Towards Object-aware Process Support in Healthcare Information Systems

The processes to be supported by healthcare information systems are highly complex, and they produce and consume a large amount of data. Besides, they require a high degree of flexibility. Despite their widespread adoption in industry, however, traditional process management systems (PrMS) have not been broadly used in healthcare environments so far. One major reason for this is the missing integration of processes with business data; i.e., business objects (e.g., medical orders or reports) are usually outside the control of a PrMS. By contrast, our PHILharmonicFlows framework offers an object-aware process management approach, which tightly integrates business objects and processes. In this paper, we use this framework to support a breast cancer diagnosis scenario. We discuss the lessons learned from this case study as well as requirements from the healthcare domain that can be effectively met by an object-aware process management system

Object-aware Process Support in Healthcare Information Systems: Requirements, Conceptual Framework and Examples

The business processes to be supported by healthcare information systems are highly complex, producing and consuming a large amount of data. Besides, the execution of these processes requires a high degree of flexibility. Despite their widespread adoption in industry, however, traditional process management systems (PrMS) have not been broadly used in healthcare environments so far. One major reason for this drawback is the missing integration of business processes and business data in existing PrMS; i.e., business objects (e.g., medical orders, medical reports) are usually maintained in specific application systems, and are hence outside the control of the PrMS. As a consequence, most existing PrMS are unable to provide integrated access to business processes and business objects in case of unexpected events, which is crucial in the healthcare domain. In this context, the PHILharmonicFlows framework offers an innovative object-aware process management approach, which tightly integrates business objects, functions, and processes. In this paper, we apply this framework to model and control the processes in the context of a breast cancer diagnosis scenario. First, we present the modeling components of PHILharmonicFlows framework applied to this scenario. Second, we give insights into the operational semantics that governs the process execution in PHILharmonicFlows. Third, we discuss the lessons learned in this case study as well as requirements from the healthcare domain that can be effectively handled when using an object-aware process management system like PHILharmonicFlows. Overall, object-aware process support will allow for a new generation of healthcare information systems treating both data and processes as first class citizens

A modular, open-source information extraction framework for identifying clinical concepts and processes of care in clinical narratives

In this thesis, a synthesis is presented of the knowledge models required by clinical informa- tion systems that provide decision support for longitudinal processes of care. Qualitative research techniques and thematic analysis are novelly applied to a systematic review of the literature on the challenges in implementing such systems, leading to the development of an original conceptual framework. The thesis demonstrates how these process-oriented systems make use of a knowledge base derived from workflow models and clinical guidelines, and argues that one of the major barriers to implementation is the need to extract explicit and implicit information from diverse resources in order to construct the knowledge base. Moreover, concepts in both the knowledge base and in the electronic health record (EHR) must be mapped to a common ontological model. However, the majority of clinical guideline information remains in text form, and much of the useful clinical information residing in the EHR resides in the free text fields of progress notes and laboratory reports. In this thesis, it is shown how natural language processing and information extraction techniques provide a means to identify and formalise the knowledge components required by the knowledge base. Original contributions are made in the development of lexico-syntactic patterns and the use of external domain knowledge resources to tackle a variety of information extraction tasks in the clinical domain, such as recognition of clinical concepts, events, temporal relations, term disambiguation and abbreviation expansion. Methods are developed for adapting existing tools and resources in the biomedical domain to the processing of clinical texts, and approaches to improving the scalability of these tools are proposed and evalu- ated. These tools and techniques are then combined in the creation of a novel approach to identifying processes of care in the clinical narrative. It is demonstrated that resolution of coreferential and anaphoric relations as narratively and temporally ordered chains provides a means to extract linked narrative events and processes of care from clinical notes. Coreference performance in discharge summaries and progress notes is largely dependent on correct identification of protagonist chains (patient, clinician, family relation), pronominal resolution, and string matching that takes account of experiencer, temporal, spatial, and anatomical context; whereas for laboratory reports additional, external domain knowledge is required. The types of external knowledge and their effects on system performance are identified and evaluated. Results are compared against existing systems for solving these tasks and are found to improve on them, or to approach the performance of recently reported, state-of-the- art systems. Software artefacts developed in this research have been made available as open-source components within the General Architecture for Text Engineering framework

The application of process mining to care pathway analysis in the NHS

Background: Prostate cancer is the most common cancer in men in the UK and the sixth-fastest increasing cancer in males. Within England survival rates are improving, however, these are comparatively poorer than other countries. Currently, information available on outcomes of care is scant and there is an urgent need for techniques to improve healthcare systems and processes. Aims: To provide prostate cancer pathway analysis, by applying concepts of process mining and visualisation and comparing the performance metrics against the standard pathway laid out by national guidelines. Methods: A systematic review was conducted to see how process mining has been used in healthcare. Appropriate datasets for prostate cancer were identified within Imperial College Healthcare NHS Trust London. A process model was constructed by linking and transforming cohort data from six distinct database sources. The cohort dataset was filtered to include patients who had a PSA from 2010-2015, and validated by comparing the medical patient records against a Case-note audit. Process mining techniques were applied to the data to analyse performance and conformance of the prostate cancer pathway metrics to national guideline metrics. These techniques were evaluated with stakeholders to ascertain its impact on user experience. Results: Case note audit revealed 90% match against patients found in medical records. Application of process mining techniques showed massive heterogeneity as compared to the homogenous path laid out by national guidelines. This also gave insight into bottlenecks and deviations in the pathway. Evaluation with stakeholders showed that the visualisation and technology was well accepted, high quality and recommended to be used in healthcare decision making. Conclusion: Process mining is a promising technique used to give insight into complex and flexible healthcare processes. It can map the patient journey at a local level and audit it against explicit standards of good clinical practice, which will enable us to intervene at the individual and system level to improve care.Open Acces

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

FAIR and bias-free network modules for mechanism-based disease redefinitions

Even though chronic diseases are the cause of 60% of all deaths around the world, the underlying causes for most of them are not fully understood. Hence, diseases are defined based on organs and symptoms, and therapies largely focus on mitigating symptoms rather than cure. This is also reflected in the most commonly used disease classifications. The complex nature of diseases, however, can be better defined in terms of networks of molecular interactions. This research applies the approaches of network medicine – a field that uses network science for identifying and treating diseases – to multiple diseases with highly unmet medical need such as stroke and hypertension. The results show the success of this approach to analyse complex disease networks and predict drug targets for different conditions, which are validated through preclinical experiments and are currently in human clinical trials

The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

This book is a reprint of the Special Issue entitled "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Artificial intelligence is extending into the world of both digital radiology and digital pathology, and involves many scholars in the areas of biomedicine, technology, and bioethics. There is a particular need for scholars to focus on both the innovations in this field and the problems hampering integration into a robust and effective process in stable health care models in the health domain. Many professionals involved in these fields of digital health were encouraged to contribute with their experiences. This book contains contributions from various experts across different fields. Aspects of the integration in the health domain have been faced. Particular space was dedicated to overviewing the challenges, opportunities, and problems in both radiology and pathology. Clinal deepens are available in cardiology, the hystopathology of breast cancer, and colonoscopy. Dedicated studies were based on surveys which investigated students and insiders, opinions, attitudes, and self-perception on the integration of artificial intelligence in this field

Rfid-based business process and workflow management in healthcare:design and implementation

The healthcare system in the United States is considered one of the most complex systems and has encountered challenges related to patient safety concerns, escalating costs, and unpredictable outcomes. Many of these problems share a common cause - a lack of efficient business process management and visibility into the real-time location, status, and condition of medical resources. The goal of this research is to propose a newly integrated system to model, automate, and monitor healthcare business processes using an automatic data collection technology to record the timing and location of activities and identify their various resources. This dissertation makes several contributions to the design and implementation of RFID-based business process and workflow management in healthcare. First, I propose a road map to implement RFID in hospitals with performance matrixes for technology evaluation, key criteria for resolution level setting, and business rules for information extraction. Second, RFID-based business process management (BPM) concepts and workflow technologies are used to transform the reprocessing procedures in a Sterile Processing Department (SPD) for the purpose of reducing infections caused by unclean reusable medical equipment. In the proposed pattern for healthcare business process management, the importance of execution status control is emphasized as a key component to handle complex and dynamic healthcare processes. A five-level framework for service-oriented business process management is designed for SPDs to share information, integrate distributed systems, and manage heterogeneous resources among multiple stakeholders. This research proposes a healthcare workflow system as a deliverable solution to manage the execution phase of reprocessing procedures, which supports the design, execution, monitoring, and automation of services supplied in SPDs. RFID techniques are adopted to collect relative real-time data for SPD performance management. Finally, by identifying key architectural requirements, the subsystems of a service-oriented architecture for the SPD workflow prototyping system, SPDFLOW, are discussed in detail. This research is the first attempt to explore healthcare workflow technologies in the SPD domain to improve the quality of reusable medical equipment and ensure patient safety