Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

Utilizing artificial intelligence in perioperative patient flow:systematic literature review

Abstract. The purpose of this thesis was to map the existing landscape of artificial intelligence (AI) applications used in secondary healthcare, with a focus on perioperative care. The goal was to find out what systems have been developed, and how capable they are at controlling perioperative patient flow. The review was guided by the following research question: How is AI currently utilized in patient flow management in the context of perioperative care? This systematic literature review examined the current evidence regarding the use of AI in perioperative patient flow. A comprehensive search was conducted in four databases, resulting in 33 articles meeting the inclusion criteria. Findings demonstrated that AI technologies, such as machine learning (ML) algorithms and predictive analytics tools, have shown somewhat promising outcomes in optimizing perioperative patient flow. Specifically, AI systems have proven effective in predicting surgical case durations, assessing risks, planning treatments, supporting diagnosis, improving bed utilization, reducing cancellations and delays, and enhancing communication and collaboration among healthcare providers. However, several challenges were identified, including the need for accurate and reliable data sources, ethical considerations, and the potential for biased algorithms. Further research is needed to validate and optimize the application of AI in perioperative patient flow. The contribution of this thesis is summarizing the current state of the characteristics of AI application in perioperative patient flow. This systematic literature review provides information about the features of perioperative patient flow and the clinical tasks of AI applications previously identified

Causality and Association: The Statistical and Legal Approaches

This paper discusses different needs and approaches to establishing ``causation'' that are relevant in legal cases involving statistical input based on epidemiological (or more generally observational or population-based) information. We distinguish between three versions of ``cause'': the first involves negligence in providing or allowing exposure, the second involves ``cause'' as it is shown through a scientifically proved increased risk of an outcome from the exposure in a population, and the third considers ``cause'' as it might apply to an individual plaintiff based on the first two. The population-oriented ``cause'' is that commonly addressed by statisticians, and we propose a variation on the Bradford Hill approach to testing such causality in an observational framework, and discuss how such a systematic series of tests might be considered in a legal context. We review some current legal approaches to using probabilistic statements, and link these with the scientific methodology as developed here. In particular, we provide an approach both to the idea of individual outcomes being caused on a balance of probabilities, and to the idea of material contribution to such outcomes. Statistical terminology and legal usage of terms such as ``proof on the balance of probabilities'' or ``causation'' can easily become confused, largely due to similar language describing dissimilar concepts; we conclude, however, that a careful analysis can identify and separate those areas in which a legal decision alone is required and those areas in which scientific approaches are useful.Comment: Published in at http://dx.doi.org/10.1214/07-STS234 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Patient and health care professional decision-making to commence and withdraw from renal dialysis: A systematic review of qualitative research

Background and objectives. To ensure decisions to start and stop dialysis in end stage kidney disease are shared, the factors that affect patients and healthcare professionals in making such decisions need to be understood. This systematic review aims to explore how and why different factors mediate the choices about dialysis treatment. Design, setting, participants, and measurements. Medline, Embase, CINAHL and PsychINFO were searched for qualitative studies of factors that affect patients’ and/or healthcare professionals’ decisions to commence or withdraw from dialysis. A thematic synthesis was conducted. Results. Of 494 articles screened, 12 studies (conducted: 1985-2014) were included. These involved 206 predominantly haemodialysis patients and 64 healthcare professionals (age range: patients 26-93; professionals 26-61 years). (i) Commencing dialysis: patients based their choice on ‘gut-instinct’ as well as deliberating the impact of treatment on quality-of-life and survival. How individuals coped with decision-making was influential, some tried to take control of the problem of progressive renal failure, whilst others focussed on controlling their emotions. Healthcare professionals weighed-up biomedical factors and were led by an instinct to prolong life. Both patients and healthcare professionals described feeling powerless. (ii) Dialysis withdrawal: Only after prolonged periods of time on dialysis, were the realities of life on dialysis fully appreciated and past choice questioned. By this stage however patients were physically treatment dependent. Similar to commencing dialysis, individuals coped with treatment withdrawal in a problem or emotion-controlling way. Families struggled to differentiate choosing versus allowing death. Healthcare teams avoided and queried discussions regarding dialysis withdrawal. Patients however missed the dialogue they experienced during pre-dialysis education. Conclusions. Decision-making in end stage kidney disease is complex, dynamic, and evolves over time and towards death. The factors at work are multi-faceted and operate differently for patients and health professionals. More training and research on open-communication and shared decision-making is needed

Computational ethics

Technological advances are enabling roles for machines that present novel ethical challenges. The study of 'AI ethics' has emerged to confront these challenges, and connects perspectives from philosophy, computer science, law, and economics. Less represented in these interdisciplinary efforts is the perspective of cognitive science. We propose a framework – computational ethics – that specifies how the ethical challenges of AI can be partially addressed by incorporating the study of human moral decision-making. The driver of this framework is a computational version of reflective equilibrium (RE), an approach that seeks coherence between considered judgments and governing principles. The framework has two goals: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms. Working jointly towards these two goals will create the opportunity to integrate diverse research questions, bring together multiple academic communities, uncover new interdisciplinary research topics, and shed light on centuries-old philosophical questions.publishedVersio

Adapting a Kidney Exchange Algorithm to Align with Human Values

The efficient and fair allocation of limited resources is a classical problem in economics and computer science. In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ. Patients and donors in kidney exchanges are prioritized using ad-hoc weights decided on by committee and then fed into an allocation algorithm that determines who gets what--and who does not. In this paper, we provide an end-to-end methodology for estimating weights of individual participant profiles in a kidney exchange. We first elicit from human subjects a list of patient attributes they consider acceptable for the purpose of prioritizing patients (e.g., medical characteristics, lifestyle choices, and so on). Then, we ask subjects comparison queries between patient profiles and estimate weights in a principled way from their responses. We show how to use these weights in kidney exchange market clearing algorithms. We then evaluate the impact of the weights in simulations and find that the precise numerical values of the weights we computed matter little, other than the ordering of profiles that they imply. However, compared to not prioritizing patients at all, there is a significant effect, with certain classes of patients being (de)prioritized based on the human-elicited value judgments

Altruism, Markets, and Organ Procurement

For decades, the dominant view among biomedical ethicists, transplantation professionals, and the public at large has been that altruism, not financial considerations, should motivate organ donors. Proposals to compensate sources of transplantable organs or their survivors, although endorsed by a number of economists and legal scholars, have been denounced as unethical and impracticable. Organ transplantation is said to belong to the world of gift, as distinct from the market realm. Paying for organs would inject commerce into a sphere where market values have no place and would transform a system based on generosity and civic spirit into one of antiseptic, bargained-for exchanges. Here, Mahoney discusses a brief history of the restriction on payments to sources of transplantable organs. She then turn to the arguments commonly advanced against compensating organ sources and explain how they are grounded in beliefs that range from the highly contestable to the demonstrably wrong. Furthermore, she examines the most popular compensation proposals, and offering preliminary assessments of their promise and feasibility. She also concludes with some thoughts about the relationship between altruism and self-interest

Splitting Deceased Donor Livers to Double the Transplant Benefits: Addressing the Legal, Ethical, and Practical Challenges

Liver transplantation is different from transplanting other solid organs because some recipients can achieve good long-term outcomes with only half of a donor’s liver (or less). This means that some deceased donor livers can be split, saving two lives instead of one. However, although more than 10 percent of cadaveric livers meet the criteria for splitting, only about 1.5 percent are actually split in the United States. This article identifies a set of ethical, legal, and logistical challenges to a more extensive use of split liver transplantation (SLT) within existing legal frameworks. We then discuss how each of these challenges can be overcome with a set of realistic clarifications and changes to the current liver transplant architecture. Three guiding values shape liver allocation policy in the United States: maximizing expected outcomes, ensuring broad access, and prioritizing the sickest patients. While the last value is in tension with SLT (because the sickest patients often need a whole liver), we maintain that greater adoption of SLT is consistent with this normative balance. In addition, the distribution infrastructure is not designed to facilitate splitting. When a surgical team is offered a liver for a specific patient, they feel duty-bound to give that specific patient the whole organ. Further discouraging SLT, performance metrics, including those used to determine a transplant program’s eligibility for Medicare and Medicaid funding, focus on surgical outcomes rather than waitlist mortality. Our preferred remedies entail clarifying the informed consent requirements for SLT, using a national clearinghouse to identify livers that are prime candidates for splitting, offering these livers to qualifying programs for SLT only, and establishing a separate regulatory reporting and outcomes evaluation pathway for SLT. Together, these reforms, many of which have precedents in the transplant field, will support the expansion of SLT in carefully controlled conditions and save more lives