Using Formal Concept Analysis for mining and interpreting patient flows within a healthcare network

International audienceThis paper presents an original experiment based on frequent itemset search and lattice based classification. This work focuses on the ability of iceberg-lattices to discover and represent flows of patient within a healthcare network. We give examples of analysis of real medical data showing how Formal Concept Analysis techniques can be helpful in the interpretation step of the knowledge discovery in databases process. This combined approach has been successfully used to assist public health managers in designing healthcare networks and planning medical resources

A Process Modelling Framework Based on Point Interval Temporal Logic with an Application to Modelling Patient Flows

This thesis considers an application of a temporal theory to describe and model the patient journey in the hospital accident and emergency (A&E) department. The aim is to introduce a generic but dynamic method applied to any setting, including healthcare. Constructing a consistent process model can be instrumental in streamlining healthcare issues. Current process modelling techniques used in healthcare such as flowcharts, unified modelling language activity diagram (UML AD), and business process modelling notation (BPMN) are intuitive and imprecise. They cannot fully capture the complexities of the types of activities and the full extent of temporal constraints to an extent where one could reason about the flows. Formal approaches such as Petri have also been reviewed to investigate their applicability to the healthcare domain to model processes. Additionally, to schedule patient flows, current modelling standards do not offer any formal mechanism, so healthcare relies on critical path method (CPM) and program evaluation review technique (PERT), that also have limitations, i.e. finish-start barrier. It is imperative to specify the temporal constraints between the start and/or end of a process, e.g., the beginning of a process A precedes the start (or end) of a process B. However, these approaches failed to provide us with a mechanism for handling these temporal situations. If provided, a formal representation can assist in effective knowledge representation and quality enhancement concerning a process. Also, it would help in uncovering complexities of a system and assist in modelling it in a consistent way which is not possible with the existing modelling techniques. The above issues are addressed in this thesis by proposing a framework that would provide a knowledge base to model patient flows for accurate representation based on point interval temporal logic (PITL) that treats point and interval as primitives. These objects would constitute the knowledge base for the formal description of a system. With the aid of the inference mechanism of the temporal theory presented here, exhaustive temporal constraints derived from the proposed axiomatic system’ components serves as a knowledge base. The proposed methodological framework would adopt a model-theoretic approach in which a theory is developed and considered as a model while the corresponding instance is considered as its application. Using this approach would assist in identifying core components of the system and their precise operation representing a real-life domain deemed suitable to the process modelling issues specified in this thesis. Thus, I have evaluated the modelling standards for their most-used terminologies and constructs to identify their key components. It will also assist in the generalisation of the critical terms (of process modelling standards) based on their ontology. A set of generalised terms proposed would serve as an enumeration of the theory and subsume the core modelling elements of the process modelling standards. The catalogue presents a knowledge base for the business and healthcare domains, and its components are formally defined (semantics). Furthermore, a resolution theorem-proof is used to show the structural features of the theory (model) to establish it is sound and complete. After establishing that the theory is sound and complete, the next step is to provide the instantiation of the theory. This is achieved by mapping the core components of the theory to their corresponding instances. Additionally, a formal graphical tool termed as point graph (PG) is used to visualise the cases of the proposed axiomatic system. PG facilitates in modelling, and scheduling patient flows and enables analysing existing models for possible inaccuracies and inconsistencies supported by a reasoning mechanism based on PITL. Following that, a transformation is developed to map the core modelling components of the standards into the extended PG (PG*) based on the semantics presented by the axiomatic system. A real-life case (from the King’s College hospital accident and emergency (A&E) department’s trauma patient pathway) is considered to validate the framework. It is divided into three patient flows to depict the journey of a patient with significant trauma, arriving at A&E, undergoing a procedure and subsequently discharged. Their staff relied upon the UML-AD and BPMN to model the patient flows. An evaluation of their representation is presented to show the shortfalls of the modelling standards to model patient flows. The last step is to model these patient flows using the developed approach, which is supported by enhanced reasoning and scheduling

Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol

Introduction In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications or finance, a methodology known as ‘process mining’ has become an established and successful method to identify how an organisation can best deploy resources to meet the needs of its clients and customers. Here and for the first time in the UK, we will apply it to primary care settings to gain a greater understanding of how patients with two of the most common chronic conditions are managed. Methods and analysis The study will be conducted in three phases; first, we will apply process mining algorithms to the data held on the clinical management system of four practices of varying characteristics in the West Midlands to determine how each interacts with patients with hypertension or type 2 diabetes. Second, we will use traditional process mapping exercises at each practice to manually produce maps of care processes for the selected condition. Third, with the aid of staff and patients at each practice, we will compare and contrast the process models produced by process mining with the process maps produced via manual techniques, review differences and similarities between them and the relative importance of each. The first pilot study will be on hypertension and the second for patients diagnosed with type 2 diabetes

Implementing health research through academic and clinical partnerships : a realistic evaluation of the Collaborations for Leadership in Applied Health Research and Care (CLAHRC)

Background: The English National Health Service has made a major investment in nine partnerships between higher education institutions and local health services called Collaborations for Leadership in Applied Health Research and Care (CLAHRC). They have been funded to increase capacity and capability to produce and implement research through sustained interactions between academics and health services. CLAHRCs provide a natural ‘test bed’ for exploring questions about research implementation within a partnership model of delivery. This protocol describes an externally funded evaluation that focuses on implementation mechanisms and processes within three CLAHRCs. It seeks to uncover what works, for whom, how, and in what circumstances. Design and methods: This study is a longitudinal three-phase, multi-method realistic evaluation, which deliberately aims to explore the boundaries around knowledge use in context. The evaluation funder wishes to see it conducted for the process of learning, not for judging performance. The study is underpinned by a conceptual framework that combines the Promoting Action on Research Implementation in Health Services and Knowledge to Action frameworks to reflect the complexities of implementation. Three participating CLARHCS will provide indepth comparative case studies of research implementation using multiple data collection methods including interviews, observation, documents, and publicly available data to test and refine hypotheses over four rounds of data collection. We will test the wider applicability of emerging findings with a wider community using an interpretative forum. Discussion: The idea that collaboration between academics and services might lead to more applicable health research that is actually used in practice is theoretically and intuitively appealing; however the evidence for it is limited. Our evaluation is designed to capture the processes and impacts of collaborative approaches for implementing research, and therefore should contribute to the evidence base about an increasingly popular (e.g., Mode two, integrated knowledge transfer, interactive research), but poorly understood approach to knowledge translation. Additionally we hope to develop approaches for evaluating implementation processes and impacts particularly with respect to integrated stakeholder involvement

Viiteraamistik turvariskide haldamiseks plokiahela abil

Turvalise tarkvara loomiseks on olemas erinevad programmid (nt OWASP), ohumudelid (nt STRIDE), turvariskide juhtimise mudelid (nt ISSRM) ja eeskirjad (nt GDPR). Turvaohud aga arenevad pidevalt, sest traditsiooniline tehnoloogiline infrastruktuur ei rakenda turvameetmeid kavandatult. Blockchain näib leevendavat traditsiooniliste rakenduste turvaohte. Kuigi plokiahelapõhiseid rakendusi peetakse vähem haavatavateks, ei saanud need erinevate turvaohtude eest kaitsmise hõbekuuliks. Lisaks areneb plokiahela domeen pidevalt, pakkudes uusi tehnikaid ja sageli vahetatavaid disainikontseptsioone, mille tulemuseks on kontseptuaalne ebaselgus ja segadus turvaohtude tõhusal käsitlemisel. Üldiselt käsitleme traditsiooniliste rakenduste TJ-e probleemi, kasutades vastumeetmena plokiahelat ja plokiahelapõhiste rakenduste TJ-t. Alustuseks uurime, kuidas plokiahel leevendab traditsiooniliste rakenduste turvaohte, ja tulemuseks on plokiahelapõhine võrdlusmudel (PV), mis järgib TJ-e domeenimudelit. Järgmisena esitleme PV-it kontseptualiseerimisega alusontoloogiana kõrgema taseme võrdlusontoloogiat (ULRO). Pakume ULRO kahte eksemplari. Esimene eksemplar sisaldab Cordat, kui lubatud plokiahelat ja finantsjuhtumit. Teine eksemplar sisaldab lubadeta plokiahelate komponente ja tervishoiu juhtumit. Mõlemad ontoloogiaesitlused aitavad traditsiooniliste ja plokiahelapõhiste rakenduste TJ-es. Lisaks koostasime veebipõhise ontoloogia parsimise tööriista OwlParser. Kaastööde tulemusel loodi ontoloogiapõhine turberaamistik turvariskide haldamiseks plokiahela abil. Raamistik on dünaamiline, toetab TJ-e iteratiivset protsessi ja potentsiaalselt vähendab traditsiooniliste ja plokiahelapõhiste rakenduste turbeohte.Various programs (e.g., OWASP), threat models (e.g., STRIDE), security risk management models (e.g., ISSRM), and regulations (e.g., GDPR) exist to communicate and reduce the security threats to build secure software. However, security threats continuously evolve because the traditional technology infrastructure does not implement security measures by design. Blockchain is appearing to mitigate traditional applications’ security threats. Although blockchain-based applications are considered less vulnerable, they did not become the silver bullet for securing against different security threats. Moreover, the blockchain domain is constantly evolving, providing new techniques and often interchangeable design concepts, resulting in conceptual ambiguity and confusion in treating security threats effectively. Overall, we address the problem of traditional applications’ SRM using blockchain as a countermeasure and the SRM of blockchain-based applications. We start by surveying how blockchain mitigates the security threats of traditional applications, and the outcome is a blockchain-based reference model (BbRM) that adheres to the SRM domain model. Next, we present an upper-level reference ontology (ULRO) as a foundation ontology and provide two instantiations of the ULRO. The first instantiation includes Corda as a permissioned blockchain and the financial case. The second instantiation includes the permissionless blockchain components and the healthcare case. Both ontology representations help in the SRM of traditional and blockchain-based applications. Furthermore, we built a web-based ontology parsing tool, OwlParser. Contributions resulted in an ontology-based security reference framework for managing security risks using blockchain. The framework is dynamic, supports the iterative process of SRM, and potentially lessens the security threats of traditional and blockchain-based applications.https://www.ester.ee/record=b551352

Analysis of social communities with iceberg and stability-based concept lattices

International audienceIn this paper, we presents a research work based on formal concept analysis and interest measures associated with formal concepts. This work focuses on the ability of concept lattices to discover and represent special groups of individuals, called social communities. Concept lattices are very useful for the task of knowledge discovery in databases, but they are hard to analyze when their size become too large. We rely on concept stability and support measures to reduce the size of large concept lattices. We propose an example from real medical use cases and we discuss the meaning and the interest of concept stability for extracting and explaining social communities within a healthcare network

Data-Driven Models, Techniques, and Design Principles for Combatting Healthcare Fraud

In the U.S., approximately $700 billion of the$2.7 trillion spent on healthcare is linked to fraud, waste, and abuse. This presents a significant challenge for healthcare payers as they navigate fraudulent activities from dishonest practitioners, sophisticated criminal networks, and even well-intentioned providers who inadvertently submit incorrect billing for legitimate services. This thesis adopts Hevner’s research methodology to guide the creation, assessment, and refinement of a healthcare fraud detection framework and recommended design principles for fraud detection. The thesis provides the following significant contributions to the field:1. A formal literature review of the field of fraud detection in Medicaid. Chapters 3 and 4 provide formal reviews of the available literature on healthcare fraud. Chapter 3 focuses on defining the types of fraud found in healthcare. Chapter 4 reviews fraud detection techniques in literature across healthcare and other industries. Chapter 5 focuses on literature covering fraud detection methodologies utilized explicitly in healthcare.2. A multidimensional data model and analysis techniques for fraud detection in healthcare. Chapter 5 applies Hevner et al. to help develop a framework for fraud detection in Medicaid that provides specific data models and techniques to identify the most prevalent fraud schemes. A multidimensional schema based on Medicaid data and a set of multidimensional models and techniques to detect fraud are presented. These artifacts are evaluated through functional testing against known fraud schemes. This chapter contributes a set of multidimensional data models and analysis techniques that can be used to detect the most prevalent known fraud types.3. A framework for deploying outlier-based fraud detection methods in healthcare. Chapter 6 proposes and evaluates methods for applying outlier detection to healthcare fraud based on literature review, comparative research, direct application on healthcare claims data, and known fraudulent cases. A method for outlier-based fraud detection is presented and evaluated using Medicaid dental claims, providers, and patients.4. Design principles for fraud detection in complex systems. Based on literature and applied research in Medicaid healthcare fraud detection, Chapter 7 offers generalized design principles for fraud detection in similar complex, multi-stakeholder systems.<br/