Clinical Electronic Medical Records Research Articles

MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records.

To address the problem of poor entity recognition performance caused by the lack of Chinese annotation in clinical electronic medical records, this paper proposes a multi-medical entity recognition method F-MNER using a fusion technique combining BART, Bi-LSTM, and CRF. First, after cleaning, encoding, and segmenting the electronic medical records, the obtained semantic representations are dynamically fused using a bidirectional autoregressive transformer (BART) model. Then, sequential information is captured using a bidirectional long short-term memory (Bi-LSTM) network. Finally, the conditional random field (CRF) is used to decode and output multi-task entity recognition. Experiments are performed on the CCKS2019 dataset, with micro avg Precision, macro avg Recall, weighted avg Precision reaching 0.880, 0.887, and 0.883, and micro avg F1-score, macro avg F1-score, weighted avg F1-score reaching 0.875, 0.876, and 0.876 respectively. Compared with existing models, our method outperforms the existing literature in three evaluation metrics (micro average, macro average, weighted average) under the same dataset conditions. In the case of weighted average, the Precision, Recall, and F1-score are 19.64%, 15.67%, and 17.58% higher than the existing BERT-BiLSTM-CRF model respectively. Experiments are performed on the actual clinical dataset with our MF-MNER, the Precision, Recall, and F1-score are 0.638, 0.825, and 0.719 under the micro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.685, 0.800, and 0.733 under the macro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.647, 0.825, and 0.722 under the weighted avg evaluation mechanism. The above results show that our method MF-MNER can integrate the advantages of BART, Bi-LSTM, and CRF layers, significantly improving the performance of downstream named entity recognition tasks with a small amount of annotation, and achieving excellent performance in terms of recall score, which has certain practical significance. Source code and datasets to reproduce the results in this paper are available at https://github.com/xfwang1969/MF-MNER .

User Perceptions of Academic and Clinical Electronic Medical Records in an Undergraduate Nursing Program

Background: Electronic Medical Records (EMRs) are a widely used technology within health care. Nursing students require preparation during undergraduate education for the use of EMR. Nursing educators in universities also need to be proficient with EMR. This study reports the experiences and attitudes of nursing students and nurse educators using a simulated Academic Electronic Medical Record (AEMR) system within a nursing degree. Aim: To determine nursing student and nursing educator experience with and attitudes towards EMR and AEMR. Methods: Nursing students across three years of a Bachelor of Nursing program and nurse educators teaching with an AEMR system completed an online survey. Results: Findings from participating nursing students (n=103) revealed a favourable view of the learning and clinical relevance of AEMR (87.5%). Less than half (45.5%) of the surveyed students had been exposed to EMR whilst on clinical placements. In contrast most of the students (38.8%) who had been employed in a health care setting within the previous five years had used an EMR. Nurse educator responses (n=7) showed that most (n=5) had experience with EMR and viewed AEMR as important for student learning, with fewer (n=3) regarding it as an easy system to use. Conclusion: The survey revealed that both students and educators were satisfied with AEMR - and that AEMR use was acceptable for use in the University setting. However, the survey shows student exposure to EMR on placement is low. AEMR applications have the potential to bridge the gap in student experience however adequate support and training for academic staff in the use of AEMR is required.

Tibetan Medical Named Entity Recognition Study for Tibetan Clinical Electronic Medical Records

Tibetan Medical Named Entity Recognition Study for Tibetan Clinical Electronic Medical Records

An Improved Method for Named Entity Recognition and Its Application to CEMR

Named Entity Recognition (NER) on Clinical Electronic Medical Records (CEMR) is a fundamental step in extracting disease knowledge by identifying specific entity terms such as diseases, symptoms, etc. However, the state-of-the-art NER methods based on Long Short-Term Memory (LSTM) fail to exploit GPU parallelism fully under the massive medical records. Although a novel NER method based on Iterated Dilated CNNs (ID-CNNs) can accelerate network computing, it tends to ignore the word-order feature and semantic information of the current word. In order to enhance the performance of ID-CNNs-based models on NER tasks, an attention-based ID-CNNs-CRF model, which combines the word-order feature and local context, is proposed. Firstly, position embedding is utilized to fuse word-order information. Secondly, the ID-CNNs architecture is used to extract global semantic information rapidly. Simultaneously, the attention mechanism is employed to pay attention to the local context. Finally, we apply the CRF to obtain the optimal tag sequence. Experiments conducted on two CEMR datasets show that our model outperforms traditional ones. The F1-scores of 94.55% and 91.17% are obtained respectively on these two datasets, and both are better than LSTM-based models.

Identification of validated case definitions for medical conditions used in primary care electronic medical record databases: a systematic review

Data derived from primary care electronic medical records (EMRs) are being used for research and surveillance. Case definitions are required to identify patients with specific conditions in EMR data with a degree of accuracy. The purpose of this study is to identify and provide a summary of case definitions that have been validated in primary care EMR data. We searched MEDLINE and Embase (from inception to June 2016) to identify studies that describe case definitions for clinical conditions in EMR data and report on the performance metrics of these definitions. We identified 40 studies reporting on case definitions for 47 unique clinical conditions. The studies used combinations of International Classification of Disease version 9 (ICD-9) codes, Read codes, laboratory values, and medications in their algorithms. The most common validation metric reported was positive predictive value, with inconsistent reporting of sensitivity and specificity. This review describes validated case definitions derived in primary care EMR data, which can be used to understand disease patterns and prevalence among primary care populations. Limitations include incomplete reporting of performance metrics and uncertainty regarding performance of case definitions across different EMR databases and countries. Our review found a significant number of validated case definitions with good performance for use in primary care EMR data. These could be applied to other EMR databases in similar contexts and may enable better disease surveillance when using clinical EMR data. Consistent reporting across validation studies using EMR data would facilitate comparison across studies. PROSPERO CRD42016040020 (submitted June 8, 2016, and last revised June 14, 2016).

Harnessing the Power of Big Data to Improve Graduate Medical Education: Big Idea or Bust?

With the advent of electronic medical records (EMRs) fueling the rise of big data, the use of predictive analytics, machine learning, and artificial intelligence are touted as transformational tools to improve clinical care. While major investments are being made in using big data to transform health care delivery, little effort has been directed toward exploiting big data to improve graduate medical education (GME). Because our current system relies on faculty observations of competence, it is not unreasonable to ask whether big data in the form of clinical EMRs and other novel data sources can answer questions of importance in GME such as when is a resident ready for independent practice.The timing is ripe for such a transformation. A recent National Academy of Medicine report called for reforms to how GME is delivered and financed. While many agree on the need to ensure that GME meets our nation's health needs, there is little consensus on how to measure the performance of GME in meeting this goal. During a recent workshop at the National Academy of Medicine on GME outcomes and metrics in October 2017, a key theme emerged: Big data holds great promise to inform GME performance at individual, institutional, and national levels. In this Invited Commentary, several examples are presented, such as using big data to inform clinical experience and provide clinically meaningful data to trainees, and using novel data sources, including ambient data, to better measure the quality of GME training.

Data quality assessment framework to assess electronic medical record data for use in research

IntroductionThe proliferation and use of electronic medical records (EMR) in the clinical setting now provide a rich source of clinical data that can be leveraged to support research on patient outcomes, comparative effectiveness, and health systems research. Once the large volume and variety of data that robust clinical EMRs provide is aggregated, the suitability of the data for research purposes must be addressed. Therefore, the purpose of this paper is two-fold. First, we present a stepwise framework capable of guiding initial data quality assessment when matching multiple data sources regardless of context or application. Then, we demonstrate a use case of initial analysis of a longitudinal data repository of electronic health record data that illustrates the first four steps of the framework, and report results. MethodsA six-step data quality assessment framework is proposed and described that includes the following data quality assessment steps: (1) preliminary analysis, (2) documentation–longitudinal concordance, (3) breadth, (4) data element presence, (5) density, and (6) prediction. The six-step framework was applied to the Transport Data Mart—a data repository that contains over 28,000 records for patients that underwent interhospital transfer that includes EMRs from the sending hospitalization, transport, and receiving hospitalization. ResultsThere were a total of 9557 log entries of which 8139 were successfully matched to corresponding hospital encounters. 2832 were successfully mapped to both the sending and receiving hospital encounters (resulting in a 93% automatic matching rate), with 590 including air medical transport EMR data representing a complete case for testing. Results from Step 2 indicate that once records are identified and matched, there appears to be relatively limited drop-off of additional records when the criteria for matching increases, indicating the a proportion of records consistently contain nearly complete data. Measures of central tendency used in Step 3 and 4 exhibit a right skewness suggesting that a small proportion of records contain the highest number of repeated measures for the measured variables. ConclusionsThe proposed six-step data quality assessment framework is useful in establishing the metadata for a longitudinal data repository that can be replicated by other studies. There are practical issues that need to be addressed including the data quality assessments—with the most prescient being the need to establish data quality metrics for benchmarking acceptable levels of EMR data inclusiveness through testing and application.

CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record.

Clinicians' ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS). The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement. There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information. Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.

Combination of symptoms, syndrome and disease: treatment of refractory diabetic gastroparesis.

To assess effect of combination of symptoms, syndrome and disease on treatment of diabetic gastroparesis with severe nausea and vomiting. Professor Tong Xiaolin's clinical electronic medical records of patients who were treated between January 1, 2006 and October 1, 2012 were used as a database. Patients who met the inclusion criteria were enrolled. General information (name, sex and age), symptoms and blood glucose levels were obtained from the clinic electronic medical record, which was supplemented by a telephone interview. The patient-rated Gastroparesis Cardinal Symptom Index (GCSI) was used to evaluate the severity of the symptoms of gastroparesis. The effects of the treatment were assessed by the change in the severity of the symptoms of gastroparesis and the change in blood glucose between the baseline levels and the post-treatment levels at 1, 2, 4, 8 and 12 wk. Forty-five patients had a mean GCSI nausea and vomiting severity score of 4.21 ± 0.67 and a total GCSI score of 2.77 ± 0.63 before treatment. There was a significant improvement in the nausea and vomiting score at every return visit compared with the baseline score (1 wk: 3.02 ± 1.04 vs 4.18 ± 0.71, P < 0.001; 2 wk: 2.32 ± 1.25 vs 4.16 ± 0.73, P < 0.001; 4 wk: 2.12 ± 1.26 vs 4.12 ± 0.73, P < 0.001; 8 wk: 1.79 ± 1.09 vs 4.24 ± 0.77, P < 0.001; 12 wk: 0.69 ± 0.92 vs 4.25 ± 0.70, P < 0.001). Twenty-five of the 45 patients had complete resolution of vomiting during the observation period (mean time to resolution was 37.9 ± 27.3 d). The postprandial fullness and early satiety subscale, bloating subscale and total GCSI scores were also improved. Finally, the blood glucose levels improved after treatment, although the change was not significant. Use of the combination of symptoms, syndrome and disease to treat diabetic gastroparesis with refractory nausea and vomiting may be a new treatment option.

Marrying Work and Technical Artifact within the Healthcare Organization: A Work Network Approach

Despite the widespread belief that IT innovations bring beneficial change in clinical practices, healthcare practitioners struggle to realize the innovation potential of electronic medical records (EMR). This paper reports on two in-depth case studies of clinical EMR implementation based on an 11 month field study with the goal of developing a richer understanding as to why, when, and how IT innovation and organizational change occur. We propose the work network as an approach for analyzing the processes of configuration, implementation and use of the system over time. The work network offers a flexible formalism that allows researchers to analyze the evolving relationships between work, actors, institutions, and technology as the EMR artifact is inscribed in the work system. By linking up specific aspects of infrastructure and organizing over time, the work network clarifies how tensions emerge around complex IT implementation and describes the evolving fitting work so important for successful innovation with IT.