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In order to assist in the standardization and maturity evaluation of national hospital information interconnection, and further standardize the application and management of hospital medical record data, a hospital carried out the practice of design of structured medical records and the corresponding quality management from April 2021. Based on the six sigma quality management method, the hospital had developed universal templates for electronic medical records and a list of candidate electronic medical record templates. The problems faced by medical record data had been analyzed, and improvement strategies had been proposed from three levels: template design, software functionality and management services. The clinical departments were guided to design and develop various structured electronic medical record templates for specialties and specialized diseases, and established a medical record template design and quality management method. The hospital had ultimately designed a total of 614 structured electronic medical record templates that met the actual needs of the hospital. This practice enhanced the scalability of structured templates and quality of the data, and achieved localization and specialization of medical record templates while meeting the requirements of information interconnection and sharing, providing reference for promoting the interconnection and sharing of electronic medical records of hospitals in China.
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In recent years, the artificial intelligence machine learning and deep learning technology have made leap progress. Using clinical decision support system for auxiliary diagnosis and treatment is the inevitable developing trend of wisdom medical. Clinicians tend to ignore the interpretability of models while pursuing its high accuracy, which leads to the lack of trust of users and hamper the application of clinical decision support system. From the perspective of explainable artificial intelligence, the authors make some preliminary exploration on the construction of clinical decision support system in the field of liver disease. While pursuing high accuracy of the model, the data governance techniques, intrinsic interpretability models, post-hoc visualization of complex models, design of human-computer interactions, providing knowledge map based on clinical guidelines and data sources are used to endow the system with interpretability.
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Abstract This article examines data-driven policy for smart cities and how the institutional analysis and development framework (IAD) is a tool to analyze data governance and data policy design. The research assumes that data-driven policy underpins smart city initiatives and incorporates collective action dilemmas, directly impacting policy design and data governance. The article's motivation is to examine the elements of the IAD framework and decompose the constitutive components of data policy in smart cities, obtaining principles that guide data governance in complex situations of digital transformation. The article is a theoretical essay to discuss data policy in smart cities and how policy design understands dilemmas and conflicts of collective action motivated by data control.
Resumen Este artículo examina la política basada en datos para ciudades inteligentes y cómo el Institutional Analysis and Development Framework (IAD) constituye una herramienta para analizar el diseño de la gobernanza de datos y de la política de datos. El examen realizado en el artículo parte de la premisa de que la política basada en datos, que sustenta las iniciativas de ciudades inteligentes, incorpora dilemas de acción colectiva e impacta directamente en el diseño de las políticas y de la gobernanza de datos. La motivación para la elaboración del artículo fue examinar los elementos del IAD framework y descomponer los componentes constitutivos de la política de datos en las ciudades inteligentes, para luego derivar de ellos los principios que guían la gobernanza de datos en situaciones complejas de transformación digital. El artículo es un ensayo teórico que pretende generar discusiones sobre la política de datos en las ciudades inteligentes y cómo el diseño de políticas entiende los dilemas y conflictos de la acción colectiva motivados por el control de datos.
Resumo Este artigo examina a política orientada por dados para cidades inteligentes e como o Institutional Analysis and Development Framework (IAD) se posiciona como uma ferramenta para analisar o design da governança de dados e de política de dados. A pesquisa realizada neste artigo sobre políticas baseadas em dados parte da premissa de que um projeto orientado por dados sustenta iniciativas de cidades inteligentes e incorpora dilemas de ação coletiva, impactando diretamente o desenho da política e da governança de dados. A motivação do artigo foi examinar dos componentes da estrutura IAD e componentes constitutivos da política de dados que orientam sua governança em sistemas complexos de transformação digital. O artigo é um ensaio que pretende levantar discussões sobre dados em cidades inteligentes e como o desenho de políticas compreende os conflitos de ação coletiva motivados pelo controle de dados.
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Cidades , Gestão da Informação , Ciência de Dados , Administração das Tecnologias da InformaçãoRESUMO
Background: This study aimed to investigate data governance (DG) related to challenges associated with healthcare information systems (HIS), by reviewing guidelines emerging from academic sources as part of a consolidated systematic literature review (SLR). The research contributed theoretically towards the body of knowledge, by reviewing challenges and guidelines related to DG within the healthcare environment. It contributed practically to the body of knowledge through understanding the healthcare information's systems status. The study also contributed methodologically and significantly to SLR strategies. Objectives: The objective of this study was to understand the features of HIS; acquire information about DG success and understand the influence noted on DG. Method: The study conducted an SLR over the period 20102020. Literature collection was not only restricted to South African publications but was extended to international sources. This study adapted a mono method. Results: The study revealed that many organisations have realised that the only method to fix the data problem is the implementation of effective DG. With the increased adoption and rise of cloud computing, DG is gaining interest amongst specialists. Conclusion: The shift from paper-based systems led organisations to seek organisational change through digital transformation. The proper collection and utilisation of electronic healthcare record is the foundation of the digital healthcare. Many organisations value DG as a promising method of maintaining data as a valuable asset.
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Sistemas de InformaçãoRESUMO
Based on 18 hospitals including the Chinese People's Liberation Army General Hospital and Peking University People's Hospital, and based on the "Specifications for Perioperative Data", explore the construction and application of perioperative multi-center data centers in the era of medical big data. The use of data ferry technology avoids hidden safety hazards in hospitals, realizes the integration and sharing of perioperative medical data of various medical institutions, and forms a complete data chain combining patient medical data and follow-up data.
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Humanos , Hospitais Militares , Militares , Estados UnidosRESUMO
Objective:To analyze the application status and problems of clinical decision support system(CDSS) in medical institutions in China, and put forward corresponding suggestions.Methods:From April to May in 2020, a questionnaire survey was conducted in 1 013 medical institutions in 31 provinces of China. The contents of the questionnaire included the current situation of CDSS installation and deployment, the purpose of establishment, the source of knowledge base, the content to be optimized and the factors hindering the use.Results:199(19.64%) medical institutions had CDSS, among which 123 were used in the whole hospital and 76 in some departments; 426 medical institutions did not use CDSS, but had plans to use it. It was found that the current CDSS system had setbacks, such as big cognitive difference, lack of authority in knowledge, high difficulty in data governance, lack of industry standards and so on.Conclusions:In the future, the standardized use of CDSS in medical institutions could be promoted from the aspects of raising awareness, establishing knowledge authority and establishing standards.
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Objective:To strengthen the integration and sharing of medical data resources, provide high-quality and usable data for clinical researchers, and promote the medical data use in clinical research.Methods:According to the development and application goals of the medical big data platform, data from different major clinical information systems in our hospital are integrated and then cleaned, processed and analyzed, and finally aggregated into a unified platform and turned to valuable and usable data resources.Results:A medical big data platform for clinical research in our hospital has been developed. It has stored over 13.42 million patients′ clinical data of more than 50 million visits since 2004 in our hospital; an analysis-oriented common data model (CDM) for clinical research has been designed; clinical researchers can query and extract clinical data according to CDM; the standard clinical research data service mechanisms have been established.Conclusions:The medical big data platform in our hospital helps to provide usable data of good quality for high-level scientific research based on medical big data, and improve the efficiency and quality of clinical research; at the same time, it also provides a efficient way to manage and control clinical research data use while ensuring data security and regulatory compliance.
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Resumo Dados ganham cada vez mais importância e valor na busca de respostas para enfrentar a COVID-19 tanto para a ciência quanto para as autoridades sanitárias. Em virtude da dificuldade de realizar diagnóstico da infecção na população em geral, iniciativas apoiadas em tecnologias digitais vêm sendo desenvolvidas por governos ou empresas privadas para possibilitar rastreamentos de sintomas, contatos e deslocamentos de modo a apoiar estratégias de acompanhamento e avaliação na vigilância de contágios. A despeito da importância e necessidade dessas iniciativas, questionamentos acerca da quantidade e tipos de dados pessoais coletados, processados, compartilhados e utilizados em nome da saúde pública, bem como os concomitantes ou posteriores usos desses dados, suscitam questionamentos éticos, legais e técnicos. Desafios que apontam para a necessidade de novos modelos de governança de dados e de tecnologias, responsáveis e transparentes, para controlar o Sars-Cov2 e as futuras emergências de saúde pública.
Abstract Data has become increasingly important and valuable for both scientists and health authorities searching for answers to the COVID-19 crisis. Due to difficulties in diagnosing this infection in populations around the world, initiatives supported by digital technologies are being developed by governments and private companies to enable the tracking of the public's symptoms, contacts and movements. Considering the current scenario, initiatives designed to support infection surveillance and monitoring are essential and necessary. Nonetheless, ethical, legal and technical questions abound regarding the amount and types of personal data being collected, processed, shared and used in the name of public health, as well as the concomitant or posterior use of this data. These challenges demonstrate the need for new models of responsible and transparent data and technology governance in efforts to control SARS-COV2, as well as in future public health emergencies.