Analysis and classification of oncology activities on the way to workflow based single source documentation in clinical information systems

BACKGROUND: Today, cancer documentation is still a tedious task involving many different information systems even within a single institution and it is rarely supported by appropriate documentation workflows. METHODS: In a comprehensive 14 step analysis we compiled diagnostic and therapeutic pathways for 13 cancer entities using a mixed approach of document analysis, workflow analysis, expert interviews, workflow modelling and feedback loops. These pathways were stepwise classified and categorized to create a final set of grouped pathways and workflows including electronic documentation forms. RESULTS: A total of 73 workflows for the 13 entities based on 82 paper documentation forms additionally to computer based documentation systems were compiled in a 724 page document comprising 130 figures, 94 tables and 23 tumour classifications as well as 12 follow-up tables. Stepwise classification made it possible to derive grouped diagnostic and therapeutic pathways for the three major classes - solid entities with surgical therapy - solid entities with surgical and additional therapeutic activities and - non-solid entities. For these classes it was possible to deduct common documentation workflows to support workflow-guided single-source documentation. CONCLUSIONS: Clinical documentation activities within a Comprehensive Cancer Center can likely be realized in a set of three documentation workflows with conditional branching in a modern workflow supporting clinical information system

Design and methodological characteristics of studies using observational routinely collected health data for investigating the link between cancer and neurodegenerative diseases: protocol for a meta-research study

Introduction: Health services generate large amounts of routine health data (eg, administrative databases, disease registries and electronic health records), which have important secondary uses for research. Increases in the availability and the ability to access and analyse large amounts of data represent a major opportunity for conducting studies on the possible relationships between complex diseases. The objective of this study will be to evaluate the design, methods and reporting of studies conducted using observational routinely collected health data for investigating the link between cancer and neurodegenerative diseases. Methods and analysis: This is the protocol for a meta-research study. We registered the study protocol within the Open Science Framework: https://osf.io/h2qjg. We will evaluate observational studies (eg, cohort and case-control) conducted using routinely collected health data for investigating the associations between cancer and neurodegenerative diseases (such as Alzheimer's disease, amyotrophic lateral sclerosis/motor neuron disease, Huntington's disease, multiple sclerosis and Parkinson's disease). The following electronic databases will be searched (from their inception onwards): MEDLINE, Embase and Web of Science Core Collection. Screening and selection of articles will be conducted by at least two researchers. Potential discrepancies will be resolved via discussion. Design, methods and reporting characteristics in each article will be extracted using a standardised data extraction form. Information on general, methodological and transparency items will be reported. We will summarise our findings with tables and graphs (eg, bar charts, forest plots). Ethics and dissemination: Due to the nature of the proposed study, no ethical approval will be required. We plan to publish the full study in an open access peer-reviewed journal and disseminate the findings at scientific conferences and via social media. All data will be deposited in a cross-disciplinary public repository.FC-L and RT-S are supported by the Institute of Health Carlos III/CIBERSAM. MJP is supported by an Australian Research Council Discovery Early Career Researcher Award (DE200101618). MR and EB-D are partially funded by the Spanish Health Services Research on Chronic Patients Network (REDISSEC)/Institute of Health Carlos III.S

Precision health approaches: ethical considerations for health data processing

This thesis provides insights and recommendations on some of the most crucial elements necessary for an effective, legally and ethically sound implementation of precision health approaches in the Swiss context (and beyond), specifically for precision medicine and precision public health. In this regard, this thesis recognizes the centrality of data in these two abovementioned domains, and the ethical and scientific imperative of ensuring the widespread and responsible sharing of high quality health data between the numerous stakeholders involved in healthcare, public health and associated research domains. It also recognizes the need to protect not only the interests of data subjects but also those of data processors. Indeed, it is only through a comprehensive assessment of the needs and expectations of each and every one regarding data sharing activities that sustainable solutions to known ethical and scientific conundrums can be devised and implemented. In addition, the included chapters in this thesis emphasize recommending solutions that could be convincingly applied to real world problems, with the ultimate objective of having a concrete impact on clinical and public health practice and policies, including research activities. Indeed, the strengths of this thesis reside in a careful and in-depth interdisciplinary assessment of the different issues at stake in precision health approaches, with the elaboration of the least disruptive solutions (as far as possible) and recommendations for an easy evaluation and subsequent adoption by relevant stakeholders active in these two domains. This thesis has three main objectives, namely (i) to investigate and identify factors influencing the processing of health data in the Swiss context and suggest some potential solutions and recommendations. A better understanding of these factors is paramount for an effective implementation of precision health approaches given their strong dependence on high quality and easily accessible health datasets; (ii) to identify and explore the ethical, legal and social issues (ELSI) of innovative participatory disease surveillance systems – also falling under precision health approaches – and how research ethics are coping within this field. In addition, this thesis aims to strengthen the ethical approaches currently used to cater for these ELSIs by providing a robust ethical framework; and lastly, (iii) to investigate how precision health approaches might not be able to achieve their social justice and health equity goals, if the impact of structural racism on these initiatives is not given due consideration. After a careful assessment, this thesis provides recommendations and potential actions that could help these precision health approaches adhere to their social justice and health equity goals. This thesis has investigated these three main objectives using both empirical and theoretical research methods. The empirical branch consists of systematic and scoping reviews, both adhering to the PRISMA guidelines, and two interview-based studies carried out with Swiss expert stakeholders. The theoretical branch consists of three chapters, each addressing important aspects concerning precision health approaches

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care