Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

An ontology-driven architecture for data integration and management in home-based telemonitoring scenarios

The shift from traditional medical care to the use of new technology and engineering innovations is nowadays an interesting and growing research area mainly motivated by a growing population with chronic conditions and disabilities. By means of information and communications technologies (ICTs), telemedicine systems offer a good solution for providing medical care at a distance to any person in any place at any time. Although significant contributions have been made in this field in recent decades, telemedicine and in e-health scenarios in general still pose numerous challenges that need to be addressed by researchers in order to take maximum advantage of the benefits that these systems provide and to support their long-term implementation. The goal of this research thesis is to make contributions in the field of home-based telemonitoring scenarios. By periodically collecting patients' clinical data and transferring them to physicians located in remote sites, patient health status supervision and feedback provision is possible. This type of telemedicine system guarantees patient supervision while reducing costs (enabling more autonomous patient care and avoiding hospital over flows). Furthermore, patients' quality of life and empowerment are improved. Specifically, this research investigates how a new architecture based on ontologies can be successfully used to address the main challenges presented in home-based telemonitoring scenarios. The challenges include data integration, personalized care, multi-chronic conditions, clinical and technical management. These are the principal issues presented and discussed in this thesis. The proposed new ontology-based architecture takes into account both practical and conceptual integration issues and the transference of data between the end points of the telemonitoring scenario (i.e, communication and message exchange). The architecture includes two layers: 1) a conceptual layer and 2) a data and communication layer. On the one hand, the conceptual layer based on ontologies is proposed to unify the management procedure and integrate incoming data from all the sources involved in the telemonitoring process. On the other hand, the data and communication layer based on web service technologies is proposed to provide practical back-up to the use of the ontology, to provide a real implementation of the tasks it describes and thus to provide a means of exchanging data. This architecture takes advantage of the combination of ontologies, rules, web services and the autonomic computing paradigm. All are well-known technologies and popular solutions applied in the semantic web domain and network management field. A review of these technologies and related works that have made use of them is presented in this thesis in order to understand how they can be combined successfully to provide a solution for telemonitoring scenarios. The design and development of the ontology used in the conceptual layer led to the study of the autonomic computing paradigm and its combination with ontologies. In addition, the OWL (Ontology Web Language) language was studied and selected to express the required knowledge in the ontology while the SPARQL language was examined for its effective use in defining rules. As an outcome of these research tasks, the HOTMES (Home Ontology for Integrated Management in Telemonitoring Scenarios) ontology, presented in this thesis, was developed. The combination of the HOTMES ontology with SPARQL rules to provide a flexible solution for personalising management tasks and adapting the methodology for different management purposes is also discussed. The use of Web Services (WSs) was investigated to support the exchange of information defined in the conceptual layer of the architecture. A generic ontology based solution was designed to integrate data and management procedures in the data and communication layer of the architecture. This is an innovative REST-inspired architecture that allows information contained in an ontology to be exchanged in a generic manner. This layer structure and its communication method provide the approach with scalability and re-usability features. The application of the HOTMES-based architecture has been studied for clinical purposes following three simple methodological stages described in this thesis. Data and management integration for context-aware and personalized monitoring services for patients with chronic conditions in the telemonitoring scenario are thus addressed. In particular, the extension of the HOTMES ontology defines a patient profile. These profiles in combination with individual rules provide clinical guidelines aiming to monitor and evaluate the evolution of the patient's health status evolution. This research implied a multi-disciplinary collaboration where clinicians had an essential role both in the ontology definition and in the validation of the proposed approach. Patient profiles were defined for 16 types of different diseases. Finally, two solutions were explored and compared in this thesis to address the remote technical management of all devices that comprise the telemonitoring scenario. The first solution was based on the HOTMES ontology-based architecture. The second solution was based on the most popular TCP/IP management architecture, SNMP (Simple Network Management Protocol). As a general conclusion, it has been demonstrated that the combination of ontologies, rules, WSs and the autonomic computing paradigm takes advantage of the main benefits that these technologies can offer in terms of knowledge representation, work flow organization, data transference, personalization of services and self-management capabilities. It has been proven that ontologies can be successfully used to provide clear descriptions of managed data (both clinical and technical) and ways of managing such information. This represents a further step towards the possibility of establishing more effective home-based telemonitoring systems and thus improving the remote care of patients with chronic diseases

Data completeness in healthcare: A literature survey.

As the adoption of eHealth has made it easier to access and aggregate healthcare data, there has been growing application for clinical decisions, health services planning, and public health monitoring with daily collected data in clinical care. Reliable data quality is a precursor of the aforementioned tasks. There is a body of research on data quality in healthcare, however, a clear picture of data completeness in this field is missing. This research aims to identify and classify current research themes related to data completeness in healthcare. In addition, the paper presents problems with data completeness in the reviewed literature and identifies methods that have been adopted to address those problems. This study has reviewed 24 papers (January 2011–April 2016) published in information and computing sciences, biomedical engineering, and medicine and health sciences journals. The paper uncovers three main research themes, including design and development, evaluation, and determinants. In conclusion, this paper improves our understanding of the current state of the art of data completeness in healthcare records and indicates future research directions.N

An ontology for formal representation of medication adherence-related knowledge : case study in breast cancer

Indiana University-Purdue University Indianapolis (IUPUI)Medication non-adherence is a major healthcare problem that negatively impacts the health and productivity of individuals and society as a whole. Reasons for medication non-adherence are multi-faced, with no clear-cut solution. Adherence to medication remains a difficult area to study, due to inconsistencies in representing medicationadherence behavior data that poses a challenge to humans and today’s computer technology related to interpreting and synthesizing such complex information. Developing a consistent conceptual framework to medication adherence is needed to facilitate domain understanding, sharing, and communicating, as well as enabling researchers to formally compare the findings of studies in systematic reviews. The goal of this research is to create a common language that bridges human and computer technology by developing a controlled structured vocabulary of medication adherence behavior—“Medication Adherence Behavior Ontology” (MAB-Ontology) using breast cancer as a case study to inform and evaluate the proposed ontology and demonstrating its application to real-world situation. The intention is for MAB-Ontology to be developed against the background of a philosophical analysis of terms, such as belief, and desire to be human, computer-understandable, and interoperable with other systems that support scientific research. The design process for MAB-Ontology carried out using the METHONTOLOGY method incorporated with the Basic Formal Ontology (BFO) principles of best practice. This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including adherence assessment, adherence determinants, adherence theories, adherence taxonomies, and tacit knowledge source types. These sources were analyzed using a systematic approach that involved some questions applied to all source types to guide data extraction and inform domain conceptualization. A set of intermediate representations involving tables and graphs was used to allow for domain evaluation before implementation. The resulting ontology included 629 classes, 529 individuals, 51 object property, and 2 data property. The intermediate representation was formalized into OWL using Protégé. The MAB-Ontology was evaluated through competency questions, use-case scenario, face validity and was found to satisfy the requirement specification. This study provides a unified method for developing a computerized-based adherence model that can be applied among various disease groups and different drug categories

Guideline-based decision support in medicine : modeling guidelines for the development and application of clinical decision support systems

Guideline-based Decision Support in Medicine Modeling Guidelines for the Development and Application of Clinical Decision Support Systems The number and use of decision support systems that incorporate guidelines with the goal of improving care is rapidly increasing. Although developing systems that are both effective in supporting clinicians and accepted by them has proven to be a difficult task, of the systems that were evaluated by a controlled trial, the majority showed impact. The work, described in this thesis, aims at developing a methodology and framework that facilitates all stages in the guideline development process, ranging from the definition of models that represent guidelines to the implementation of run-time systems that provide decision support, based on the guidelines that were developed during the previous stages. The framework consists of 1) a guideline representation formalism that uses the concepts of primitives, Problem-Solving Methods (PSMs) and ontologies to represent guidelines of various complexity and granularity and different application domains, 2) a guideline authoring environment that enables guideline authors to define guidelines, based on the newly developed guideline representation formalism, and 3) a guideline execution environment that translates defined guidelines into a more efficient symbol-level representation, which can be read in and processed by an execution-time engine. The described methodology and framework were used to develop and validate a number of guidelines and decision support systems in various clinical domains such as Intensive Care, Family Practice, Psychiatry and the areas of Diabetes and Hypertension control

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

Behind the screens: Clinical decision support methodologies - A review

Clinical decision support systems (CDSSs) are interactive software systems designed to assist clinicians with decision making tasks, such as determining diagnosis of patient data. CDSSs are a widely researched topic in the Computer Science community but their workings are less well understood by clinicians. The purpose of this review is to introduce clinicians and policy makers to the most commonly computer-based methodologies employed to construct decision models to compute clinical decisions in a non-technical manner. We hope that a better understanding of CDSSs will open up discussion about the future of CDSSs as a part of healthcare delivery as well as engage clinicians and policy makers in the development and deployment of CDSSs that can meaningfully help with decision making tasks