Referral to rehabilitation following traumatic brain injury: practitioners and the process of decision-making

The study aimed to examine the factors influencing referral to rehabilitation following traumatic brain injury (TBI) by using social problems theory as a conceptual model to focus on practitioners and the process of decision-making in two Australian hospitals. The research design involved semi-structured interviews with 18 practitioners and observations of 10 team meetings, and was part of a larger study on factors influencing referral to rehabilitation in the same settings. Analysis revealed that referral decisions were influenced primarily by practitioners' selection and their interpretation of clinical and non-clinical patient factors. Further, practitioners generally considered patient factors concurrently during an ongoing process of decision-making, with the combinations and interactions of these factors forming the basis for interpretations of problems and referral justifications. Key patient factors considered in referral decisions included functional and tracheostomy status, time since injury, age, family, place of residence and Indigenous status. However, rate and extent of progress, recovery potential, safety and burden of care, potential for independence and capacity to cope were five interpretative themes, which emerged as the justifications for referral decisions. The subsequent negotiation of referral based on patient factors was in turn shaped by the involvement of practitioners. While multi-disciplinary processes of decision-making were the norm, allied health professionals occupied a central role in referral to rehabilitation, and involvement of medical, nursing and allied health practitioners varied. Finally, the organizational pressures and resource constraints, combined with practitioners' assimilation of the broader efficiency agenda were central factors shaping referral. (C) 2004 Elsevier Ltd. All rights reserved.

Clinical decision-making and myocardial viability: current perspectives

Not all myocardium involved in a myocardial infarction is dead or irreversibly damaged. The balance between the amount of scar and live tissue, and the nature of the live tissue, determine the likelihood that contractile function will improve after revascularisation. This improvement (which defines viability) may be predicted with about 80% accuracy using several techniques. This review examines the determinants of functional recovery and how they may be integrated in making decisions regarding revascularisation. (Intern Med J 2005; 35: 118–125)

Factors influencing Hong Kong Chinese patients' decision-making in seeking early treatment for acute myocardial infarction

The purpose of this study was to identify, through in-depth interview, factors that influenced 27 Hong Kong Chinese patients' decision-making in seeking early treatment for acute myocardial infarction (AMI). The median delay time from the onset of symptoms to arrival at the hospital was 15.6 hours for men and 53.7 hours for women. Three major categories emerged from the data: (a) becoming aware of the threat, (b) maintaining a sense of normality, and (c) struggling to mobilize resources. A variety of decisions were made by patients from the onset of chest Pain to seeking help. These decisions were heavily influenced by healthcare factors (access to emergency medical service (EMS) and treatment), personal factors (cognitive interpretations of symptoms), sociocultural factors (family situation, cultural beliefs, and practices), and coping strategies. (c) 2006 Wiley Periodicals, Inc.

End-of-life decision making in Europe and Australia - A physician survey

Background: The frequencies with which physicians make different medical end-of-life decisions (ELDs) may differ between countries, but comparison between countries has been difficult owing to the use of dissimilar research methods. Methods: A written questionnaire was sent to a random sample of physicians from 9 specialties in 6 European countries and Australia to investigate possible differences in the frequencies of physicians' willingness to perform ELDs and to identify predicting factors. Response rates ranged from 39% to 68% (N= 10 139). Using hypothetical cases, physicians were asked whether they would ( probably) make each of 4 ELDs. Results: In all the countries, 75% to 99% of physicians would withhold chemotherapy or intensify symptom treatment at the request of a patient with terminal cancer. In most cases, more than half of all physicians would also be willing to deeply sedate such a patient until death. However, there was generally less willingness to administer drugs with the explicit intention of hastening death at the request of the patient. The most important predictor of ELDs was a request from a patient with decisional capacity (odds ratio, 2.1-140.0). Shorter patient life expectancy and uncontrollable pain were weaker predictors but were more stable across countries and across the various ELDs (odds ratios, 1.1-2.4 and 0.9-2.4, respectively). Conclusion: Cultural and legal factors seem to influence the frequencies of different ELDs and the strength of their determinants across countries, but they do not change the essence of decision making.

Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector

Guest editorial Ali Emrouznejad is a Senior Lecturer at the Aston Business School in Birmingham, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining. He has published widely in various international journals. He is an Associate Editor of IMA Journal of Management Mathematics and Guest Editor to several special issues of journals including Journal of Operational Research Society, Annals of Operations Research, Journal of Medical Systems, and International Journal of Energy Management Sector. He is in the editorial board of several international journals and co-founder of Performance Improvement Management Software. William Ho is a Senior Lecturer at the Aston University Business School. Before joining Aston in 2005, he had worked as a Research Associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University. His research interests include supply chain management, production and operations management, and operations research. He has published extensively in various international journals like Computers & Operations Research, Engineering Applications of Artificial Intelligence, European Journal of Operational Research, Expert Systems with Applications, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: An International Journal, and so on. His first authored book was published in 2006. He is an Editorial Board member of the International Journal of Advanced Manufacturing Technology and an Associate Editor of the OR Insight Journal. Currently, he is a Scholar of the Advanced Institute of Management Research. Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector This special issue aims to focus on holistic, applied research on performance measurement in energy sector management and for publication of relevant applied research to bridge the gap between industry and academia. After a rigorous refereeing process, seven papers were included in this special issue. The volume opens with five data envelopment analysis (DEA)-based papers. Wu et al. apply the DEA-based Malmquist index to evaluate the changes in relative efficiency and the total factor productivity of coal-fired electricity generation of 30 Chinese administrative regions from 1999 to 2007. Factors considered in the model include fuel consumption, labor, capital, sulphur dioxide emissions, and electricity generated. The authors reveal that the east provinces were relatively and technically more efficient, whereas the west provinces had the highest growth rate in the period studied. Ioannis E. Tsolas applies the DEA approach to assess the performance of Greek fossil fuel-fired power stations taking undesirable outputs into consideration, such as carbon dioxide and sulphur dioxide emissions. In addition, the bootstrapping approach is deployed to address the uncertainty surrounding DEA point estimates, and provide bias-corrected estimations and confidence intervals for the point estimates. The author revealed from the sample that the non-lignite-fired stations are on an average more efficient than the lignite-fired stations. Maethee Mekaroonreung and Andrew L. Johnson compare the relative performance of three DEA-based measures, which estimate production frontiers and evaluate the relative efficiency of 113 US petroleum refineries while considering undesirable outputs. Three inputs (capital, energy consumption, and crude oil consumption), two desirable outputs (gasoline and distillate generation), and an undesirable output (toxic release) are considered in the DEA models. The authors discover that refineries in the Rocky Mountain region performed the best, and about 60 percent of oil refineries in the sample could improve their efficiencies further. H. Omrani, A. Azadeh, S. F. Ghaderi, and S. Abdollahzadeh presented an integrated approach, combining DEA, corrected ordinary least squares (COLS), and principal component analysis (PCA) methods, to calculate the relative efficiency scores of 26 Iranian electricity distribution units from 2003 to 2006. Specifically, both DEA and COLS are used to check three internal consistency conditions, whereas PCA is used to verify and validate the final ranking results of either DEA (consistency) or DEA-COLS (non-consistency). Three inputs (network length, transformer capacity, and number of employees) and two outputs (number of customers and total electricity sales) are considered in the model. Virendra Ajodhia applied three DEA-based models to evaluate the relative performance of 20 electricity distribution firms from the UK and the Netherlands. The first model is a traditional DEA model for analyzing cost-only efficiency. The second model includes (inverse) quality by modelling total customer minutes lost as an input data. The third model is based on the idea of using total social costs, including the firm’s private costs and the interruption costs incurred by consumers, as an input. Both energy-delivered and number of consumers are treated as the outputs in the models. After five DEA papers, Stelios Grafakos, Alexandros Flamos, Vlasis Oikonomou, and D. Zevgolis presented a multiple criteria analysis weighting approach to evaluate the energy and climate policy. The proposed approach is akin to the analytic hierarchy process, which consists of pairwise comparisons, consistency verification, and criteria prioritization. In the approach, stakeholders and experts in the energy policy field are incorporated in the evaluation process by providing an interactive mean with verbal, numerical, and visual representation of their preferences. A total of 14 evaluation criteria were considered and classified into four objectives, such as climate change mitigation, energy effectiveness, socioeconomic, and competitiveness and technology. Finally, Borge Hess applied the stochastic frontier analysis approach to analyze the impact of various business strategies, including acquisition, holding structures, and joint ventures, on a firm’s efficiency within a sample of 47 natural gas transmission pipelines in the USA from 1996 to 2005. The author finds that there were no significant changes in the firm’s efficiency by an acquisition, and there is a weak evidence for efficiency improvements caused by the new shareholder. Besides, the author discovers that parent companies appear not to influence a subsidiary’s efficiency positively. In addition, the analysis shows a negative impact of a joint venture on technical efficiency of the pipeline company. To conclude, we are grateful to all the authors for their contribution, and all the reviewers for their constructive comments, which made this special issue possible. We hope that this issue would contribute significantly to performance improvement of the energy sector.

Investigating the influence of African American and African Caribbean race on primary care doctors' decision making about depression

This paper explores differences in how primary care doctors process the clinical presentation of depression by African American and African-Caribbean patients compared with white patients in the US and the UK. The aim is to gain a better understanding of possible pathways by which racial disparities arise in depression care. One hundred and eight doctors described their thought processes after viewing video recorded simulated patients presenting with identical symptoms strongly suggestive of depression. These descriptions were analysed using the CliniClass system, which captures information about micro-components of clinical decision making and permits a systematic, structured and detailed analysis of how doctors arrive at diagnostic, intervention and management decisions. Video recordings of actors portraying black (both African American and African-Caribbean) and white (both White American and White British) male and female patients (aged 55 years and 75 years) were presented to doctors randomly selected from the Massachusetts Medical Society list and from Surrey/South West London and West Midlands National Health Service lists, stratified by country (US v.UK), gender, and years of clinical experience (less v. very experienced). Findings demonstrated little evidence of bias affecting doctors' decision making processes, with the exception of less attention being paid to the potential outcomes associated with different treatment options for African American compared with White American patients in the US. Instead, findings suggest greater clinical uncertainty in diagnosing depression amongst black compared with white patients, particularly in the UK. This was evident in more potential diagnoses. There was also a tendency for doctors in both countries to focus more on black patients' physical rather than psychological symptoms and to identify endocrine problems, most often diabetes, as a presenting complaint for them. This suggests that doctors in both countries have a less well developed mental model of depression for black compared with white patients. © 2014 The Authors.

Personal autonomy in discharge planning decision -making in mentally alert hospitalized elderly individuals: The social workers' perspective

This study was conducted to understand (a) hospital social workers' perspectives about patients' personal autonomy and self-determination, (b) their experiences, and (c) their beliefs and behaviors. The study used the maximum variation sampling strategy to select hospitals and hospital social work respondents. Individual interviews were conducted with 31 medical/surgical and mental health hospital social workers who worked in 13 hospitals. The data suggest the following four points. First, the hospital setting as an outside influence as it relates to illness and safety, and its four categories, mentally alert patients, family members, health care professionals, and social work respondents, seems to enhance or diminish patients' autonomy in discharge planning decision making. Second, respondents report they believe patients must be safe both inside and outside the hospital. In theory, respondents support autonomy and self-determination, respect patients' wishes, and believe patients are the decision makers. However, in practice, respondents respect autonomy and self-determination to a point. Third, a model, The Patient's Decision in Discharge Planning: A Continuum, is presented where a safe discharge plan is at one end of a continuum, while an unsafe discharge plan is at the other end. Respondents respect personal autonomy and the patient's self-determination to a point. This point is likely to be located in a gray area where the patient's decision crosses from one end of the continuum to the other. When patients decide on an unsafe discharge plan, workers' interventions range from autonomy to paternalism. And fourth, the hospital setting as an outside influence may not offer the best opportunity for patients to make decisions (a) because of beliefs family members and health care professionals hold about the value of patient self-determination, and (b) because patients may not feel free to make decisions in an environment where they are surrounded by family members, health care professionals, and social work respondents who have power and who think they know best. Workers need to continue to educate elderly patients about their right to self-determination in the hospital setting. ^

Combined Machine Learning Techniques for Decision Making Support in Medicine

Computational intelligent support for decision making is becoming increasingly popular and essential among medical professionals. Also, with the modern medical devices being capable to communicate with ICT, created models can easily find practical translation into software. Machine learning solutions for medicine range from the robust but opaque paradigms of support vector machines and neural networks to the also performant, yet more comprehensible, decision trees and rule-based models. So how can such different techniques be combined such that the professional obtains the whole spectrum of their particular advantages? The presented approaches have been conceived for various medical problems, while permanently bearing in mind the balance between good accuracy and understandable interpretation of the decision in order to truly establish a trustworthy ‘artificial’ second opinion for the medical expert.

Identifying and enhancing risk thresholds in the detection of elder financial abuse: A signal detection analysis of professionals' decision making

Background: Financial abuse of elders is an under acknowledged problem and professionals' judgements contribute to both the prevalence of abuse and the ability to prevent and intervene. In the absence of a definitive "gold standard" for the judgement, it is desirable to try and bring novice professionals' judgemental risk thresholds to the level of competent professionals as quickly and effectively as possible. This study aimed to test if a training intervention was able to bring novices' risk thresholds for financial abuse in line with expert opinion. Methods: A signal detection analysis, within a randomised controlled trial of an educational intervention, was undertaken to examine the effect on the ability of novices to efficiently detect financial abuse. Novices (n = 154) and experts (n = 33) judged "certainty of risk" across 43 scenarios; whether a scenario constituted a case of financial abuse or not was a function of expert opinion. Novices (n = 154) were randomised to receive either an on-line educational intervention to improve financial abuse detection (n = 78) or a control group (no on-line educational intervention, n = 76). Both groups examined 28 scenarios of abuse (11 "signal" scenarios of risk and 17 "noise" scenarios of no risk). After the intervention group had received the on-line training, both groups then examined 15 further scenarios (5 "signal" and 10 "noise" scenarios). Results: Experts were more certain than the novices, pre (Mean 70.61 vs. 58.04) and post intervention (Mean 70.84 vs. 63.04); and more consistent. The intervention group (mean 64.64) were more certain of abuse post-intervention than the control group (mean 61.41, p = 0.02). Signal detection analysis of sensitivity (Á) and bias (C) revealed that this was due to the intervention shifting the novices' tendency towards saying "at risk" (C post intervention -.34) and away from their pre intervention levels of bias (C-.12). Receiver operating curves revealed more efficient judgments in the intervention group. Conclusion: An educational intervention can improve judgements of financial abuse amongst novice professionals.

Clinicians’ perceptions of decision making regarding discharge from public hospitals to in-patient rehabilitation following trauma

Objective: The aim of the present study was to investigate the perceptions of consultant surgeons, allied health clinicians and rehabilitation consultants regarding discharge destination decision making from the acute hospital following trauma.Methods: A qualitative study was performed using individual in-depth interviews of clinicians in Victoria (Australia) between April 2013 and September 2014. Thematic analysis was used to derive important themes. Case studies provided quantitative information to enhance the information gained via interviews.Results: Thirteen rehabilitation consultants, eight consultant surgeons and 13 allied health clinicians were interviewed. Key themes that emerged included the importance of financial considerations as drivers of decision making and the perceived lack of involvement of medical staff in decisions regarding discharge destination following trauma. Other themes included the lack of consistency of factors thought to be important drivers of discharge and the difficulty in acting on trauma patients’ requests in terms of discharge destination. Importantly, as the complexity of the patient increases in terms of acquired brain injury, the options for rehabilitation become scarcer.Conclusions: The information gained in the present study highlights the large variation in discharge practises between and within clinical groups. Further consultation with stakeholders involved in the care of trauma patients, as well as government bodies involved in hospital funding, is needed to derive a more consistent approach to discharge destination decision making.

Rise of the machines: a critical consideration of automated leadership decision making in organizations

Machines are increasingly becoming a substitute for human skills and intelligence in a number of fields where decisions that are crucial to group performance have to be taken under stringent constraints—for example, when an army contingent has to devise battlefield tactics or when a medical team has to diagnose and treat a life-threatening condition or illness. We hypothesize a scenario where similar machine-based intelligent technology is available to support, and even substitute human decision making in an organizational leadership context. We do not engage in any metaphysical debate on the plausibility of such a scenario. Rather, we contend that given what we observe in several other fields of human decision making, such a scenario may very well eventuate in the near future. We argue a number of “positives” that can be expected to emerge out of automated group and organizational leadership decision making. We also posit several anti-theses—“negatives” that can also potentially emerge from the hypothesized scenario and critically consider their implications. We aim to bring leadership and organization theorists, as well as researchers in machine intelligence, together at the discussion table for the first time and postulate that while leadership decision making in a group/organizational context could be effectively delegated to an artificial-intelligence (AI)-based decision system, this would need to be subject to the devising of crucial safeguarding conditions.

Group decision making in health care: a case study of multidisciplinary meetings

Recent studies have demonstrated that Multi-Disciplinary Meetings (MDM) practiced in some medical contexts can contribute to positive health care outcomes. The group reasoning and decision-making in MDMs has been found to be most effective when deliberations revolve around the patient’s needs, comprehensive information is available during the meeting, core members attend and the MDM is effectively facilitated. This article presents a case study of the MDMs in cancer care in a region of Australia. The case study draws on a group reasoning model called the Reasoning Community model to analyse MDM deliberations to illustrate that many factors are important to support group reasoning, not solely the provision of pertinent information. The case study has implications for the use of data analytics in any group reasoning context.