabilidade de cura. Diversas abordagens baseadas em Estatística foram desenvolvidas para auxiliar na sua detecção precoce. Este artigo apresenta um método para a seleção de variáveis para classificação dos casos em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. As variáveis são ordenadas de acordo com um novo índice de importância de variáveis que combina os pesos de importância da Análise de Componentes Principais e a variância explicada a partir de cada componente retido. Observações da amostra de treino são categorizadas em duas classes através das ferramentas k-vizinhos mais próximos e Análise Discriminante, seguida pela eliminação da variável com o menor índice de importância. Usa-se o subconjunto com a máxima acurácia para classificar as observações na amostra de teste. Aplicando ao Wisconsin Breast Cancer Database, o método proposto apresentou uma média de 97,77% de acurácia de classificação, retendo uma média de 5,8 variáveis.

In the majority of countries, breast cancer among women is highly prevalent. If diagnosed in the early stages, there is a high probability of a cure. Several statistical-based approaches have been developed to assist in early breast cancer detection. This paper presents a method for selection of variables for the classification of cases into two classes, benign or malignant, based on cytopathological analysis of breast cell samples of patients. The variables are ranked according to a new index of importance of variables that combines the weighting importance of Principal Component Analysis and the explained variance based on each retained component. Observations from the test sample are categorized into two classes using the k-Nearest Neighbor algorithm and Discriminant Analysis, followed by elimination of the variable with the index of lowest importance. The subset with the highest accuracy is used to classify observations in the test sample. When applied to the Wisconsin Breast Cancer Database, the proposed method led to average of 97.77% in classification accuracy while retaining an average of 5.8 variables.