The role of blockchain technology in ensuring security and immutability of open data in healthcare

Clinical information is highly confidential due to its sensitive nature. Implementing health information systems has raised concerns regarding in teroperability, privacy, and security. The storage and retrieval of this infor mation also present the same problems. Therefore, any effort to introduce healthcare information systems must ensure patient data's safety, privacy, integ rity, and immutability. Blockchain technology and the openEHR open data model have emerged to address these concerns, providing a solution that guar antees data security, interoperability between systems, and the accuracy of stored data queries. Two different architectures were developed and subjected to several performance tests to enhance security and immutability in open data models implemented in healthcare institutions. The results were analysed to de termine which architecture provides more value to a healthcare institution. Sub sequently, a discussion was held to draw appropriate conclusions.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope

The nephrology eHealth-system of the metropolitan region of Hannover for digitalization of care, establishment of decision support systems and analysis of health care quality

Background Even though a high demand for sector spanning communication exists, so far no eHealth platform for nephrology is established within Germany. This leads to insufficient communication between medical providers and therefore suboptimal nephrologic care. In addition, Clinical Decision Support Systems have not been used in Nephrology until now. Methods The aim of NEPHRO-DIGITAL is to create a eHealth platform in the Hannover region that facilitates integrated, cross-sectoral data exchange and includes teleconsultation between outpatient nephrology, primary care, pediatricians and nephrology clinics to reduce communication deficits and prevent data loss, and to enable the creation and implementation of an interoperable clinical decision support system. This system will be based on input data from multiple sources for early identification of patients with cardiovascular comorbidity and progression of renal insufficiency. Especially patients will be able to enter and access their own data. A transfer to a second nephrology center (metropolitan region of Erlangen-Nuremburg) is included in the study to prove feasibility and scalability of the approach. Discussion A decision support system should lead to earlier therapeutic interventions and thereby improve the prognosis of patients as well as their treatment satisfaction and quality of life. The system will be integrated in the data integration centres of two large German university medicine consortia (HiGHmed (highmed.org) and MIRACUM (miracum.org)). Trial registration ISRCTN16755335 (09.07.2019)

Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

[Abstract] The advances achieved in recent decades regarding cardiac surgery have led to a new risk that goes beyond surgeons’ dexterity; postoperative hours are crucial for cardiac surgery patients and are usually spent in intensive care units (ICUs), where the patients need to be continuously monitored to adjust their treatment. Clinical decision support systems (CDSSs) have been developed to take this real-time information and provide clinical suggestions to physicians in order to reduce medical errors and to improve patient recovery. In this review, an initial total of 499 papers were considered after identification using PubMed, Web of Science, and CINAHL. Twenty-two studies were included after filtering, which included the deletion of duplications and the exclusion of titles or abstracts that were not of real interest. A review of these papers concluded the applicability and advances that CDSSs offer for both doctors and patients. Better prognosis and recovery rates are achieved by using this technology, which has also received high acceptance among most physicians. However, despite the evidence that well-designed CDSSs are effective, they still need to be refined to offer the best assistance possible, which may still take time, despite the promising models that have already been applied in real ICUs.Xunta de Galicia; ED431C 2018/4

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

[EN] In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? 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Data representation structure to support clinical decision-making in the pediatric intensive care unit: Interview study and preliminary decision support interface design

ABSTRACT: Background: Clinical decision-making is a complex cognitive process that relies on the interpretation of a large variety of data from different sources and involves the use of knowledge bases and scientific recommendations. The representation of clinical data plays a key role in the speed and efficiency of its interpretation. In addition, the increasing use of clinical decision support systems (CDSSs) provides assistance to clinicians in their practice, allowing them to improve patient outcomes. In the pediatric intensive care unit (PICU), clinicians must process high volumes of data and deal with ever-growing workloads. As they use multiple systems daily to assess patients’ status and to adjust the health care plan, including electronic health records (EHR), clinical systems (eg, laboratory, imaging and pharmacy), and connected devices (eg, bedside monitors, mechanical ventilators, intravenous pumps, and syringes), clinicians rely mostly on their judgment and ability to trace relevant data for decision-making. In these circumstances, the lack of optimal data structure and adapted visual representation hinder clinician’s cognitive processes and clinical decision-making skills. Objective: In this study, we designed a prototype to optimize the representation of clinical data collected from existing sources (eg, EHR, clinical systems, and devices) via a structure that supports the integration of a home-developed CDSS in the PICU. This study was based on analyzing end user needs and their clinical workflow. Methods: First, we observed clinical activities in a PICU to secure a better understanding of the workflow in terms of staff tasks and their use of EHR on a typical work shift. Second, we conducted interviews with 11 clinicians from different staff categories (eg, intensivists, fellows, nurses, and nurse practitioners) to compile their needs for decision support. Third, we structured the data to design a prototype that illustrates the proposed representation. We used a brain injury care scenario to validate the relevance of integrated data and the utility of main functionalities in a clinical context. Fourth, we held design meetings with 5 clinicians to present, revise, and adapt the prototype to meet their needs. Results: We created a structure with 3 levels of abstraction—unit level, patient level, and system level—to optimize clinical data representation and display for efficient patient assessment and to provide a flexible platform to host the internally developed CDSS. Subsequently, we designed a preliminary prototype based on this structure. Conclusions: The data representation structure allows prioritizing patients via criticality indicators, assessing their conditions using a personalized dashboard, and monitoring their courses based on the evolution of clinical values. Further research is required to define and model the concepts of criticality, problem recognition, and evolution. Furthermore, feasibility tests will be conducted to ensure user satisfaction

Understanding Clinicians’ Requirements, Perception and Acceptance of Clinical Decision Support Systems: User Study for Implementation of Sepsis Best Practice Advisory in General Paediatric Care

Clinicians are faced with ever-increasing patient data as well as medical evidence which are all required for them to make the best possible decisions. Clinical Decision Support Systems (CDSS) are widely used to support clinicians’ information processing and decision making. However, clinicians as end users are hardly involved in the design and development of these decision support tools. In addition, some of these CDSS designs and processes are not properly implemented to fit into the clinicians’ workflow. The study specifically investigated clinicians’ decision-making regarding Sepsis, design and workflow requirements as well as their perception and acceptance of the Sepsis best practice advisory (BPA). Sepsis is a life-threatening disease, and it is important to identify early manifestations rapidly and reliably for timely interventions as every hour of delay increases mortality by 5-10% (37). The aim was to identify the factors that can aid the implementation of the CDSS such that there is no reduced or incorrect usage and interference with clinicians’ decision making. Successful implementation of the CDSS can further improve patient’s safety especially with regards to Sepsis care. The study was in two phases, a user interview and a moderated usability testing. Both phases were qualitative studies obtaining data from a total of 13 participants from a target population of clinicians working in the general paediatrics unit of the hospital. Decision ladders from control task analysis (ConTa) and cognitive work analysis (CWA) were used to model clinicians’ decision making and the support provided by the Sepsis BPA. The unified theory of acceptance and use of technology (UTUAT) was used to measure clinicians’ satisfaction and acceptance of the tool. The first phase of the study discovered the general experience, knowledge, challenges caring for patients with Sepsis as well as experiences with CDSS and clinicians’ projections or expectations of the Sepsis BPA. Key findings were translated into user requirements which were checked against the minimum viable product (MVP) of the Sepsis BPA and recommendations provided. The second phase discovered particular design feedback and usability issues on the MVP with more recommendations provided. The UTAUT survey results showed highly positive feedback on satisfaction, acceptance and intentions of clinicians to use the Sepsis BPA

Artificial Intelligence and the Situational Rationality of Diagnosis: Human Problem-Solving and the Artifacts of Health and Medicine

What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem-solving for medical sociology regarding the limits of automation for decision-based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem-solving process in doctor-patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem-solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects of seeking optimization in the problem-solving process. Using these parameters as a heuristic for evaluating the capacity of AI to address issues of diagnostic error through design, the paper presents a conceptual review of the discipline of AI in medicine. Studies relying on procedural rationality describe models that treat diagnosis as a “natural artifact” by employing symbolic methods designed to simulate human problem-solving. Research adhering to probabilistic rationality describes models that treat diagnosis as a “natural artifact” of an ecological image by utilizing sub-symbolic methods designed to simulate neural networks. Research guided by situational rationality describes models that require treating diagnosis as a “socio-cognitive artifact,” the artificiality of which is organized in discourses of patient-centered decision-making. The paper concludes with a commentary on the ethical application of AI in health and medicine, given the logical differences underlying diagnostic task performance

Implementación de un Sistema Inteligente Semiautomático para la asistencia de pacientes en unidades de cuidados críticos

Programa Oficial de Doutoramento en Ciencias da Saúde. 5007V01[Resumen] El soporte hemodinámico de los pacientes en las unidades de cuidados intensivos (UCI) de Anestesia cardiaca (Reanimación) resulta complejo por la cantidad de variables aportadas por los diversos dispositivos con escasa integración, que permiten al clínico evaluar las necesidades en la variación del tratamiento. La valoración de todos los parámetros clínicos disponibles y la ejecución de las modificaciones necesarias en las infusiones de fármacos intravenosos supone un esfuerzo de tiempo necesario que no aporta valor a la asistencia a pacientes. La integración de datos, facilitando la toma de decisiones y la ayuda en agilizar las variaciones del tratamiento puede suponer una optimización del trabajo que permita conseguir rangos de objetivos más precisos y continuos de los pacientes. El uso de los sistemas de apoyo a la decisión clínica (CDSS) para ayudar a los clínicos puede contribuir a mejorar la calidad y la eficiencia de la atención. Este trabajo describe el desarrollo y la implementación de un CDSS denominado IOSC3 (Sistema Inteligente basado en Ontologías en Cuidados Críticos), para el manejo de pacientes de la unidad de cuidados intensivos cardíacos. Este CDSS implementó un sistema basado en conocimiento experto para ofrecer consejos terapéuticos en tiempo real basados en la monitorización continua de las constantes vitales cardiovasculares de los pacientes. Cuando la propuesta es evaluada por el clínico, se determina su adecuación siendo aceptada, el sistema actúa de manera semiautomática, controlando de manera las bombas de infusión de fármacos modificando la cantidad de fármacos suministrados al paciente. IOSC3 ha sido probado en pacientes en tiempo real de la UCI del ComplejoHospitalario Universitario de Vigo. El sistema IOSC3 fue integrado y aceptado por el personal de la UCI por representar una ayuda a la toma de decisiones, ya que las recomendaciones de dosis propuestas son aceptadas en el 90% de los casos. Es visto como una herramienta útil para su trabajo diario. Será necesario seguir investigando en diferentes escenarios clínicos para ver si el sistema IOSC3 representa puntos finales más ventajosos en aspectos como la administración total de dosis, estancias más cortas o mortalidad.[Resumo] O soporte hemodinámico dos pacientes nas unidades de coidados intensivos (UCI) de Anestesia cardíaca (Reanimación) resulta complexo pola cantidade de variables achegadas polos diversos dispositivos con escasa integración, que permiten ao clínico avaliar as necesidades na variación do tratamento. A valoración de todos os parámetros clínicos dispoñibles e a execución das modificacións necesarias nas infusións de fármacos intravenosos supón un esforzo de tempo necesario que non achega valor á asistencia a pacientes. A integración de datos, facilitando a toma de decisións e a axuda en axilizar as variacións do tratamento pode supoñer unha optimización do traballo que permita conseguir rangos de obxectivos máis precisos e continuos dos pacientes. O uso dos sistemas de apoio á decisión clínica (CDSS) para axudar aos clínicos pode contribuír a mellorar a calidade e a eficiencia da atención. Este traballo describe o desenvolvemento e a implementación dun CDSS denominado IOSC3 (Sistema Intelixente baseado en Ontologías en Coidados Críticos), para o manexo de pacientes da unidade de coidados intensivos cardíacos. Este CDSS implementou un sistema baseado en coñecemento experto para ofrecer consellos terapéuticos en tempo real baseados na monitorización continua das constantes vitais cardiovasculares dos pacientes. Cando a proposta é avaliada polo clínico, determínase a súa adecuación sendo aceptada, o sistema actúa de maneira semiautomática, controlando de maneira as bombas de infusión de fármacos modificando a cantidade de fármacos fornecidos ao paciente. IOSC3 foi probado en pacientes en tempo real da UCI do Complexo Hospitalario Universitario de Vigo. O sistema IOSC3 foi integrado e aceptado polo persoal da UCI por representar unha axuda á toma de decisións, xa que as recomendacións de doses propostas son aceptadas no 90% dos casos. É visto como unha ferramenta útil para o seu traballo diario. Será necesario seguir investigando en diferentes escenarios clínicos para ver se o sistema IOSC3 representa puntos finais máis vantaxosos en aspectos como a administración total de dose, estancias máis curtas ou mortalidade.[Abstract] Hemodynamic support of patients in cardiac anesthesia (resuscitation) intensive care units (ICU) is complex due to the number of variables provided by the various devices with little integration, allowing the clinician to assess the needs for treatment variation. The assessment of all available clinical parameters and the execution of the necessary modifications in intravenous drug infusions is a time-consuming effort that does not add value to patient care. The integration of data, facilitating decision making and helping to streamline treatment variations can optimize work to achieve more accurate and continuous patient target ranges. The use of clinical decision support systems (CDSS) to assist clinicians can help improve the quality and efficiency of care. This paper describes the development and implementation of a CDSS called IOSC3 (Intelligent Ontology-based System in Critical Care), for the management of cardiac intensive care unit patients. This CDSS implemented an expert knowledge-based system to provide real-time therapeutic advice based on continuous monitoring of patients' cardiovascular vitals. When the proposal is evaluated by the clinician, its appropriateness is determined and accepted, the system acts semi-automatically, controlling the drug infusion pumps and modifying the number of drugs delivered to the patient. IOSC3 has been tested in real time on patients in the ICU of the Complejo Hospitalario Universitario de Vigo. The IOSC3 system was integrated and accepted by the ICU staff as an aid to decision making, since the proposed dosage recommendations are accepted in 90% of the cases. It is seen as a useful tool for their daily work. Further research will be needed in different clinical scenarios to see if the IOSC3 system represents more advantageous endpoints in aspects such as total dose administration, shorter stays or mortality