Methods of Information in Medicine最新文献

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.

Methods: As part of the National Research Network for Applied Surveillance and Testing project within the Network University Medicine, a cross-sectional survey was conducted to assess the situation of IC IT systems in 36 university hospitals in Germany.

Results: Among the most prominent findings were the lack of standardization of IC IT systems and the predominant use of commercial IC IT systems, while the vast majority of hospitals reported inadequacies in the features their IC IT systems provide for their daily work. However, as the pandemic has shown that there is a need for systems that can help improve health care, several German university hospitals have already started this upgrade independently.

Conclusions: The deep challenges faced by the German health care sector regarding the integration and interoperability of IT systems designed for IC and surveillance are unlikely to be solved through punctual interventions and require collaboration between educational, medical, and administrative institutions.

Introduction: In the health care environment, a huge volume of data is produced on a daily basis. However, the processes of collecting, storing, sharing, analyzing, and reporting health data usually face with numerous challenges that lead to producing incomplete, inaccurate, and untimely data. As a result, data quality issues have received more attention than before.

Objective: The purpose of this article is to provide an insight into the data quality definitions, dimensions, and assessment methodologies.

Methods: In this article, a scoping literature review approach was used to describe and summarize the main concepts related to data quality and data quality assessment methodologies. Search terms were selected to find the relevant articles published between January 1, 2012 and September 31, 2022. The retrieved articles were then reviewed and the results were reported narratively.

Results: In total, 23 papers were included in the study. According to the results, data quality dimensions were various and different methodologies were used to assess them. Most studies used quantitative methods to measure data quality dimensions either in paper-based or computer-based medical records. Only two studies investigated respondents' opinions about data quality.

Conclusion: In health care, high-quality data not only are important for patient care, but also are vital for improving quality of health care services and better decision making. Therefore, using technical and nontechnical solutions as well as constant assessment and supervision is suggested to improve data quality.

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.

Methods: We utilized the VAERS dataset of COVID-19 vaccine reports from November 2020 to August 2022 of 725,246 individuals. We first developed a gold-standard (GS) dataset of randomly selected 1,500 records. Second, the GS was split into training, testing, and validation sets. The training dataset was used to develop the NLP applications (supervised and unsupervised) and testing and validation datasets were used to test the performances of the NLP application.

Results: The NLP application automatically extracted vaccine side effects, body locations of the side effects, medication, and possibly misinformation with moderate to high accuracy (84% sensitivity, 82% specificity, and 83% F-1 measure). We found that 23% people (386,270) faced arm soreness, 31% body swelling (226,208), 23% fatigue/body weakness (168,160), and 22% (159,873) cold/flue-like symptoms. Most of the complications occurred in the body locations such as the arm, back, chest, neck, face, and head. Over-the-counter pain medications such as Tylenol and Ibuprofen and allergy medication like Benadryl were most reported self-reported medications. Death due to COVID-19, changes in the DNA, and infertility were possible false/misinformation reported by people.

Conclusion: Some self-reported side effects such as syncope, arthralgia, and blood clotting need further clinical investigations. Our NLP application may help in extracting information from big free-text electronic datasets to help policy makers and other researchers with decision making.

Background: The introduction of a computerized physician order entry (CPOE) system is changing workflows and redistributing tasks among health care professionals.

Objectives: The aim of this study is to describe exemplary changes in workflow, to objectify the time required for medication documentation, and to evaluate documentation quality with and without a CPOE system (Cerner® i.s.h.med).

Methods: Workflows were assessed either through direct observation and in-person interviews or through semistructured online interviews with clinical staff involved in medication documentation. Two case scenarios were developed consisting of exemplary medications (case 1 = 6 drugs and case 2 = 11 drugs). Physicians and nurses/documentation assistants were observed documenting the case scenarios according to workflows established prior to CPOE implementation and those newly established with CPOE implementation, measuring the time spent on each step in the documentation process. Subsequently, the documentation quality of the documented medication was assessed according to a previously established and published methodology.

Results: CPOE implementation simplified medication documentation. The overall time needed for medication documentation increased from a median of 12:12 min (range: 07:29-21:10 min) without to 14:40 min (09:18-25:18) with the CPOE system (p = 0.002). With CPOE, less time was spent documenting peroral prescriptions and more time documenting intravenous/subcutaneous prescriptions. For physicians, documentation time approximately doubled, while nurses achieved time savings. Overall, the documentation quality increased from a median fulfillment score of 66.7% without to 100.0% with the CPOE system (p < 0.001).

Conclusion: This study revealed that CPOE implementation simplified the medication documentation process but increased the time spent on medication documentation by 20% in two fictitious cases. This increased time resulted in higher documentation quality, occurred at the expense of physicians, and was primarily due to intravenous/subcutaneous prescriptions. Therefore, measures to support physicians with complex prescriptions in the CPOE system should be established.

Background: Temporal dataset shift can cause degradation in model performance as discrepancies between training and deployment data grow over time. The primary objective was to determine whether parsimonious models produced by specific feature selection methods are more robust to temporal dataset shift as measured by out-of-distribution (OOD) performance, while maintaining in-distribution (ID) performance.

Methods: Our dataset consisted of intensive care unit patients from MIMIC-IV categorized by year groups (2008-2010, 2011-2013, 2014-2016, and 2017-2019). We trained baseline models using L2-regularized logistic regression on 2008-2010 to predict in-hospital mortality, long length of stay (LOS), sepsis, and invasive ventilation in all year groups. We evaluated three feature selection methods: L1-regularized logistic regression (L1), Remove and Retrain (ROAR), and causal feature selection. We assessed whether a feature selection method could maintain ID performance (2008-2010) and improve OOD performance (2017-2019). We also assessed whether parsimonious models retrained on OOD data performed as well as oracle models trained on all features in the OOD year group.

Results: The baseline model showed significantly worse OOD performance with the long LOS and sepsis tasks when compared with the ID performance. L1 and ROAR retained 3.7 to 12.6% of all features, whereas causal feature selection generally retained fewer features. Models produced by L1 and ROAR exhibited similar ID and OOD performance as the baseline models. The retraining of these models on 2017-2019 data using features selected from training on 2008-2010 data generally reached parity with oracle models trained directly on 2017-2019 data using all available features. Causal feature selection led to heterogeneous results with the superset maintaining ID performance while improving OOD calibration only on the long LOS task.

Conclusions: While model retraining can mitigate the impact of temporal dataset shift on parsimonious models produced by L1 and ROAR, new methods are required to proactively improve temporal robustness.

Background: Deep generative models (DGMs) present a promising avenue for generating realistic, synthetic data to augment existing health care datasets. However, exactly how the completeness of the original dataset affects the quality of the generated synthetic data is unclear.

Objectives: In this paper, we investigate the effect of data completeness on samples generated by the most common DGM paradigms.

Methods: We create both cross-sectional and panel datasets with varying missingness and subset rates and train generative adversarial networks, variational autoencoders, and autoregressive models (Transformers) on these datasets. We then compare the distributions of generated data with original training data to measure similarity.

Results: We find that increased incompleteness is directly correlated with increased dissimilarity between original and generated samples produced through DGMs.

Conclusions: Care must be taken when using DGMs to generate synthetic data as data completeness issues can affect the quality of generated data in both panel and cross-sectional datasets.

Introduction: Health care 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." The publications were then reviewed, compared, summarized, and structured using a bottom-up approach, to provide an operational taxonomy of data quality problems. The latter were illustrated with fictional examples (though based on reality) from clinical databases.

Results: Twelve publications were selected, and 286 instances of data quality problems were identified and were classified according to six distinct levels of granularity. We used the classification defined by Oliveira et al to structure our taxonomy. The extracted items were grouped into 53 data quality problems.

Discussion: This taxonomy facilitated the systematic assessment of data quality in databases by presenting the data's quality according to their granularity. The definition of this taxonomy is the first step in the data cleaning process. The subsequent steps include the definition of associated quality assessment methods and data cleaning methods.

Conclusion: Our new taxonomy enabled the classification and illustration of 53 data quality problems found in hospital databases.

Objective: To provide high-quality data for coronavirus disease 2019 (COVID-19) research, we validated derived COVID-19 clinical indicators and 22 associated machine learning phenotypes, in the Mass General Brigham (MGB) COVID-19 Data Mart.

Methods: Fifteen reviewers performed a retrospective manual chart review for 150 COVID-19-positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered a natural language processing (NLP)-based chart review tool, the Digital Analytic Patient Reviewer (DAPR). For this work, we designed a dedicated patient summary view and developed new 127 NLP logics to extract COVID-19 relevant medical concepts and target phenotypes. Moreover, we transformed DAPR for research purposes so that patient information is used for an approved research purpose only and enabled fast access to the integrated patient information. Lastly, we performed a survey to evaluate the validation difficulty and usefulness of the DAPR.

Results: The concepts for COVID-19-positive cohort, COVID-19 index date, COVID-19-related admission, and the admission date were shown to have high values in all evaluation metrics. However, three phenotypes showed notable performance degradation than the positive predictive value in the prepandemic population. Based on these results, we removed the three phenotypes from our data mart. In the survey about using the tool, participants expressed positive attitudes toward using DAPR for chart review. They assessed that the validation was easy and DAPR helped find relevant information. Some validation difficulties were also discussed.

Conclusion: Use of NLP technology in the chart review helped to cope with the challenges of the COVID-19 data validation task and accelerated the process. As a result, we could provide more reliable research data promptly and respond to the COVID-19 crisis. DAPR's benefit can be expanded to other domains. We plan to operationalize it for wider research groups.

Background: Although the process-mining algorithms have evolved in the past decade, the lack of attention to extracting event logs from raw data of databases in an automatic manner is evident. These logs are available in a process-oriented manner in the process-aware information systems. Still, there are areas where their extraction is a challenge to address (e.g., trauma registries).

Objective: The registry data are recorded manually and follow an unstructured ad hoc pattern; prone to high noises and errors; consequently, registry logs are classified at a maturity level of one, and extracting process-centric information is not a trivial task therein. The experiences made during the event log building from the trauma registry are the subjects to be studied.

Results: The result indicates that the three-phase self-service registry log builder tool can withstand the mentioned issues by filtering and enriching the raw data and making them ready for any level of process-mining analysis. This proposed tool is demonstrated through process discovery in the National Trauma Registry of Iran, and the encountered challenges and limitations are reported.

Conclusion: This tool is an interactive visual event log builder for trauma registry data and is freely available for studies involving other registries. In conclusion, future research directions derived from this case study are suggested.