Big data management in healthcare: Adoption challenges and implications

Factors impacting the adoption of big data in healthcare: A systematic literature review

BACKGROUND

The term "big data" refers to the vast volume, variety, and velocity of data generated from various sources-e.g., sensors, social media, and online platforms. Big data adoption within healthcare poses an intriguing possibility for improving patients' health, increasing operational efficiency, and enabling data-driven decision-making. Despite considerable interest in the adoption of big data in healthcare, empirical research assessing the factors impacting the adoption process is lacking. Therefore, this review aimed to investigate the literature using a systematic approach to explore the factors that affect big data adoption in healthcare.

METHODS

A systematic literature review was conducted. The methodical and thorough process of discovering, assessing, and synthesizing relevant studies provided a full review of the available data. Several databases were used for the information search. Most of the articles retrieved from the search came from popular medical research databases, such as Scopus, Taylor & Francis, ScienceDirect, Emerald Insights, PubMed, Springer, IEEE, MDPI, Google Scholar, ProQuest Central, ProQuest Public Health Database, and MEDLINE.

RESULTS AND CONCLUSION

The results of the systematic literature review indicated that several theoretical frameworks (including the technology acceptance model; the technology, organization, and environment framework; the interactive communication technology adoption model; diffusion of innovation theory; dynamic capabilities theory; and the absorptive capability framework) can be used to analyze and understand technology acceptance in healthcare. It is vital to consider the safety of electronic health records during the use of big data. Furthermore, several elements were found to determine technological acceptance, including environmental, technological, organizational, political, and regulatory factors.

Examining the Effect of Entrepreneurial Leadership on Nursing Team Creativity in New Hospitals: A Structural Equation Model

METHODS

This cross-sectional study sampled 833 nurses from 2 new hospitals in Guizhou Province, China. They completed a questionnaire on entrepreneurial leadership, nursing team creativity, innovation climate, creative self-efficacy, team psychological safety, and knowledge sharing. Data were analyzed using structural equation modeling.

RESULTS

Entrepreneurial leadership positively influenced nursing team creativity. Innovation climate, creative self-efficacy, team psychological safety, and knowledge sharing mediated the relationship between entrepreneurial leadership and nursing team creativity in new hospitals.

CONCLUSIONS

This study confirmed the significant role of innovation climate, creative self-efficacy, team psychological safety, and knowledge sharing in mediating the relationship between entrepreneurial leadership and nursing team creativity through empirical analysis.

Dilemmas and prospects of artificial intelligence technology in the data management of medical informatization in China: A new perspective on SPRAY-type AI applications

Objectives: This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data within the healthcare sector, particularly within the context of Chinese medical information data management. The research seeks to propose a solution in the form of a medical metadata governance framework that is efficient and suitable for clinical research and transformation. Methods: The article begins by outlining the background of medical information data management and reviews the advancements in artificial intelligence (AI) technology relevant to the field. It then introduces the "Service, Patient, Regression, base/Away, Yeast" (SPRAY)-type AI application as a case study to illustrate the potential of AI in EMR data management. Results: The research identifies the scarcity of scientific research on the transformation of EMR data in Chinese hospitals and proposes a medical metadata governance framework as a solution. This framework is designed to achieve scientific governance of clinical data by integrating metadata management and master data management, grounded in clinical practices, medical disciplines, and scientific exploration. Furthermore, it incorporates an information privacy security architecture to ensure data protection. Conclusion: The proposed medical metadata governance framework, supported by AI technology, offers a structured approach to managing and transforming EMR data into valuable scientific research outcomes. This framework provides guidance for the identification, cleaning, mining, and deep application of EMR data, thereby addressing the bottlenecks currently faced in the healthcare scenario and paving the way for more effective clinical research and data-driven decision-making.

Embedded Sensor Systems in Medical Devices: Requisites and Challenges Ahead

The evolution of technology enables the design of smarter medical devices. Embedded Sensor Systems play an important role, both in monitoring and diagnostic devices for healthcare. The design and development of Embedded Sensor Systems for medical devices are subjected to standards and regulations that will depend on the intended use of the device as well as the used technology. This article summarizes the challenges to be faced when designing Embedded Sensor Systems for the medical sector. With this aim, it presents the innovation context of the sector, the stages of new medical device development, the technological components that make up an Embedded Sensor System and the regulatory framework that applies to it. Finally, this article highlights the need to define new medical product design and development methodologies that help companies to successfully introduce new technologies in medical devices.

Implementation of Health Information Systems to Improve Patient Identification

Wellbeing can be ensured in society through quality healthcare, a minimum of medical errors, and the improved performance of healthcare professionals. To this end, health information systems have been implemented in hospitals, with this implementation representing progress in medicine and information technologies. As a result, life expectancy has significantly increased, standards in healthcare have been raised, and public health has improved. This progress is influenced by the process of managing healthcare organizations and information systems. While hospitals tend to adapt health information systems to reduce errors related to patient misidentification, the rise in the occurrence and recording of medical errors in Lebanon resulting from failures to correctly identify patients reveals that such measures remain insufficient due to unknown factors. This research aimed to investigate the effect of health information systems (HISs) and other factors related to work-related conditions on reductions in patient misidentification and related consequences. The empirical data were collected from 109 employees in Neioumazloum Hospital in Lebanon. The results revealed a correlation between HISs and components and the effects of other factors on patient identification. These other factors included workload, nurse fatigue, a culture of patient safety, and lack of implementation of patient identification policies. This paper provides evidence from a Lebanese hospital and paves the way for further studies aiming to explore the role of information technologies in adopting HISs for work performance and patient satisfaction. Improved care for patients can help achieve health equality, enhance healthcare delivery performance and patient safety, and decrease the numbers of medical errors.

National IoMT platform strategy portfolio decision model under the COVID-19 environment: based on the financial and non-financial value view

The Internet of Medical Things (IoMT) is an emerging technology in the healthcare revolution which provides real-time healthcare information communication and reasonable medical resource allocation. The COVID-19 pandemic has had a significant effect on people's lives and has affected healthcare capacities. It is important for integrated IoMT platform development to overcome the global pandemic challenges. This study proposed the national IoMT platform strategy portfolio decision-making model from the non-financial (technology, organization, environment) and financial perspectives. As a solution to the decision problem, initially, the decision-making trial and evaluation laboratory (DEMATEL) technology were employed to capture the cause-effect relationship based on the perspectives and criteria obtained from the insight of an expert team. The analytic network process (ANP) and pairwise comparisons were then used to determine the weights for the strategy. Simultaneously, this study incorporated IoMT platform resource limitations into the zero-one goal programming (ZOGP) method to obtain an optimal portfolio selection for IoMT platform strategy planning. The results showed that the integrated MCDM method produced reasonable results for selecting the most appropriate IoMT platform strategy portfolio when considering resource constraints such as system installation costs, consultant fees, infrastructure costs, reduction of medical staff demand, and improvement rates for diagnosis efficiency. The decision-making model of the IoMT platform in this study was conclusive and significantly compelling to aid government decision makers in concentrating their efforts on planning IoMT strategies in response to various pandemic and medical resource allocations.

Government regulation and organizational effectiveness in the health-care supply chain

Purpose

In the context of the debate on ensuring health-care efficiency, this study aims to identify and prioritize factors and subfactors that influence organizational effectiveness (OE) in the health-care supply chain.

Design/methodology/approach

For the purpose of this qualitative study, triangulation was applied to identify, explore and systematically analyze the OE-related practices used by diverse stakeholders along the health-care supply chain. Sixty-two OE practices were thus identified. Subsequently, these were grouped in six different nodes before the analytical hierarchical process (AHP) was used to identify the weightings of specific practices (and related factors) and their impact on OE.

Findings

The findings suggest that external factors associated with government regulation, including government directives and branding, are the most critical factors that influence OE-related practices, while cost-related factors are the least important.

Originality/value

The originality of this study derives from the introduction of system theory supported by a modified supplier-input-process-output-customer (SIPOC) framework. Two important factors – government regulation and branding – have been introduced to the existing SIPOC chart as a valuable process structure for the health-care chain.

Digital Twins for Managing Health Care Systems: Rapid Literature Review

BACKGROUND

Although most digital twin (DT) applications for health care have emerged in precision medicine, DTs can potentially support the overall health care process. DTs (twinned systems, processes, and products) can be used to optimize flows, improve performance, improve health outcomes, and improve the experiences of patients, doctors, and other stakeholders with minimal risk.

OBJECTIVE

This paper aims to review applications of DT systems, products, and processes as well as analyze the potential of these applications for improving health care management and the challenges associated with this emerging technology.

METHODS

We performed a rapid review of the literature and reported available studies on DTs and their applications in health care management. We searched 5 databases for studies published between January 2002 and January 2022 and included peer-reviewed studies written in English. We excluded studies reporting DT usage to support health care practice (organ transplant, precision medicine, etc). Studies were analyzed based on their contribution toward DT technology to improve user experience in health care from human factors and systems engineering perspectives, accounting for the type of impact (product, process, or performance/system level). Challenges related to the adoption of DTs were also summarized.

RESULTS

The DT-related studies aimed at managing health care systems have been growing over time from 0 studies in 2002 to 17 in 2022, with 7 published in 2021 (N=17 studies). The findings reported on applications categorized by DT type (system: n=8; process: n=5; product: n=4) and their contributions or functions. We identified 4 main functions of DTs in health care management including safety management (n=3), information management (n=2), health management and well-being promotion (n=3), and operational control (n=9). DTs used in health care systems management have the potential to avoid unintended or unexpected harm to people during the provision of health care processes. They also can help identify crisis-related threats to a system and control the impacts. In addition, DTs ensure privacy, security, and real-time information access to all stakeholders. Furthermore, they are beneficial in empowering self-care abilities by enabling health management practices and providing high system efficiency levels by ensuring that health care facilities run smoothly and offer high-quality care to every patient.

CONCLUSIONS

The use of DTs for health care systems management is an emerging topic. This can be seen in the limited literature supporting this technology. However, DTs are increasingly being used to ensure patient safety and well-being in an organized system. Thus, further studies aiming to address the challenges of health care systems challenges and improve their performance should investigate the potential of DT technology. In addition, such technologies should embed human factors and ergonomics principles to ensure better design and more successful impact on patient and doctor experiences.

Green service production and environmental performance in healthcare emergencies: role of big-data management and green HRM practices

Purpose

This study aims to examine the impact of big data management on green service production (GSP) and environmental performance (ENPr) while considering green HRM practices (GHRM) in healthcare emergencies.

Design/methodology/approach

The authors collected primary data from major healthcare organizations in India by surveying healthcare professionals. The data analysis through structural equation modelling (PLS-SEM) reveals several significant relationships to extricate the underlying dynamics.

Findings

Grounded in the theories of service production and natural resource-based view (NRBV), this study conceptualizes GSP with its three dimensions of green procurement (GP), green service design (GSD) and green service practices (GSPr). The study conducted in India's healthcare sector with a sample size limited to healthcare professionals serving in COVID-19 identifies the positive and significant impact of big data management on GSP and ENPr that organizations seek to deploy in such emergencies. The findings of the study explain the moderating effects of GHRM on GSP-ENPr relationships.

Research limitations/implications

The study was conducted in the healthcare sector in India, and its sample size was limited to healthcare professionals serving in COVID-19. The practical ramifications for healthcare administrators and policymakers are suggested, and future avenues of research are discussed.

Originality/value

This paper develops a holistic model of big data analytics, GP, GSD, GSPr, GHRM and ENPr. This study is a first step in investigating how big data management contributes to ENPr in an emergency and establishing the facets of GSP as a missing link in this relationship, which is currently void in the literature. This study contributes to the theory and fills the knowledge gap in this area.

Information Technology Ambidexterity, Digital Dynamic Capability, and Knowledge Processes as Enablers of Patient Agility: Empirical Study

BACKGROUND

There is a limited understanding of information technology's (IT) role as an enabler of patient agility and the department's ability to respond to patients' needs and wishes adequately.

OBJECTIVE

This study aims to contribute to the insights of the validity of the hypothesized relationship among IT resources, practices and capabilities, and hospital departments' knowledge processes, and the department's ability to adequately sense and respond to patient needs and wishes (ie, patient agility).

METHODS

This study conveniently sampled data from 107 clinical hospital departments in the Netherlands and used structural equation modeling for model assessment.

RESULTS

IT ambidexterity positively enhanced the development of a digital dynamic capability (β=.69; t4999=13.43; P<.001). Likewise, IT ambidexterity also positively impacted the hospital department's knowledge processes (β=.32; t4999=2.85; P=.005). Both digital dynamic capability (β=.36; t4999=3.95; P<.001) and knowledge processes positively influenced patient agility (β=.33; t4999=3.23; P=.001).

CONCLUSIONS

IT ambidexterity promotes taking advantage of IT resources and experiments to reshape patient services and enhance patient agility.

Comprehension, Perception, and Projection

Health social networks (HSNs) allow individuals with health information needs to connect and discuss health-related issues online. Political-technology intertwinement (e.g. GDPR and Digital Technology) highlights that users need to be aware, understand, and willing to provide electronic consent (eConsent) when sharing personal information online. The objective of this study is to explore the ‘As-Is’ factors which impact individuals’ decisional autonomy when consenting to the privacy policy (PP) and Terms and Conditions (T&Cs) on a HSN. We use a Situational Awareness (SA) lens to examine decision autonomy when providing eConsent. A mixed-methods approach reveals that technical and privacy comprehension, user perceptions, and projection of future consequences impact participants’ decision autonomy in providing eConsent. Without dealing with the privacy paradox at the outset, decision awareness and latterly decision satisfaction is negatively impacted. Movement away from clickwrap online consent to customised two-way engagement is the way forward for the design of eConsent.

Multiple additive regression trees with hybrid loss for classification tasks across heterogeneous clinical data in distributed environments: a case study

Multiple additive regression trees (MART) have been widely used in the literature for various classification tasks. However, the overfitting effects of MART across heterogeneous and highly imbalanced big data structures within distributed environments has not yet been investigated. In this work, we utilize distributed MART with hybrid loss to resolve overfitting effects during the training of disease classification models in a case study with 10 heterogeneous and distributed clinical datasets. Lexical and semantic analysis methods were utilized to match heterogeneous terminologies with 80% overlap. Data augmentation was used to resolve class imbalance yielding virtual data with goodness of fit 0.01 and correlation difference 0.02. Our results highlight the favorable performance of the proposed distributed MART on the augmented data with an average increase by 7.3% in the accuracy, 6.8% in sensitivity, 10.4% in specificity, for a specific loss function topology.

A structural equation model for big data adoption in the healthcare supply chain

Purpose

The purpose of this study is to examine the barriers to the implementation of big data (BD) in the healthcare supply chain (HSC).

Design/methodology/approach

First, the barriers concerning BD adoption in the HSC were found by conducting a detailed literature survey and with the expert's opinion. Then the exploratory factor analysis (EFA) was employed to categorize the barriers. The obtained results are verified using the confirmatory factor analysis (CFA). Structural equation modeling (SEM) analysis gives the path diagram representing the interrelationship between latent variables and observed variables.

Findings

The segregation of 13 barriers into three categories, namely “data governance perspective,” “technological and expertise perspective,” and “organizational and social perspective,” is performed using EFA. Three hypotheses are tested, and all are accepted. It can be concluded that the “data governance perspective” is positively related to “technological and expertise perspective” and “organizational and social perspective” factors. Also, the “technological and expertise perspective” is positively related to “organizational and social perspective.”

Research limitations/implications

In literature, very few studies have been performed on finding the barriers to BD adoption in the HSC. The systematic methodology and statistical verification applied in this study empowers the healthcare organizations and policymakers in further decision-making.

Originality/value

This paper is first of its kind to adopt an approach to classify barriers to BD implementation in the HSC into three distinct perspectives.

Towards the Representation of Network Assets in Health Care Environments Using Ontologies

Objectives  The aim of the study is to design an ontology model for the representation of assets and its features in distributed health care environments. Allow the interchange of information about these assets through the use of specific vocabularies based on the use of ontologies.

Methods  Ontologies are a formal way to represent knowledge by means of triples composed of a subject, a predicate, and an object. Given the sensitivity of network assets in health care institutions, this work by using an ontology-based representation of information complies with the FAIR principles. Federated queries to the ontology systems, allow users to obtain data from multiple sources (i.e., several hospitals belonging to the same public body). Therefore, this representation makes it possible for network administrators in health care institutions to have a clear understanding of possible threats that may emerge in the network.

Results  As a result of this work, the “Software Defined Networking Description Language—CUREX Asset Discovery Tool Ontology” (SDNDL-CAO) has been developed. This ontology uses the main concepts in network assets to represent the knowledge extracted from the distributed health care environments: interface, device, port, service, etc.

Conclusion  The developed SDNDL-CAO ontology allows to represent the aforementioned knowledge about the distributed health care environments. Network administrators of these institutions will benefit as they will be able to monitor emerging threats in real-time, something critical when managing personal medical information.

Facilities technology management framework for public health-care institutions in a developing country

Purpose

This study aims to develop a Facilities technology management framework for public health-care institutions in a developing country.

Design/methodology/approach

The study used descriptive research design to identify the specifications of the framework via strategic initiatives anchored on efficiency, sustainability, ecological-friendliness and technological innovation. These measures are wrapped into a facilities TM framework which incorporates concepts and practices on risk management, facility management (FM) and TM.

Findings

Results of the survey of the public HCIs in the Philippines, show high levels of acceptability of proposed measures which identify the technologies, innovations and materials which are in the viable context of public hospital circumstances in the country.

Research limitations/implications

The findings of this study are limited to the public HCIs in a developing country, and thus cannot be generalized to other HCIs particularly the private institutions.

Practical implications

The framework seeks to help improve the operational efficiency and sustainability of public HCIs in a developing country like the Philippines. The discussions on TM revolve around the application of TM approaches. Also, the study incorporates discussions on sustainability, technology innovation and the conformity of these with HCI standards, best practices and government requirements.

Social implications

The study takes into consideration the identification of FM principles and practices that are deemed suitable and applicable for public HCIs in a developing country. This study is intended to develop a TM framework for FM services which is cost-effective but not sacrificing safety, security, employees and the environment. Then the foremost consideration is the perceived suitability of the framework in the public HCI environment.

Originality/value

This is an original study. It has as its scope the fusion of FM and TM approaches that would help in the identification of challenges, requirements for manpower, processes and technologies (especially, information and communications technolog-based technologies), and a corresponding TM system framework for public HCI facilities in a developing country.

A Deep Learning Approach to Predict Diabetes' Cardiovascular Complications From Administrative Claims

People with diabetes require lifelong access to healthcare services to delay the onset of complications. Their disease management processes generate great volumes of data across several domains, from clinical to administrative. Difficulties in accessing and processing these data hinder their secondary use in an institutional setting, even for highly desirable applications, such as the prediction of cardiovascular disease, the main driver of excess mortality in diabetes. Hence, in the present work, we propose a deep learning model for the prediction of major adverse cardiovascular events (MACE), developed and validated using the administrative claims of 214,676 diabetic patients of the Veneto region, in North East Italy. Specifically, we use a year of pharmacy and hospitalisation claims, together with basic patient's information, to predict the 4P-MACE composite endpoint, i.e., the first occurrence of death, heart failure, myocardial infarction, or stroke, with a variable prediction horizon of 1 to 5 years. Adapting to the time-to-event nature of this task, we cast our problem as a multi-outcome (4P-MACE and components), multi-label (1 to 5 years) classification task with a custom loss to account for the effect of censoring. Our model, purposefully specified to minimise data preparation costs, exhibits satisfactory performance in predicting 4P-MACE at all prediction horizons: AUROC from 0.812 (C.I.: 0.797 - 0.827) to 0.792 (C.I.: 0.781 - 0.802); C-index from 0.802 (C.I.: 0.788 - 0.816) to 0.770 (C.I.: 0.761 - 0.779). Components' prediction performance is also adequate, ranging from death's 0.877 1-year AUROC to stroke's 0.689 5-year AUROC.

Clouds Proportionate Medical Data Stream Analytics for Internet of Things-based Healthcare Systems

Internet of Things (IoT) assisted healthcare systems are designed for providing ubiquitous access and recommendations for personal and distributed electronic health services. The heterogeneous IoT platform assists healthcare services with reliable data management through dedicated computing devices. Healthcare services' reliability depends upon the efficient handling of heterogeneous data streams due to variations and errors. A Proportionate Data Analytics (PDA) for heterogeneous healthcare data stream processing is introduced in this manuscript. This analytics method differentiates the data streams based on variations and errors for satisfying the service responses. The classification is streamlined using linear regression for segregating errors from the variations in different time intervals. The time intervals are differentiated recurrently after detecting errors in the stream's variation. This process of differentiation and classification retains a high response ratio for healthcare services through spontaneous regressions. The proposed method's performance is analyzed using the metrics accuracy, identification ratio, delivery, variation factor, and processing time.

Understanding providers' attitudes and key concerns toward incorporating CVD risk prediction into clinical practice: a qualitative study

Background

Although risk prediction has become an integral part of clinical practice guidelines for cardiovascular disease (CVD) prevention, multiple studies have shown that patients’ risk still plays almost no role in clinical decision-making. Because little is known about why this is so, we sought to understand providers’ views on the opportunities, barriers, and facilitators of incorporating risk prediction to guide their use of cardiovascular preventive medicines.

Methods

We conducted semi-structured interviews with primary care providers (n = 33) at VA facilities in the Midwest. Facilities were chosen using a maximum variation approach according to their geography, size, proportion of MD to non-MD providers, and percentage of full-time providers. Providers included MD/DO physicians, physician assistants, nurse practitioners, and clinical pharmacists. Providers were asked about their reaction to a hypothetical situation in which the VA would introduce a risk prediction-based approach to CVD treatment. We conducted matrix and content analysis to identify providers’ reactions to risk prediction, reasons for their reaction, and exemplar quotes.

Results

Most providers were classified as Enthusiastic (n = 14) or Cautious Adopters (n = 15), with only a few Non-Adopters (n = 4). Providers described four key concerns toward adopting risk prediction. Their primary concern was that risk prediction is not always compatible with a “whole patient” approach to patient care. Other concerns included questions about the validity of the proposed risk prediction model, potential workflow burdens, and whether risk prediction adds value to existing clinical practice. Enthusiastic, Cautious, and Non-Adopters all expressed both doubts about and support for risk prediction categorizable in the above four key areas of concern.

Conclusions

Providers were generally supportive of adopting risk prediction into CVD prevention, but many had misgivings, which included concerns about impact on workflow, validity of predictive models, the value of making this change, and possible negative effects on providers’ ability to address the whole patient. These concerns have likely contributed to the slow introduction of risk prediction into clinical practice. These concerns will need to be addressed for risk prediction, and other approaches relying on “big data” including machine learning and artificial intelligence, to have a meaningful role in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06540-y.

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications.

Could amantadine interfere with COVID-19 vaccines based on the LNP-mRNA platform?

INTRODUCTION

Amantadine is a well-known medication with indications in neurology and infectious diseases. It is currently FDA approved for Parkinson's disease, drug-induced extrapyramidal symptoms, and influenza.

METHODS

The article is the author's original research hypothesis.

RESULTS

Because more people are going to be vaccinated and additional similar vaccines are going to be introduced, we should take into consideration the potential of amantadine to interfere with LNP-mRNA COVID-19 vaccine delivery into the target cells.

CONCLUSIONS

A more cautious approach to the patients taking amantadine as far as vaccination utilizing LNP-mRNA platform should be considered.

Overcoming Resistance to Big Data and Operational Changes Through Interactive Data Visualization

Research has shown that the use of big data can modify operational processes in organizations. However, little research has been conducted on overcoming resistance to the process changes needed for adoption of big data technologies. In this article, we address this gap in the literature by investigating the impact of interactive data visualization on decision-making around operational process changes with big data. Our goal is to demonstrate how the choice of visualization of workflow and operational processes impacts decisions to embrace real-time, big data technology. To do so, we conduct a case study of patient/provider interactions in a large health care practice and compare the initial state with a revised workflow using a big data, real-time analytics platform. We then investigate the impact of the data visualization strategy on decision-making to implement operational changes caused by big data. The study demonstrates that interactive data visualization of operational processes can be an enabler in overcoming organizational resistance to big data technologies in a change-resistant organization. The concomitant benefit is that big data analytics is placed directly into the hands of primary decision makers.

Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy

The objective of this article is to review how Artificial Intelligence (AI) tools have helped the process of formulating agricultural public policies in the world. For this, a search process was carried out in the main scientific repositories finding different publications. The findings have shown that, first, the most commonly used AI tools are agent-based models, cellular automata, and genetic algorithms. Secondly, they have been utilized to determine land and water use, and agricultural production. In the end, the large usefulness that AI tools have in the process of formulating agricultural public policies is concluded.

Implementing a digital patient feedback system: an analysis using normalisation process theory

BACKGROUND

Patient feedback in the English NHS is now widespread and digital methods are increasingly used. Adoption of digital methods depends on socio-technical and contextual factors, alongside human agency and lived experience. Moreover, the introduction of these methods may be perceived as disruptive of organisational and clinical routines. The focus of this paper is on the implementation of a particular digital feedback intervention that was co-designed with health professionals and patients (the DEPEND study).

METHODS

The digital feedback intervention was conceptualised as a complex intervention and thus the study focused on the contexts within which it operated, and how the different participants made sense of the intervention and engaged with it (or not). Four health care sites were studied: an acute setting, a mental health setting, and two general practices. Qualitative data was collected through interviews and focus groups with professionals, patients and carers. In total 51 staff, 24 patients and 8 carers were included. Forty-two observations of the use of the digital feedback system were carried out in the four settings. Data analysis was based on modified grounded theory and Normalisation Process Theory (NPT) formed the conceptual framework.

RESULTS

Digital feedback made sense to health care staff as it was seen as attractive, fast to complete and easier to analyse. Patients had a range of views depending on their familiarity with the digital world. Patients mentioned barriers such as kiosk not being visible, privacy, lack of digital know-how, technical hitches with the touchscreen. Collective action in maintaining participation again differed between sites because of workload pressure, perceptions of roles and responsibilities; and in the mental health site major organisational change was taking place. For mental health service users, their relationship with staff and their own health status determined their digital use.

CONCLUSION

The potential of digital feedback was recognised but implementation should take local contexts, different patient groups and organisational leadership into account. Patient involvement in change and adaptation of the intervention was important in enhancing the embedding of digital methods in routine feedback. NPT allowed for a in-depth understanding of actions and interactions of both staff and patients.