Methodologies for designing healthcare analytics solutions: a literature analysis

© The Author(s) 2019. Healthcare analytics has been a rapidly emerging research domain in recent years. In general, healthcare solution design studies focus on developing analytic solutions that enhance product, process and practice values for clinical and non-clinical decision support. The objective of this study is to explore the scope of healthcare analytics research and in particular its utilisation of design and development methodologies. Using six prominent electronic databases, qualifying articles between 2010 and mid-2018 were sourced and categorised. A total of 52 articles on healthcare analytics solutions were selected for relevant content on public healthcare. The research team scrutinised the articles, using established content analysis protocols. Analysis identified that various methodologies have been used for developing analytics solutions, such as prototyping, traditional software engineering, agile approaches and others, but despite its clear advantages, few show the use of design science. Key topic areas are also identified throughout the content analysis suggesting topical research priorities in the field

Emerging Healthcare Innovations in Eastern Europe: Trends and Prospects

Eastern Europe has been undergoing significant changes in healthcare over the past few years. Many countries in the region have been investing in healthcare innovation, which has led to the development of new technologies, treatments, and healthcare systems. In this response, this study examines the emerging healthcare innovations in Eastern Europe, focusing on the trends and prospects for the region. The study analyzed the current state of the healthcare industry in Eastern Europe and identified five key areas of innovation: telemedicine and digital health, artificial intelligence and big data, personalized medicine, medical devices and robotics, and health IT infrastructure. The study found that many startups in the region are developing innovative solutions to some of the biggest challenges facing the healthcare industry. Telemedicine has become increasingly popular, particularly in countries with large rural populations. Digital health solutions, including mobile apps and wearable devices, are also gaining traction. The use of AI and big data in healthcare is growing rapidly, with startups using these technologies to develop new diagnostic tools, improve patient outcomes, and reduce healthcare costs. Personalized medicine, medical devices, and robotics are also gaining popularity in Eastern Europe. Finally, there is a growing focus on improving healthcare IT infrastructure in the region. The study concludes that the healthcare industry in Eastern Europe is experiencing rapid innovation and growth, with a strong focus on research and development, and a growing ecosystem of startups. The region is poised to continue driving healthcare innovation for years to come

Collaborative systems for telemedicine diagnosis accuracy

The transmission of medical data and the possibility for distant healthcare structures to share experiments about a given medical case raises several conceptual and technical questions. Good remote healthcare monitoring deals with more problems in personalized heath data processing compared to the traditional methods nowadays used in several parts of hospitals in the world. The adoption of telemedicine in the healthcare sector has significantly changed medical collaboration. However, to provide good telemedicine services through new technologies such as cloud computing, cloud storage, and so on, a suitable and adaptable framework should be designed. Moreover, in the chain of medical information exchange, between requesting agencies, including physicians, a secure and collaborative platform enhanced the decision-making process. This paper provides an in-depth literature review on the interaction that telemedicine has with cloud-based computing. On the other hand, the paper proposes a framework that can allow various research organizations, healthcare sectors, and government agencies to log data, develop collaborative analysis, and support decision-making. The electrocardiogram (ECG) and electroencephalogram EEG case studies demonstrate the benefit of the proposed approach in data reduction and high-fidelity signal processing to a local level; this can make possible the extracted characteristic features to be communicated to the cloud database

An investigation into usability of big data analytics in the management of Type 2 Diabetes Mellitus

The global prevalence of Type 2 Diabetes Mellitus (T2DM) has been on the rise over the last four decades and is expected to rise further in the future. Big Data applications such as Artificial Intelligence (AI) and Machine learning (ML) are increasingly being used in the healthcare industry to manage various aspects of patient care. Researchers have so far studied the adoption of technologies including AI and ML in various contexts using technology adoption frameworks in the information systems (IS) domain, where the usability of technology is just viewed as one factor. Although, researches on technology adoption models in the IS domain has indicated that usability has a significant influence on the adoption of a technology, it appears that there are limited attempts made to study the factors influencing the usability of big data applications such as AI and ML for the management of T2DM. Since usability not only a factor that impacts the adoption of a technology, but also determines the outcomes of the management process, there is a need to understand the factors that influence the usability of a big data analytics application for the management of T2DM, this research aims to identify and analyse the factors influencing the usability of big data applications such as AI and ML in management of T2DM. The research is designed as mixed method research with qualitative research undertaken first to confirm the conceptualised research model followed by quantitative research to genaralise the model. This research would contribute to the academic literature in the areas of Information Systems Quality, Human-Computer Interaction (HCI), design and development big data applications, usability engineering, user experience (UX), and usability measurement model. The contributions from this research would also benefit the healthcare industry, predominantly that part of an industry that is directly involved in the management of T2DM and indirectly involved in the management of comorbidities on T2DM. The learnings from this research can also be extended to the management of many other chronic conditions and many other contexts

Predictive analysis in healthcare

The Emergency departments (ED) are the major entry point to the healthcare system. With the growing demand due to the increase of life expectancy and the greater number of diseases, it is mandatory for the ED’s to have a more efficient resource management in order to try and provide the best experience possible to its patients. If the resource demand is greater than the resources available, then ED crowding occurs. This phenomenon leads to several problems that affect the patient experience, like longer waiting times, lack of beds, patients in hallways, etc. One of the ways to improve patient satisfaction is through patient waiting time prediction, since it would allow for a better resource management in the ED and providing patients with a waiting time estimation on the triage increases patient satisfaction. The author collaborated with a Portuguese hospital near Lisbon using real ED data and built a prototype to predict the ED waiting time. The researcher complemented the ED original dataset with external data like weather information, DGS Announcements and number of football games, to try to find the most accurate model. To perform the prediction, the Naïve Bayes (NB) and Random Forest (RF) algorithms were applied in three different scenarios: the first one only with data from the original dataset, the second one where the number of football games and DGS announcements attributes were added and finally, a third one with the same dataset as the previous scenario but added weather information (temperature, wind, humidity and precipitation). The RF algorithm was the one with the best performance, especially in the third scenario. For this reason, the author used the RF algorithm with the variable inputs from the third scenario to perform the predictions on the prototype. The author concluded that the external data attributes added in both second and third scenarios were not the most important attributes for the waiting times, being the most important variables, the triage colors, disease category.As urgências dos hospitais são o maior ponto de entrada para o sistema de saúde. Com o aumento da esperança média de vida e o aumento do número de doenças, aumentou a necessidade e a procura dos serviços de saúde, levando a que seja importante que as urgências dos hospitais consigam fazer uma gestão eficiente dos seus recursos de forma a proporcionar a melhor experiência possível aos seus utentes. Se a procura por recursos nas urgências dos hospitais for superior aos recursos disponíveis, ocorre um fenómeno de concentração excessiva de pessoas nas urgências, o que pode causar vários problemas como por exemplo tempos de espera mais longos, falta de camas, utentes nos corredores, o que acaba por afetar a satisfação dos utentes. Uma forma de aumentar a satisfação dos utentes é através da previsão do tempo de espera nas urgências do hospital, visto que ajuda a administração do hospital a fazer uma melhor gestão dos recursos disponíveis e oferecer uma previsão do tempo de espera aos utentes leva a maior satisfação. O autor desenvolveu em conjunto com um hospital Português perto de Lisboa, usando dados reais, um protótipo que permite fazer a previsão do tempo de espera nas urgências do hospital. Para complementar os dados providenciados pelo hospital, o autor adicionou alguns atributos como informação do estado meteorológico por dia (temperatura, humidade, precipitação e vento), anúncios da Direção-Geral de Saúde (DGS) e o número de jogos de futebol das duas principais equipas de Lisboa (Sporting CP e SL Benfica) por dia. O autor aplicou os algoritmos Naive Bayes e Random Forest em três cenários diferentes: o primeiro em que apenas se utilizam os dados originais providenciados pelo hospital, o segundo em que se adicionam os atributos dos anúncios da DGS e o número de jogos de futebol e o terceiro em que para além dos atributos do cenário anterior, se adicionou os atributos relativos ao estado meteorológico do dia mencionados anteriormente. O algoritmo com melhor performance foi o Random Forest, principalmente no terceiro cenário, fator que levou a que este tenha sido o modelo escolhido para ser utilizado no protótipo. Depois de fazer as previsões do tempo de espera e analisar os resultados, podese concluir que para além do algoritmo Random Forest apresentar melhores resultados para a previsão do tempo de espera nas urgências, tendo em conta o tipo de dados fornecido, os atributos externos adicionados posteriormente e que não pertenciam ao conjunto de dados original providenciado pelo hospital, não são dos atributos que mais afetam os tempos de espera, sendo que os atributos que têm mais importância para os tempos de espera das urgências são a cor de triagem e a categoria da doença

Proactive IT Incident Prevention: Using Data Analytics to Reduce Service Interruptions

The cost of resolving user requests for IT assistance rises annually. Researchers have demonstrated that data warehouse analytic techniques can improve service, but they have not established the benefit of using global organizational data to reduce reported IT incidents. The purpose of this quantitative, quasi-experimental study was to examine the extent to which IT staff use of organizational knowledge generated from data warehouse analytical measures reduces the number of IT incidents over a 30-day period, as reported by global users of IT within an international pharmaceutical company headquartered in Germany. Organizational learning theory was used to approach the theorized relationship between organizational knowledge and user calls received. Archival data from an internal help desk ticketing system was the source of data, with access provided by the organization under study. The population for this study was all calls logged and linked to application systems registered in a configuration database, and the sample was the top 14 application systems with the highest call volume that were under the control of infrastructure management. Based on an analysis of the data using a split-plot ANOVA (SPANOVA) of Time 1, Time 2, treatment, and nontreatment data, there was a small reduction in calls in the number of reported IT incidents in the treatment group, though the reduction was not statistically significant. Implications for positive social change include reassigning employees to other tasks, rather than continuing efforts in this area, enabling employees to support alternative initiatives to drive the development of innovative therapies benefiting patients and improving employee satisfaction

Exploring Digital Government transformation in the EU

This report presents the findings of the analysis of the state of the art conducted as part of the JRC research on “Exploring Digital Government Transformation in the EU: understanding public sector innovation in a data-driven society” (DIGIGOV), within the framework of the “European Location Interoperability Solutions for eGovernment (ELISE)" Action of the ISA2 Programme on Interoperability solutions for public administrations, businesses and citizens, coordinated by DIGIT. The results of the review of literature, based on almost 500 academic and grey literature sources, as well as the analysis of digital government policies in the EU Member States provide a synthetic overview of the main themes and topics of the digital government discourse. The report depicts the variety of existing conceptualisations and definitions of the digital government phenomenon, measured and expected effects of the application of more disruptive innovations and emerging technologies in government, as well as key drivers and barriers for transforming the public sector. Overall, the literature review shows that many sources appear overly optimistic with regard to the impact of digital government transformation, although the majority of them are based on normative views or expectations, rather than empirically tested insights. The authors therefore caution that digital government transformation should be researched empirically and with a due differentiation between evidence and hope. In this respect, the report paves the way to in-depth analysis of the effects that can be generated by digital innovation in public sector organisations. A digital transformation that implies the redesign of the tools and methods used in the machinery of government will require in fact a significant change in the institutional frameworks that regulate and help coordinate the governance systems in which such changing processes are implemented.JRC.B.6-Digital Econom