Högskolan i Skövde

Background: The global shift towards an ageing population and workforce shortages increases demands on health and social care systems. Proactive, personalized, and resource-efficient care models are therefore needed. Continuous monitoring using wearables, together with artificial intelligence (AI), offers potential for early detection of health deterioration and timely intervention. Still, existing solutions often target single diseases and lack integration into clinical workflows.

Objective: This article presents the study protocol for the project Predicting Care Needs of Older Adults in the Healthcare System through AI-Enabled Analysis of Patient Monitoring Data (PAI), which aims to develop an AI-driven predictive model based on data from wearables (smartwatches) to identify health decline among older persons in the near future. In addition, the project will evaluate the pilot implementation of this model within health and social care settings in three countries and generate recommendations for future adoption.

Methods: To answer the project aim, the AI model will be developed using historical datasets from wearables linked to emergency health care visits. To guide and evaluate the pilot implementation in Sweden, Norway, and Spain, staff from health and social care, managers, and IT professionals will be interviewed before implementation to explore current work practices and relevant contextual factors. Users of the smartwatch (older persons) will also be interviewed to explore their experiences, needs, and expectations. Follow-up interviews will be conducted after implementation to capture users’ and health and social care staff’s experiences of use. In addition, quantitative data on the AI model’s performance will be collected. The recommendations will be developed in an iterative process, including both researchers and stakeholders.

Expected Results: The project will identify key data requirements, deliver a predictive AI model, and develop evidence-based recommendations to support the responsible and scalable implementation of the proposed AI-solution in healthcare and social care settings.

Conclusions: By combining technical innovation with real-world implementation research, the PAI project seeks to advance the use of AI in care for older persons, support proactive and person-centered interventions, and contribute to more sustainable healthcare systems.

Plain Language summary

As people live longer, many older persons develop complex health conditions that require extended care. At the same time, health and social care services are facing growing pressure from staff shortages and increasing demand. Therefore, they need to act early to preserve older persons’ health while conditions can be managed at home. This project explores how artificial intelligence (AI) can assist by using information from smartwatches and other monitoring data to identify early signs of health decline. The aim is to predict when someone may be at risk of a serious health issue in the next days or weeks, giving health and social care staff the chance to act earlier and possibly prevent it. To achieve this, historical data will be used to build an AI model that later will be pilot tested in health and social care settings in Sweden, Norway, and Spain. It will assess whether this technology can improve care, reduce the strain on health services, and support older persons in living independently at home for longer.

This article aims to increase the body of knowledge on the practical use of business process modeling to support process redesign and organizational development. A qualitative case study inspired by Action Research has been performed in an SME. The data was deductively analyzed using an established project phase model aimed to support Enterprise Modeling activities. The main contribution is the detailed descriptions of the usage of business process modeling for process redesign, adding knowledge to the practice of business process modeling. In addition, this work also increases knowledge of aspects that support the Enterprise Modeling project phase model and provides suggestions for further development of the Enterprise Modeling project phase model. Other contributions highlight the critical issues of having a holistic perspective, including the three levels of inquiry, the need to include parts of later phases in the Enterprise Modeling project phase model in a pre-phase activity, and suggestions to extend the “what to avoid” list. The work also revealed that the list could be used for evaluation purposes. Furthermore, some advice to practitioners in the form of lessons learned has been formulated, e.g., the importance of establishing the problem owner and having the future state in focus instead of the current state.

Situation pictures are helpful to make sense of what is happening and to prevent further escalation. These situation pictures are typically text- or map-based and focus on the current effects of the crisis. For long-lasting transboundary crises that impact many critical infrastructures and different parts of society directly and indirectly, such situation pictures have limitations. Crisis management teams might benefit from continuous monitoring of societal performance indicators, so the current situation can easily be compared with historical and future data to reveal trends and escalations. This research project explored how a successful approach for systematic monitoring of indicators in crime prevention could be transferred to crisis management. Several pilot studies revealed nine challenging pitfalls and six promising possibilities. The findings of this study can inform future research on how continuous systematic situation monitoring can strengthen societal resilience.

Establishing business intelligence analytics (BIA) in small- and medium-sized manufacturing enterprises is a pervasive problem. SME’s - the majority of businesses - play an important role in creating jobs, but research is primarily focused on large corporations. The authors worked with small manufacturing companies at the aspirational capability level but found that their motivation to introduce BIA was low. They had many business challenges but perceived the obstacles (primarily cost and effort) as too great, and their priorities were with operational issues. A two-phase approach based on a well-known analytics maturity model was devised to help raise company motivation. The article describes three studies in different companies using variations of the approach. Comparative analysis of the cases shows that demonstrating a clear path to improved functional efficiency is key to improving motivation, and that simple, easy to learn tools can provide these insights at little cost.

This work concerns how to support Small and Medium sized Manufacturing Enterprises(SMMEs) with their Business Intelligence (BI) adoption, with the long term aim of supporting them in making better use of their BI investments and becoming (more)data-driven in their decision-making processes. Current BI research focuses primarily on larger enterprises, despite the fact that the majority of businesses are small or mediumsized. Therefore, this research focuses on the body of knowledge concerning how SMMEs can be more intelligent about their business, and better adopt BI to improve decision-making. Accordingly, the overall research aim is to create an artefact that can support SMMEs to facilitate BI adoption. An understanding of the current situation of BI adoption within SMMEs needs to be attained to achieve this, which is the focus for the first research question: What is the current state-of-practice in relation to BI adoption in SMMEs? The research question adds to current knowledge on how SMMEs are taking advantage of BI and highlights which functions within companies are currently supported by BI. Research question two identifies the main challenges that SMMEs are facing in this context: What are the main challenges for BI adoption in SMMEs? This question adds to knowledge regarding some of the barriers and hindrances SMMEs face in BI adoption. Finally, the third research question addresses how SMMEs can address the challenges in successfully adopting BI: How can the main challenges be addressed? The research question is answered by providing descriptions of work in four participating companies addressing different types of problems. Many of the challenges from literature (and from empirical data from the participating companies) regarding BI adoption are met. The outcome adds to the literature a hands-on approach for companies to address chosen problems in their settings, and addressing many of the factors previously found in the BI adoption literature. An action design research (ADR) method is used to fulfill the overall research aim. The ADR method is used to guide the development of a framework artefact based on previousliterature, and on empirical findings from working with participating companies. Theoretical background was obtained through a literature review of BI adoption and usage. Empirical material was gathered both through interviews and by reviewing documents from the companies. The work that was done in participating companies was supported by previous literature in several ways: through the use of an elicitation activity, through the core concepts of BI, and by focusing on categories presented in a BI maturity model. The principal contribution of the research is in the form of a framework: the Business Intelligence Facilitation Framework (BIFF), which includes four phases. All phases contain activities that support companies in addressing BI adoption challenges from the literature and empirical data, in order to achieve the overall research aim. This research contributes both to research and practice. From a research point of view, the framework provides a way to address many of the factors previously identified in literature that need to be in place to increase the likelihood of successful BI adoption. From a practice perspective, the framework supports practitioners offering guidance in how to improve their BI adoption, providing activities for them to take, and guidance in how to carry out the activities.

Helhetlig virksomhetsledelse, digitalisering og gjennomføring av endring er viktige kompetanse- og fokusområder i lederens hverdag. Grunnen til dette er at en ny virkelighet har vokst frem for organisasjoner og deres ledere i dagens turbulente forretningsverden. Den nye realiteten fordrer at organisasjoner ikke bare blir bedre på å drive forretningen, men også på å bli fremragende i å endre måten virksomheten drives. Storparten av stoffet i Kontinuerlig endringsarbeid handler om forutsetninger, verktøy og tiltak for økt kontroll med drift og endring. Materialet som presenteres er hentet fra litteratur og forfatternes egen forskning i praksisfeltet. Målgruppen for Kontinuerlig endringsarbeid er ledere og endringsagenter, men også fremtidige ledere som nå er studenter innen ulike ledelses-, teknologi- og organisasjonsfag. 

Current research lacks details on how SMMEs are able to capitalize on how their IT-solutions supports data-driven decision-making. Such details are important for being able to support further development of SMMEs and assuring their sustainability and competitive edge. Prosperous SMMEs are vital due to their economical and societal importance. To alleviate the lack of details, this paper presents the results of four case studies towards SMMEs partly aimed at investigating their current state of data-driven decision-making. The findings reveal that IT-solutions in some areas are either underdeveloped or unexplored. Instead, the SMMEs tend to focus on traditional manufacturing techniques, continuous improvements in the manufacturing process, and manual support routines and thereby neglects opportunities offered in relation to e.g. incident management, product quality monitoring, and the usage of KPIs not directly linked to manufacturing.

Business intelligence (BI) has become a well-known umbrella term both amongst academics and practitioners. Researchers have studied how companies can take advantage of BI and what challenges companies are facing when working with BI. However, research is mostly focused on large companies, despite the importance of small- and medium sized companies (SMEs) in both society and economically. This paper presents results of an in-depth qualitative case study on challenges faced by SMEs when adopting BI. The challenges are categorized according to a BI maturity model adopted as unit of assessment. The contribution of the results presented is two-folded; 1) It increases current literature regarding challenges when adopting BI in SMEs, and 2) It serves as guidance for SMEs on common pitfalls that ought to be avoided.

This paper aims at providing insights into the complex world of managing goals as part of change. The paper describes a digital prototype tool to support goal oriented improvement efforts towards company survival and growth. The prototype was developed based on the needs of practitioners in a SME construction company. Initial results indicates that a tool like the prototype can be helpful. This stimulates further research and development, and might inspire others to make and take advantage of IT solutions that go beyond traditional project scheduling to support change.