Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

A process model for quality in use evaluation on clinical decision support systems

Developing or purchasing software is an expensive investment and needs to be justified. Furthermore, the software must be useful in its purpose, reliable, efficient and, among other characteristics, meet the expectations of users [1, 2]. It would be no different in the case of a clinical Decision Support System - CDSS. CDSS are systems developed to support clinicians and other health professionals in a medical decision making [3]. They are developed within a clinical context, following medical guidelines, with varied purposes such as diagnoses [4, 5, 6] patient monitoring [7, 8, 9], prevention [10] and disease treatment [11, 12]. Conversely, even with all the benefits offered by a CDSS, its acceptance in the medical field is still a matter of debate [13, 14]. The CDSS acceptance is linked to the perception of the end user, such as 1) the system’s ease of use and utility, 2) the quality of its results and its reliability [14], 3) the contextual accessibility of the system, sometimes not included in the health professional’s routine and workflow, and 4) the fact that numerous CDSSs are not integrated with existing systems [15]. One manner to extend the use and disseminate positive contributions of CDSSs to the medical world is to develop them in a reliable and useful way. For this, one must follow the best practices of software engineering (SE, acronym in English) [16] and be concerned with its quality, both in the design and development process and in its effective use. Evaluating the quality of the software is to measure its characteristics and sub-characteristics of quality. In order to better structure the assessment, a series of international standards, with models and frameworks, were developed for assisting software developers in assessing the quality of software products. The latest series is the ISO/IEC 25000 - System and Software Quality Requirements and Evaluation (SQuaRE) [17]. Two of the SQuaRE divisions are addressed in this thesis: 1) Division of quality models standard (ISO/IEC 25010) [18], and 2) Quality measurement division standard (ISO/IEC 25022) [19]. The ISO/IEC 25010 are divided in product quality model and the quality model in use. Quality in use (QiU), a model of ISO/IEC 25010, is the focus of this study, through its evaluation in the context of a CDSS. The quality in use model refers to the quality of the software when executed, mentioning the result of the interaction between users and the software system/product in a specific context. This model consists of five quality characteristics: • Effectiveness - means the level of precision and completeness with which users achieve their specific goals when using the system; • Efficiency - refers to the resources spent to achieve the goals and its measure is related to the level of effectiveness achieved with the consumed resources; • Satisfaction - refers to whether user requirements are satisfied in a particular context of system use; • Freedom from risk - refers to the degree to which the quality of a system reduces or avoids potential risks to human life, the economic situation, and health of the environment; • Context coverage - deals with the use of the system in all specific contexts and/or in contexts that extend beyond the initially identified contexts. Context completeness and flexibility are the sub-characteristics that represent context coverage. Thus, when measuring the quality of a CDSS, we must consider both the context of use and the choice of the characteristic and sub-characteristic that best suits the purpose of the measurement [20]. The QiU model provides a powerful contribution to the practice of evaluating a system and determining its quality. According to Harrison et al. [21], Effectiveness, Efficiency and Satisfaction are considered the key criteria to reflect the quality of use. Therefore, these QiU characteristics meet the needs and expectations of the users of the systems, in our case of CDSSs, as they consider the user experience. As a contribution, we proposed a process model to evaluate two QiU characteristics in a CDSS: satisfaction and efficiency. We believe these characteristics are important in the evaluation of a CDSS because, due to its links with the user experience and the usability of the system, when measured, can corroborate the quality of the CDSS and mitigate the non-use and non-acceptance of this type of software. Other contributions from our work are 1) in the academic context, a significant study in the area of software quality, focusing on its characteristics, especially on the quality in use. A guideline for collecting and measuring these characteristics was built into our process model; 2) in the area of software development, professionals can make use of a simple and adaptable process, applicable to other types of systems, to measure the quality-in-use characteristics of their products.Desenvolver ou adquirir software é um investimento caro e precisa ser justificado. Além de útil, o sistema deve ser confiável, eficiente e, entre outras características, atender às expectativas dos usuários [1, 2]. Não seria diferente no caso de um sistema de apoio à decisão clínica (CDSS, acrônimo em inglês), sistemas desenvolvidos para apoiar médicos e outros profissionais de saúde na tomada de uma decisão médica [3]. CDSSs são elaborados dentro de um contexto clínico, seguindo guidelines com propósitos variados, sejam para diagnósticos [4, 5, 6], acompanhamento do paciente [7, 8, 9], na prevenção [10] e tratamento de doenças [11, 12]. No entanto, apesar de todo os benefícios oferecidos por um CDSS, sua aceitação na área médica ainda é motivo de debate [13, 14]. Essa aceitação está ligada à percepção do usuário final, como 1) a facilidade de uso e utilidade do sistema; 2) a qualidade dos resultados produzidos e sua confiabilidade [14]; 3) a acessibilidade contextual do sistema, muitas vezes não incluída na rotina e no fluxo de trabalho do profissional de saúde, e 4) o fato de muitos CDSSs não estarem integrados aos sistemas existentes [15]. Uma forma de estender o uso de CDSSs e disseminar suas contribuições positivas entre os profissionais de saúde é garantir a confiabilidade de seus resultados e a satisfação do usuáriofinal. Para tal deve-se seguir as melhores práticas da engenharia de software (SE, acrônimo em inglês) em sua concepção [16]. Isso implica em preocupar-se com a qualidade do sistema tanto no processo do projeto e desenvolvimento quanto em sua efetiva utilização. Uma forma de certificar se um software obedece a essa premissa é realizando avaliações de qualidade. Avaliar a qualidade do software é medir suas características e subcaracterísticas de qualidade. Para uma melhor estruturação desta medição foram desenvolvidos séries de padrões internacionais como guidelines de avaliação de qualidade de produtos de software. A série mais recente trata-se da ISO/IEC 25000 System and Software Quality Requirements and Evaluation (SQUARE) [17]. Dois padrões desta série foram abordadas nesta tese, sendo 1) o modelos de qualidade de software e sistemas (ISO/IEC 25010) [18], no qual trabalhamos especificamente com o modelo de qualidade em uso, e 2) o padrão de medição da qualidade em uso (ISO/IEC 25022) [19]. Qualidade em uso é o foco desta tese, através de sua avaliação no contexto de utilização de um CDSS. O Modelo de qualidade em uso trata da qualidade do software quando em execução, referindose ao resultado da interação dos usuários e o software em um cenário específico. Este modelo é composto de cinco características de qualidade: • Eficácia (ou efetividade) - esta característica representa o nível de precisão e completude com que os usuários alcançam os objetivos específicos, durante a utilização do sistema ou produto de software; • Eficiência - sua medição representa o nível de eficácia alcançada em relação aos recursos consumidos para o alcance das metas; • Satisfação - trata do quanto as necessidades do usuário são satisfeitas dentro de um determinado contexto de uso do sistema ou produto de software. Esta característica é composta pelas subcaracterísticas Utilidade, Confiança, Prazer e Conforto do usuário em relação ao sistema; • Livre de risco - trata do grau em que a qualidade de um sistema ou produto permite mitigar ou evitar riscos potenciais à vida humana, à situação econômica, à saúde ou ao meio ambiente, sendo estas suas três subcaracterísticas; • Cobertura de contexto - trata do uso do sistema em todos os contextos específicos e/ou em contextos além dos inicialmente identificados, sendo composta pelas subcaracterísticas completude de contexto e flexibilidade do sistema. Assim, para se medir a qualidade de um CDSS deve-se considerar tanto o contexto de utilização quanto a escolha da característica e subcaracterística que melhor condizem ao propósito da avaliação [20]. De acordo com Harrison et al. [21], Eficácia, Eficiência e Satisfação são considerados os principais critérios a serem avaliados para refletir a qualidade de uso. Tais características de qualidades em uso refletem o atendimento das necessidades e expectativas dos usuários dos sistemas, em especial ao usuário primário ou final, uma vez que estão diretamente relacionadas com a experiência do usuário. O modelo de qualidade em uso fornece uma contribuição poderosa para a prática de avaliar um sistema e determinar sua qualidade. Como contribuição, propusemos um modelo de processo para avaliação de qualidade em uso de um CDSS através da medição, a priori, de duas características de qualidade - satisfação e eficiência. Acreditamos que tais características são importantes na avaliação de um CDSS devido estreita relação destas com a experiência do usuário-final e a usabilidade do sistema. Assim, quando mensuradas, tais características podem corroborar com a qualidade do CDSS e mitigar a não utilização e não aceitação desse tipo de software. Nosso modelo proposto é definido por cinco (5) fases, a saber: 1) Identificação de cenário e contexto de uso do sistema, 2) seleção das medidas, métricas e métodos para mensurar as características, 3) a medição da qualidade, 4) a análise dos valores encontrados na medição e 5) a apresentação dos resultados obtidos. O resultado da aplicação do modelo de processo traduz-se em um conjunto de informações que nortearão um melhoramento do software, caso a medição das características fique abaixo de um padrão pré-definido pelos atores envolvidos no processo de medição do sistema. Por outro lado, se a medição for positiva, isso vem ratificar a qualidade do sistema e ações poderão ser tomadas para disseminar esse bom resultado, buscando a adesão de mais utilizadores. Como forma de validação do modelo proposto, após sua utilização para identificação de cenários e contexto-de-uso possíveis de serem mensurados, foi apresentado um CDSS da área oncológica a profissionais de saúde, estudantes de medicina e profissionais da área de qualidade de software que, ao final de sua utilização, responderam a um inquérito com o objetivo de avaliar o sistema. A aplicação se deu de forma online, dado a necessidade de mantermos o distanciamento social e o de cumprirmos as orientações sanitárias. As respostas serviram como fonte de dados para a medição das características de qualidadeem- uso do sistema. Os resultados da aplicação revelou que nosso modelo de processo de avaliação é válido, relevante e de fácil utilização para identificar as características importantes em um sistema, bem como suas medições por meio das funções matemáticas do modelo ISO/IEC 25022. Outras contribuições do nosso trabalho, temos 1) no âmbito acadêmico, um estudo significativo na área de qualidade de software, com foco em suas características, especialmente na qualidade em uso. Uma guideline para a coleta e mensuração dessas características foi construída em nosso modelo de processo; 2) na área de desenvolvimento de software, os profissionais podem contar com um processo simples e adaptável, aplicável a outros tipos de sistema, para mensuração da qualidade em uso de seus produtos.The research has been partially funded by the FCT/MCTES through national funds, and when applicable, co-funded EU funds under the project UIDB/EEA/50008/2020 and Operação Centro 01-0145-FEDER-000019 – C4 – Centro de Competências em Cloud Computing, co-financed by the Programa Operacional Regional do Centro (CENTRO 2020), through the Sistema de Apoio à Investigação Científica e Tecnológica – Programas Integrados de IC&DT. I would also like to acknowledge the contribution of the COST Action IC1303: AAPELE—Archi- tectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226; SHELD-ON—Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology)

Clinical foundations and information architecture for the implementation of a federated health record service

Clinical care increasingly requires healthcare professionals to access patient record information that may be distributed across multiple sites, held in a variety of paper and electronic formats, and represented as mixtures of narrative, structured, coded and multi-media entries. A longitudinal person-centred electronic health record (EHR) is a much-anticipated solution to this problem, but its realisation is proving to be a long and complex journey. This Thesis explores the history and evolution of clinical information systems, and establishes a set of clinical and ethico-legal requirements for a generic EHR server. A federation approach (FHR) to harmonising distributed heterogeneous electronic clinical databases is advocated as the basis for meeting these requirements. A set of information models and middleware services, needed to implement a Federated Health Record server, are then described, thereby supporting access by clinical applications to a distributed set of feeder systems holding patient record information. The overall information architecture thus defined provides a generic means of combining such feeder system data to create a virtual electronic health record. Active collaboration in a wide range of clinical contexts, across the whole of Europe, has been central to the evolution of the approach taken. A federated health record server based on this architecture has been implemented by the author and colleagues and deployed in a live clinical environment in the Department of Cardiovascular Medicine at the Whittington Hospital in North London. This implementation experience has fed back into the conceptual development of the approach and has provided "proof-of-concept" verification of its completeness and practical utility. This research has benefited from collaboration with a wide range of healthcare sites, informatics organisations and industry across Europe though several EU Health Telematics projects: GEHR, Synapses, EHCR-SupA, SynEx, Medicate and 6WINIT. The information models published here have been placed in the public domain and have substantially contributed to two generations of CEN health informatics standards, including CEN TC/251 ENV 13606

Rethink Digital Health Innovation: Understanding Socio-Technical Interoperability as Guiding Concept

Diese Dissertation sucht nach einem theoretischem Grundgerüst, um komplexe, digitale Gesundheitsinnovationen so zu entwickeln, dass sie bessere Erfolgsaussichten haben, auch in der alltäglichen Versorgungspraxis anzukommen. Denn obwohl es weder am Bedarf von noch an Ideen für digitale Gesundheitsinnovationen mangelt, bleibt die Flut an erfolgreich in der Praxis etablierten Lösungen leider aus. Dieser unzureichende Diffusionserfolg einer entwickelten Lösung - gern auch als Pilotitis pathologisiert - offenbart sich insbesondere dann, wenn die geplante Innovation mit größeren Ambitionen und Komplexität verbunden ist. Dem geübten Kritiker werden sofort ketzerische Gegenfragen in den Sinn kommen. Beispielsweise was denn unter komplexen, digitalen Gesundheitsinnovationen verstanden werden soll und ob es überhaupt möglich ist, eine universale Lösungsformel zu finden, die eine erfolgreiche Diffusion digitaler Gesundheitsinnovationen garantieren kann. Beide Fragen sind nicht nur berechtigt, sondern münden letztlich auch in zwei Forschungsstränge, welchen ich mich in dieser Dissertation explizit widme. In einem ersten Block erarbeite ich eine Abgrenzung jener digitalen Gesundheitsinnovationen, welche derzeit in Literatur und Praxis besondere Aufmerksamkeit aufgrund ihres hohen Potentials zur Versorgungsverbesserung und ihrer resultierenden Komplexität gewidmet ist. Genauer gesagt untersuche ich dominante Zielstellungen und welche Herausforderung mit ihnen einhergehen. Innerhalb der Arbeiten in diesem Forschungsstrang kristallisieren sich vier Zielstellungen heraus: 1. die Unterstützung kontinuierlicher, gemeinschaftlicher Versorgungsprozesse über diverse Leistungserbringer (auch als inter-organisationale Versorgungspfade bekannt); 2. die aktive Einbeziehung der Patient:innen in ihre Versorgungsprozesse (auch als Patient Empowerment oder Patient Engagement bekannt); 3. die Stärkung der sektoren-übergreifenden Zusammenarbeit zwischen Wissenschaft und Versorgungpraxis bis hin zu lernenden Gesundheitssystemen und 4. die Etablierung daten-zentrierter Wertschöpfung für das Gesundheitswesen aufgrund steigender bzgl. Verfügbarkeit valider Daten, neuen Verarbeitungsmethoden (Stichwort Künstliche Intelligenz) sowie den zahlreichen Nutzungsmöglichkeiten. Im Fokus dieser Dissertation stehen daher weniger die autarken, klar abgrenzbaren Innovationen (bspw. eine Symptomtagebuch-App zur Beschwerdedokumentation). Vielmehr adressiert diese Doktorarbeit jene Innovationsvorhaben, welche eine oder mehrere der o.g. Zielstellung verfolgen, ein weiteres technologisches Puzzleteil in komplexe Informationssystemlandschaften hinzufügen und somit im Zusammenspiel mit diversen weiteren IT-Systemen zur Verbesserung der Gesundheitsversorgung und/ oder ihrer Organisation beitragen. In der Auseinandersetzung mit diesen Zielstellungen und verbundenen Herausforderungen der Systementwicklung rückte das Problem fragmentierter IT-Systemlandschaften des Gesundheitswesens in den Mittelpunkt. Darunter wird der unerfreuliche Zustand verstanden, dass unterschiedliche Informations- und Anwendungssysteme nicht wie gewünscht miteinander interagieren können. So kommt es zu Unterbrechungen von Informationsflüssen und Versorgungsprozessen, welche anderweitig durch fehleranfällige Zusatzaufwände (bspw. Doppeldokumentation) aufgefangen werden müssen. Um diesen Einschränkungen der Effektivität und Effizienz zu begegnen, müssen eben jene IT-System-Silos abgebaut werden. Alle o.g. Zielstellungen ordnen sich dieser defragmentierenden Wirkung unter, in dem sie 1. verschiedene Leistungserbringer, 2. Versorgungsteams und Patient:innen, 3. Wissenschaft und Versorgung oder 4. diverse Datenquellen und moderne Auswertungstechnologien zusammenführen wollen. Doch nun kommt es zu einem komplexen Ringschluss. Einerseits suchen die in dieser Arbeit thematisierten digitalen Gesundheitsinnovationen Wege zur Defragmentierung der Informationssystemlandschaften. Andererseits ist ihre eingeschränkte Erfolgsquote u.a. in eben jener bestehenden Fragmentierung begründet, die sie aufzulösen suchen. Mit diesem Erkenntnisgewinn eröffnet sich der zweite Forschungsstrang dieser Arbeit, der sich mit der Eigenschaft der 'Interoperabilität' intensiv auseinandersetzt. Er untersucht, wie diese Eigenschaft eine zentrale Rolle für Innovationsvorhaben in der Digital Health Domäne einnehmen soll. Denn Interoperabilität beschreibt, vereinfacht ausgedrückt, die Fähigkeit von zwei oder mehreren Systemen miteinander gemeinsame Aufgaben zu erfüllen. Sie repräsentiert somit das Kernanliegen der identifizierten Zielstellungen und ist Dreh- und Angelpunkt, wenn eine entwickelte Lösung in eine konkrete Zielumgebung integriert werden soll. Von einem technisch-dominierten Blickwinkel aus betrachtet, geht es hierbei um die Gewährleistung von validen, performanten und sicheren Kommunikationsszenarien, sodass die o.g. Informationsflussbrüche zwischen technischen Teilsystemen abgebaut werden. Ein rein technisches Interoperabilitätsverständnis genügt jedoch nicht, um die Vielfalt an Diffusionsbarrieren von digitalen Gesundheitsinnovationen zu umfassen. Denn beispielsweise das Fehlen adäquater Vergütungsoptionen innerhalb der gesetzlichen Rahmenbedingungen oder eine mangelhafte Passfähigkeit für den bestimmten Versorgungsprozess sind keine rein technischen Probleme. Vielmehr kommt hier eine Grundhaltung der Wirtschaftsinformatik zum Tragen, die Informationssysteme - auch die des Gesundheitswesens - als sozio-technische Systeme begreift und dabei Technologie stets im Zusammenhang mit Menschen, die sie nutzen, von ihr beeinflusst werden oder sie organisieren, betrachtet. Soll eine digitale Gesundheitsinnovation, die einen Mehrwert gemäß der o.g. Zielstellungen verspricht, in eine existierende Informationssystemlandschaft der Gesundheitsversorgung integriert werden, so muss sie aus technischen sowie nicht-technischen Gesichtspunkten 'interoperabel' sein. Zwar ist die Notwendigkeit von Interoperabilität in der Wissenschaft, Politik und Praxis bekannt und auch positive Bewegungen der Domäne hin zu mehr Interoperabilität sind zu verspüren. Jedoch dominiert dabei einerseits ein technisches Verständnis und andererseits bleibt das Potential dieser Eigenschaft als Leitmotiv für das Innovationsmanagement bislang weitestgehend ungenutzt. An genau dieser Stelle knüpft nun der Hauptbeitrag dieser Doktorarbeit an, in dem sie eine sozio-technische Konzeptualisierung und Kontextualisierung von Interoperabilität für künftige digitale Gesundheitsinnovationen vorschlägt. Literatur- und expertenbasiert wird ein Rahmenwerk erarbeitet - das Digital Health Innovation Interoperability Framework - das insbesondere Innovatoren und Innovationsfördernde dabei unterstützen soll, die Diffusionswahrscheinlichkeit in die Praxis zu erhöhen. Nun sind mit diesem Framework viele Erkenntnisse und Botschaften verbunden, die ich für diesen Prolog wie folgt zusammenfassen möchte: 1. Um die Entwicklung digitaler Gesundheitsinnovationen bestmöglich auf eine erfolgreiche Integration in eine bestimmte Zielumgebung auszurichten, sind die Realisierung eines neuartigen Wertversprechens sowie die Gewährleistung sozio-technischer Interoperabilität die zwei zusammenhängenden Hauptaufgaben eines Innovationsprozesses. 2. Die Gewährleistung von Interoperabilität ist eine aktiv zu verantwortende Managementaufgabe und wird durch projektspezifische Bedingungen sowie von externen und internen Dynamiken beeinflusst. 3. Sozio-technische Interoperabilität im Kontext digitaler Gesundheitsinnovationen kann über sieben, interdependente Ebenen definiert werden: Politische und regulatorische Bedingungen; Vertragsbedingungen; Versorgungs- und Geschäftsprozesse; Nutzung; Information; Anwendungen; IT-Infrastruktur. 4. Um Interoperabilität auf jeder dieser Ebenen zu gewährleisten, sind Strategien differenziert zu definieren, welche auf einem Kontinuum zwischen Kompatibilitätsanforderungen aufseiten der Innovation und der Motivation von Anpassungen aufseiten der Zielumgebung verortet werden können. 5. Das Streben nach mehr Interoperabilität fördert sowohl den nachhaltigen Erfolg der einzelnen digitalen Gesundheitsinnovation als auch die Defragmentierung existierender Informationssystemlandschaften und trägt somit zur Verbesserung des Gesundheitswesens bei. Zugegeben: die letzte dieser fünf Botschaften trägt eher die Färbung einer Überzeugung, als dass sie ein Ergebnis wissenschaftlicher Beweisführung ist. Dennoch empfinde ich diese, wenn auch persönliche Erkenntnis als Maxim der Domäne, der ich mich zugehörig fühle - der IT-Systementwicklung des Gesundheitswesens

Data Journeys in the Sciences

This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research

HEALTH INFORMATION STANDARDISATION AS A BASIS FOR LEARNING HEALTH SYSTEMS

PhD ThesisStandardisation of healthcare has been the focus of hospital management and clinicians since the 1990’s. Electronic health records were already intended to provide clinicians with real-time access to clinical knowledge and care plans while also recording and storing vast amounts of patient data. It took more than three decades for electronic health records to start to become ubiquitous in all aspects of healthcare. Learning health systems are the next stage in health information systems whose potential benefits have been promoted for more than a decade - yet few are seen in clinical practice. Clinical care process specifications are a primary form of clinical documentation used in all aspects of healthcare, but they lack standardisation. This thesis contends that this lack of standardisation was inherited by electronic health records and that this is a significant issue holding back the development and adoption of learning health systems. Standardisation of clinical documents is used to mitigate issues in electronic health records as a basis for enabling learning health systems. One type of clinical document, the caremap, is standardised in order to achieve an effective approach to containing resources and ensuring consistency and quality. This led not only to improved clinicians’ comprehension and acceptance of the clinical document, but also to reduced time expended in developing complicated learning health systems built using the input of clinical experts

The Knowledge Grid: A Platform to Increase the Interoperability of Computable Knowledge and Produce Advice for Health

Here we demonstrate how more highly interoperable computable knowledge enables systems to generate large quantities of evidence-based advice for health. We first provide a thorough analysis of advice. Then, because advice derives from knowledge, we turn our focus to computable, i.e., machine-interpretable, forms for knowledge. We consider how computable knowledge plays dual roles as a resource conveying content and as an advice enabler. In this latter role, computable knowledge is combined with data about a decision situation to generate advice targeted at the pending decision. We distinguish between two types of automated services. When a computer system provides computable knowledge, we say that it provides a knowledge service. When computer system combines computable knowledge with instance data to provide advice that is specific to an unmade decision we say that it provides an advice-giving service. The work here aims to increase the interoperability of computable knowledge to bring about better knowledge services and advice-giving services for health. The primary motivation for this research is the problem of missing or inadequate advice about health topics. The global demand for well-informed health advice far exceeds the global supply. In part to overcome this scarcity, the design and development of Learning Health Systems is being pursued at various levels of scale: local, regional, state, national, and international. Learning Health Systems fuse capabilities to generate new computable biomedical knowledge with other capabilities to rapidly and widely use computable biomedical knowledge to inform health practices and behaviors with advice. To support Learning Health Systems, we believe that knowledge services and advice-giving services have to be more highly interoperable. I use examples of knowledge services and advice-giving services which exclusively support medication use. This is because I am a pharmacist and pharmacy is the biomedical domain that I know. The examples here address the serious problems of medication adherence and prescribing safety. Two empirical studies are shared that demonstrate the potential to address these problems and make improvements by using advice. But primarily we use these examples to demonstrate general and critical differences between stand-alone, unique approaches to handling computable biomedical knowledge, which make it useful for one system, and common, more highly interoperable approaches, which can make it useful for many heterogeneous systems. Three aspects of computable knowledge interoperability are addressed: modularity, identity, and updateability. We demonstrate that instances of computable knowledge, and related instances of knowledge services and advice-giving services, can be modularized. We also demonstrate the utility of uniquely identifying modular instances of computable knowledge. Finally, we build on the computing concept of pipelining to demonstrate how computable knowledge modules can automatically be updated and rapidly deployed. Our work is supported by a fledgling technical knowledge infrastructure platform called the Knowledge Grid. It includes formally specified compound digital objects called Knowledge Objects, a conventional digital Library that serves as a Knowledge Object repository, and an Activator that provides an application programming interface (API) for computable knowledge. The Library component provides knowledge services. The Activator component provides both knowledge services and advice-giving services. In conclusion, by increasing the interoperability of computable biomedical knowledge using the Knowledge Grid, we demonstrate new capabilities to generate well-informed health advice at a scale. These new capabilities may ultimately support Learning Health Systems and boost health for large populations of people who would otherwise not receive well-informed health advice.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146073/1/ajflynn_1.pd