Development, Implementation and Evaluation of Medical Decision Support Systems Based on Mortality Prediction Algorithms from an Operations Research Perspective.

Wide implementation of electronic health record systems provides rich data for personalized medicine. One topic of great interest is to develop methods to assist physicians in prognosis for example mortality. While many studies have reported on various new prediction models and algorithms there is relatively little literature on if and how these new prediction methods translate into actual benefits. My dissertation consists of three theses that aims at filling this gap between prognostic predictions and clinical decisions in end-of-life care and intensive care settings. In the first thesis, we develop an approach to using temporal trends in physiologic data as an input into mortality prediction models. The approach uses penalized b-spline smoothing and functional PCA to summarize time series of patient data. we apply the methodology in two settings to demonstrate the value of using the shapes of health data time series as a predictor of patient prognosis. The first application a mortality predictor for advanced cancer patients that can help oncologists decide which patients should stop aggressive treatments and switch to palliative care such as that provided in hospice. The second one is a real-time near term mortality predictor for MICU patients that can work as an early alarm system to guide timely interventions. In the second thesis, we investigate the integration of a prediction algorithm with physician decision making, focusing on the advanced cancer patient setting. We design a retrospective study to compare prognoses made by doctors and those that would be recommended by the IMPAC algorithm developed in Chapter 1. We used the doctor\u27s discharge decision as a proxy of what they predict the patient as dying in 90 days and show that doctor\u27s predictions tend to very conservative. Although IMPAC on its own does not perform better than doctors in terms of precision and recall, we find that IMPAC and doctors identify significantly different group of positive cases. IMPAC and doctors are also good at identifying very different groups of patients in terms of survival time. We propose a new way to augment decisions of doctors with IMPAC. At the same recall, the augment method identifies 43\% more patients close to death than the doctors do. We also estimate potential hospitalizations and hospital length of stays avoided if the doctors use augmented procedure instead of acting on their own beliefs. In the third thesis, we look at the integration of a prediction algorithm with physician decision making, focusing on the ICU setting. We use a POMDP framework to evaluate how decision support systems based on ICU mortality predictions can help physicians allocate time to inspect the patients at highest risk of death. We assume physicians have limited time and seek to optimally allocate it to patients in order to minimize their mortality rate. Physicians can do Bayesian updates on observations of patient health state. A prediction algorithm can augment this process by sending alerts to physicians. We represent the algorithm by an arbitrary point on an ROC curve representing a particular alert threshold. We study two approaches to using the algorithm input: (1) Belief based policy (BBP) that integrates algorithm outputs using Bayesian updating; (2) Alarm triggered policy (ATP) where the physician responds only to the algorithm without updating, and compare them to benchmarks that do not rely on the algorithm at all. By running simulations, we explore how the accuracy of predictions can translate into lower mortality rates

Efectos de la fatiga anticipatoria y la sintomatología emocional en la percepción de fatiga física y cognitiva

El objetivo de este estudio fue doble, primero, analizar los efectos de la fatiga anticipatoria, la sintomatología emocional y la pertenencia a un grupo clínico sobre la percepción de fatiga física y mental; segundo, explorar el potencial efecto moderador de la anticipación de la fatiga en las relaciones entre la sintomatología o la condición clínica y la sensación percibida de fatiga. Se analiza mediante un diseño ex post facto y correlacional los efectos parciales y condicionados de las variables predictivas mediante diferentes análisis de regresión jerárquica. Participaron 317 sujetos (29% procedentes de población clínica). Se evaluó la fatiga anticipatoria (Escala elaborada ad hoc), la experiencia percibida de fatiga (Escala de Fatiga de Chalder et al., 1993), y la sintomatología emocional (GHQ-28 de Goldberg, 1996). Los resultados mostraron efectos significativos de la fatiga anticipatoria y la sintomatología emocional, predominantemente de la sintomatología depresiva sobre la percepción de fatiga física y mental. La pertenencia a un grupo clínico predecía de forma significativa y exclusiva la fatiga cognitiva. Además, la fatiga anticipatoria moderaba el efecto del grupo (clínico versus general) sobre la fatiga cognitiva. En conclusión, la sintomatología, principalmente la depresiva, y la fatiga anticipatoria, tienen un valor predictivo significativo en la experiencia percibida de la fatiga física y mental. La anticipación de fatiga moderaba el efecto del grupo clínico sobre la experiencia de fatiga cognitiva una vez controlada la sintomatología depresiva.The two fold aim of this study was first, to analyze the effects of anticipatory fatigue, emotional symptomatology and belonging to a clinical group on the physical and cognitive perception of fatigue, and second, to explore the potential moderating effect of anticipatory fatigue on the relationship between symptomatology or clinical condition and perceived fatigue. The conditional and partial effects of independent variables were analyzed by hierarchical regression in an ex-post-facto correlational design. The sample was composed of 317 participants (29% from a clinical population). Anticipatory fatigue (by an ad hoc scale), and perception of fatigue (by the Chalder Fatigue Scale) were measured. Emotional symptoms were assessed by Goldberg’s GHQ-28 questionnaire. Anticipatory fatigue and emotional symptoms (mainly depressive) had significant effects on cognitive and physical fatigue. Belonging to the clinical group significantly and exclusively predicted cognitive fatigue. Furthermore, anticipatory fatigue moderated between-group effects (clinical versus general) and cognitive fatigue. In brief, emotional symptoms (mainly depressive) and anticipatory fatigue significantly predicted perceived cognitive and physical fatigue. Anticipation of fatigue moderated the effect of clinical group on cognitive fatigue after controlling for depressive symptomatolog

Assessment and Management of Older People in the General Hospital Setting

Worldwide, populations are ageing. Older people, particularly centurions, represent the fastest growing sector and are counted as the success of the society. But not everyone ages successfully and enjoys good health. Many older people have multiple long-term medical, physical, mental, psychological and social problems. This can result in reduced quality of life, higher cost and poorer health outcome including increased mortality. Chronic diseases are associated with disability and low self-reported general health. In addition, physiological changes of ageing and consequent loss of functional reserve of the organ systems lead to the increased physical disability and dependency. Therefore, geriatric medicine could warrant a more holistic approach than general adult medicine. Nearly two-thirds of people admitted to hospital are over 65 years old and an increasing number are frail or have a diagnosis of dementia [1]. Our current training not only generates relatively low number of geriatricians but there also remains a huge need for better staff training and support to provide safe, holistic and dignified care. The cornerstone of modern geriatric medicine is the comprehensive geriatric assessment (CGA). This is defined as multidimensional, interdisciplinary diagnostic process that aims to determine a frail older person’s medical conditions, mental health, functional capability and social circumstances in order to develop a coordinated and integrated plan for treatment, rehabilitation and long-term follow-up [2]. All older people admitted to hospital with an acute medical illness, geriatric syndromes including falls, incontinence, delirium or immobility, unexplained functional dependency or need for rehabilitation warrant CGA. CGA could screen for treatable illnesses, establish the key diagnosis leading to hospital admission and formulate a rational therapeutic plan thus resulting in the improved outcome. This chapter starts with an introduction to the ageing nation and impact of ageing on hospitals. This will be followed by discussing physiological changes of ageing and the various components of multidisciplinary assessment for older people admitted to hospital with an acute illness that could lead to high-level holistic care. It also covers a wide range of issues and challenges which medical team/multidisciplinary teams often come across during routine care of acutely unwell older people. The chapter concludes by a literature review on current evidence on the effectiveness of CGA and recommendations to enhance clinical care

Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

BACKGROUND: Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. METHODS: We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. RESULTS: Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. CONCLUSIONS: We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting

Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions

Devices and Data Workflow in COPD Wearable Remote Patient Monitoring: A Systematic Review

Background: With global increase in Chronic Obstructive Pulmonary Disease (COPD) prevalence and mortality rates, and socioeconomical burden continuing to rise, current disease management strategies appear inadequate, paving the way for technological solutions, namely remote patient monitoring (RPM), adoption considering its acute disease events management benefit. One RPM’s category stands out, wearable devices, due to its availability and apparent ease of use. Objectives: To assess the current market and interventional solutions regarding wearable devices in the remote monitoring of COPD patients through a systematic review design from a device composition, data workflow, and collected parameters description standpoint. Methods: A systematic review was conducted to identify wearable device trends in this population through the development of a comprehensive search strategy, searching beyond the mainstream databases, and aggregating diverse information found regarding the same device. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed, and quality appraisal of identified studies was performed using the Critical Appraisal Skills Programme (CASP) quality appraisal checklists. Results: The review resulted on the identification of 1590 references, of which a final 79 were included. 56 wearable devices were analysed, with the slight majority belonging to the wellness devices class. Substantial device heterogeneity was identified regarding device composition type and wearing location, and data workflow regarding 4 considered components. Clinical monitoring devices are starting to gain relevance in the market and slightly over a third, aim to assist COPD patients and healthcare professionals in exacerbation prediction. Compliance with validated recommendations is still lacking, with no devices assessing the totality of recommended vital signs. Conclusions: The identified heterogeneity, despite expected considering the relative novelty of wearable devices, alerts for the need to regulate the development and research of these technologies, specially from a structural and data collection and transmission standpoints.Introdução: Com o aumento global das taxas de prevalência e mortalidade da Doença Pulmonar Obstrutiva Crónica (DPOC) e o seu impacto socioeconómico, as atuais estratégias de gestão da doença parecem inadequadas, abrindo caminho para soluções tecnológicas, nomeadamente para a adoção da monitorização remota, tendo em conta o seu benefício na gestão de exacerbações de doenças crónicas. Dentro destaca-se uma categoria, os dispositivos wearable, pela sua disponibilidade e aparente facilidade de uso. Objetivos: Avaliar as soluções existentes, tanto no mercado, como na área de investigação, relativas a dispositivos wearable utilizados na monitorização remota de pacientes com DPOC através de uma revisão sistemática, do ponto de vista da composição do dispositivo, fluxo de dados e descrição dos parâmetros coletados. Métodos: Uma revisão sistemática foi realizada para identificar tendências destes dispositivos, através do desenvolvimento de uma estratégia de pesquisa abrangente, procurando pesquisar para além das databases convencionais e agregar diversas informações encontradas sobre o mesmo dispositivo. Para tal, foram seguidas as diretrizes PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), e a avaliação da qualidade dos estudos identificados foi realizada utilizando a ferramenta CASP (Critical Appraisal Skills Programme). Resultados: A revisão resultou na identificação de 1590 referências, das quais 79 foram incluídas. Foram analisados 56 dispositivos wearable, com a ligeira maioria a pertencer à classe de dispositivos de wellness. Foi identificada heterogeneidade substancial nos dispositivos em relação à sua composição, local de uso e ao fluxo de dados em relação a 4 componentes considerados. Os dispositivos de monitorização clínica já evidenciam alguma relevância no mercado e, pouco mais de um terço, visam auxiliar pacientes com DPOC e profissionais de saúde na previsão de exacerbações. Ainda assim, é notória a falta do cumprimento das recomendações validadas, não estando disponíveis dispositivos que avaliem a totalidade dos sinais vitais recomendados. Conclusão: A heterogeneidade identificada, apesar de esperada face à relativa novidade dos dispositivos wearable, alerta para a necessidade de regulamentação do desenvolvimento e investigação destas tecnologias, especialmente do ponto de vista estrutural e de recolha e transmissão de dados

Development of the Assessment of Clinical Prediction Model Transportability (APT) Checklist

Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and tools. However, naïve implementations of external CPMs are prone to failure due to the incompatibilities between the environments of the development and implementation sites. Although prior research has described methods for estimating the external validity of predictive models, quantifying dataset shift, updating models, as well as numerous CPM-specific frameworks for guiding the development, evaluation, reporting, and systematic reviews of CPMs, there are no frameworks for assessing the compatibility between a CPM and the target environment. This dissertation addresses this critical gap by proposing a novel CPM transportability checklist for guiding the adoption of externally developed CPMs.To guide the development of the checklist, four extant CPM-relevant frameworks (TRIPOD, CHARMS, PROBAST, and GRASP) were reviewed and synthesized, thereby identifying the key domains of CPMs. Then, four individual studies were conducted, each identifying, assessing the impact of, and/or proposing solutions for the disparity between CPM and environment in those domains. The first two studies target disparities in features, with the first characterizing the non-generalizability impact of a particular class of commonly used, EHR-idiosyncratic features. The second study was conducted to identify and propose a solution for the semantic discrepancy in features across sites caused by the insufficient coverage of EHR data by standards. The third study focused on the prediction target of CPMs, identifying significant heterogeneity in disease understanding, phenotyping algorithms, and cohort characteristics of the same clinical condition. In the fourth study investigating CPM evaluation, the gap between typical CPM evaluation design and expected implemented behavior was identified, and a novel evaluative framework was proposed to bridge that gap. Finally, the APT checklist was developed using the synthesis of the aforementioned CPM frameworks as the foundation, enriched through the incorporation of innovations and findings from these four conducted studies. While rigorous meta-evaluation remains, the APT checklist shows promise as a tool for assessing CPM transportability thereby reducing the risk of failure of externally implemented CPMs. The key contributions to informatics include: the discovery of healthcare process (HCP) variables as a driver of CPM non-transportability, the fragility of clinical phenotyping used to identify CPM targets, a novel classification system and meta-heuristics for an aspect of EHR data previously lacking in standards, a novel CPM evaluation design termed the pseudo-prospective trial, and the APT checklist. Overall, this work contributes to the body of biomedical informatics literature guiding the success of informatics interventions

Psychological aspects of relapse in schizophrenia

Following a review of the relevant literature a Cognitive Behavioural treatment protocol for the prevention of relapse in schizophrenia is presented. This treatment protocol is investigated in a 12-month non-blind randomised controlled trial comparing Cognitive Behavioural Therapy and Treatment as Usual (CBT + TAU) versus Treatment as Usual (TAU) alone. Three studies of treatment outcome are described: relapse and admission, remission and social functioning, and psychological distress. 144 participants with a DSM-IV Schizophrenia spectrum disorder were randomised to receive either CBT + TAU (n = 72) or TAU alone (n = 72). 11 participants dropped out (6 from CBT + TAU, 5 from TAU alone) leaving a completers sample of 133. Participants were assessed at entry, 12-weeks, 26-weeks, and 52 weeks. CBT was delivered over two stages: a 5-session engagement phase which was provided between entry and 12-weeks, and a targeted CBT phase which was delivered on the appearance of early signs of relapse. Over 12-months CBT + TAU was associated with significant reductions in relapse and admission rate. The clinical significance of the reduced relapse and admission rate amongst the CBT + TAU group was investigated. First, receipt of CBT + TAU was associated with improved rates of remission over 12-months. Second, clinically significant improvements in social functioning were investigated. Again, receipt of CBT + TAU was associated with clinically significant improvements in prosocial activities. However, receipt of CBT + TAU was not associated with improvements in psychological distress over 12-months. The theory underpinning the cognitive behavioural treatment protocol predicted that negative appraisals of self and psychosis represent a cognitive vulnerability to relapse. This hypothesis was investigated during the present 2 Abstract study. After controlling for clinical, treatment and demographic variables, negative appraisals of self and entrapment in psychosis were associated with increased vulnerability to relapse, whilst negative appraisals of self were associated with reduced duration to relapse. Finally, an explorative study of changes in negative appraisals of psychosis and self over time, which were associated with relapsers versus non-relapsers from the TAU alone group, was conducted. This study found a strong association between the experience of relapse, increasing negative appraisals of psychosis and self, and the development of psychological co-morbidity in schizophrenia. Results of treatment outcome and theoretical analyses are discussed in terms of their relevance to the further development of psychological models and treatments for psychosis