Care of the Dying: The Doctor and Euthanasia

The quality of the doctor patient relationship is central in this discussion of the euthanasia problem. Robert Rizzo, Ph.D., is Assistant Professor in the Department of Religious Studies at Canisius College, Buffalo, N.Y. Joseph Yonder teaches medical ethics at Trocaire College, Buffalo, N.Y. and is also an Inhalation Therapy Technician at Buffalo\u27s Columbus Hospital

Changing preferences: an experiment and estimation of market-incentive effects on altruism

This paper studies how altruistic preferences are changed by markets and incentives. We conduct a laboratory experiment in a within-subject design. Subjects are asked to choose health care qualities for hypothetical patients in monopoly, duopoly, and quadropoly. Prices, costs, and patient benefits are experimental incentive parameters. In monopoly, subjects choose quality to tradeoff between profits and altruistic patient benefits. In duopoly and quadropoly, we model subjects playing a simultaneous-move game. Each subject is uncertain about an opponent's altruism, and competes for patients by choosing qualities. Bayes-Nash equilibria describe subjects' quality decisions as functions of altruism. Using a nonparametric method, we estimate the population altruism distributions from Bayes-Nash equilibrium qualities in different markets and incentive configurations. Markets tend to reduce altruism, although duopoly and quadropoly equilibrium qualities are much higher than those in monopoly. Although markets crowd out altruism, the disciplinary powers of market competition are stronger. Counterfactuals confirm markets change preferences.Accepted manuscrip

Understanding safety-critical interactions with a home medical device through Distributed Cognition

As healthcare shifts from the hospital to the home, it is becoming increasingly important to understand how patients interact with home medical devices, to inform the safe and patient-friendly design of these devices. Distributed Cognition (DCog) has been a useful theoretical framework for understanding situated interactions in the healthcare domain. However, it has not previously been applied to study interactions with home medical devices. In this study, DCog was applied to understand renal patients’ interactions with Home Hemodialysis Technology (HHT), as an example of a home medical device. Data was gathered through ethnographic observations and interviews with 19 renal patients and interviews with seven professionals. Data was analyzed through the principles summarized in the Distributed Cognition for Teamwork methodology. In this paper we focus on the analysis of system activities, information flows, social structures, physical layouts, and artefacts. By explicitly considering different ways in which cognitive processes are distributed, the DCog approach helped to understand patients’ interaction strategies, and pointed to design opportunities that could improve patients’ experiences of using HHT. The findings highlight the need to design HHT taking into consideration likely scenarios of use in the home and of the broader home context. A setting such as home hemodialysis has the characteristics of a complex and safety-critical socio-technical system, and a DCog approach effectively helps to understand how safety is achieved or compromised in such a system

Effective patient–clinician interaction to improve treatment outcomes for patients with psychosis: a mixed-methods design

BACKGROUND:At least 100,000 patients with schizophrenia receive care from community mental health teams (CMHTs) in England. These patients have regular meetings with clinicians, who assess them, engage them in treatment and co-ordinate care. As these routine meetings are not commonly guided by research evidence, a new intervention, DIALOG, was previously designed to structure consultations. Using a hand-held computer, clinicians asked patients to rate their satisfaction with eight life domains and three treatment aspects, and to indicate whether or not additional help was needed in each area, with responses being graphically displayed and compared with previous ratings. In a European multicentre trial, the intervention improved patients’ quality of life over a 1-year period. The current programme builds on this research by further developing DIALOG in the UK. RESEARCH QUESTIONS:(1) How can the practical procedure of the intervention be improved, including the software used and the design of the user interface? (2) How can elements of resource-oriented interventions be incorporated into a clinician manual and training programme for a new, more extensive ‘DIALOG+’ intervention? (3) How effective and cost-effective is the new DIALOG+ intervention in improving treatment outcomes for patients with schizophrenia or a related disorder? (4) What are the views of patients and clinicians regarding the new DIALOG+ intervention? METHODS:We produced new software on a tablet computer for CMHTs in the NHS, informed by analysis of videos of DIALOG sessions from the original trial and six focus groups with 18 patients with psychosis. We developed the new ‘DIALOG+’ intervention in consultation with experts, incorporating principles of solution-focused therapy when responding to patients’ ratings and specifying the procedure in a manual and training programme for clinicians. We conducted an exploratory cluster randomised controlled trial with 49 clinicians and 179 patients with psychosis in East London NHS Foundation Trust, comparing DIALOG+ with an active control. Clinicians working as care co-ordinators in CMHTs (along with their patients) were cluster randomised 1 : 1 to either DIALOG+ or treatment as usual plus an active control, to prevent contamination. Intervention and control were to be administered monthly for 6 months, with data collected at baseline and at 3, 6 and 12 months following randomisation. The primary outcome was subjective quality of life as measured on the Manchester Short Assessment of Quality of Life; secondary outcomes were also measured. We also established the cost-effectiveness of the DIALOG intervention using data from the Client Service Receipt Inventory, which records patients’ retrospective reports of using health- and social-care services, including hospital services, outpatient services and medication, in the 3 months prior to each time point. Data were supplemented by the clinical notes in patients’ medical records to improve accuracy. We conducted an exploratory thematic analysis of 16 video-recorded DIALOG+ sessions and measured adherence in these videos using a specially developed adherence scale. We conducted focus groups with patients (n = 19) and clinicians (n = 19) about their experiences of the intervention, and conducted thematic analyses. We disseminated the findings and made the application (app), manual and training freely available, as well as producing a protocol for a definitive trial. RESULTS:Patients receiving the new intervention showed more favourable quality of life in the DIALOG+ group after 3 months (effect size: Cohen’s d = 0.34), after 6 months (Cohen’s d = 0.29) and after 12 months (Cohen’s d = 0.34). An analysis of video-recorded DIALOG+ sessions showed inconsistent implementation, with adherence to the intervention being a little over half of the possible score. Patients and clinicians from the DIALOG+ arm of the trial reported many positive experiences with the intervention, including better self-expression and improved efficiency of meetings. Difficulties reported with the intervention were addressed by further refining the DIALOG+ manual and training. Cost-effectiveness analyses found a 72% likelihood that the intervention both improved outcomes and saved costs. LIMITATIONS:The research was conducted solely in urban east London, meaning that the results may not be broadly generalisable to other settings. CONCLUSIONS:(1) Although services might consider adopting DIALOG+ based on the existing evidence, a definitive trial appears warranted; (2) applying DIALOG+ to patient groups with other mental disorders may be considered, and to groups with physical health problems; (3) a more flexible use with variable intervals might help to make the intervention even more acceptable and effective; (4) more process evaluation is required to identify what mechanisms precisely are involved in the improvements seen in the intervention group in the trial; and (5) what appears to make DIALOG+ effective is that it is not a separate treatment and not a technology that is administered by a specialist; rather, it changes and utilises the existing therapeutic relationship between patients and clinicians in CMHTs to initiate positive change, helping the patients to improve their quality of life. FUTURE RESEARCH:Future studies should include a definitive trial on DIALOG+ and test the effectiveness of the intervention with other populations, such as people with depression. TRIAL REGISTRATION:Current Controlled Trials ISRCTN34757603. FUNDING:The National Institute for Health Research Programme Grants for Applied Research programme

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

Secure Management of Personal Health Records by Applying Attribute-Based Encryption

The confidentiality of personal health records is a major problem when patients use commercial Web-based systems to store their health data. Traditional access control mechanisms, such as Role-Based Access Control, have several limitations with respect to enforcing access control policies and ensuring data confidentiality. In particular, the data has to be stored on a central server locked by the access control mechanism, and the data owner loses control on the data from the moment when the data is sent to the requester. Therefore, these mechanisms do not fulfil the requirements of data outsourcing scenarios where the third party storing the data should not have access to the plain data, and it is not trusted to enforce access control policies. In this paper, we describe a new approach which enables secure storage and controlled sharing of patient’s health records in the aforementioned scenarios. A new variant of a ciphertext-policy attribute-based encryption scheme is proposed to enforce patient/organizational access control policies such that everyone can download the encrypted data but only authorized users from the social domain (e.g. family, friends, or fellow patients) or authorized users from the professional\ud domain (e.g. doctors or nurses) are allowed to decrypt it

Enhancing declarative process models with DMN decision logic

Modeling dynamic, human-centric, non-standardized and knowledge-intensive business processes with imperative process modeling approaches is very challenging. Declarative process modeling approaches are more appropriate for these processes, as they offer the run-time flexibility typically required in these cases. However, by means of a realistic healthcare process that falls in the aforementioned category, we demonstrate in this paper that current declarative approaches do not incorporate all the details needed. More specifically, they lack a way to model decision logic, which is important when attempting to fully capture these processes. We propose a new declarative language, Declare-R-DMN, which combines the declarative process modeling language Declare-R with the newly adopted OMG standard Decision Model and Notation. Aside from supporting the functionality of both languages, Declare-R-DMN also creates bridges between them. We will show that using this language results in process models that encapsulate much more knowledge, while still offering the same flexibility

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Cultural Transformation in Health Care

Describes the role of organizational culture in healthcare organizations. Recommends strategies for innovative approaches to improve the overall performance of the U.S. healthcare system