Mitigate cross-market competition caused by the risk of uncertainty and improve firm performance through business intelligence.

In the face of various risks and uncertainties including political instability, technological advancements, and natural disasters, businesses involved in cross-market activities are encountering a more competitive environment. This study investigates the relationship between competitive intensity, business intelligence, internal process efficiency, and the performance of small and medium-sized manufacturing enterprises in China. By incorporating the Technology-Organization-Environment (TOE) framework and Dynamic Capabilities Theory, a research model is developed to demonstrate that competitive intensity improves firms' performance through the utilization of business intelligence capabilities and internal process efficiency. Through the use of a structural equation model, data collected from 15 industrial parks in Henan province, China, involving 429 participants, was analyzed. The findings show a positive correlation between competitive intensity and business intelligence sensing capability (both internal and external). The impact of business intelligence sensing capability on the performance of small and medium-sized manufacturing enterprises is shown to be mediated through internal process efficiency. Our study reveals the mediating roles of business intelligence capability and internal process efficiency in improving organizational performance among Chinese small and medium-sized manufacturing enterprises. This research not only fills gaps in business intelligence research from a management perspective but also contributes to the literature on the interactions among competitive intensity, business intelligence, internal processes, and organizational performance. It also sheds light on the relationship between competitive intensity and organizational performance, offering insights for companies involved in cross-market activities. By highlighting the importance of business intelligence capabilities in adapting to environmental influences, this study offers practical guidance for enterprise digital transformation efforts.

A Nursing Clinical Care Services Platform to Support Leadership Decision Making.

Like most organizations, The Children's Hospital of Philadelphia (CHOP) generates a lot of data of varying types, from clinical to staffing and operations. However, these datasets are separated, preventing effective analytics. To improve workforce management and care delivery, CHOP's Nursing department collaborated with the Data & Analytics team to create a dashboard-based platform for analyzing workforce metrics and patient outcomes, aiding nursing leaders in decision-making. This project involved multiple phases with contributions from Nursing, Data & Analytics, CHOP's Data Trust Office, and Human Resources. Key performance indicators were identified, and a variety of data sources were aggregated to provide a comprehensive view of the nursing enterprise. The resulting platform offers automated, current, and reliable analytics on various aspects, including nursing demographics, education, survey results, staffing actuals by job group, and patient and family experience data. The platform's usability was assessed using a modified Health Information Technology Usability Evaluation Scale survey, with a 29% response rate primarily from senior directors and managers. The findings showed high usability and satisfaction, indicating the dashboard is a valuable decision-support tool. Lessons learned include the need for analytics education for nurses and mid-managers, the inclusion of critical nursing-specific metrics (and development of the data pipelines making them possible), and the integration of multidisciplinary team metrics for comprehensive nursing analytics.

Enhancing the Autonomy of Establishing Indicator Informatization.

Quality indicators are essential for hospital management, clinical decision-making, and patient care. In today's technologically advanced era, it is increasingly critical to derive effective statistical data analysis from intricate datasets. However, the administrative department within our hospital often fails to prioritize information needs due to limited resources. Consequently, the collection of indicator data continues to rely on paper-based operations.To tackle common issues in collecting indicator data, such as incomplete or inaccurate data, time-consuming aggregation and statistical analysis, and a lack of timeliness, we have implemented appropriate Business Intelligence (BI) tools. We provide educational training to empower users to create visual templates for indicators that are tailored to their specific requirements. This approach saves time, reduces data duplication, and minimizes writing errors. It enhances supervisors' understanding of the current situation and future prospects in terms of data analysis and timeliness. Ultimately, this initiative improves healthcare quality and management effectiveness.

Spurring SMEs' performance through business intelligence, organizational and network learning, customer value anticipation, and innovation - Empirical evidence of the creative economy sector in East Java, Indonesia.

Several studies have explored firm performance in the post-Covid-19 pandemic era. However, there is not much research to find reports divulging the complex relationship dynamics between business intelligence, organizational and network learning, customer value anticipation, and creative economy-based small-medium enterprises (SMEs) performance in developing countries. This study aims to uncover the complexity of those relationships. The quantitative data were collected from 313 creative economy-based SMEs in East Java, Indonesia. Using PLS-SEM, this study disclosed that business intelligence practices could not directly impact SMEs' performance. Business intelligence will be crucial to SMEs' performance with the support of organizational learning as a mediator. The finding also confirmed the presence of serial mediation of organizational learning and innovation in the relationship between business intelligence and SMEs' performance. However, the role of network learning and innovation is also important, considering their relatively large direct impact on SMEs' performance. The theoretical implications of this research broke the boundaries of strategic management theory in resource-based view and knowledge-based view in the latest era, where creative economy-based SMEs have been able to mobilize resources to carry out business intelligence to realize innovation and high performance. Further research is suggested to explore the role of business intelligence in promoting specific performance areas, such as marketing performance, financial performance, and human resource management. In addition, it is advisable to choose more specific research subjects, including those in the culinary subsector, and pay attention to other areas, e.g., the demographics of respondents in the model as a control variable.

A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme.

Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

Business intelligence and the leverage of information in healthcare organizations from a managerial perspective: a systematic literature review and research agenda.

PURPOSE: Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations' (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions. DESIGN/METHODOLOGY/APPROACH: A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis. FINDINGS: In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research's stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness. ORIGINALITY/VALUE: To the authors' knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

Retrospective evaluation of clinical decision support for within-laboratory optimization of SARS-CoV-2 NAAT workflow.

The unprecedented demand for severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) testing led to challenges in prioritizing and processing specimens efficiently. We describe and evaluate a novel workflow using provider- and patient-facing ask at order entry (AOE) questions to generate distinctive icons on specimen labels for within-laboratory clinical decision support (CDS) for specimen triaging. A multidisciplinary committee established target turnaround times (TATs) for SARS-CoV-2 nucleic acid amplification test (NAAT) based on common clinical scenarios. A set of AOE questions was used to collect relevant clinical information that prompted icon generation for triaging SARS-CoV-2 NAAT specimens. We assessed the collect-to-verify TATs among relevant clinical scenarios. Our study included a total of 1,385,813 SARS-CoV-2 NAAT conducted from March 2020 to June 2022. Most testing met the TAT targets established by institutional committees, but deviations from target TATs occurred during periods of high demand and supply shortages. Median TATs for emergency department (ED) and inpatient specimens and ambulatory pre-procedure populations were stable over the pandemic. However, healthcare worker and other ambulatory test TATs varied substantially, depending on testing volume and community transmission rates. Median TAT significantly differed throughout the pandemic for ED and inpatient clinical scenarios, and there were significant differences in TAT among label icon-signified ambulatory clinical scenarios. We describe a novel approach to CDS for triaging specimens within the laboratory. The use of CDS tools could help clinical laboratories prioritize and process specimens efficiently, especially during times of high demand. Further studies are needed to evaluate the impact of our CDS tool on overall laboratory efficiency and patient outcomes. IMPORTANCE We describe a novel approach to clinical decision support (CDS) for triaging specimens within the clinical laboratory for severe acute respiratory syndrome coronavirus 2 (SARS­CoV­2) nucleic acid amplification tests (NAAT). The use of our CDS tool could help clinical laboratories prioritize and process specimens efficiently, especially during times of high demand. There were significant differences in the turnaround time for specimens differentiated by icons on specimen labels. Further studies are needed to evaluate the impact of our CDS tool on overall laboratory efficiency and patient outcomes.

Identifying and prioritizing the key performance indicators for hospital management dashboard at a national level: Viewpoint of hospital managers.

Participation of main users in identifying key performance indicators (KPIs) for management dashboards contributes to their success. The aim of this study was to identify and prioritize the KPIs of hospital management dashboards from the viewpoint of hospital managers. This study was conducted on managers of public hospitals at a national level in Iran in 2020. Data were collected using a self-administrated questionnaire. The KPIs were classified into five categories, namely financial, operational, human resources, safety and quality of care, services provided to patients. A total of 234 hospital managers participated in this study. Totally, 25 KPIs were determined for the hospital management dashboard, including the patient falls rate, waiting time for patients in the emergency department, patient satisfaction, total hospital revenue, financial balance, bed occupancy rate, patients' discharge with own agreement, average length of stay, and personnel satisfaction. For designing hospital management dashboards, the domains of services provided to patients, safety and quality of care, financial resources, human resources, and operational are important from the hospital managers' viewpoint, respectively. The results of this study can be helpful for developers of business intelligence tools, such as hospital management dashboards, to visualize the most important indicators for managers.

A method of Mapping Process for scientific production using the Smart Bibliometrics.

Big data launches a modern way of producing science and research around the world. Due to an explosion of data available in scientific databases, combined with recent advances in information technology, the researcher has at his disposal new methods and technologies that facilitate scientific development. Considering the challenges of producing science in a dynamic and complex scenario, the main objective of this article is to present a method aligned with tools recently developed to support scientific production, based on steps and technologies that will help researchers to materialize their objectives efficiently and effectively. Applying this method, the researcher can apply science mapping and bibliometric techniques with agility, taking advantage of an easy-to-use solution with cloud computing capabilities. From the application of the "Scientific Mapping Process", the researcher will be able to generate strategic information for a result-oriented scientific production, assertively going through the main steps of research and boosting scientific discovery in the most diverse fields of investigation. •The Scientific Mapping Process provides a method and a system to boost scientific development.•It automates Science Mapping and bibliometric analysis from scientific datasets.•It facilitates the researcher's work, increasing the assertiveness in scientific production.

Making a Virtue of Necessity - A Highly Structured Clinical Data Warehouse as the Source of Assured Truth in a Hospital.

Data-driven decision-making in health care is becoming increasingly important in daily clinical use. A data warehouse, storing all the clinically relevant information in a highly structured way, is a primary basis for achieving this goal. We are developing a clinical data warehouse where more than 20 years of clinical data can be persisted, and newly generated data from different sources can be integrated. A back room was created to store all hospital information system data in a PostgreSQL database. Due to the enormous number of diverse forms in the hospital information system, a broker service was developed that integrates the individual data sources into the data warehouse as soon as they are released for storage. The front room represents the interface from the infrastructure to the targeted analysis. Database query and visualization tools or business intelligence tools can display and analyze processed and interleaved data. In all areas of business and medicine, structured and quality-adjusted data is of major importance. With the help of a clinical data warehouse system, it is possible to perform patient-centered analyses and thus realize optimal therapy. Furthermore, it is possible to provide staff and management with dashboards for control purposes.

Optimizing Medication Distribution in Automated Dispensing Cabinets: Dashboard Implementation and Evaluation.

Purpose: To determine the impact of a business intelligence dashboard tool to optimize automated dispensing cabinets (ADCs). Methods: A pre-post implementation design was used to evaluate key performance indicators (KPI) before and after the implementation of a dashboard tool to optimize ADCs. Eleven ADCs were optimized in 2 phases according to dashboard recommendations: (1) removal of unused medications over 90 days, (2) adjusting periodic automatic replenishment (PAR) levels, and (3) addition of commonly dispensed medications. The KPI measures that were assessed included inventory cost, no. of stocked medications, stockout percentage, vend to refill ratio, and missing dose messages from nursing. An interrupted-time-series regression was used to quantify the impact of ADCs on the means of measured KPIs. Results: Differences in mean distribution of all KPIs, except missing dose, between the pre- and post-ADC periods during the Phase 1 period were statistically significant: inventory cost (54.2 vs 56), stockout percentage (1.55 vs 1.12), vend to refill ratio (6.83 vs 6.14), and missing dose messages (221 vs 229). Only the mean ADC utilization (57.3 vs 64) and missing dose (228 vs 179) were statistically different between the pre- and post-ADC periods in Phase 2. The interrupted-time-series analysis showed that Phase 1 optimization significantly reduced the cost of inventory (ß = -$1.238.00, P < .01), no. Stocked medications (ß = -8.2, P < .01), percent stockout (ß = -.49%, P < .01), vend-to-refill ratio (ß = -1.29%, P<.01) and ADC utilization (ß = -.2, P < .01). Conclusion: Automated dispensing cabinets optimization, through the use of a dashboard tool, had a positive impact on almost all measured KPIs.

Approaching towards sustainable supply chain under the spotlight of business intelligence.

These two main objectives of this study are to present a theoretical model to explain how business intelligence capabilities influence the company's supply chain sustainability and to examine the relationships among different BI and CSCS dimensions. This study was conducted with the use of a standard BI questionnaire along with the United Nations CSCS questionnaire among 234 Iranian pharmaceutical companies, from which 188 were also surveyed. Smart pls3 and partial least squares methods were used for validity as well as reliability evaluation of the measurement model. According to the findings, BI significantly affects the sustainability of the pharmaceutical supply chain and some of its dimensions, including vision, scope, and internal aspects, thereby the hypothesis indicating the effect of BI on these dimensions was accepted. However, there was an insignificantly positive relationship between BI and the other dimensions of CSCS, including expectation, engagement, and goals; hence, the hypothesis indicating the effect of BI on these dimensions was rejected. If the policy of the board is to implement supply chain sustainability, BI can have a greater impact on the company. Otherwise, BI may be implemented with not much effect though it can be indirectly beneficial to these companies. No studies have been performed on direct examination of the relationship of BI and CSCS and their various dimensions with the use of an extensive survey among Iran's pharmaceutical companies. Also, this study reveals some facts about the sustainability of the pharmaceutical supply chain, BI, and relevant issues as significant obstacles against a sustainable supply chain and BI. This article also supports the UN questionnaire on supply chain sustainability and adopts it in the surveys. Furthermore, various social networks such as Facebook, Twitter, and Instagram were compared, and it was concluded that the data required for the pharmaceutical industry was more accessible from Twitter, in comparison to the other social networks.

Diseño de un Programa Virtual en Archivística e Inteligencia de Negocios para la Universidad de La Salle, Bogotá-Colombia / Design of a Virtual Program in Archiving and Business Intelligence for Universidad de La Salle, Bogotá, Colombia

Se presenta la propuesta de diseño de un Programa Virtual en Archivística e Inteligencia de Negocios para la Universidad de La Salle, de Bogotá-Colombia. Dicha propuesta se asume como un ejercicio curricular disruptivo, debido a que se pretende generar articulaciones entre los conocimientos y las prácticas típicamente archivísticas-informacionales-organizacionales con el conocimiento, métodos y técnicas de la inteligencia de negocios y, con ello, formar un profesional desde un enfoque que potencie el carácter estratégico de la toma de decisiones en todo tipo de instituciones, en conexión con las necesidades socio-productivas. La comparación de programas de pregrado, el análisis de tendencias conceptuales y metódicas de ambos campos, la caracterización de la demanda académico-profesional para la modalidad virtual y los criterios técnico-curriculares-normativos del Ministerio de Educación Nacional de Colombia, así como los lineamientos de la Universidad de La Salle fueron los elementos sustantivos de la metodología. Los principales resultados se traducen en el perfil profesional por competencias, resultados de aprendizaje, plan de estudios y aspectos de la gestión curricular del programa que se propone. Como conclusión se enfatiza en lo disruptivo como el elemento transversal que debe guiar el diseño y planeación de programas académicos en Ciencias de la Información, los cuales deben ofrecer alternativas que estén en sintonía con las demandas sociales, configuradas a partir de las transformaciones producidas por las tecnologías digitales, en articulación con procesos analíticos de alto valor agregado en las organizaciones(AU)

A design proposal of Virtual Program in Archiving and Business Intelligence is presented for Universidad de La Salle, in Bogotá, Colombia. Said proposal is assumed as a disruptive curricular exercise, since it is intended to generate articulations between typically archival-informational-organizational knowledge and practices with the knowledge, methods and techniques of business intelligence and, with this, training a professional from an approach that enhances the strategic nature of decision-making in all kinds of institutions, in connection with socio-productive needs. The comparison of undergraduate programs, the analysis of conceptual and methodical trends of both fields, the characterization of the academic-professional demand for the virtual modality and the technical-curricular-regulatory criteria of Colombia National Ministry of Education, as well as the guidelines from the Universidad de La Salle were the major methodological elements. The main results are translated into the professional profile by competencies, learning outcomes, study plan and aspects of the curricular management of the proposed program. In conclusion, disruption is emphasized as the transversal element that must guide designing and planning academic programs in Information Sciences, which must offer alternatives that are in tune with social demands, shaped from the transformations produced by the digital technologies, in coordination with analytical processes of high added value in organizations(AU)

Modernization of a Large Spinal Cord Injuries and Disorders Registry: The Veterans Administration Experience.

Since the 1990s, Veterans Health Administration (VHA) has maintained a registry of Veterans with Spinal Cord Injuries and Disorders (SCI/Ds) to guide clinical care, policy, and research. Historically, methods for collecting and recording data for the VHA SCI/D Registry (VSR) have required significant time, cost, and staffing to maintain, were susceptible to missing data, and caused delays in aggregation and reporting. Each subsequent data collection method was aimed at improving these issues over the last several decades. This paper describes the development and validation of a case-finding and data-capture algorithm that uses primary clinical data, including diagnoses and utilization across 9 million VHA electronic medical records, to create a comprehensive registry of living and deceased Veterans seen for SCI/D services since 2012. A multi-step process was used to develop and validate a computer algorithm to create a comprehensive registry of Veterans with SCI/D whose records are maintained in the enterprise wide VHA Corporate Data Warehouse. Chart reviews and validity checks were used to validate the accuracy of cases that were identified using the new algorithm. An initial cohort of 28,202 living and deceased Veterans with SCI/D who were enrolled in VHA care from 10/1/2012 through 9/30/2017 was validated. Tables, reports, and charts using VSR data were developed to provide operational tools to study, predict, and improve targeted management and care for Veterans with SCI/Ds. The modernized VSR includes data on diagnoses, qualifying fiscal year, recent utilization, demographics, injury, and impairment for 38,022 Veterans as of 11/2/2022. This establishes the VSR as one of the largest ongoing longitudinal SCI/D datasets in North America and provides operational reports for VHA population health management and evidence-based rehabilitation. The VSR also comprises one of the only registries for individuals with non-traumatic SCI/Ds and holds potential to advance research and treatment for multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), and other motor neuron disorders with spinal cord involvement. Selected trends in VSR data indicate possible differences in the future lifelong care needs of Veterans with SCI/Ds. Future collaborative research using the VSR offers opportunities to contribute to knowledge and improve health care for people living with SCI/Ds.

Indicadores claves de desempeño y su aplicación en la gerencia estratégica de las empresas de salud / Key performance indicators and their application in the strategic management of healthcare companies

Introducción: La gerencia empresarial, a lo largo de la historia, se ha preocupado por alcanzar objetivos y metas mediante la utilización de estrategias con el fin de tratar de garantizar los réditos que le permitan seguir trabajando en el tiempo. El interés de lograr alcanzar la eficiencia empresarial ha sido clave para que los investigadores modernos hayan realizado importantes aportes a la epistemología de las ciencias administrativas. Surge entonces la pregunta en la mente de los administradores: ¿Es de importancia la aplicación de indicadores claves de desempeño en la gerencia estratégica de las empresas de salud? Los indicadores claves de desempeño, o KPIs por sus siglas en inglés, forman parte del conjunto de métricos que la administración moderna utiliza para saber si el negocio está logrando los objetivos y metas planteadas por la gerencia. El tablero de mando integral de Kaplan y Norton, desde su aparición en el año de 1992, ha sido también de gran valor para la gerencia estratégica empresarial. Objetivo: Presentar la importancia que tiene la utilización de los indicadores claves de desempeño al ser aplicados en la gerencia estratégica de las empresas de salud. Metodología: Basada en el paradigma cualitativo-descriptivo de investigación acción, el cual se sustentó en literatura científica publicada entre los años 2017 y 2021 apoyado en las plataformas Google Scholar y SciELO. Los criterios de inclusión fueron artículos y textos que abordaran los temas de: indicadores claves de desempeño, inteligencia de negocios, gerencia estratégica y tablero de mando integral. Resultado y Conclusiones: El continuo monitoreo del ambiente competitivo en el que la empresa está inmerso, permite la adaptación de los métricos de acuerdo con los cambios del entorno. En la medida en que los métricos permitan a la empresa volverse líquida, es decir, facilitar que se amolde a los cambios en el medioambiente económico, se facilitará la toma de decisiones de negocios eficaces y oportunas.

Introduction: Business management throughout history has been concerned with achieving objectives and goals, through the use of strategies in order to try to guarantee the revenues that allow it to continue operating over time. The interest in achieving business efficiency has been key for modern researchers to have made important contributions to the epistemology of administrative sciences. The question then arises in the minds of administrators: Is it important to apply key performance indicators in the strategic management of health companies? Key performance indicators, or KPIs for its acronym in English, are part of the set of metrics that modern management uses to know if the business is achieving the objectives and goals set by senior management. Kaplan and Norton's Balanced Scorecard, since its appearance in 1992, has also been of great value for strategic business management. Objective: To present the importance of the use of key performance indicators when applied in the strategic management of health companies Methodology: Based on the qualitative-descriptive and hermeneutical paradigm in the which was based on studies between 2017 and 2021 supported by the Google Scholar and SciELO platforms. The inclusion criteria were articles and texts that addressed the topics of: key performance indicators, business intelligence, strategic management and balanced scorecard. Results and Conclusions: The continuous monitoring of the competitive environment in which the company is immersed, allows the adaptation of the metrics according to changes in the environment. To the extent that the metrics allow the company to become liquid, that is, make it easier to adapt to changes in the economic environment, effective and timely business decision-making will be facilitated.

Intention to use business intelligence tools in decision making processes: applying a UTAUT 2 model.

The pressure on the speed of information processing ranks business intelligence technologies among the fastest growing decision support tools. The main goal of this article is, applying the UTAUT 2 (the unified theory of acceptance and use of technology), to verify the factors determining the implementation of business intelligence tools in business processes, especially decision-making, and their subsequent optimal use in business practice. The researched scheme was modified according to the specifics of business intelligence tools and was supplemented by user behaviour in decision-making. The verification was performed using a questionnaire survey based on UTAUT 2 theory and 152 respondents were included in the analysis. According to the results, the most important variable of influence on both the behavioural intention and the users' behaviour itself in decision-making was the factor of habit. And surprisingly, some previously recognised links were not confirmed, especially the factors influencing the intention of behaviour (effort expectancy, social influence, facilitating conditions). So, there is room after almost 10 years and experience gained during the Covid-19 pandemic to modify the latest version of a model.

Inteligencia empresarial para la visualización y análisis de datos de indicadores de actividad de Telefarmacia en un cuadro de mando de un servicio de farmacia de hospital / Business intelligence for the visualization and data analysis of Telepharmacy activity indicators in a hospital pharmacy service scorecard

Objetivo: La consolidación de la Telefarmacia en el contexto de la pandemia por la COVID-19 exige manejar a tiempo real un gran volumen dedatos de actividad mediante análisis de datos. El objetivo de este trabajofue diseñar un cuadro de mando ágil, personalizable y dinámico para lavisualización y análisis de indicadores de actividad en Telefarmacia en unservicio de farmacia de hospital, mediante el empleo de herramientas avanzadas de inteligencia empresarial (business intelligence).Método: Un equipo de trabajo multidisciplinar desarrolló una herramienta de software entre abril y mayo de 2021 impulsado desde el servicio de farmacia de hospital. Una vez consensuados los indicadoresde interés en Telefarmacia, se extrajeron los datos a partir de bases dedatos brutas (base de datos de Telefarmacia, programa de dispensación de pacientes externos, bases de datos administrativas, catálogosde fármacos) mediante análisis de datos. La integración de las diferentesfuentes de datos en el cuadro de mando se realizó mediante PowerBI®.Se definió el manejo de los datos perdidos y duplicados y se aplicópreprocesamiento, normalización y transformación de los datos. Una vez validado el piloto por diferentes tipos de usuarios, se diseñó la estructurapara actualización automática de los paneles con las sucesivas actualizaciones de las fuentes de datos.Resultados: Diseño e implementación de un cuadro de mando de laactividad en Telefarmacia: panel descriptivo general (perfil demográficode pacientes, recuento y condiciones de envíos, programa y serviciomédico); geolocalización de destino; perfil farmacológico; análisis relativo de los pacientes beneficiarios de Telefarmacia respecto del totalde pacientes externos. En el último corte, a enero de 2022, se habíanincluido datos de 16.000 dispensaciones con entrega informada a másde 4.000 pacientes, lo que supone que el 21,93% de los pacientes externos han estado en algún momento en el programa de Telefarmacia. (AU)

Objective: The consolidation of Telepharmacy during the COVID-19pandemic has raised the need for managing large volumes of real-timeactivity data through data analysis. The aim of this project was to designa dynamic, user-friendly, customizable scorecard in a hospital pharmacyservice for the visualization and analysis of Telepharmacy activity indicators through the use of advanced business intelligence technology.Method: The software tool was developed by a multidisciplinary teambetween April and May 2021, driven from the hospital pharmacy service.Once the Telepharmacy indicators of interest were established, datasetswere extracted from raw databases (administrative databases, Telepharmacy database, outpatient dispensing software, drug catalogues) throughdata analysis. The different data sources were integrated in a scorecardusing PowerBI®. The criteria for processing missing and duplicated datawere defined, and data pre-processing, normalization and transformationwere performed. Once the pilot scorecard was validated by differentprofiles of users, the structure was designed for the panels to automaticallyupdate as databases were updated. Results: Design and implementation of a scorecard of Telepharmacyactivity: general descriptive panel (demographic profile of patients, countand delivery conditions, program and medical service); geolocation ofdestination; pharmacological profile; relative analysis of patients involvedin the Telepharmacy program with respect to the total of outpatients. Inthe last updating as of January 2022, data from 16,000 dispensations tomore than 4,000 patients had been collected. This means that 21.93%of outpatients had benefited at some time point from the Telepharmacyservice. (AU)

Augmented Analytics Driven by AI: A Digital Transformation beyond Business Intelligence.

Lately, Augmented Analytics (AA) has increasingly been introduced as a tool for transforming data into valuable insights for decision-making, and it has gained attention as one of the most advanced methods to facilitate modern analytics for different types of users. AA can be defined as a combination of Business Intelligence (BI) and the advanced features of Artificial Intelligence (AI). With the massive growth in data diversity, the traditional approach to BI has become less useful and requires additional work to obtain timely results. However, the power of AA that uses AI can be leveraged in BI platforms with the use of Machine Learning (ML) and natural language comprehension to automate the cycle of business analytics. Despite the various benefits for businesses and end users in converting from BI to AA, research on this trend has been limited. This study presents a comparison of the capabilities of the traditional BI and its augmented version in the business analytics cycle. Our findings show that AA enhances analysis, reduces time, and supports data preparation, visualization, modelling, and generation of insights. However, AI-driven analytics cannot fully replace human decision-making, as most business problems cannot be solved purely by machines. Human interaction and perspectives are essential, and decision-makers still play an important role in sharing and operationalizing findings.

A Business Analytics Framework for Primary Care.

In this paper, we present a Business Analytics (BA) framework, which addresses the challenge of analysing primary care outcomes for both patients and clinicians from multiple data sources in an accurate manner. A review of the process monitoring literature has been conducted in the context of healthcare management and decision making and its findings have informed the formulation of a BA conceptual framework for process monitoring and decision support in primary care. Furthermore, a real case study is conducted to demonstrate the application of the BA framework to implement a BA dashboard tool within one of the largest primary care providers in England. Findings: The main contributions of the presented work are the development of a conceptual BA framework and a BA dashboard tool to support management and decision making in primary care. This was evaluated through a case study of the implementation of the BA dashboard tool in London's largest primary care provider. This BA tool provides real-time information to enable simpler decision-making processes and to inform business transformation in a number of areas. The resulting increased efficiency has led to significant cost savings and improved delivery of patient care.

Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study.

BACKGROUND: Although lung protective strategy and adjunctive intervention are associated with improved survival in patients with acute respiratory distress syndrome (ARDS), the implementation of effective therapies remains low. This study aimed to evaluate whether the use of business intelligence (BI) for real-time data visualization is associated with an improvement in lung protective strategy and adjunctive therapy. METHODS: A retrospective observational cohort study was conducted on patients with ARDS admitted between September 2020 and June 2021 at two intensive care units (ICUs) of a tertiary referral hospital in Taiwan. BI was imported for data visualization and integration to assist in clinical decision in one of the ICUs. The primary outcomes were the implementation of low tidal volume ventilation (defined as tidal volume/predicted body weight ≤ 8 mL/kg) within 24 h from ARDS onset. The secondary outcomes included ICU and hospital mortality rates. RESULTS: Among the 1201 patients admitted to the ICUs during the study period, 148 (12.3%) fulfilled the ARDS criteria, with 86 patients in the BI-assisted group and 62 patients in the standard-of-care (SOC) group. Disease severity was similar between the two groups. The application of low tidal volume ventilation strategy was significantly improved in the BI-assisted group compared with that in the SOC group (79.1% vs. 61.3%, p = 0.018). Despite their ARDS and disease severity, the BI-assisted group tended to achieve low tidal volume ventilation. The ICU and hospital mortality were lower in the BI-assisted group. CONCLUSIONS: The use of real-time visualization system for data-driven decision support was associated with significantly improved compliance to low tidal volume ventilation strategy, which enhanced the outcomes of patients with ARDS in the ICU.