Disparate Information Systems Research Articles

Combining Frameworks to Improve Military Health System Quality and Cybersecurity

Existing conceptual frameworks and commercially available technology could be considered to rapidly operationalize the use of Quality Measures (QM) within military health systems (Costantino et al. 2020). Purchased healthcare as well as digital healthcare services have paved the way for data collection from multiple information systems thus offering stakeholders actionable intelligence to both guide and measure healthcare outcomes. However, the collection of data secondary to Smart Devices, disparate information systems, cloud services, and the Internet of Medical Things (IOMT) is a complication for security experts that also affect clients, stakeholders, organizations, and businesses delivering patient care. We have combined three conceptual frameworks: (1) Donabedian’s Quality Attributes Framework (DQA), (2) The National Academy of Medicine (NAM) framework, and the (3) Healthcare and Public Health (HPH) Cybersecurity Framework (HCF). Each of these frameworks is well-tested, widely accepted, and referenced as the gold standard in delivering quality care (DQA and NAM) and cybersecurity (HCF) for healthcare. Separately, each framework provides long-used analysis, guidance, and application of quality reliable measures of healthcare services. The combined frameworksmay enhance data integrity (collection and standardized safety of data in regard to regulatory compliance). The combined frameworks also support identifying gaps in quality of care, support the prioritization of cost-effective methodologies that predictably improve patient outcomes, and support DHA’s quality measures. Finally, we offer the importance of understanding the components of quality measures in a more precise manner while maintaining information technology (IT) governance compliance.

Новые подходы к диагностическим информационным системам в радиологии.

The first electronic information systems to help compile radiological reports appeared in the mid-1960s and were introduced primarily in radiology departments of healthcare. Due to the development and achievements in the field of information technology in radiology, a system of archiving and transmission of images (Picture Archiving and Communication System (PACS)) was introduced, this helped to reduce the stages of work processes in the routine practice of a radiologist in comparison with the use of classical systems based on films and paper. Also, the use of PACS has led to an increase in the effective work of the radiation diagnostics department by speeding up the acquisition of images for diagnosticians and clinicians, reducing the costs of the department, speeding up the writing of research protocols, increasing the number of studies to conduct and describe. The use of modern information systems has a number of advantages, storing a large amount of data, combining and storing disparate information systems of a medical organization, obtaining full access to the electronic medical record of the patient, which in turn accelerates work processes and effective interaction of hospital staff. Thus, dependence on these information systems by radiation diagnosticians and doctors of other specializations can affect the diagnosis and improvement of the quality of patient care. This review contains up-to-date information about the work of information systems in radiology departments.

Trends and features of the informatization of higher education modern stage

Problem statement. Nowadays, people are increasingly talking not about the progressive development of higher education in the context of the use of digital technologies, but about its more drastic digital transformation. The problem of identifying and systematizing patterns characterizing the development of universities in the context of the mass introduction of new digital technologies, as well as changes in external influences on student training systems, is urgent. The solution of this problem is significant for determining further scientific and pedagogical research, as well as ways to develop teacher training systems. The purpose of the study is to identify on the basis of domestic and foreign analytical data (OECD, UNESCO and others) directions, characteristics, problems and prospects of informatization of higher education. Methodology. The analysis of scientific publications on the development of didactics and the use of modern teaching tools in universities is based on the study of the species composition and specifics of existing digital resources, the use of mathematical methods for processing numerical data and technologies for their visualization. Results. It is shown that modern informatization of higher education is characterized by the spread of online courses, collections of digital resources, a decrease in the number of computer equipment in universities, the penetration of technologies of the new industrial revolution and many other factors. The development of a fundamental component of higher education that is invariant with respect to the development of technologies, the preparation of students for the use of promising technologies in professional activities, the pooling of resources into a single digital educational environment are significant. Conclusion. Research should be continued to ensure and evaluate the quality of all types of learning tools, to identify theoretical and practical approaches to the integration and unification of disparate information systems. There is a need for a scientifically based substantive and methodological update of the systems of training and retraining of all specialists working in the conditions of digital transformation of higher education.

Philosophical foundations of intelligence collection and analysis: a defense of ontological realism

ABSTRACT It is a common belief within the Intelligence Community (IC) that data residing in disparate information systems can be mined in useful ways by means of artificial intelligence (AI) and natural language processing (NLP) methods working alone, which is to say, without the aid of some kind of integrating framework. Here, in contrast, we argue that the sort of integration and analysis that is required if we are to connect data and information deriving from heterogeneous sources in useful ways needs semantic integration, in other words integration that rests on the ability to identify shared meanings across different bodies of data. To achieve such integration requires what we shall call an Integrating Semantic Framework (ISF). A framework of this sort is based on ontologies, which are controlled structured vocabularies designed to foster interoperability in the collection and curation of data and thereby to prevent the sorts of siloing of information that arise where there is inconsistency in the use of terms.

Formation of a single information space in the context of the digital transformation of machine-building holding

Subject. The article considers the development of approaches to the integration of digital technologies into production processes of industrial enterprises to improve operational efficiency. Objectives. The purpose is to develop a mechanism for digital transformation of a machine-building holding in relation to the formation of a single information space based on modern approaches to business process reengineering. Methods. I apply methods of systems and economic analysis, modeling of business processes of enterprises. Results. The key factor in the formation of positive effects of digital transformation of industrial enterprises is the integration of business processes within a single information environment. The result of automation of key processes of products’ life cycle within the framework of a single digital platform will be the replacement of "patchwork" automation in disparate information systems of enterprises with integrated monitoring and life cycle management systems, operating on the basis of a single information space. Conclusions. The introduction of "seamless" information exchange based on a single information space is urgent for holding structures characterized by heterogeneous and poorly compatible information systems. The integration of systems of development, modeling, management, and analytical processing based on digital twins, the introduction of augmented and virtual reality tools, and the use of artificial intelligence elements for production management are a necessary stage for the formation of new methods of technological preparation and organization of production, and for the creation of a full-fledged digital twin of production and product life cycle processes.

Informing the information requirements of a digital twin: a rail industry case study

The right data to the right people at the right time are a crucial component to developing an effective digital twin (DT). A challenge in the industry is that existing legacy data systems have been tailored independently to meet specific technical requirements, leading to disparate information systems that collectively provide a suboptimal foundation needed to support a modern digital environment. To address this challenge, a detailed study was conducted in partnership with Network Rail to test a method that was adapted for the industry to identify appropriate, relevant and effective asset information requirements (AIRs). A robust library of AIRs will determine what data the organisation should be collecting and why these are necessary through design, construction, operation and disposal, hence informing the information requirements of a DT. The methodology was modified with four adaptations to improve its compatibility with the industry and was tested with 24 subject matter experts in the rail sector. It was concluded that integration of the AIR method would ensure that the rail industry is well positioned for digital transformation and provides a solid foundation to develop a DT. However, industry integration of the method would be a significant undertaking due to the amount of information that needs to be considered throughout the process, and so further research is required to improve its scalability.

Towards Unified Data Exchange Formats for Reporting Molecular Drug Susceptibility Testing.

With the rapid development of new advanced molecular detection methods, identification of new genetic mutations conferring pathogen resistance to an ever-growing variety of antimicrobial substances will generate massive genomic datasets for public health and clinical laboratories. Keeping up with specialized standard coding for these immense datasets will be extremely challenging. This challenge prompted our effort to create a common molecular resistance Logical Observation Identifiers Names and Codes (LOINC) panel that can be used to report any identified antimicrobial resistance pattern. To develop and utilize a common molecular resistance LOINC panel for molecular drug susceptibility testing (DST) data exchange in the U.S. National Tuberculosis Surveillance System using California Department of Public Health (CDPH) and New York State Department of Health as pilot sites. We developed an interface and mapped incoming molecular DST data to the common molecular resistance LOINC panel using Health Level Seven (HL7) v2.5.1 Electronic Laboratory Reporting (ELR) message specifications through the Orion Health™ Rhapsody Integration Engine v6.3.1. Both pilot sites were able to process and upload/import the standardized HL7 v2.5.1 ELR messages into their respective systems; albeit CDPH identified areas for system improvements and has focused efforts to streamline the message importation process. Specifically, CDPH is enhancing their system to better capture parent-child elements and ensure that the data collected can be accessed seamlessly by the U.S. Centers for Disease Control and Prevention. The common molecular resistance LOINC panel is designed to be generalizable across other resistance genes and ideally also applicable to other disease domains. The study demonstrates that it is possible to exchange molecular DST data across the continuum of disparate healthcare information systems in integrated public health environments using the common molecular resistance LOINC panel.

The Employer Perspective of Managing Access to Federated Systems and the Need for an Ontological Approach

Identification, authentication, and authorization (IAA) play a significant role in federated identity management for disparate information systems (IS) within an organization. Although all three components are vital to system access control processes and help minimize unauthorized access to resources, authorization is the most cumbersome and time-consuming process (relative to identification and authentication) in many organizations today. However, most prior studies focus on the user perspective and only address authentication issues (e.g., single sign-on). In order to fill a gap in the literature, our work studies the organizational perspective of managing IS access and aims to address the issues associated with managing the authorization process. In doing so, we develop an ontology-based design artifact, called Onto-IAA, describing how disparate IS perform authorization natively. Via this ontology, organizations can significantly reduce the effort required to manage the authorization processes. Onto-IAA operationalization is demonstrated for two real IS, and its utility is evaluated using an observational approach in a global retail chain with approximately 25 disparate IS and over 5000 employees. Additionally, the experimental approach is used to test its effectiveness to control access to other IS, which are not utilized to “train” or develop the ontological model.

A Survey of LOINC Code Selection Practices Among Participants of the College of American Pathologists Coagulation (CGL) and Cardiac Markers (CRT) Proficiency Testing Programs.

Biomedical terminologies such as Logical Observation Identifiers, Names, and Codes (LOINC) were developed to enable interoperability of health care data between disparate health information systems to improve patient outcomes, public health, and research activities. To ascertain the utilization rate and accuracy of LOINC terminology mapping to 10 commonly ordered tests by participants of the College of American Pathologists (CAP) Proficiency Testing program. Questionnaires were sent to 1916 US and Canadian laboratories participating in the 2018 CAP coagulation (CGL) and/or cardiac markers (CRT) surveys requesting information on practice setting, instrument(s) and test method(s), and LOINC code selection and usage in the laboratory and electronic health records. Ninety of 1916 CGL and/or CRT participants (4.7%) responded to the questionnaire. Of the 275 LOINC codes reported, 54 (19.6%) were incorrect: 2 codes (5934-2 and 12345-1) (0.7%) did not exist in the LOINC database and the highest error rates were observed in the property (27 of 275, 9.8%), system (27 of 275, 9.8%), and component (22 of 275, 8.0%) LOINC axes. Errors in LOINC code selection included selection of the incorrect component (eg, activated clotting time instead of activated partial thromboplastin time); selection of panels that can never be used to obtain an individual analyte (eg, prothrombin time panel instead of international normalized ratio); and selection of an incorrect specimen type. These findings of real-world LOINC code implementation across a spectrum of laboratory settings should raise concern about the reliability and utility of using LOINC for clinical research or to aggregate data.

Interoperable Health Care System Using Blockchain Technology

The need for integration is not new but it existed since applications moved from central processors to distributed systems and networks. This need has emerged as disparate Information Systems (IS) that automate business processes which run on different computer platforms and have been based on a diversity of standards, operating systems and computer languages. In healthcare, interoperability is the important because of different information systems (IS) to communicate, interchange data, and use of the exchanged information among systems. Blockchain technology can address the interoperability challenges in healthcare systems; it can help healthcare providers, healthcare stakeholders and researchers to share health data in secure way. In this paper, we have proposed a Blockchain based healthcare systems which can address the interoperability issues in healthcare in secure in transparent way.

Lessons Learned in the Development of a Computable Phenotype for Response in Myeloproliferative Neoplasms.

Determining response status in patients with myeloproliferative neoplasms is a complex problem requiring the integration of both structured and unstructured data elements from disparate information systems. By applying multiple techniques, a collaborative team of informatics professionals and research personnel were able to determine which elements were amenable to automated extraction and which required expert adjudication. With this knowledge in mind, we were able to build a system that joins together programmatically-derived and manually-abstracted data elements to facilitate response assessment - an important end point in clinical and translational research in this disease area.

Advanced Visualization and Analysis of Data Quality for Syndromic Surveillance Systems

ObjectiveTo extend an open source analytics and visualization platform for measuring the quality of electronic health data transmitted to syndromic surveillance systems.IntroductionEffective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems. However, the quality of data in these systems can be poor or “unfit for use.” Therefore measuring and monitoring data quality is an essential activity for clinical and public health professionals as well as researchers1. Current methods for examining data quality largely rely on manual queries and processes conducted by epidemiologists. Better, automated tools for examining data quality are desired by the surveillance community.MethodsUsing the existing, open-source platform Atlas developed by the Observational Health Data Sciences and Informatics collaborative (OHDSI; www.ohdsi.org), we added new functionality to measure and visualize the quality of data electronically reported from disparate information systems. Our extensions focused on analysis of data reported electronically to public health agencies for disease surveillance. Specifically, we created methods for examining the completeness and timeliness of data reported as well as the information entropy of the data within syndromic surveillance messages sent from emergency department information systems.ResultsTo date we transformed 111 million syndromic surveillance message segments pertaining to 16.4 million emergency department encounters representing 6 million patients into the OHDSI common data model. We further measured completeness, timeliness and entropy of the syndromic surveillance data. In Figure-1, the OHDSI tool Atlas summarizes the analysis of data completeness for key fields in over one million syndromic surveillance messages sent to Indiana’s health department in 2014. Completeness is reported by age category (e.g., 0-10, 20-30, 60+). Gender is generally complete, but both race and ethnicity fields are often complete for less than half of the patients in the cohort. These results suggest areas for improvement with respect to data quality that could be actionable by the syndromic surveillance coordinator at the state health department.ConclusionsOur project remains a work-in-progress. While functions that assess completeness, timeliness and entropy are complete, there may be other functions important to public health that need to be developed. We are currently soliciting feedback from syndromic surveillance stakeholders to gather ideas for what other functions would be useful to epidemiologists. Suggestions could be developed into functions over the next year. We are further working with the OHDSI collaborative to distribute the Atlas enhancements to other platforms, including the National Syndromic Surveillance Platform (NSSP). Our goal is to enable epidemiologists to quickly analyze data quality at scale.

System Safety in Healthcare

Electronic Medical Records (EMR) are the digital version of paper charts. They are the electronic record of an individual’s health-related information that is created, gathered, managed, and consulted by licensed clinicians and staff [Refs. 1-3]. This article will focus on some of the challenges of health record integration between often disparate systems — internally, across a mix of systems, or externally, such as entities ranging from federal and state agencies, insurance companies, physician practices, medical facilities and pharmacies. The challenges of data collection, assimilation and integration remain at the forefront of our electronic medical record era [Refs. 1, 6-9]. Seamlessly moving information across disparate information systems becomes even more challenging when the data involves confidential medical information.

A case for a pan-Indian Ocean information grid for improved maritime domain awareness

ABSTRACTThe Indian Ocean region, in recent years, has witnessed a proliferation of information centers which have emerged in response to various sub-regional requirements and sometimes representing diverse interest groups. This paper highlights the gaps in regional maritime domain awareness (MDA) and argues that the multiple disparate information systems have not contributed to improving overall maritime safety and security in the Indian Ocean region. It puts forward a case for a pan-Indian Ocean information grid that could integrate the various information sources to provide comprehensive MDA for the region.

Current applications of big data in obstetric anesthesiology.

The narrative review aims to highlight several recently published 'big data' studies pertinent to the field of obstetric anesthesiology. Big data has been used to study rare outcomes, to identify trends within the healthcare system, to identify variations in practice patterns, and to highlight potential inequalities in obstetric anesthesia care. Big data studies have helped define the risk of rare complications of obstetric anesthesia, such as the risk of neuraxial hematoma in thrombocytopenic parturients. Also, large national databases have been used to better understand trends in anesthesia-related adverse events during cesarean delivery as well as outline potential racial/ethnic disparities in obstetric anesthesia care. Finally, real-time analysis of patient data across a number of disparate health information systems through the use of sophisticated clinical decision support and surveillance systems is one promising application of big data technology on the labor and delivery unit. 'Big data' research has important implications for obstetric anesthesia care and warrants continued study. Real-time electronic surveillance is a potentially useful application of big data technology on the labor and delivery unit.

Measuring and Improving the Quality of Data Used for Syndromic Surveillance

ObjectiveTo extend an open source platform for measuring the qualityof electronic health data by adding functions useful for syndromicsurveillance.IntroductionNearly all of the myriad activities (or use cases) in clinical andpublic health (e.g., patient care, surveillance, community healthassessment, policy) involve generating, collecting, storing, analyzing,or sharing data about individual patients or populations. Effectiveclinical and public health practice in the twenty-first century requiresaccess to data from an increasing array of information systems,including but not limited to electronic health records. However, thequality of data in electronic health record systems can be poor or“unfit for use.” Therefore measuring and monitoring data quality isan essential activity for clinical and public health professionals aswell as researchers.MethodsUsing the Health Data Stewardship Framework1, we will extendAutomated Characterization of Health Information at Large-scaleLongitudinal Evidence Systems (ACHILLES), a software packagepublished open-source by the Observational Health Data Sciencesand Informatics collaborative (OHDSI; www.ohdsi.org) to measurethe quality of data electronically reported from disparate informationsystems. Our extensions will focus on analysis of data reportedelectronically to public health agencies for disease surveillance. Nextwe will apply the ACHILLES extensions to explore the quality ofdata captured from multiple real-world health systems, hospitals,laboratories, and clinics. We will further demonstrate the extendedsoftware to public health professionals, gathering feedback on theability of the methods and software tool to support public healthagencies’ efforts to routinely monitor the quality of data received forsurveillance of disease prevalence and burden.ResultsTo date we have mapped key surveillance data fields into theOHDSI common data model, and we have transformed 111 millionsyndromic surveillance message segments pertaining to 16.4 millionemergency department encounters representing 6 million patientsfor importation into ACHILLES. Using these data, we are exploringthe existing 167 metrics across 16 categories available withinACHILLES, including a person (e.g., number of unique persons);and observation period (e.g., Distribution of age at first observationperiod). Syndromic surveillance (SS), however, is driven largelyby monitoring patient stated chief complaints (non-standard freetext clinical data) in addition to coded diagnoses. Consequently,ACHILLES must be extended to maximally support use in analyzingSS datasets.ConclusionsThis work remains a work-in-progress. Over the coming year, wewill not only explore existing ACHILLES constructs using real-worldpublic health data but also introduce new functionality to explore1) patient demographics; 2) facility and location (e.g., emergencydepartment where care was delivered); and 3) clinical observations(e.g., chief complaint). The design and methods for examining theseaspects of surveillance data will be included on the poster, and theywill be made freely available for distribution with a future instance ofthe ACHILLES software. We ultimately envision these tools beingavailable for use on platforms such as the CDC’s Biosense – open toall local and state health agencies as a one-stop portal for surveillancedata analysis – or research environments where they can be used toexamine and improve the quality of data output from informaticssystems.

Exploring Health Information Exchange (HIE) Through Collaboration Framework: Normative Guidelines for IT Leadership of Healthcare Organizations

ABSTRACTHealth Information Exchanges (HIEs) hold the promise to integrate patient data residing across disparate information systems in various hospitals to improve care coordination, patient engagement, and provisioning of real-time information to physicians. This research posits that collaboration is the key to HIE’s success. Drawing from the extant literature on collaboration, we discuss collaboration-related challenges that healthcare IT leaders are facing and provide normative guidelines that they can implement during the HIE initiation phase as well as the maintenance phase.

Integrating Health Information Technology to Achieve Seamless Care Transitions

Improving care transitions, or "handoffs" as patients migrate from one care setting to another, is a priority across stakeholder groups and health-care settings and additionally is included in national health-care goals set forth in the National Quality Strategy. Although many demonstrations of improved care transitions have succeeded, particularly for hospital discharges, ensuring consistent, high-quality, and safe transitions of care remains challenging. This paper highlights the potential for health information technology to become an increasing part of effective transitional care interventions, with the potential to reduce the resource burden currently associated with effective care transitions, the ability to spread improved practices to larger numbers of patients and providers efficiently and at scale, and, as health technology interoperability increases, the potential to facilitate critical information flow and feedback loops to clinicians, patients, and caregivers across disparate information systems and care settings.

Use of introspective interfaces in application layer protocols

Technologies and architectures of applied software systems have significant achievements in the field of creating monolithic and new solutions, which are expressed in well designed and thought out software development methodologies, programming paradigms, platforms and libraries, but the task of integrating disparate information systems (IS) is still poorly elaborated. Movement towards uniform and centralized solutions cannot fully eliminate the integration problem until all the software in the world will become an integral software package with a common architecture, cross-cutting standards and continuous synchronous update process. Under the conditions of heterogeneous distributed IS, the relevance of integration tasks increases. Summarizing the theoretical and practical experience of binding application web-service type IS, the authors propose an approach to solving the problem of dynamic binding of application programming interfaces (API) in distributed information systems, using the methods of metaprogramming and its various techniques, such as introspection, dynamic modification of the structure and functions of the software modules, use of metadata and dynamic interpretation of metamodels. At interaction of two or more systems through network interfaces with introspection and interpretation of the metamodel, there is dynamical binding of their interfaces, allowing to modify the functionality of applied information systems without specialized adaptation of calls and change in the code, and also to bind the systems, interaction of which was not originally intended. This method increases the IS flexibility and allows significantly improve the code reuse and IS development and operating efficiency in general.

Enterprise Content Management and the Records Continuum Model as strategies for long-term preservation of digital information

Purpose – The purpose of the paper is to establish whether Enterprise Content Management (ECM) and Records Continuum Model (RCM) frameworks could be used to mitigate long-term preservation challenges. Design/methodology/approach – Qualitative research was undertaken using two case studies and interviews were conducted with the different categories of the municipal personnel to solicit answers to the research questions. The questions were designed using the lens of the RCM and its four dimensions that cover the creation, capture, organization and pluralization of records and prescribed factors of ECM which include business process management, enterprise architecture, collaboration, system integration, re-purposing of information, change management, knowledge management and the life cycle management of information. Not all the ECM factors are dealt with in this paper: the remainder have been dealt with in the author's earlier works. Findings – Challenges of long-term preservation of information still persist despite the enormous research that has been generated over the years. The municipalities that were subjects of this research are still grappling with issues of lack of long-term information management policies, enterprise architecture, disparate information systems, collaboration and system integration. This is likely to work against the investments that are being ploughed into e-Government developments should the municipalities fail to espouse strong information and records management regimes. Embracing the ECM prescribed factors and the RCM thinking might mitigate these challenges. Originality/value – The author's licentiate research proved that there was no discourse between records managers/archivists and ECM proponents. Therefore, the originality of this article lies in the application of the two frameworks of ECM and RCM. The findings confirmed that even within the records management framework the municipalities were addressing factors similar to ECM prescribed factors. Embracing both the RCM model and the ECM prescribed factors might mitigate the challenges of long-term preservation and hence the re-use of information and enhancement of the societal memory.