OBJECTIVE: Demand forecasting is a vital step for production planning and consequently, for supply chain efficiency, especially for the pharmaceutical (pharma) supply chain due to its unique characteristics. Numerous models and techniques that are proposed in the literature but little in concrete and generic framework to forecasting process, mainly for pharmaceutical supply chain. Unlike studies in the literature, this study not only perfectly predict the sales of a pharma manufacturer, but also visualize the results via a developed dashboard using modern information technology and business intelligence.
METHODS: In this research, a rolling forecasting framework comprising of different steps and specialized tools is proposed that can assist supply chain managers to perform an accurate sales forecasting and consequently a better performance and specifically patient satisfaction. The proposed generic framework combines the use of Visual studio C++ software to extract optimal forecasting and the Power BI software to monitor the accuracy of the obtained sales forecasts. Three exponential smoothing methods are integrated in the proposed framework, which is open to adding more new forecasting methods.
RESULTS: The proposed framework is tested for many data sets from a pharmaceutical manufacturer company, and the results obtained show superior performance, especially a clear decline in both forecast errors, which can reach 75% and a drop of stock level to 50%. Therefore, the company is currently using it and a future integration with their ERP is being carried out.
CONCLUSIONS: The proposed rolling forecasting framework contributes to insightful decision-making through the visualization of accurate future sales and turnover, and consequently, an efficient stock management and effective production planning.

Health management information systems implemented in low-and middle-income countries (LMICs) have provided availability of HIV-data. As such, dashboards have become increasingly popular as they provide a potentially powerful avenue for deriving insights at glance. This promotes use of data for decision-making by various stakeholders such as Ministries of Health as well as international donor organizations. Nonetheless, despite the use of dashboards in LMICs, their potential may go unrealized with underutilization of good design principles. In various LMICs, health facilities are required to submit HIV-indicator data on time for its use in decision-making. Hence, dashboards can be utilized in assessing facility reporting performance overtime in order to identify where interventions are needed. In this study, we applied good design principles in developing a dashboard, which presents the performance of facilities in reporting HIV-indicator data overtime (2011-2018). Timeliness and completeness in reporting were used as performance indicators and were extracted from the District Health Information Software Version 2 (DHIS2) in Kenya. Results for the system usability scale used in evaluating the dashboard was 87, which meant the dashboard usability was good.

Under pandemic conditions, it is important to communicate local infection risks to better enable the general population to adjust their behaviors accordingly. In Japan, our team operates a popular non-government and not-for-profit dashboard project - \"Japan LIVE Dashboard\" - which allows the public to easily grasp the evolution of the pandemic on the internet. We presented the Dashboard design concept with a generic framework integrating socio-technical theories, disease epidemiology and related contexts, and evidence-based approaches. Through synthesizing multiple types of reliable and real-time local data sources from all prefectures across the country, the Dashboard allows the public access to user-friendly and intuitive disease visualization in real time and has gained an extensive online followership. To date, it has attracted c.30 million visits (98% domestic access) testifying to the reputation it has acquired as a user-friendly portal for understanding the progression of the pandemic. Designed as an open-source solution, the Dashboard can also be adopted by other countries as well as made applicable for other emerging outbreaks in the future. Furthermore, the conceptual design framework may prove applicable into other ehealth scaled for global pandemics.

About 30 million people suffer from a rare disease in Europe. Those affected face a variety of problems. These include the lack of information and difficult access to scientific knowledge for physicians. For a higher visibility of rare diseases and high-quality research, effective documentation and use of data are essential. The aim of this work is to optimize the processing, use and accessibility of data on rare diseases and thus increase the added value from existing information. While dashboards are already being used to visualize clinical data, it is unclear what requirements are prevalent for rare diseases and how these can be implemented with available development tools so that a highly accepted dashboard can be designed. For this purpose, based on an analysis of the current situation and a requirements analysis, a prototype dashboard for the visualization of up-to-date key figures on rare diseases was developed at the University Hospital Carl Gustav Carus in Dresden. The development was based on the user-centered design process in order to achieve a high-level user-friendliness. The requirements analysis identified parameters that stakeholders wanted to see, focusing primarily on statistical analyses. The dashboard handles the automated calculation of statistics as well as their preparation and provision. The evaluations showed the prototypical dashboard would be considered valuable and used by potential users. This work demonstrates that stakeholders are interested in access to prepared information and exemplifies a way to implement it. The dashboard can increase the usage of existing information in terms of a higher accessibility and thus improve the knowledge about rare diseases.

OBJECTIVE: The surgical morbidity and mortality (M&M) conferences at a regional children\'s hospital achieved the goals of case by case peer review and education for trainees but provided limited data for trending and analysis. In 2019, an institution-wide effort was initiated to create an electronic case review system with the goals of improving event capture and real-time practice performance feedback. Surgical M&M was migrated to this structured case review format to provide a platform for surgical performance improvement.
METHODS: An online secure database was created with a 3-step classification system based on Clavien-Dindo severity score, peer review, and causality fishbone analysis. The data entered were available in an interactive dashboard. Retrospective tabulation of the 2018 M&M data was performed using the archived paper system used prior to 2019.
RESULTS: For the calendar year of 2019, the division of pediatric surgery captured and categorized 193 complications in the case review system. The capture rate was 50 per 1000 surgical procedures. For a similar time frame in 2018, the capture rate was 35 per 1000 surgical procedures. The dashboard provided run charts of the incidence and types of complications by procedure and by surgeon. Similar trend data were not available in 2018. The dashboard output has made possible the creation of (non- risk adjusted) individual surgeon performance reports. The output has been used to direct process improvement projects and educational content.
CONCLUSIONS: Creation of an online database with interactive dashboard has allowed surgical M&M to evolve into a systematic case review that greatly facilitates quality improvement efforts. This system increased the event capture rate and provided novel practice performance feedback, resulting in process improvement projects and educational objectives predicated on the trending data. These electronic reporting tools are now available to all surgical divisions and represent a transformative approach to surgical case review.
METHODS: Retrospective Historical control; Quality improvement.
METHODS: Level III.

Knowledge management is one of the key priorities of many organizations. They face different challenges in the implementation of knowledge management processes, including the transformation of tacit knowledge-experience, skills, insights, intuition, judgment and know-how-into explicit knowledge. Furthermore, the increasing number of information sources and services in some domains, such as healthcare, increase the amount of information available. Therefore, there is a need to transform that information in knowledge. In this context, learning ecosystems emerge as solutions to support knowledge management in a different context. On the other hand, the dashboards enable the generation of knowledge through the exploitation of the data provided from different sources. The model-driven development of these solutions is possible through two meta-models developed in previous works. Even though those meta-models solve several problems, the learning ecosystem meta-model has a lack of decision-making support. In this context, this work provides two main contributions to face this issue. First, the definition of a holistic meta-model to support decision-making processes in ecosystems focused on knowledge management, also called learning ecosystems. The second contribution of this work is an instantiation of the presented holistic meta-model in the healthcare domain.

Current acute kidney injury (AKI) literature focuses on diagnosis, treatment, and outcomes. While little literature exists studying the quality of care delivered to patients with AKI. However, improving outcomes for patients is dependent on the specifics of the delivered care (i.e., the who, what, when, and how). Therefore, it is necessary to direct attention to process measures to assess the relationship between care and outcomes. The application of quality improvement science to the care of AKI, uses a series of metrics encompassing both processes and outcomes to better understand, evaluate, and ensure the delivery high quality care.

BACKGROUND: Introducing self-collected health data from patients with diabetes into consultation can be beneficial for both patients and clinicians. Such an initiative can allow patients to be more proactive in their disease management and clinicians to provide more tailored medical services. Optimally, electronic health record systems (EHRs) should be able to receive self-collected health data in a standard representation of medical data such as Fast Healthcare Interoperability Resources (FHIR), from patients systems like mobile health apps and display the data directly to their users-the clinicians. However, although Norwegian EHRs are working on implementing FHIR, no solution or graphical interface is available today to display self-collected health data.
OBJECTIVE: The objective of this study was to design and assess a dashboard for displaying relevant self-collected health data from patients with diabetes to clinicians.
METHODS: The design relied on an iterative participatory process involving workshops with patients, clinicians, and researchers to define which information should be available and how it should be displayed. The assessment is based on a case study, presenting an instance of the dashboard populated with data collected from one patient with diabetes type 1 (in-house researcher) face-to-face by 14 clinicians. We performed a qualitative analysis based on usability, functionality, and expectation by using responses to questionnaires that were distributed to the 14 clinicians at the end of the workshops and collected before the participants left. The qualitative assessment was guided by the Standards for Reporting Qualitative Research.
RESULTS: We created a dashboard permitting clinicians to assess the reliability of self-collected health data, list all collected data including medical calculations, and highlight medical situations that need to be investigated to improve the situation of the patients. The dashboard uses a combination of tables, graphs, and other visual representations to display the relevant information. Clinicians think that this type of solution will be useful during consultations every day, especially for patients living in remote areas or those who are technologically interested.
CONCLUSIONS: Displaying self-collected health data during consultations is not enough for clinicians; the data reliability has to be assured and the relevant information needs to be extracted and displayed along with the data to ease the introduction during a medical encounter. The prestudy assessment showed that the system provides relevant information to meet clinicians\' need and that clinicians were eager to start using it during consultations. The system has been under testing in a medical trial since November 2018, and the first results of its assessment in a real-life situation are expected in the beginning of next year (2020).

BACKGROUND: The task of monitoring and managing the entire emergency department (ED) is becoming more important due to increasing pressure on the ED. Recently, dashboards have received the spotlight as health information technology to support these tasks.
OBJECTIVE: This study aimed to describe the development of a real-time autonomous dashboard for the ED and to evaluate perspectives of clinical staff on its usability.
METHODS: We developed a dashboard based on three principles-\"anytime, anywhere, at a glance;\" \"minimal interruption to workflow;\" and \"protect patient privacy\"-and 3 design features-\"geographical layout,\" \"patient-level alert,\" and \"real-time summary data.\" Items to evaluate the dashboard were selected based on the throughput factor of the conceptual model of ED crowding. Moreover, ED physicians and nurses were surveyed using the system usability scale (SUS) and situation awareness index as well as a questionnaire we created on the basis of the construct of the Situation Awareness Rating Technique.
RESULTS: The first version of the ED dashboard was successfully launched in 2013, and it has undergone 3 major revisions since then because of geographical changes in ED and modifications to improve usability. A total of 52 ED staff members participated in the survey. The average SUS score of the dashboard was 67.6 points, which indicates \"OK-to-Good\" usability. The participants also reported that the dashboard provided efficient \"concentration support\" (4.15 points), \"complexity representation\" (4.02 points), \"variability representation\" (3.96 points), \"information quality\" (3.94 points), and \"familiarity\" (3.94 points). However, the \"division of attention\" was rated at 2.25 points.
CONCLUSIONS: We developed a real-time autonomous ED dashboard and successfully used it for 5 years with good evaluation from users.

In April 2013, a case of wild polio virus (WPV) was detected in the Somalia capital Mogadishu. This inaugurated what is now referred to as the 2013-2014 Horn of Africa Polio outbreak with cases reported in Somalia, Kenya and Ethiopia. By the notification of the last polio case in August 2014, 223 cases of WPV had been reported in Somalia, Kenya and Ethiopia of which 199 in Somalia alone. The outbreak response required timely exchange of information between the outbreak response coordination unit (in Nairobi) and local staff located in multiple locations inside the country. The need to track and timely respond to information requests, to satisfy the information/data needs of polio partners and to track key outbreak response performance indicators dictated the need to urgently set up an online dashboard. The Somalia Polio Room dashboard provided a graphical display of the polio outbreak data to track progress and inform decision making. The system was designed using free and open sources components and seamlessly integrated existing polio surveillance data for real time monitoring of key outbreak response performance indicators. In this article, we describe the design and operation of an electronic dashboard for disease surveillance in an outbreak situation and used the lessons learned to propose key design considerations and functional requirements for online electronic dashboards for disease outbreak response.