Doctor of Philosophy

dissertationMedical knowledge learned in medical school can become quickly outdated given the tremendous growth of the biomedical literature. It is the responsibility of medical practitioners to continuously update their knowledge with recent, best available clinical evidence to make informed decisions about patient care. However, clinicians often have little time to spend on reading the primary literature even within their narrow specialty. As a result, they often rely on systematic evidence reviews developed by medical experts to fulfill their information needs. At the present, systematic reviews of clinical research are manually created and updated, which is expensive, slow, and unable to keep up with the rapidly growing pace of medical literature. This dissertation research aims to enhance the traditional systematic review development process using computer-aided solutions. The first study investigates query expansion and scientific quality ranking approaches to enhance literature search on clinical guideline topics. The study showed that unsupervised methods can improve retrieval performance of a popular biomedical search engine (PubMed). The proposed methods improve the comprehensiveness of literature search and increase the ratio of finding relevant studies with reduced screening effort. The second and third studies aim to enhance the traditional manual data extraction process. The second study developed a framework to extract and classify texts from PDF reports. This study demonstrated that a rule-based multipass sieve approach is more effective than a machine-learning approach in categorizing document-level structures and iv that classifying and filtering publication metadata and semistructured texts enhances the performance of an information extraction system. The proposed method could serve as a document processing step in any text mining research on PDF documents. The third study proposed a solution for the computer-aided data extraction by recommending relevant sentences and key phrases extracted from publication reports. This study demonstrated that using a machine-learning classifier to prioritize sentences for specific data elements performs equally or better than an abstract screening approach, and might save time and reduce errors in the full-text screening process. In summary, this dissertation showed that there are promising opportunities for technology enhancement to assist in the development of systematic reviews. In this modern age when computing resources are getting cheaper and more powerful, the failure to apply computer technologies to assist and optimize the manual processes is a lost opportunity to improve the timeliness of systematic reviews. This research provides methodologies and tests hypotheses, which can serve as the basis for further large-scale software engineering projects aimed at fully realizing the prospect of computer-aided systematic reviews

The burden of proof: the current state of atrial fibrillation prevention and treatment trials

Atrial fibrillation (AF) is an age-related arrhythmia of enormous socioeconomic significance. In recent years, our understanding of the basic mechanisms that initiate and perpetuate AF has evolved rapidly, catheter ablation of AF has progressed from concept to reality, and recent studies suggest lifestyle modification may help prevent AF recurrence. Emerging developments in genetics, imaging, and informatics also present new opportunities for personalized care. However, considerable challenges remain. These include a paucity of studies examining AF prevention, modest efficacy of existing antiarrhythmic therapies, diverse ablation technologies and practice, and limited evidence to guide management of high-risk patients with multiple comorbidities. Studies examining the long-term effects of AF catheter ablation on morbidity and mortality outcomes are not yet completed. In many ways, further progress in the field is heavily contingent on the feasibility, capacity, and efficiency of clinical trials to incorporate the rapidly evolving knowledge base and to provide substantive evidence for novel AF therapeutic strategies. This review outlines the current state of AF prevention and treatment trials, including the foreseeable challenges, as discussed by a unique forum of clinical trialists, scientists, and regulatory representatives in a session endorsed by the Heart Rhythm Society at the 12th Global CardioVascular Clinical Trialists Forum in Washington, DC, December 3–5, 2015

Classification of Human Ventricular Arrhythmia in High Dimensional Representation Spaces

We studied classification of human ECGs labelled as normal sinus rhythm, ventricular fibrillation and ventricular tachycardia by means of support vector machines in different representation spaces, using different observation lengths. ECG waveform segments of duration 0.5-4 s, their Fourier magnitude spectra, and lower dimensional projections of Fourier magnitude spectra were used for classification. All considered representations were of much higher dimension than in published studies. Classification accuracy improved with segment duration up to 2 s, with 4 s providing little improvement. We found that it is possible to discriminate between ventricular tachycardia and ventricular fibrillation by the present approach with much shorter runs of ECG (2 s, minimum 86% sensitivity per class) than previously imagined. Ensembles of classifiers acting on 1 s segments taken over 5 s observation windows gave best results, with sensitivities of detection for all classes exceeding 93%.Comment: 9 pages, 2 tables, 5 figure

Citation analysis may severely underestimate the impact of clinical research as compared to basic research

Background: Citation analysis has become an important tool for research performance assessment in the medical sciences. However, different areas of medical research may have considerably different citation practices, even within the same medical field. Because of this, it is unclear to what extent citation-based bibliometric indicators allow for valid comparisons between research units active in different areas of medical research. Methodology: A visualization methodology is introduced that reveals differences in citation practices between medical research areas. The methodology extracts terms from the titles and abstracts of a large collection of publications and uses these terms to visualize the structure of a medical field and to indicate how research areas within this field differ from each other in their average citation impact. Results: Visualizations are provided for 32 medical fields, defined based on journal subject categories in the Web of Science database. The analysis focuses on three fields. In each of these fields, there turn out to be large differences in citation practices between research areas. Low-impact research areas tend to focus on clinical intervention research, while high-impact research areas are often more oriented on basic and diagnostic research. Conclusions: Popular bibliometric indicators, such as the h-index and the impact factor, do not correct for differences in citation practices between medical fields. These indicators therefore cannot be used to make accurate between-field comparisons. More sophisticated bibliometric indicators do correct for field differences but still fail to take into account within-field heterogeneity in citation practices. As a consequence, the citation impact of clinical intervention research may be substantially underestimated in comparison with basic and diagnostic research

A Cox-based Model for Predicting the Risk of Cardiovascular Disease

This research is aimed to develop a 10-year risk prediction model and identify key contributing Cardiovascular Disease (CVD) risk factors. A Cox proportional hazard regression method was adopted to design and develop the risk model. We used Framingham Original Cohort dataset of 5079 men and women aged 30 - 62 years, who had no overt symptoms of CVD at the baseline. Out of them, 3189 (62.78%) had an actual CVD event. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure, cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel contributing risk factors. We validated the model via statistical and empirical validation methods. The proposed model achieved an acceptable discrimination and calibration with C-index (receiver operating characteristic (ROC)) being 0.71 from the validation dataset

Chapter Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety

This chapter aims to describe current and emerging roles of spontaneous reporting systems (SRSs) for assessing and monitoring drug safety. Moreover, it offers a perspective on the near future, which entails the so-called era of Big Data, keeping in mind both regulator and researcher viewpoints. After a panorama on key data sources and analyses of post-marketing data of adverse drug reactions, a critical appraisal of methodological issues and debated future applications of SRSs will be presented, including the exploitation and challenges in evidence integration (i.e., merging and combining heterogeneous sources of data into a unique indicator of risk) and patient’s reporting via social media. Finally, a call for a responsible use of these studies is offered, with a proposal on a set of minimum requirements to assess the quality of disproportionality analysis in terms of study conception, performing and reporting

Evolving Roles of Spontaneous Reporting Systems to Assess and Monitor Drug Safety

This chapter aims to describe current and emerging roles of spontaneous reporting systems (SRSs) for assessing and monitoring drug safety. Moreover, it offers a perspective on the near future, which entails the so-called era of Big Data, keeping in mind both regulator and researcher viewpoints. After a panorama on key data sources and analyses of post-marketing data of adverse drug reactions, a critical appraisal of methodological issues and debated future applications of SRSs will be presented, including the exploitation and challenges in evidence integration (i.e., merging and combining heterogeneous sources of data into a unique indicator of risk) and patient’s reporting via social media. Finally, a call for a responsible use of these studies is offered, with a proposal on a set of minimum requirements to assess the quality of disproportionality analysis in terms of study conception, performing and reporting

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention’s generalizability and interoperability with existing systems, as well as the inner settings’ data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting

Counting what counts : time-driven activity-based costing in health care

Introduction: Patients with multiple chronic conditions consume over 40% of health care resources. The si- loed nature of the health care system exacerbates these costs, and integrated care solutions are required to adequately meet their needs. However, such integrated multidisciplinary ap- proaches are seen as costly. Therefore, costing care for patients with multiple chronic condi- tions becomes important to support health care professionals, management, and policy makers understand the true financial impact of integrated multidisciplinary care. Aim: The aim of this thesis is to explore how Time-Driven Activity-Based Costing (TDABC) can be applied to capture and compare the cost of integrated multidisciplinary versus traditional siloed care processes for patients with multiple chronic conditions. Method: This thesis is comprised of four studies. Study I was a systematic review performed according to the PRISMA statement and used qualitative methods to analyze data through content analy- sis. Studies II to IV were based on a randomized controlled trial CareHND (NCT03362983). Study II used descriptive statistics to describe patient diagnostic data, Charlson Comorbidity Index scores, and performed a comparison of care utilization patterns between integrated mul- tidisciplinary care and traditional care. Study III adopted a mixed-methods approach to perform a TDABC analysis of integrated multidisciplinary care. Study IV expanded on Study III to compare the costs of integrated multidisciplinary care to that of traditional siloed care. Findings: Study I found that TDABC is an efficient and accurate tool for costing processes in health care, but has not been demonstrated to effectively cost care across the care continuum. Study II found that patients with multiple chronic conditions experience care that is characterized by high vol- ume and high variation, and no difference in care utilization was detected when comparing integrated multidisciplinary care to traditional siloed care. The TDABC cost analysis in Study III successfully estimated the outpatient care costs for patients with multiple chronic condi- tions. Study IV found that the integrated multidisciplinary care center saved a hospital an av- erage of 5,098.00 € per patient per year. Discussion: This thesis demonstrates how TDABC can be applied to capture and compare costs of pro- cesses for patients with multiple chronic conditions. More broadly, this thesis demonstrates how to conceptualize and evaluate real-world care pathways for patients with multiple chronic conditions in order inform actionable changes to clinical management within hospitals. This thesis lays the groundwork for empowering hospitals and other providers to incorporate finan- cial analyses into their evidence development, quality improvement, and decision making, and to contribute to the wider financial and economic systems in health care. Conclusion: This thesis demonstrates that a hospital-based integrated multidisciplinary care approach to a complex medical condition makes economic sense for the hospital and the system. The TDABC approach developed in this thesis project brought to light a set of core capacities which can be prioritized in future quality improvement efforts. Through these core capacities, clinical organizations will hopefully become empowered to make wise, value-driven decisions that will serve as the new incentive for organizational improvement. Information that demonstrates value delivery will make financial needs clear to managers and policy makers, who in turn should understand that evidence-based investment in care facilities and services will ultimately demonstrate a return, benefiting not only IMD-Care patients, but also the larger populations they serve