A Reference Architecture for Data-Driven and Adaptive Internet-Delivered Psychological Treatment Systems: Software Architecture Development and Validation Study

Background: Internet-delivered psychological treatment (IDPT) systems are software applications that offer psychological treatments via the internet. Such IDPT systems have become one of the most commonly practiced and widely researched forms of psychotherapy. Evidence shows that psychological treatments delivered by IDPT systems can be an effective way of treating mental health morbidities. However, current IDPT systems have high dropout rates and low user adherence. The primary reason is that the current IDPT systems are not flexible, adaptable, and personalized as they follow a fixed tunnel-based treatment architecture. A fixed tunnel-based architecture follows predefined, sequential treatment content for every patient, irrespective of their context, preferences, and needs. Moreover, current IDPT systems have poor interoperability, making it difficult to reuse and share treatment materials. There is a lack of development and documentation standards, conceptual frameworks, and established (clinical) guidelines for such IDPT systems. As a result, several ad hoc forms of IDPT models exist. Consequently, developers and researchers have tended to reinvent new versions of IDPT systems, making them more complex and less interoperable. Objective: This study aimed to design, develop, and evaluate a reference architecture (RA) for adaptive systems that can facilitate the design and development of adaptive, interoperable, and reusable IDPT systems. Methods: This study was conducted in collaboration with a large interdisciplinary project entitled INTROMAT (Introducing Mental Health through Adaptive Technology), which brings together information and communications technology researchers, information and communications technology industries, health researchers, patients, clinicians, and patients’ next of kin to reach its vision. First, we investigated previous studies and state-of-the-art works based on the project’s problem domain and goals. On the basis of the findings from these investigations, we identified 2 primary gaps in current IDPT systems: lack of adaptiveness and limited interoperability. Second, we used model-driven engineering and Domain-Driven Design techniques to design, develop, and validate the RA for building adaptive, interoperable, and reusable IDPT systems to address these gaps. Third, based on the proposed RA, we implemented a prototype as the open-source software. Finally, we evaluated the RA and open-source implementation using empirical (case study) and nonempirical approaches (software architecture analysis method, expert evaluation, and software quality attributes). Results: This paper outlines an RA that supports flexible user modeling and the adaptive delivery of treatments. To evaluate the proposed RA, we developed an open-source software based on the proposed RA. The open-source framework aims to improve development productivity, facilitate interoperability, increase reusability, and expedite communication with domain experts. Conclusions: Our results showed that the proposed RA is flexible and capable of adapting interventions based on patients’ needs, preferences, and context. Furthermore, developers and researchers can extend the proposed RA to various health care interventions.publishedVersio

Towards Adaptive Technology in Routine Mental Healthcare

This paper summarizes the information technology-related research findings after 5 years with the INTROducing Mental health through Adaptive Technology project. The aim was to improve mental healthcare by introducing new technologies for adaptive interventions in mental healthcare through interdisciplinary research and development. We focus on the challenges related to internet-delivered psychological treatments, emphasising artificial intelligence, human-computer interaction, and software engineering. We present the main research findings, the developed artefacts, and lessons learned from the project before outlining directions for future research. The main findings from this project are encapsulated in a reference architecture that is used for establishing an infrastructure for adaptive internet-delivered psychological treatment systems in clinical contexts. The infrastructure is developed by introducing an interdisciplinary design and development process inspired by domain-driven design, user-centred design, and the person based approach for intervention design. The process aligns the software development with the intervention design and illustrates their mutual dependencies. Finally, we present software artefacts produced within the project and discuss how they are related to the proposed reference architecture. Our results indicate that the proposed development process, the reference architecture and the produced software can be practical means of designing adaptive mental health care treatments in correspondence with the patients’ needs and preferences. In summary, we have created the initial version of an information technology infrastructure to support the development and deployment of Internet-delivered mental health interventions with inherent support for data sharing, data analysis, reusability of treatment content, and adaptation of intervention based on user needs and preferences.publishedVersio

Predicting the next click with Web log Process Mining

Process mining allows for extraction of visual models describing general sequence patterns from event log data. This analysis technique is most commonly applied in business process analytics settings comparing expected and actual process execution. In this thesis work we will examine how process mining can be applied on click logs from media sites and reveal contents relationships and readers behavioural characteristics

A software framework for adaptive and interoperable internet-delivered psychological treatments

BACKGROUND Statistics unveil the predominance of mental and neurological disorders globally. Handling these mental and neurological disorders is economically, physically and emotionally challenging. Proper healthcare treatments would have been an ideal solution for these people suffering from these disorders. However, provided limited healthcare resources, an alternative solution is to use the Internet to provide psychological treatments. The use of Internet-Delivered Psychological Treatments (IDPT) can accelerate treatments for people globally at a lower cost. While such IDPT systems have been practised at volume, user adherence is low with high dropout rates. Low adherence in treatments is primarily due to the IDPT system’s inability to adapt treatments according to user needs, context and preferences. OBJECTIVE This study is accomplished in collaboration with a large interdisciplinary project entitled as INTROMAT. INTROMAT brings together ICT researchers, ICT industries, health researchers, patients, clinicians, and patients next of kin to reach its vision. The project’s vision is to improve public mental health through innovative technologies. The main objective of this thesis, inclined to fulfil this vision, is to design, develop and evaluate an adaptive IDPT framework. METHODS Based on the INTROMAT project’s problem domain and goals, we started with the study of state-of-the-art works, including systematic literature review, evaluation of available systems, analysis of the previous studies on the problem domain, and evaluation of past case studies. Based on these studies, we identified two primary gaps in the current IDPT systems, lack of adaptiveness and limited interoperability. Then, we used MDE and Domain-Driven Design (DDD) techniques to address these two gaps. RESULTS We proposed a software framework for developing adaptive, reusable, and interoperable Internet-Delivered Psychological Treatments (IDPT), referred hereto as OpenIDPT Framework. The OpenIDPT Framework includes a) a Reference Model (RM), b) a Reference Architecture (RA), c) an Information Architecture (IA), and d) an open-source implementation of an adaptive IDPT system.The reference model reveals the adaptive elements (what to adapt), adaptive dimensions (on what basis to adapt), information architecture (how to structure content), and strategies (how to adapt) of an adaptive IDPT system. The Reference Architecture unveils the technical architecture of an adaptive IDPT system. The information architecture guides how to structure and organize the content for better discoverability and comprehensibility. To evaluate the proposed RA of adaptive IDPT systems, we implemented a prototype as an Open-Source Software. We refer to it as Open-Source Adaptive IDPT System (OSAIS). We used Design Science Research (DSR) evaluation methods to assess the efficacy of the proposed artefacts and their ability to address identified research gaps. Our preliminary results demonstrate that the proposed artefacts exhibit capabilities to use comprehensive user profiling techniques to adapt interventions using different rule-based engines, recommendation systems, and artificial intelligence (AI) based algorithms. As a Proof-of-Concept of AI-based algorithms, we present an adaptive strategy based on Natural Language Processing (NLP) techniques that analyze patientauthored text data and extract depression symptoms corresponding to a clinically established psychometric assessment questionnaire PHQ-9. The strategy utilizes the proposed novel Word Embedding (Depression2Vec) to extract depression symptoms from patient-authored text and adapts psychological treatments based on the absence or presence of depression symptoms. Furthermore, to obtain interoperability in the OpenIDPT Framework, we created an open-source Resource Server (RS) based on GraphQL. An RS is a web application that can read, write, update, and delete (CRUD) HL7 FHIR resources. HL7 FHIR is an open healthcare IT standard (analogous to data structure) for healthcare data exchange. GraphQL is a data query and manipulation language for Web-Service communications. CONCLUSION This study demonstrates the feasibility of using an adaptive system to enhance user adherence. With the ubiquity of ambient intelligence and predictive algorithms, further study on how to combine these IoT technologies with the adaptive system is prudent and exciting

An HL7 FHIR and GraphQL approach for interoperability between heterogeneous Electronic Health Record systems

Heterogeneities in data representation and care processes create interoperability complexity among Electronic Health Record systems (EHRs). We can resolve such data and process level heterogeneities by following consistent healthcare standards like Clinical Document Architecture (CDA), OpenEHR, and HL7 FHIR. However, these standards also differ at the structural and implementation level, making interoperability more complex. Hence, there is a need to investigate mechanisms that can resolve data level heterogeneity to achieve semantic data interoperability between heterogeneous systems. As a solution to this, we offer an architecture that utilizes a resource server based on GraphQL and HL7 FHIR that establishes communication between two heterogeneous EHRs. This paper describes how the proposed architecture is implemented to achieve interoperability between two heterogeneous EHRs, HL7 FHIR and OpenMRS. The presented approach establishes secure communication between the EHRs and provides accurate mappings that enable timely health information exchange between EHRs

Adaptive Systems for Internet-Delivered Psychological Treatments

Internet-Delivered Psychological Treatments (IDPT) are based on evidence-based psychological treatment models adjusted for interaction through the Internet. The use of Internet technologies has the potential to increase the availability of evidence-based mental health services for a far-reaching population with the use of fewer resources. Despite evidence that Internet Interventions can be effective means in mental health morbidities, most current IDPT systems are tunnel-based, inflexible, and non-interoperable. Hence it becomes essential to understand which elements of an Internet intervention contribute to effectiveness and treatment outcomes. By analogy, adaptation is a central aspect of successful face-to-face mental health therapy. Adaptability to patient needs can be regarded as an essential outcome factor in online systems for mental health interventions as well. While some aspects of rule-based and machine-learning-based adaptation have attracted attention in recent IDPT development, systematic reporting of core components, dimensions of adaptiveness, information architecture, and strategies for adaptation in the IDPT system are still lacking. To bridge this gap, we propose a model that shows how adaptive systems are represented in classical control theory and discuss how the model can be used to specify adaptive IDPT systems. Concerning the reference model, we outline the core components of adaptive IDPT systems, the main adaptive elements, dimensions of adaptiveness, information architecture applied to adaptive systems, and strategies used in the adaptation process. We also provide comprehensive guidelines on how to develop an adaptive IDPT system based on the Person-Based Approach

Adaptive Systems for Internet-Delivered Psychological Treatments

Internet-Delivered Psychological Treatments (IDPT) are based on evidence-based psychological treatment models adjusted for interaction through the Internet. The use of Internet technologies has the potential to increase the availability of evidence-based mental health services for a far-reaching population with the use of fewer resources. Despite evidence that Internet Interventions can be effective means in mental health morbidities, most current IDPT systems are tunnel-based, inflexible, and non-interoperable. Hence it becomes essential to understand which elements of an Internet intervention contribute to effectiveness and treatment outcomes. By analogy, adaptation is a central aspect of successful face-to-face mental health therapy. Adaptability to patient needs can be regarded as an essential outcome factor in online systems for mental health interventions as well. While some aspects of rule-based and machine-learning-based adaptation have attracted attention in recent IDPT development, systematic reporting of core components, dimensions of adaptiveness, information architecture, and strategies for adaptation in the IDPT system are still lacking. To bridge this gap, we propose a model that shows how adaptive systems are represented in classical control theory and discuss how the model can be used to specify adaptive IDPT systems. Concerning the reference model, we outline the core components of adaptive IDPT systems, the main adaptive elements, dimensions of adaptiveness, information architecture applied to adaptive systems, and strategies used in the adaptation process. We also provide comprehensive guidelines on how to develop an adaptive IDPT system based on the Person-Based Approach

A GraphQL approach to Healthcare Information Exchange with HL7 FHIR

Interoperability is accepted as a fundamental necessity for the successful realization of Healthcare Information Systems. It can be achieved by utilizing consistent standards defining syntactic and semantic meaning of the information being exchanged. HL7 FHIR is one of such open standards for Health Information Exchange (HIE). While HL7 FHIR supports Representational State Transfer (REST) architecture and Service-oriented Architecture (SOA) for seamless information exchange, it inherits the inflexibility and complexity associated with the RESTful approach. GraphQL is a query language developed by Facebook that provides promising techniques to overcome these issues. In this paper, we exploit the use of GraphQL and HL7 FHIR for HIE; present an algorithm to map HL7 FHIR resources to a GraphQL schema, and created a prototype implementation of the approach and compare it with a RESTful approach. Our experimental results indicate that the combination of GraphQL and HL7 FHIR-based web APIs for HIE is performant, cost-effective, scalable and flexible to meet web and mobile clients requirements

Adaptive Elements in Internet-Delivered Psychological Treatment Systems: Systematic Review

Background: Internet-delivered psychological treatments (IDPTs) are built on evidence-based psychological treatment models, such as cognitive behavioral therapy, and are adjusted for internet use. The use of internet technologies has the potential to increase access to evidence-based mental health services for a larger proportion of the population with the use of fewer resources. However, despite extensive evidence that internet interventions can be effective in the treatment of mental health disorders, user adherence to such internet intervention is suboptimal. Objective: This review aimed to (1) inspect and identify the adaptive elements of IDPT for mental health disorders, (2) examine how system adaptation influences the efficacy of IDPT on mental health treatments, (3) identify the information architecture, adaptive dimensions, and strategies for implementing these interventions for mental illness, and (4) use the findings to create a conceptual framework that provides better user adherence and adaptiveness in IDPT for mental health issues. Methods: The review followed the guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The research databases Medline (PubMed), ACM Digital Library, PsycINFO, CINAHL, and Cochrane were searched for studies dating from January 2000 to January 2020. Based on predetermined selection criteria, data from eligible studies were analyzed. Results: A total of 3341 studies were initially identified based on the inclusion criteria. Following a review of the title, abstract, and full text, 31 studies that fulfilled the inclusion criteria were selected, most of which described attempts to tailor interventions for mental health disorders. The most common adaptive elements were feedback messages to patients from therapists and intervention content. However, how these elements contribute to the efficacy of IDPT in mental health were not reported. The most common information architecture used by studies was tunnel-based, although a number of studies did not report the choice of information architecture used. Rule-based strategies were the most common adaptive strategies used by these studies. All of the studies were broadly grouped into two adaptive dimensions based on user preferences or using performance measures, such as psychometric tests. Conclusions: Several studies suggest that adaptive IDPT has the potential to enhance intervention outcomes and increase user adherence. There is a lack of studies reporting design elements, adaptive elements, and adaptive strategies in IDPT systems. Hence, focused research on adaptive IDPT systems and clinical trials to assess their effectiveness are needed