On Intelligence Augmentation and Visual Analytics to Enhance Clinical Decision Support Systems

Human-in-the-loop intelligence augmentation (IA) methods combined with visual analytics (VA) have the potential to provide additional functional capability and cognitively driven interpretability to Decision Support Systems (DSS) for health risk assessment and patient-clinician shared decision making. This paper presents some key ideas underlying the synthesis of IA with VA (IA/VA) and the challenges in the design, implementation, and use of IA/VA-enabled clinical decision support systems (CDSS) in the practice of medicine through data driven analytical models. An illustrative IA/VA solution provides a visualization of the distribution of health risk, and the impact of various parameters on the assessment, at the population and individual levels. It also allows the clinician to ask “what-if” questions using interactive visualizations that change actionable risk factors of the patient and visually assess their impact. This approach holds promise in enhancing decision support systems design, deployment and use outside the medical sphere as well

Process Mining in Frail Elderly Care: A Literature Review

Process mining has proved to be a valuable technique for extracting process knowledge from data within information systems. Much work has been conducted in applying process mining to domains such as logistics, banking, transportation and many areas of the government, including healthcare. Frail elderly people who have an increased risk of adverse outcomes are amongst the main users of healthcare services and understanding healthcare processes for the frail elderly is challenging because of their diverse and complex needs combined with an often high number of co-morbidities. This paper aims to provide an overview of work applying process mining techniques to improving the care of frail elderly people. We conducted a literature search using broad criteria to identify 1,047 potential papers followed by a review of titles, abstract and content which identified eight papers where process mining techniques have been successfully applied to the care of frail elderly people. Our review shows that, to date, there has been limited application of process mining to support this important segment of the population. We summarise the results based on five themes that emerged: types of source data and process; geographical location; analysis methodology; medical domain; and challenges. Our paper concludes with a discussion on the issues and opportunities for process mining to improve the care pathways for frail elderly people

Status report - Individual, programmatic and systemic indicators of the quality of mental health care using a large health administrative database: an avenue for preventing suicide mortality

Suicide is a major public health issue in Canada. The quality of health care services, in addition to other individual and population factors, has been shown to affect suicide rates. In publicly managed care systems, such as systems in Canada and the United Kingdom, the quality of health care is manifested at the individual, program and system levels. Suicide audits are used to assess health care services in relation to the deaths by suicide at individual level and when aggregated at the program and system levels. Large health administrative databases comprise another data source used to inform population-based decisions at the system, program and individual levels regarding mental health services that may affect the risk of suicide. This status report paper describes a project we are conducting at the Institut national de santé publique du Québec (INSPQ) with the Quebec Integrated Chronic Disease Surveillance System (QICDSS) in collaboration with colleagues from Wales (United Kingdom) and the Norwegian Institute of Public Health. This study describes the development of quality of care indicators at three levels and the corresponding statistical analysis strategies designed. We propose 13 quality of care indicators, including system-level and several population-level determinants, primary care treatment, specialist care, the balance between care sectors, emergency room utilization, and mental health and addiction budgets, that may be drawn from a chronic disease surveillance system

Modified Needleman-Wunsch algorithm for clinical pathway clustering

Clinical pathways are used to guide clinicians to provide a standardised delivery of care. Because of their standardisation, the aim of clinical pathways is to reduce variation in both care process and patient outcomes. When learning clinical pathways from data through data mining, it is common practice to represent each patient pathway as a string corresponding to their movements through activities. Clustering techniques are popular methods for pathway mining, and therefore this paper focuses on distance metrics applied to string data for k-medoids clustering. The two main aims are to firstly, develop a technique that seamlessly integrates expert information with data and secondly, to develop a string distance metric for the purpose of process data. The overall goal was to allow for more meaningful clustering results to be found by adding context into the string similarity calculation. Eight common distance metrics and their applicability are discussed. These distance metrics prove to give an arbitrary distance, without consideration for context, and each produce different results. As a result, this paper describes the development of a new distance metric, the modified Needleman–Wunsch algorithm, that allows for expert interaction with the calculation by assigning groupings and rankings to activities, which provide context to the strings. This algorithm has been developed in partnership with UK’s National Health Service (NHS) with the focus on a lung cancer pathway, however the handling of the data and algorithm allows for application to any disease type. This method is contained within Sim.Pro.Flow, a publicly available decision support tool

Análise de conformidade em processos de saúde: obesidade no Hospital da Luz de Lisboa

Mestrado Bolonha em Métodos Quantitativos para a Decisão Económica e EmpresarialA adoção de protocolos clínicos por parte das organizações de saúde surge da necessidade de reforçar a qualidade do tratamento prestado, aliando as exigências do setor à evolução tecnológica verificada na análise de procedimentos médicos. Tendo em conta a complexidade dos processos clínicos, verifica-se a existência de desvios entre o percurso idealmente percorrido pelo paciente, descrito nos protocolos de prática clínica, e o seu trajeto real, o que acaba por condicionar a qualidade do serviço prestado e, potencialmente, os resultados clínicos obtidos. Desta forma, a avaliação da conformidade existente nos processos de saúde é crucial para que os protocolos clínicos implementados possam ser melhorados e, consequentemente, aumentada a eficiência no tratamento de cada paciente. O presente estudo tem como objetivo avaliar a conformidade existente entre a prática clínica observada e o percurso clínico pré-estabelecido, recorrendo a técnicas de Process Mining. O modelo proposto avalia a conformidade existente numa amostra de 69 pacientes do Hospital da Luz de Lisboa, que realizaram cirurgia bariátrica como forma de tratamento para a obesidade, utilizando o software ProM. Tendo em conta os resultados obtidos, não existe evidência de conformidade total com o protocolo clínico, pois foi possível concluir que nenhum paciente cumpre na integralidade o trajeto pré-definido. Verifica-se, assim, um grande espaço para melhoria, uma vez que os valores obtidos para a fitness individual se encontram muito aquém do considerado como satisfatório e, em média, apenas um quarto do trajeto teórico foi concluído pelos pacientes.The implementation of clinical protocols by health organizations arises from the need to increase the quality of the treatment provided, combining the demands of the sector with the technological evolution verified in the analysis of medical procedures. Considering the complexity of clinical processes, there are deviations between the pathway ideally taken by the patient, described in the clinical practice protocols, and their actual pathway, which ultimately affect the quality of the service provided and, potentially, the clinical outcomes. In this way, the evaluation of existing conformance in health processes is crucial so that the implemented clinical protocols can be improved and, consequently, the efficiency in the treatment of each patient increases. This study intends to evaluate the existing conformance between the observed clinical practice and the pre-established clinical pathway, using Process Mining techniques. The proposed model evaluates the existing compliance in a sample of 69 clinical cases from Hospital da Luz de Lisboa, who underwent bariatric surgery as a form of treatment for obesity, using the Process Mining software - ProM. Taking into account the obtained results, it was concluded that no patient fully complies with the pre-defined pathway, with no evidence of full conformance with the clinical protocol. There is a large room for improvement, since the values obtained for the individual fitness fall far short of what is considered satisfactory and, on average, only a quarter of the theoretical pathway was completed by the patients.info:eu-repo/semantics/publishedVersio