Development of a Shared Decision-Making Program Theory: A Realist Synthesis Examining Contexts and Mechanisms to Engagement

Purpose: Shared Decision-making (SDM) is a style of medical decision-making that focuses on balancing the relationship between patients, physicians, and other key players. SDM is purported to improve patient and system outcomes; however, the potential effectiveness is challenged in part due to gaps in the current literature between theory and implementation. With my team, I conducted a realist synthesis of SDM literature to identify “In which situations, how, why, and for whom does SDM between patients and health care providers contribute to improved patient-centered decisions?” Method: We conducted a seven step iterative process, including: preliminary theory development, establishment of a search strategy, selection and appraisal of literature, data extraction, identification of formal theories, analysis and synthesis of extracted results from literature, and formation of a revised program theory with the input of patients, physicians, nurse navigators, and policy makers from a stakeholder session Results: We developed a program theory comprised of eight complex, interrelated mechanisms, three contexts, and a single outcome of engagement in SDM. Conclusion: Our realist synthesis produced a program theory for SDM through the identification of mechanisms which shape the characteristics of when, how, and why SDM will, and will not, work. This research hypothesizes that by facilitating high engagement of SDM, medical consultations will lead to informed, patient-centered decisions

Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

The Effects of Pattern Recognition Based Simulation Scenarios on Symptom Recognition of Myocardial Infarction, Critical Thinking, Clinical Decision-Making, and Clinical Judgment in Nursing Students

In the United States nearly 1 million annual new and recurrent myocardial infarctions (MI) occur with 10% of patients hospitalized with MI having unrecognized ischemic symptoms. Inexperienced nurses are expected to accurately interpret cardiac symptom cues, possibly without ever having experienced care of patients with MI, yet have been shown to be less able to classify symptom cues and reach accurate conclusions than experienced nurses. The purpose of this study was to test an educational intervention using theories of pattern recognition to develop CT in MI and improve nursing students’ clinical decision-making and clinical judgment using high fidelity patient simulation. This study used a quasi-experimental three group pre-/post-test design and qualitative data to triangulate information on critical thinking, clinical decision-making, and clinical judgment in MI. A sample of junior baccalaureate in nursing students (N = 54) from a large metropolitan university were divided in pairs and randomized to one of two control groups. Data were collected with instruments which measured pattern recognition in MI, critical thinking in MI, and self-perception of clinical decision-making. In addition, diagnostic efficiency and accuracy were measured. Triangulation on clinical decision making with semi-structured interviews using ‘thinking aloud’ technique was conducted. Data were analyzed as qualitative data and compared among groups. Students who were given prototypes for MI using simulation significantly improved critical thinking as measured by pattern recognition in MI (t(3.153(2), p = .038) compared with the non-simulation control group. Qualitative findings showed that students receiving the experimental simulation with a non-MI scenario and feedback-based debriefing had greatest gains in clinical reasoning which included development of clinical decision-making using analytic hypothetico-deductive and Bayesian reasoning processes and learned avoidance of heuristics. Students receiving the experimental simulation learned to identify salient symptom cues, analyzed data more complexly, and reflected on their simulation experience in a way which students reported improved learning. Students who were given MI only simulation scenarios developed deleterious heuristics and showed fewer gains in clinical reasoning, though both simulation groups demonstrated greater critical thinking ability than the non-simulation control group. Findings support the use of simulation to improve clinical reasoning including pattern recognition and clinical decision-making, and emphasize the significance of simulation scenario construction and debriefing to achieving learning outcomes. The findings could be used to guide further research to improve critical thinking, clinical decision-making, and clinical judgment in nursing students using simulation. Funding for this study was provided by the American Association of Critical Care Nurses and Philips Medical Systems and a testing grant from Elsevier, Assessment

Benchmarking in cluster analysis: A white paper

To achieve scientific progress in terms of building a cumulative body of knowledge, careful attention to benchmarking is of the utmost importance. This means that proposals of new methods of data pre-processing, new data-analytic techniques, and new methods of output post-processing, should be extensively and carefully compared with existing alternatives, and that existing methods should be subjected to neutral comparison studies. To date, benchmarking and recommendations for benchmarking have been frequently seen in the context of supervised learning. Unfortunately, there has been a dearth of guidelines for benchmarking in an unsupervised setting, with the area of clustering as an important subdomain. To address this problem, discussion is given to the theoretical conceptual underpinnings of benchmarking in the field of cluster analysis by means of simulated as well as empirical data. Subsequently, the practicalities of how to address benchmarking questions in clustering are dealt with, and foundational recommendations are made

Rational decision-making in medicine: implications for overuse and underuse

open access articleIn spite of substantial spending and resource utilization, today's health care remains characterized by poor outcomes, largely due to overuse (over-testing/treatment) or underuse (under-testing/treatment) of services. To a significant extent, this is a consequence of low-quality decision-making that appears to violate various rationality criteria. Such sub-optimal decision-making is considered a leading cause of death and is responsible for more than 80% of health expenses. In this paper, we address the issue of overuse or underuse of healthcare interventions from the perspective of rational choice theory. We show that what is considered rational under one decision theory may not be considered rational under a different theory. We posit that the questions and concerns regarding both underuse and overuse have to be addressed within a specific theoretical framework. The applicable rationality criterion, and thus the “appropriateness” of health care delivery choices, depends on theory selection that is appropriate to specific clinical situations. We provide a number of illustrations showing how the choice of theoretical framework influences both our policy and individual decision-making. We also highlight the practical implications of our analysis for the current efforts to measure the quality of care and link such measurements to the financing of healthcare services