Construction of Empirical Care Pathways Process Models from Multiple Real-World Datasets

Care pathways (CPWs) are "multidisciplinary care plans that detail essential care steps for patients with specific clinical problems." While CPWs impact on health or cost outcomes is vastly studied, an in-depth analysis of the real-world implementation of the CPWs is an area that still remains underexplored. The present work describes how to apply an existing process mining methodology to construct the empirical CPW process models. These process models are a unique piece of information for health services research: for example to evaluate their conformance against the theoretical CPW described on clinical guidelines or to evaluate the impact of the process in health outcomes. To this purpose, this work relies on the design and implementation of a solution that a) synthesizes the expert knowledge on how health care is delivered within and across providers as an activity log, and b) constructs the CPW process model from that activity log using process mining techniques. Unlike previous research based on ad hoc data captures, current approach is built on the linkage of various heterogeneous real-world data (RWD) sets that share a minimum semantic linkage. RWD, defined as secondary use of routinely collected data as opposite to ad hoc data extractions, is a unique source of information for the CPW analysis due to its coverage of the caregiving activities and its wide availability. The viability of the solution is demonstrated by constructing the CPW process model of Code Stroke (Acute Stroke CPW) in the Aragon region (Spain)

Reversible collapse of insoluble monolayers

II Encuentro sobre nanociencia y nanotecnología de investigadores y tecnólogos de la Universidad de Córdoba. NANOUC

Colapso reversible de monocapas insolubles. Influencia de la línea de tensión de los dominios

II Encuentro sobre nanociencia y nanotecnología de investigadores y tecnólogos de la Universidad de Córdoba. NANOUC

Prevalence and predictors of inadequate patient medication knowledge

© 2016 John Wiley & Sons, Ltd. Objectives: To assess medication knowledge in adult patients and to explore its determinants. Method: Cross-sectional study. Medication knowledge was the primary outcome and was assessed using a previously validated questionnaire. A multivariate logistic regression analysis was performed to explore the association between medication knowledge and the factors included in the model. Results: Seven thousand two hundred seventy-eight patients participated in the study. 71.9% (n = 5234) (95% CI: 70.9%–73.0%) of the surveyed patients had an inadequate knowledge of the medication they were taking. The dimensions obtaining the highest level of knowledge were the ‘medication use process’ and ‘therapeutic objective of medication’. The items ‘frequency’ (75.4%), ‘dosage’ (74.5%) and ‘indication’ (70.5%) had the highest percentage of knowledge. Conversely, ‘medication safety’ represented the dimension with the lowest scores, ranging from 12.6% in the item “contraindications” to 15.3% in the item ‘side effects’. The odds ratio (OR) of having an inadequate medication knowledge increased for unskilled workers (OR: 1.33; 85% CI:1.00–1.78; P = 0.050), caregivers (OR:1.46; 95% CI:1.18–1.81; P < 0.001), patients using more than one medication (OR: 1.14; 95% CI: 1.00–1.31; P = 0.050) and patients who did not know the name of the medication they were taking (OR: 2.14, 95% CI: 1.71–2.68 P < 0.001). Conclusion: Nearly three quarters of the analysed patients had inadequate knowledge regarding the medicines they were taking. Unskilled workers and caregivers were at a higher risk of lacking of medication knowledge. Other factors that correlated with inadequate medication knowledge were the use of more than one drug and not knowing the name of the medication dispensed

Vibration-based adaptive novelty detection method for monitoring faults in a kinematic chain

Postprint (published version

Interactivity in the Generation of Test Cases with Evolutionary Computation

Test generation is a costly but necessary testing activity to increase the quality of software projects. Automated testing tools based on evolutionary computation principles constitute an appealing modern approach to support testing tasks. However, these tools still find difficulties to detect certain types of plausible faults in real-world projects. Besides, recent studies have shown that, in general, automatically-generated tests do not resemble those manually written and, consequently, testers are reluctant to adopt them. We observe two key issues, namely the opacity of the process and the lack of cooperation with the tester, currently hampering the acceptance of automated results. Based on these findings, we explore in this paper how the interaction between current tools and expert testers would help address the test case generation problem. More specifically, we identify a number of interaction opportunities related to the object-oriented test case design driven to boost their readability and detection power. Using EvoSuite as base implementation, we present a proof of concept focused on the possibility to integrate readability assessment of the most promising test suites into a genetic algorithm.Work partially funded by the European Commission (ERDF), the Spanish Ministry of Science, Innovation and Universities [RTI2018-093608-BC33 and RED2018-102472-T], the University of Cordoba (Plan Propio - mod. 2.4), and the University of Malag

Interventions to facilitate shared decision-making using decision aids with patients in Primary Health Care: A systematic review

BACKGROUND: Shared decision making (SDM) is a process within the physician-patient relationship applicable to any clinical action, whether diagnostic, therapeutic, or preventive in nature. It has been defined as a process of mutual respect and participation between the doctor and the patient. The aim of this study is to determine the effectiveness of decision aids (DA) in primary care based on changes in adherence to treatments, knowledge, and awareness of the disease, conflict with decisions, and patients'' and health professionals'' satisfaction with the intervention. METHODS: A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted in Medline, CINAHL, Embase, the Cochrane Central Register of Controlled Trials, and the NHS Economic Evaluation Database. The inclusion criteria were randomized clinical trials as study design; use of SDM with DA as an intervention; primary care as clinical context; written in English, Spanish, and Portuguese; and published between January 2007 and January 2019. The risk of bias of the included studies in this review was assessed according to the Cochrane Collaboration''s tool. RESULTS: Twenty four studies were selected out of the 201 references initially identified. With the use of DA, the use of antibiotics was reduced in cases of acute respiratory infection and decisional conflict was decreased when dealing with the treatment choice for atrial fibrillation and osteoporosis. The rate of determination of prostate-specific antigen (PSA) in the prostate cancer screening decreased and colorectal cancer screening increased. Both professionals and patients increased their knowledge about depression, type 2 diabetes, and the perception of risk of acute myocardial infarction at 10 years without statins and with statins. The satisfaction was greater with the use of DA in choosing the treatment for depression, in cardiovascular risk management, in the treatment of low back pain, and in the use of statin therapy in diabetes. Blinding of outcomes assessment was the most common bias. CONCLUSIONS: DA used in primary care are effective to reduce decisional conflict and improve knowledge on the disease and treatment options, awareness of risk, and satisfaction with the decisions made. More studies are needed to assess the impact of shared decision making in primary care