Machine learning-driven clinical decision support systems (ML-CDSSs) seem impressively promising for future routine and emergency care. However, reflection on their clinical implementation reveals a wide array of ethical challenges. The preferences, concerns and expectations of professional stakeholders remain largely unexplored. Empirical research, however, may help to clarify the conceptual debate and its aspects in terms of their relevance for clinical practice. This study explores, from an ethical point of view, future healthcare professionals’ attitudes to (...) potential changes of responsibility and decision-making authority when using ML-CDSS. Twenty-seven semistructured interviews were conducted with German medical students and nursing trainees. The data were analysed based on qualitative content analysis according to Kuckartz. Interviewees’ reflections are presented under three themes the interviewees describe as closely related: (self-)attribution of responsibility, decision-making authority and need of (professional) experience. The results illustrate the conceptual interconnectedness of professional responsibility and its structural and epistemic preconditions to be able to fulfil clinicians’ responsibility in a meaningful manner. The study also sheds light on the four relata of responsibility understood as a relational concept. The article closes with concrete suggestions for the ethically sound clinical implementation of ML-CDSS. (shrink)

Research regarding the drivers of acceptance of clinical decision support systems by physicians is still rather limited. The literature that does exist, however, tends to focus on problems regarding the user-friendliness of CDSS. We have performed a thematic analysis of 24 interviews with physicians concerning specific clinical case vignettes, in order to explore their underlying opinions and attitudes regarding the introduction of CDSS in clinical practice, to allow a more in-depth analysis of factors underlying (...) acceptance of CDSS. We identified three general themes from the results. First, ‘the perceived role of the AI’, including items referring to the tasks that may properly be assigned to the CDSS according to the respondents. Second, ‘the perceived role of the physician’, referring to the aspects of clinical practice that were seen as being fundamentally ‘human’ or non-automatable. Third, ‘concerns regarding AI’, including items referring to more general issues that were raised by the respondents regarding the introduction of CDSS in general and/or in clinical medicine in particular. Apart from the overall concerns expressed by the respondents regarding user-friendliness, we will explain how our results indicate that our respondents were primarily occupied by distinguishing between parts of their job that should be automated and aspects that should be kept in human hands. We refer to this distinction as ‘the division of clinical labor.’ This division is not based on knowledge regarding AI or medicine, but rather on which parts of a physician’s job were seen by the respondents as being central to who they are as physicians and as human beings. Often the respondents’ view that certain core parts of their job ought to be shielded from automation was closely linked to claims concerning the uniqueness of medicine as a domain. Finally, although almost all respondents claimed that they highly value their final responsibility, a closer investigation of this concept suggests that their view of ‘final responsibility’ was not that demanding after all. (shrink)

Background Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the (...) conditions that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. Methods This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. Results There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. Conclusions Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice. (shrink)

Making good decisions in extremely complex and difficult processes and situations has always been both a key task as well as a challenge in the clinic and has led to a large amount of clinical, legal and ethical routines, protocols and reflections in order to guarantee fair, participatory and up-to-date pathways for clinical decision-making. Nevertheless, the complexity of processes and physical phenomena, time as well as economic constraints and not least further endeavours as well as achievements in (...) medicine and healthcare continuously raise the need to evaluate and to improve clinical decision-making. This article scrutinises if and how clinical decision-making processes are challenged by the rise of so-called artificial intelligence-driven decision support systems (AI-DSS). In a first step, this article analyses how the rise of AI-DSS will affect and transform the modes of interaction between different agents in the clinic. In a second step, we point out how these changing modes of interaction also imply shifts in the conditions of trustworthiness, epistemic challenges regarding transparency, the underlying normative concepts of agency and its embedding into concrete contexts of deployment and, finally, the consequences for (possible) ascriptions of responsibility. Third, we draw first conclusions for further steps regarding a ‘meaningful human control’ of clinical AI-DSS. (shrink)

This paper demonstrates how argumentation schemes can be used in decision support systems that help clinicians in making treatment decisions. The work builds on the use of computational argumentation, a rigorous approach to reasoning with complex data that places strong emphasis on being able to justify and explain the decisions that are recommended. The main contribution of the paper is to present a novel set of specialised argumentation schemes that can be used in the context of a (...) clinical decision support system to assist in reasoning about what treatments to offer. These schemes provide a mechanism for capturing clinical reasoning in such a way that it can be handled by the formal reasoning mechanisms of formal argumentation. The paper describes how the integration between argumentation schemes and formal argumentation may be carried out, sketches how this is achieved by an implementation that we have created and illustrates the overall process on a small set of case studies. (shrink)

BackgroundMachine learning-based clinical decision support systems (ML_CDSS) are increasingly employed in various sectors of health care aiming at supporting clinicians’ practice by matching the characteristics of individual patients with a computerised clinical knowledge base. Some studies even indicate that ML_CDSS may surpass physicians’ competencies regarding specific isolated tasks. From an ethical perspective, however, the usage of ML_CDSS in medical practice touches on a range of fundamental normative issues. This article aims to add to the ethical (...) discussion by using professionalisation theory as an analytical lens for investigating how medical action at the micro level and the physician–patient relationship might be affected by the employment of ML_CDSS.Main textProfessionalisation theory, as a distinct sociological framework, provides an elaborated account of what constitutes client-related professional action, such as medical action, at its core and why it is more than pure expertise-based action. Professionalisation theory is introduced by presenting five general structural features of professionalised medical practice: (i) the patient has a concern; (ii) the physician deals with the patient’s concern; (iii) s/he gives assistance without patronising; (iv) s/he regards the patient in a holistic manner without building up a private relationship; and (v) s/he applies her/his general expertise to the particularities of the individual case. Each of these five key aspects are then analysed regarding the usage of ML_CDSS, thereby integrating the perspectives of professionalisation theory and medical ethics.ConclusionsUsing ML_CDSS in medical practice requires the physician to pay special attention to those facts of the individual case that cannot be comprehensively considered by ML_CDSS, for example, the patient’s personality, life situation or cultural background. Moreover, the more routinized the use of ML_CDSS becomes in clinical practice, the more that physicians need to focus on the patient’s concern and strengthen patient autonomy, for instance, by adequately integrating digital decision support in shared decision-making. (shrink)

Statistics is the science of collection, summarizing, presentation and interpretation of data. Moreover, it yields methods used in the verification of research hypotheses. The presence of a statistician in a research group remarkably improves both the quality of design and research and the optimization of financial resources. Moreover, the involvement of a statistician in a research team helps the physician to effectively utilize the time and energy spent on diagnosing, which is an important aspect in view of limited healthcare resources. (...) Precise, properly designed and implemented Computerized Clinical Decision Support Systems certainly lead to the improvement of healthcare and the quality of medical services, which increases patient satisfaction and reduces financial burdens on healthcare systems. (shrink)

Physicians and other healthcare professionals are increasingly finding ways to use artificial intelligent decision support systems in their work. IBM Watson Health, for example, is a commercially available technology that is providing AI-DDS services in genomics, oncology, healthcare management and more.1 AI’s ability to scan massive amounts of data, detect patterns, and derive solutions from data is vastly more superior than that of humans. AI technology is undeniably integral to the future of healthcare and public health, and (...) thoughtful consideration of the legal, ethical and moral issues surrounding this technology is a must. The authors of the article, Responsibility, Second Opinions, and Peer-Disagreement—Ethical and Epistemological Challenges of Using AI in Clinical Diagnostic Contexts, provide an informed discussion of how AI-DSS may be used, both practically and ethically, to assist healthcare professionals in cooperative diagnostic processes2. The authors propose a process, whereby AI-DSS would provide a physician a second opinion, and when there is a mismatch between opinions, another physician would provide a third opinion. This approach maintains a ‘physician-in-charge’ perspective, and suggests that decision-making must ultimately be made by a person. This approach is also consistent with the ‘physician in-the-loop’ concept, such that even when increasingly autonomous AI-DSSs are put to use, physicians will still be providing checks and overseeing clinical decisions. The authors conceptualise AI as a replacement of human cognitive labour. That is, AI is used to supplant a human professional in particular functions …. (shrink)

Clinical ethical support services (CESS) represent a multifaceted field of aims, consultancy models, and methodologies. Nevertheless, the overall aim of CESS can be summed up as contributing to healthcare of high ethical standards by improving ethically competent decision-making in clinical healthcare. In order to support clinical care adequately, CESS must pay systematic attention to all real-life ethical issues, including those which do not fall within the ‘favourite’ ethical issues of the day. In this paper (...) we attempt to capture a comprehensive overview of categories of ethical tensions in clinical care. We present an analytical exposition of ethical structural features in judgement-based clinical care predicated on the assumption of the moral equality of human beings and the assessment of where healthcare contexts pose a challenge to achieving moral equality. The account and the emerging overview is worked out so that it can be easily contextualized with regards to national healthcare systems and specific branches of healthcare, as well as local healthcare institutions. By considering how the account and the overview can be applied to i) improve the ethical competence of healthcare personnel and consultants by broadening their sensitivity to ethical tensions, ii) identify neglected areas for ethical research, and iii) clarify the ethical responsibility of healthcare institutions' leadership, as well as specifying required institutionalized administration, we conclude that the proposed account should be considered useful for CESS. (shrink)

Clinical ethical support services (CESS) represent a multifaceted field of aims, consultancy models, and methodologies. Nevertheless, the overall aim of CESS can be summed up as contributing to healthcare of high ethical standards by improving ethically competent decision‐making in clinical healthcare. In order to support clinical care adequately, CESS must pay systematic attention to all real‐life ethical issues, including those which do not fall within the ‘favourite’ ethical issues of the day. In this paper (...) we attempt to capture a comprehensive overview of categories of ethical tensions in clinical care. We present an analytical exposition of ethical structural features in judgement‐based clinical care predicated on the assumption of the moral equality of human beings and the assessment of where healthcare contexts pose a challenge to achieving moral equality. The account and the emerging overview is worked out so that it can be easily contextualized with regards to national healthcare systems and specific branches of healthcare, as well as local healthcare institutions. By considering how the account and the overview can be applied to i) improve the ethical competence of healthcare personnel and consultants by broadening their sensitivity to ethical tensions, ii) identify neglected areas for ethical research, and iii) clarify the ethical responsibility of healthcare institutions' leadership, as well as specifying required institutionalized administration, we conclude that the proposed account should be considered useful for CESS. (shrink)

This paper aims to identify the decision support systems and their role on the strategic management development in the Universities- Case Study: Islamic University of Gaza. The descriptive approach was used where a questionnaire was developed and distributed to a stratified random sample. (230) questionnaires were distributed and (204) were returned with response rate (88.7%). The most important findings of the study: The presence of a statistically significant positive correlation between the decision support systems (...) and strategic management in the Islamic University in Gaza, and this shows that there is a role for decision support systems in strategic management development. The senior management is convinced of the importance of strategic management of the organization, and this conviction paid to clearly understand the process of the development of strategic management and thus led to a commitment to this process to improve its performance. There is an impact on the strategic management of the capabilities available for the use of decision support systems, and the type of decision support systems used, while there is little effect of top management support for the use of decision support systems. Also, the results showed that there were no statistically significant differences between the averages of the respondents answers attributed to personal data. The research also concluded a series of recommendations: The Islamic University of Gaza need to develop its infrastructure of decision support systems when it develops the strategic management. It should increase its interest in organizational potential through the necessity of a separate unit for decision support systems. As well as increased interest in the physical and technical capabilities available to decision support systems through continuity in keeping pace with technological means and techniques, continued support of senior management in the organization of strategic management for its positive role on decision support systems, that senior management will continue in the organization in creating a culture of strategic management, and it should encourage employees to participate in the strategic management process. (shrink)

Background Although the importance of clinical ethics in contemporary clinical environments is established, development of formal clinical ethics services in the Australia health system has, to date, been ad hoc. This study was designed to systematically follow and reflect upon the first 18 months of activity by a newly established service, to examine key barriers and facilitators to establishing a new service in an Australian hospital setting. Methods: how the study was performed and statistical tests used A (...) qualitative case study approach was utilised. The study gathered and analysed data using observations of service committee meetings, document analysis of agendas and minutes, and semi-structured interviews with committee members to generate semantic themes. By interpreting the thematic findings in reference to national capacity building resources, this study also aimed to provide practice-based reflections for other health agencies. Results: the main findings An overarching theme identified in the data was a strong commitment to supporting clinicians facing difficult patient care decisions and navigating difficult discussions with patients and families. Another key theme was the role of the new clinical ethics support service in providing clinicians with a pathway to raise system-wide issues with the organisation Executive. While there was strong clinical engagement, consumer and community participation remained a challenge, as did unresolved governance issues and a need for clearer policy relationship between the service and the organisation. Conclusions: brief summary and potential implications Considering these themes in relation to the national capacity building resources, the study identifies three areas likely to require ongoing development and negotiation. These are: the role of the clinical ethics support service as a link between the workforce and the Executive; the incorporation of consumers and patients; and ethical reasoning. To improve the effectiveness of the service, it is necessary to increase clarity on the service’s role at the governance and policy level, as well as develop strategies for engaging consumers, patients and families. Finally, the capacity of the service to reflect on complex cases may be enhanced through explicit discussions of various different ethical frameworks and ways of deliberating. (shrink)

BackgroundHealthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use.MethodsPubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was (...) title and abstract screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis.ResultsArtificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process.ConclusionsThe prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human–machine interaction, have been neglected in the debate on AI for clinical ethics so far.Trial registrationThis review is registered at Open Science Framework (https://osf.io/wvcs9). (shrink)

In this article I consider two pertinent questions that practitioners must consider when they deploy an algorithmic system as support in clinical shared decision‐making. The first question concerns how to interpret and assess the significance of different performance measures for clinical decision‐making. The second question concerns the professional obligations that practitioners have to communicate information about the quality of an algorithm's output to patients in light of the principles of autonomy, beneficence, and justice. In the (...) article I review the four standard performance measures used to evaluate and validate algorithms, outline their role in the discussion of algorithmic fairness, and discuss the professional responsibilities that practitioners face when communicating information about these measures to patients. (shrink)

Although clinical ethics support services are becoming increasingly prevalent in Europe and North America, they remain an uncommon feature of the Irish healthcare system and Irish health professionals lack formal support when faced with ethically challenging cases. We have developed a variant on existing clinical ethics decision-making tools which is designed to build capacity and confidence amongst Irish practitioners and enable them to confront challenging situations in the absence of any dedicated support structure. The (...) tool provided below follows a transparent stepwise procedure which avoids an overcomplicated analysis while remaining sensitive to the complexity of the issues addressed. The novelty of the tool lies in the explicit association it makes between each step in the process and one or more responsibilities based on established norms of ethical decision-making in the public domain. (shrink)

With the rise and public accessibility of AI-enabled decision-support systems, individuals outsource increasingly more of their decisions, even those that carry ethical dimensions. Considering this trend, scholars have highlighted that uncritical deference to these systems would be problematic and consequently called for investigations of the impact of pertinent technology on humans’ ethical decision-making. To this end, this article conducts a systematic review of existing scholarship and derives an integrated framework that demonstrates how intelligent decision- (...) class='Hi'>support systems (IDSSs) shape humans’ ethical decision-making. In particular, we identify resulting consequences on an individual level (i.e., deliberation enhancement, motivation enhancement, autonomy enhancement and action enhancement) and on a societal level (i.e., moral deskilling, restricted moral progress and moral responsibility gaps). We carve out two distinct methods/operation types (i.e., processoriented and outcome-oriented navigation) that decision-support systems can deploy and postulate that these determine to what extent the previously stated consequences materialize. Overall, this study holds important theoretical and practical implications by establishing clarity in the conceptions, underlying mechanisms and (directions of) influences that can be expected when using particular IDSSs for ethical decisions. (shrink)

Objective Gather information from physicians about factors contributing to unnecessary noninvasive imaging and impact of possible solutions. Methods Qualitative study of 14 physicians using a phenomenological approach and the Theoretical Domains Framework. Results Most participants self-reported that >10% of the imaging tests they order are unnecessary. External sources of pressure included: peer-review, patient demands, nursing expectations, specialist requests, as well as prior experience with patient advocates, and the compensation and pension system. Internal sources of pressure included reliance on anecdote, self-doubt (...) about diagnoses, and fear of missing a diagnosis and of professional liability. Participants expressed both optimism and concern about potential solutions, such as adopting decision support software. Conclusion Physicians are under pressure from multiple sources to order unnecessary imaging. Peer review, nursing expectations, and perceptions about Veteran compensation and pension are newly reported contributing factors. (shrink)

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI (...) systems to support decision-making around cardiopulmonary resuscitation and the determination of a patient’s Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions—conceptual, methodological and procedural—that need to be considered in further development and implementation efforts. (shrink)

Based on applied linguistics, this study looked at the decision support system for emphasizing self-assurance in academic writing. From a generic perspective, academic writing has been considered both a process and a product. It has highlighted the planning composite processes, editing, composing, revising, and assessment, which depend upon the familiarity of someone with confidence in their capability for engagement in these activities. As a product, it has focused on the writing results through the product’s characteristics. These contain specific (...) content areas in acceptable depth and well-structured technical vocabulary. Higher education aims to support students in optimizing their potential for achieving satisfactory outcomes. For example, the assessment of grades involves academic writing, contributing to the degree course classification. Students have differences in many respects, such as expectations, background knowledge, and study and learning approaches. There were varying students’ beliefs about what academic writing is for evaluation. Modern-day motivations and theories highlight the significance of students’ confidence in their studies. The role of high confidence can support students to apply more effort toward setting challenging goals. Students may find it more difficult to succeed in higher education if they lack confidence in their academic writing abilities. A DSS has many applications in diverse areas and can play a significant role in the ranking and prioritization process. The current study has considered the DSS for prioritizing self-assurance in academic writing based on applied linguistics. Various criteria were considered for the evaluation of the research. The Super Decision software was used for the experimental process of the proposed research. The results of the study show the effectiveness of the proposed research. (shrink)

In this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level machine learning (ML) technique. The neural network used a three-level classifier approach for the prediction of Pakistani student achievement. A self-recorded dataset of 1,011 respondents of graduate students of English and Physics (...) courses are used. The ten interviews along with ten questions were conducted to determine the perception of the individual student. The chi-squared ( χ ) \left(\chi ) test was applied to test statistical significancy of the questionnaire. The statistical calculations and computation of data were performed by using the statistical package of IBMM SPSS version 21.0. The seven different algorithms were tested to improve the data classification. The Java-based environment was used for the development of numerous prediction classifiers. C4.5 algorithm shows the finest accuracy, whereas Naïve Bayes (NB) algorithm shows the least. The results depict that the classifier’s efficiency was improved by using a three-level proposed scheme from 83.2% to 88.8%. This prediction has shown remarkable results when compared with the individual level classifier technique of ML. This improvement in the accuracy of DSSs is used to identify more efficiently the gray areas in the education stratum of Pakistan. This will pave a path for making policies in the higher education system of Pakistan. The presented framework can be deployed on different platforms under numerous operating systems. (shrink)

In this article we present the foundations of a decision support system for blockage management in Fire Service. Blockage refers to the situation when all fire units are out and a new incident occurs. The approach is based on two phases: off-line data preparation and online blockage estimation. The off-line phase consists of methods from data mining and natural language processing and results in semantically coherent information granules. The online phase is about building the probabilistic models that estimate (...) the block-age probability based on these granules. Finally, the selected classifier judges whether a blockage can occur and whether the resources from neighbour fire stations should be asked for assistance. (shrink)

Recruitment processes for clinical trials are governed by guidelines and regulatory systems intended to ensure participation is informed and voluntary. Although the guidelines and systems provide some protection to potential participants, current recruitment processes often result in limited understanding and experiences of inadequate decision support. Many trials also have high drop-out rates among participants, which are ethically troubling because they can be indicative of poor experiences and they limit the usefulness of the knowledge the trials (...) were designed to generate. Drawing on recent social-psychological and philosophical-ethical research on trial recruitment and patient participation in treatment decision-making, this paper identifies possibilities for improving communicative support for both initial decisions and ongoing participation in clinical trials. It highlights the potential of a shift in thinking about ‘voluntariness’, underpinned by relational understandings of autonomy, to encourage more nuanced judgements about the ethics of communication between trial staff and (potential) participants. The paper suggests that the idea of responsively enabling people to consider invitations or requests to participate in particular trials could serve as a general guide to communication. This might help ensure decisions about trial participation are meaningfully informed and voluntary, and that relationships between trial staff and participants contribute to positive experiences of trial participation and ultimately to the generation of the robust knowledge. (shrink)

Recent proposals for computer-assisted argumentation have drawn on dialectical models of argumentation. When used to assist public policy planning, such systems also raise questions of political legitimacy. Drawing on deliberative democratic theory, we elaborate normative criteria for deliberative legitimacy and illustrate their use for assessing two argumentation systems. Full assessment of such systems requires experiments in which system designers draw on expertise from the social sciences and enter into the policy deliberation itself at the level of participants.

The paper sets out the challenges facing the Police in respect of the detection and prevention of the volume crime of burglary. A discussion of data mining and decision support technologies that have the potential to address these issues is undertaken and illustrated with reference the authors’ work with three Police Services. The focus is upon the use of “soft” forensic evidence which refers to modus operandi and the temporal and geographical features of the crime, rather than “hard” (...) evidence such as DNA or fingerprint evidence. Three objectives underpin this paper. First, given the continuing expansion of forensic computing and its role in the emergent discipline of Crime Science, it is timely to present a review of existing methodologies and research. Second, it is important to extract some practical lessons concerning the application of computer science within this forensic domain. Finally, from the lessons to date, a set of conclusions will be advanced, including the need for multidisciplinary input to guide further developments in the design of such systems. The objectives are achieved by first considering the task performed by the intended systems users. The discussion proceeds by identifying the portions of these tasks for which automation would be both beneficial and feasible. The knowledge discovery from databases process is then described, starting with an examination of the data that police collect and the reasons for storing it. The discussion progresses to the development of crime matching and predictive knowledge which are operationalised in decision support software. The paper concludes by arguing that computer science technologies which can support criminal investigations are wide ranging and include geographical information systems displays, clustering and link analysis algorithms and the more complex use of data mining technology for profiling crimes or offenders and matching and predicting crimes. We also argue that knowledge from disciplines such as forensic psychology, criminology and statistics are essential to the efficient design of operationally valid systems. (shrink)

Negotiation Support Systems have traditionally modelled the process of negotiation. They often rely on mathematical optimisation techniques and ignore heuristics and other methods derived from practice. Our goal is to develop systems capable of decision support to help resolve a given dispute. A system we have constructed, Family_Winner, uses empirical evidence to dynamically modify initial preferences throughout the negotiation process. It sequentially allocates issues using trade-offs and compensation opportunities inherent in the dispute.

Decision support systems, which incorporate artificial intelligence and big data, are receiving significant attention in the public sector. Decision support systems are sociocultural artefacts that are subject to a mix of technical and political choices, and critical investigation of these choices and the rationales they reflect are paramount since they are inscribed into and may cause harm, violate fundamental rights and reproduce negative social patterns. Applying and merging the concepts of sense-making and translation, this (...) article investigates the rationales, translations and critical reflections that shape the development of a decision support system to support social workers assessing referrals concerning child neglect. It presents findings from a qualitative case study conducted in 2019–2020 at the Citizen Centre Children and Young People, Copenhagen Municipality, Denmark. The analysis shows how key actors through processes of translation construct, negotiate and readjust problem definitions, roles, interests, responsibilities and ideas of ambiguity and accountability. Although technological solutionism is present in these processes, it is not the only rationale invested. Rather, technological and data-driven rationales are adjusted to and merged with rationales of efficiency, return on investment and child welfare. Through continuous renegotiation of roles, responsibilities and problems according to these rationales, the key actors attempt to orchestrate ways of managing the complexity facing child welfare services by projecting images of future potentials of the decision support system that are yet to be realised. (shrink)

Background Non-invasive prenatal testing (NIPT), which can screen for aneuploidies such as trisomy 21, is being implemented in several public healthcare systems across Europe. Comprehensive communication and information have been highlighted in the literature as important elements in supporting women’s reproductive decision-making and addressing relevant ethical concerns such as routinisation. Countries such as England and France are adopting broadly similar implementation models, offering NIPT for pregnancies with high aneuploidy probability. However, we do not have a deeper understanding of (...) how professionals’ counselling values and practices may differ between these contexts. Methods In this paper, we explore how professionals in England and France support patient decision-making in the provision of NIPT and critically compare professional practices and values. We draw on data from semi-structured interviews with healthcare professionals. Results Both English and French professionals emphasised values relating to patient choice and consent. However, understandings and application of these values into the practice of NIPT provision differed. English interviewees placed a stronger emphasis on interpreting and describing the process of counselling patients and clinical care through a “principle” lens. Their focus was on non-directiveness, standardisation, and the healthcare professional as “decision-facilitator” for patients. French interviewees described their approach through a “procedural” lens. Their focus was on formal consent, information, and the healthcare professional as “information-giver”. Both English and French professionals indicated that insufficient resources were a key barrier in effectively translating their values into practice. Conclusion Our findings illustrate that supporting patient choice in the provision of NIPT may be held as an important value in common on a surface level, but can be understood and translated into practice in different ways. Our findings can guide further research and beneficially inform practice and policy around NIPT provision. (shrink)

Methodological strategies for the identification and synthesis of ‘evidence’ to support decision‐making in relation to complex healthcare systems and practices This paper addresses the limitations of current methods supporting ‘evidence‐based health‐care’ in relation to complex aspects of care, including those questions that are best supported by descriptive or non‐empirical evidence. The paper identifies some new methods, which may be useful in aiding the synthesis of data in these areas. The methods detailed are broadly divided into those that (...) facilitate the identification of evidence and those that enable the interpretation of the data retrieved. To illustrate some of the issues involved, reference is made to a multimethod review recently completed by the authors, which aimed to identify factors that promote continuity in the transition from child to adult health and social care. It is argued that as healthcare organisations are becoming increasingly preoccupied with the evidence base of practice, such methods may help ensure that aspects of care and approaches that are outside the dominant pharmaco‐medical domain maintain a prominent position on the healthcare agenda while remaining open to external scrutiny. Healthcare professionals who use such approaches need to know their relative utility and benefits to inform clinical decisions, so as to ensure that best practice is observed. (shrink)

This article draws on analysis of a variety of problems emerging from practical applications of Group Decision Support Systems (GDSS) to propose a fundamental evolution of decision support models from the traditional single decision-spine model to the decision-hedgehog. It positions decision making through the construction of narratives making the rhizome that constitutes the body of the hedgehog with the fundamental aim of enriching understanding of the contexts of decision making. Localized processes (...) constructing and exploring prescriptions for action within a plethora of decision spines are rooted in this rhizome. It identifies a synthesis of theories that influence decision making within organizations and proposes a comprehensive system of Group Decision Authoring and Communication Support (GDACS). In doing so it proposes that the iterative development of collective narrative within an organizing system engaged with complex decision making leads to active engagement with implementation - a process we call Collaborative Authoring of Outcomes. Throughout, the article outlines the implications for the organization of GDACS and proposes a comprehensive architecture that enables this approach. (shrink)

Contemporary medical diagnostics has a dynamic moral landscape, which includes a variety of agents, factors, and components. A significant part of this landscape is composed of information technologies that play a vital role in doctors’ decision-making. This paper focuses on the so-called Intelligent Decision-Support System that is widely implemented in the domain of contemporary medical diagnosis. The purpose of this article is twofold. First, I will show that the IDSS may be considered a moral agent in the (...) practice of medicine today. To develop this idea I will introduce the approach to artificial agency provided by Luciano Floridi. Simultaneously, I will situate this approach in the context of contemporary discussions regarding the nature of artificial agency. It is argued here that the IDSS possesses a specific sort of agency, includes several agent features (e.g. autonomy, interactivity, adaptability), and hence, performs an autonomous behavior, which may have a substantial moral impact on the patient’s well-being. It follows that, through the technology of artificial neural networks combined with ‘deep learning’ mechanisms, the IDSS tool achieves a specific sort of independence (autonomy) and may possess a certain type of moral agency. Second, I will provide a conceptual framework for the ethical evaluation of the moral impact that the IDSS may have on the doctor’s decision-making and, consequently, on the patient’s wellbeing. This framework is the Object-Oriented Model of Moral Action developed by Luciano Floridi. Although this model appears in many contemporary discussions in the field of information and computer ethics, it has not yet been applied to the medical domain. This paper addresses this gap and seeks to reveal the hidden potentialities of the OOP model for the field of medical diagnosis. (shrink)

Due to increasing empiricalinformation on farm animal welfare since the1960s, the prospects for sound decisionmakingconcerning welfare have improved. This paperdescribes a strategy to develop adecision-making aid, a decision support system,for assessment of farm-animal welfare based onavailable scientific knowledge. Such a decisionsupport system allows many factors to be takeninto account. It is to be developed accordingto the Evolutionary Prototyping Method, inwhich an initial prototype is improved inreiterative updating cycles. This initialprototype has been constructed. It useshierarchical representations to analysescientific statements (...) and statements describingthe housing system. Welfare is assessed fromwhat is known about the biological needs of theanimals, using a welfare model in the form of atree that contains these needs as welfarecomponents. Each state of need is assessedusing welfare relevant attributes of thehousing system and weighting factors.Attributes are measurable properties of thehousing system. Weighting factors are assignedaccording to heuristic rules based on theprinciple of weighting all components(attributes and needs) equally, unless thereare strong reasons to do otherwise. Preliminarytests of the prototype indicate that it may bepossible to perform assessment of farm-animalwelfare in an explicit way and based onempirical findings. The procedure needs to berefined, but its prospects are promising. (shrink)

Crisis management is a complex problem raised by the scientific community currently. Decision support systems are a suitable solution for such issues, they are indeed able to help emergency managers to prevent and to manage crisis in emergency situations. However, they should be enough flexible and adaptive in order to be efficient to solve complex problems that are plunged in dynamic and unpredictable environments. The approach we propose in this paper addresses this challenge. First, we expose a (...) modelling of information for an emergency environment. Then, we describe an architecture of a multiagent DSS that deals with these information in order to prevent and to manage crisis situations. We focus here on the first level of the system mechanism which intends to perceive and to reflect the evolution of the current situation. Finally, we provide an implementation and an experimentation of the approach using the RoboCupRescue Simulation System. (shrink)

Artificial intelligence (AI) based clinical decision support systems (CDSS) are becoming ever more widespread in healthcare and could play an important role in diagnostic and treatment processes. For this reason, AI‐based CDSS has an impact on the doctor–patient relationship, shaping their decisions with its suggestions. We may be on the verge of a paradigm shift, where the doctor–patient relationship is no longer a dual relationship, but a triad. This paper analyses the role of AI‐based CDSS for (...) shared decision‐making to better comprehend its promises and associated ethical issues. Moreover, it investigates how certain AI implementations may instead foster the inappropriate paradigm of paternalism. Understanding how AI relates to doctors and influences doctor–patient communication is essential to promote more ethical medical practice. Both doctors' and patients' autonomy need to be considered in the light of AI. (shrink)

Can machine intelligence do clinical ethics? And if so, would applying it to actual medical cases be desirable? In a recent target article (Meier et al. 2022), we described the piloting of our advisory algorithm METHAD. Here, we reply to commentaries published in response to our project. The commentaries fall into two broad categories: concrete criticism that concerns the development of METHAD; and the more general question as to whether one should employ decision-support systems of this (...) kind—the debate we set out to ignite with our target article. (shrink)

At the Donald Berman Laboratory for Information Technology and Law, La TrobeUniversity Australia, we have been building legal decision support systems for a dozenyears. Whilst most of our energy has been devoted to conducting research in ArtificialIntelligence and Law, over the past few years we have increasingly focused uponbuilding legal decision support systems that have a commercial focus.In this paper we discuss the evolution of our systems. We begin with a discussion ofrule-based (...) class='Hi'>systems and discuss the transition to hybrid rule-based/case-based systems.We next discuss how we have used machine learning in building legal decision supportsystems. Our focus on using machine learning led us to investigate the domains ofexplanation and argumentation. We conclude by discussing our current work onbuilding negotiation support systems and tools for constructing web-based legaldecision support systems. (shrink)

BackgroundSystems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology ; “big data” statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working (...) to generate systems medicine knowledge and clinicians working to apply it.DiscussionThis article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through “big data” approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the “death computer,” to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their “quantified self.”SummaryThis paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care. (shrink)