Methods of Information in Medicine

BACKGROUND: Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is high value. Covid-19 offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance. OBJECTIVES: This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the Covid-19 emergency in the United States...

BACKGROUND:  Data quality issues can cause false decisions of clinical decision support systems (CDSSs). Analyzing local data quality has the potential to prevent data quality-related failure of CDSS adoption. OBJECTIVES:  To define a shareable set of applicable measurement methods (MMs) for a targeted data quality assessment determining the suitability of local data for our CDSS. METHODS:  We derived task-specific MMs using four approaches: (1) a GUI-based data quality analysis using the open source tool openCQA ...

BACKGROUND:  Digitalization is playing a major role in mastering the current coronavirus 2019 (COVID-19) pandemic. However, several outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in German hospitals last year have shown that many of the surveillance and warning mechanisms related to infection control (IC) in hospitals need to be updated. OBJECTIVES:  The main objective of the following work was to assess the state of information technology (IT) systems supporting IC and surveillance in German university hospitals in March 2021, almost a year into the SARS-CoV-2 pandemic...

INTRODUCTION:  The informed consent is the legal basis for research with human subjects. Therefore, the consent form (CF) as legally binding document must be valid, that is, be completely filled-in stating the person's decision clearly and signed by the respective person. However, especially paper-based CFs might have quality issues and the transformation into machine-readable information could add to low quality. This paper evaluates the quality and arising quality issues of paper-based CFs using the example of the Baltic Fracture Competence Centre (BFCC) fracture registry...

BACKGROUND:  The short time frame between the coronavirus disease 2019 (COVID-19) pandemic declaration and the vaccines authorization led to concerns among public regarding the safety and efficacy of the vaccines. The Food and Drug Administration uses the Vaccine Adverse Events Reporting System (VAERS) where general population can report their vaccine side effects in the text box. This information could be utilized to determine self-reported vaccine side effects. OBJECTIVE:  To develop a supervised and unsupervised natural language processing (NLP) pipeline to extract self-reported COVID-19 vaccination side effects, location of the side effects, medications, and possibly false/misinformation seeking further investigation in a structured format for analysis and reporting...

BACKGROUND:  Synthetic tabular data generation is a potentially valuable technology with great promise for data augmentation and privacy preservation. However, prior to adoption, an empirical assessment of generated synthetic tabular data is required across dimensions relevant to the target application to determine its efficacy. A lack of standardized and objective evaluation and benchmarking strategy for synthetic tabular data in the health domain has been found in the literature...

BACKGROUND: As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from COVID-19 patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO...

BACKGROUND: Multisite research networks such as the project "Collaboration on Rare Diseases" connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data. OBJECTIVES: The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research...

BACKGROUND: FAIR Guiding Principles present a synergy with the use cases for digital health records, in that clinical data needs to be found, accessible within a range of environments, data must interoperate between systems and subsequently reused. The use of HL7 FHIR, openEHR, IHE XDS and SNOMED CT (FOXS) together represent a specification to create an open digital health platform for modern healthcare applications. OBJECTIVES: To describe where logical FOXS components align to the European Open Science Cloud Interoperability Framework (EOSC-IF) reference architecture for semantic interoperability...

BACKGROUND:  Automated clinical decision support for risk assessment is a powerful tool in combating cardiovascular disease (CVD), enabling targeted early intervention that could avoid issues of overtreatment or undertreatment. However, current CVD risk prediction models use observations at baseline without explicitly representing patient history as a time series. OBJECTIVE:  The aim of this study is to examine whether by explicitly modelling the temporal dimension of patient history event prediction may be improved...

BACKGROUND:  Privacy management is a key issue when dealing with storage and distribution of health information. However, FAIR (Findability, Accessibility, Interoperability, and Reusability) principles when sharing information are in increasing demand in several organizations, especially for information generated in public-funded research projects. OBJECTIVES:  The two main objectives of the presented work are the definition of a secure and interoperable modular architecture to manage different kinds of medical content (xIPAMS [x, for Any kind of content, Information Protection And Management System] and HIPAMS [Health Information Protection And Management System]), and the application of FAIR principles to that architecture in such a way that privacy and security are compatible with FAIR...

BACKGROUND:  Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains...

BACKGROUND:  MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward. OBJECTIVE:  To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance. METHODS:  MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input...

OBJECTIVE:  Our objective was to phenotype periodontal disease (PD) diagnoses from three different sections (diagnosis codes, clinical notes, and periodontal charting) of the electronic dental records (EDR) by developing two automated computer algorithms. METHODS:  We conducted a retrospective study using EDR data of patients ( n  = 27,138) who received care at Temple University Maurice H. Kornberg School of Dentistry from January 1, 2017 to August 31, 2021...

BACKGROUND: Current information systems do not effectively support nurse managers' duties, such as reporting, resource management and assessing clinical performance. Few performance management information systems are available and features in many are scattered. OBJECTIVES: The purpose of the study was to determine nurse managers' opinions of information system support for performance management. METHODS: An online questionnaire was used to collect data from nurse managers (n = 419)...

BACKGROUND:  Since COVID-19 (coronavirus disease 2019) was discovered in December 2019, it has spread worldwide. Early isolation and medical observation management of cases and their close contacts are the key to controlling the spread of the epidemic. However, traditional medical observation requires medical staff to measure body temperature and other vital signs face to face and record them manually. There is a general shortage of human and personal protective equipment and a high risk of occupational exposure, which seriously threaten the safety of medical staff...

BACKGROUND:  While there is a rapid increase in digital health initiatives focusing on the processing of personal data for strengthening the delivery of reproductive, maternal, newborn, and child health (RMNCH) services in fragile settings, these are often unaccompanied at both the policy and operational levels with adequate legal and regulatory frameworks. OBJECTIVE:  The main aim was to understand the maturity level of digital personal data initiatives for RMNCH services within fragile contexts...

INTRODUCTION: Healthcare information systems can generate and/or record huge volumes of data, some of which may be reused for research, clinical trials, or teaching. However, these databases can be affected by data quality problems; hence, an important step in the data reuse process consists in detecting and rectifying these issues. With a view to facilitating the assessment of data quality, we developed a taxonomy of data quality problems in operational databases. MATERIAL: We searched the literature for publications that mentioned "data quality problems", "data quality taxonomy", "data quality assessment", or "dirty data"...

BACKGROUND:  Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation. OBJECTIVE:  The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures...

BACKGROUND:  During the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based on transparent, homogeneous, and formal methodologies, making them understandable, reproducible, and auditable...