Electronic Health Record Analysis Research Articles

Multi-task heterogeneous graph learning on electronic health records

Learning electronic health records (EHRs) has received emerging attention because of its capability to facilitate accurate medical diagnosis. Since the EHRs contain enriched information specifying complex interactions between entities, modeling EHRs with graphs is shown to be effective in practice. The EHRs, however, present a great degree of heterogeneity, sparsity, and complexity, which hamper the performance of most of the models applied to them. Moreover, existing approaches modeling EHRs often focus on learning the representations for a single task, overlooking the multi-task nature of EHR analysis problems and resulting in limited generalizability across different tasks. In view of these limitations, we propose a novel framework for EHR modeling, namely MulT-EHR (Multi-Task EHR), which leverages a heterogeneous graph to mine the complex relations and model the heterogeneity in the EHRs. To mitigate the large degree of noise, we introduce a denoising module based on the causal inference framework to adjust for severe confounding effects and reduce noise in the EHR data. Additionally, since our model adopts a single graph neural network for simultaneous multi-task prediction, we design a multi-task learning module to leverage the inter-task knowledge to regularize the training process. Extensive empirical studies on MIMIC-III and MIMIC-IV datasets validate that the proposed method consistently outperforms the state-of-the-art designs in four popular EHR analysis tasks — drug recommendation, and predictions of the length of stay, mortality, and readmission. Thorough ablation studies demonstrate the robustness of our method upon variations to key components and hyperparameters.

Applications of machine learning to electronic health record data in liver-related disease

Electronic Health Records (EHRs) has gained its increasing significance in modern healthcare as its promising prospects in the application of machine learning. The accumulation of vast clinical data holds potential for repurposing in clinical research such as prediction, diagnosis and prognosis. However, the collection and preparation of EHR data present challenges, primarily due to the inherent incompleteness of the data and the associated privacy and security concerns. To address the issue, text-mining tools based on domain-specific lexicons, data sharing and multiple de-identification methods have been suggested. In terms of methodologies, various machine learning models and algorithms used in EHR data analysis are analyzed, including logistic regression, decision trees, random forests, and natural language processing, each with its unique application scenarios in the healthcare domain. Liver related diseases, including HAV, HBV, HCV and especially Liver Cancer, has affected hundreds of millions of people around the world. The incidence and mortality rates for these diseases are still rising continually. With recent advancements of Machine Learning techniques, such as the attention mechanism and BERT-based embedding, which have shown exceptional results in EHR analysis when applying to liver diseases. While EHRs offer a treasure trove of data for clinical research, the challenges associated with their collection, processing, and analysis cannot be ignored. It underscores the need for robust methodologies and tools to harness the full potential of EHRs while ensuring data integrity and patient privacy. In this paper, we will gather and review the existing application in the realm of liver-related diseases.

Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.

In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline. A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline. Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%). In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.

The Risk Assessment of the Security of Electronic Health Records Using Risk Matrix

The healthcare industry has been shifting toward electronic health records to improve operations, reduce overhead expenditure, and provide better healthcare. Electronic health records (EHRs) are supposed to offer the same levels of confidentiality and privacy as paper records, which have been used for decades. However, this is not the case, as the technology used to access, transmit, and store records poses a high risk to patients and healthcare organizations. Employees are a big risk to EHRs, as they use their devices to access information about a patient and discuss such records with other employees. Healthcare professionals also access patients’ records illegally. Such security loopholes have a high impact on EHRs, as people with malicious intent can use the records to access their financial records or blackmail them. External access to EHRs by cyber attackers poses the highest risk to the records and patients, as attackers are primarily driven by financial gain. On the contrary, internal access to data, though unethical, does not pose a grave danger to patients, as the employees mainly discuss the cases within themselves without any financial incentive to access the data. The current research provides a risk analysis of EHRs, the source of security problems, the impact of the risks involved, and risk management best practices that healthcare organizations can use to protect patients’ data.

Using patient portals for large-scale recruitment of individuals underrepresented in biomedical research: an evaluation of engagement patterns throughout the patient portal recruitment process at a single site within the All of Us Research Program.

To evaluate the use of patient portal messaging to recruit individuals historically underrepresented in biomedical research (UBR) to the All of Us Research Program (AoURP) at a single recruitment site. Patient portal-based recruitment was implemented at Columbia University Irving Medical Center. Patient engagement was assessed using patient's electronic health record (EHR) at four recruitment stages: Consenting to be contacted, opening messages, responding to messages, and showing interest in participating. Demographic and socioeconomic data were also collected from patient's EHR and univariate logistic regression analyses were conducted to assess patient engagement. Between October 2022 and November 2023, a total of 59592 patients received patient portal messages inviting them to join the AoURP. Among them, 24445 (41.0%) opened the message, 8983 (15.1%) responded, and 3765 (6.3%) showed interest in joining the program. Though we were unable to link enrollment data with EHR data, we estimate about 2% of patients contacted ultimately enrolled in the AoURP. Patients from underrepresented race and ethnicity communities had lower odds of consenting to be contacted and opening messages, but higher odds of showing interest after responding. Patient portal messaging provided both patients and recruitment staff with a more efficient approach to outreach, but patterns of engagement varied across UBR groups. Patient portal-based recruitment enables researchers to contact a substantial number of participants from diverse communities. However, more effort is needed to improve engagement from underrepresented racial and ethnic groups at the early stages of the recruitment process.

Post-ROSC Atrial fibrillation is not associated with rearrest but is associated with stroke and mortality following out of hospital cardiac arrest

BackgroundAtrial fibrillation (AF) in patients resuscitated from cardiac arrest (CA) is associated with increased short-term mortality. However, whether this is because AF adversely affects early resuscitation success, causes post-resuscitation morbidity, or because it is a marker for patient co-morbidities, remains unclear. We aimed to determine the prevalence of AF in patients with ROSC to test the hypothesis that AF is associated with increased risk of rearrest and to determine its impact on mortality and stroke risk. MethodsWe performed a retrospective study of emergency medical services patients with OHCA and ROSC. To examine long-term morbidity and mortality due to AF, an additional observational cohort analysis was performed using a large electronic health record (EHR) database. ResultsOne hundred nineteen patients with ROSC prior to ED arrival were identified. AF was observed in 39 (33%) of patients. Rearrest was not different between AF and no AF groups (44% vs. 41%, p = 0.94). In the EHR analysis, mortality at one year in patients who developed AF was 59% vs. 39% in no AF patients. Odds of stroke was 5x greater in AF patients (p < 0.001), with the majority not anticoagulated (93%, p < 0.001) and comorbidities were greater p < 0.001). ConclusionsAF was common following ROSC and not associated with rearrest. AF after CA was associated with increased mortality and stroke risk. These data suggest rhythm control for AF in the immediate post-ROSC period is not warranted; however, vigilance is required for patients who develop persistent AF, particularly with regards to stroke risk and prevention.

The i2b2 Temporal Data-Mining Solution for Automated Reconstruction of Different Breast Cancer Patterns of Care over a Decade of Observation

Background: In oncology, Patterns of Care (PoC) provide a detailed overview of all cancer-treatment interventions and recapitulate the entire patient’s journey. While manual-chart PoC reviewing is time-consuming, the use of Electronic Health Records (EHRs) enables the creation of data-mining solutions to automatically reconstruct the whole cancer trajectory with different granularity. Methods: We tested the ability of the i2b2 (Informatics for Integrating Biology and Bedside) solution to support the automatic reconstruction of the PoC for consecutive and unselected HER2+ and TNBC breast cancer patients through a retrospective EHRs analysis over a decade of observations. Results: From 2008 to 2017, 561 HER2+ and 412 TNBC patients were retrospectively identified by i2b2 platform at the Papa Giovanni XXIII Hospital in Bergamo. Most patients, 74.3% in the HER2 group and 71.8% in the TNBC group, received a (neo) adjuvant chemotherapy, with anti-HER2 drugs whenever indicated. Among the HER2 cohort, the 5-year Time to Treatment Change (TTC) and Overall Survival (OS) were 69.4% and 77.4% respectively, with 25% of patients receiving up to 3 lines of treatment in metastatic setting. Among the TNBC cohort, the 5-year TTC and OS were 59.3% and 69.4% respectively, with only 2% of patients receiving active treatment as third line of therapy. The consistency of the automated PoC reconstruction had to be reviewed in 1/3 of cases to clean conflicting data. Conclusion: The i2b2 solution has the potential to provide a retrospective, automated reconstruction of the different PoC, with limited manual-chart review refinement and might contribute to support investigations in the field of real-world data.

Leveraging Electronic Health Records (EHR) for Enhanced Patient Care

This study analyzes how electronic health records (EHR) can be utilized to enhance patient care. The objectives of the study are to; design predictive models to recognize patients are risk of developing some specific conditions; evaluate the effect of EHR systems on patient care outcomes; ascertain the impact of EHR systems on physicians’ work; and develop a framework for incorporating EHR analysis into hospital systems, thus enabling real-time decision-making by healthcare specialists. This study adopts a qualitative research using semi-structured interviews, which was directed to physicians who had at least a year experience at Primary Health Care Centres (PHCs) to gather information about the current content and scope of Electronic Health Record Systems. Most of the respondents interviewed said that the health information system’s adaptation and the availability of Electronic Health Records influenced encounters for patients positively in their PHCs. They talked about the many benefits of EHR in terms of effectiveness, which include the availability of patient information and accuracy of patient documentation; these are often hard to find in their paper documents. The interviewees also indicated that EHRs allow eprescribe of medication, which is a huge plus to the old system of prescribing and administering medicine to patients. The physicians stressed the need for private clinics and primary healthcare centres to share and link their systems to enhance the functionality of the EHR. The study recommends that; there is need for the PHC EHR system to integrate across other government and private institutions. There is also a need for increased capacity to support patients with physical activity and nutrition.

Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare

The digitization of healthcare systems has led to the proliferation of electronic health records (EHRs), serving as comprehensive repositories of patient information. However, the vast volume and complexity of EHR data present challenges in extracting meaningful insights. This paper addresses the need for automated analysis of EHRs by proposing a novel graph learning model with label attention (GLLA) for temporal event prediction. GLLA utilizes graph neural networks to capture intricate relationships between medical codes and patients, incorporating hierarchical structures and shared risk factors. Furthermore, it introduces the Label Attention and Attention-based Transformer (LAAT) algorithm to analyze unstructured clinical notes as a multi-label classification problem. Evaluation on the widely-used MIMIC III dataset demonstrates the efficacy of GLLA in enhancing diagnostic prediction performance. The contributions of this research include a comprehensive analysis of existing models, the identification of limitations, and the development of innovative approaches to improve the accuracy and effectiveness of EHR analysis. Ultimately, GLLA aims to advance healthcare decision-making, disease management strategies, and patient outcomes.

A Quantitative Bias Analysis Approach to Informative Presence Bias in Electronic Health Records.

Accurate outcome and exposure ascertainment in electronic health record (EHR) data, referred to as EHR phenotyping, relies on the completeness and accuracy of EHR data for each individual. However, some individuals, such as those with a greater comorbidity burden, visit the health care system more frequently and thus have more complete data, compared with others. Ignoring such dependence of exposure and outcome misclassification on visit frequency can bias estimates of associations in EHR analysis. We developed a framework for describing the structure of outcome and exposure misclassification due to informative visit processes in EHR data and assessed the utility of a quantitative bias analysis approach to adjusting for bias induced by informative visit patterns. Using simulations, we found that this method produced unbiased estimates across all informative visit structures, if the phenotype sensitivity and specificity were correctly specified. We applied this method in an example where the association between diabetes and progression-free survival in metastatic breast cancer patients may be subject to informative presence bias. The quantitative bias analysis approach allowed us to evaluate robustness of results to informative presence bias and indicated that findings were unlikely to change across a range of plausible values for phenotype sensitivity and specificity. Researchers using EHR data should carefully consider the informative visit structure reflected in their data and use appropriate approaches such as the quantitative bias analysis approach described here to evaluate robustness of study findings.

Development of an Automatic Rule-Based Algorithm for the Detection of Ovarian Cancer Recurrence From Electronic Health Records.

As the onset of cancer recurrence is not explicitly recorded in the electronic health record (EHR), a high volume of manual chart review is required to detect the cancer recurrence. This study aims to develop an automatic rule-based algorithm for detecting ovarian cancer (OC) recurrence on the basis of minimally preprocessed EHR data. The automatic rule-based recurrence detection algorithm (Auto-Recur), using notes on image reading (positron emission tomography-computed tomography [PET-CT], CT, magnetic resonance imaging [MRI]), biomarker (CA125), and treatment information (surgery, chemotherapy, radiotherapy), was developed to detect the first OC recurrence. Auto-Recur contains three single algorithms (images, biomarkers, treatments) and hybrid algorithms (combinations of the single algorithms). The performance of Auto-Recur was assessed using sensitivity, specificity, and accuracy of the recurrence time detected. The recurrence-free survival probabilities were estimated and compared with the retrospective chart review results. The proposed Auto-Recur considerably reduced human resources and time; it saved approximately 1,340 days when scaled to 100,000 patients compared with the conventional retrospective chart review. The hybrid algorithm on the basis of a combination of image, biomarker, and treatment information was the most efficient (sensitivity: 93.4%, specificity: 97.4%) and precisely captured recurrence time (average time error: 8.5 days). The estimated 3-year recurrence-free survival probability (44%) was close to the estimates by the retrospective chart review (45%, log-rank P value = .894). Our rule-based algorithm effectively captured the first OC recurrence from large-scale EHR while closely approximating the recurrence-free survival estimates obtained by conventional retrospective chart reviews. The study findings facilitate large-scale EHR analysis, enhancing clinical research opportunities.

Applying Advanced Practice Nurses in Rural Japan: A Mixed-Methods Perspective.

Introduction Rural hospitals, particularly those in geographically isolated regions like Shimane Prefecture, Japan, face significant healthcare delivery challenges. These include limited resources, an aging population, and a scarcity of healthcare professionals. Advanced practice nurses (APNs) have emerged as pivotal in addressing these gaps, offering specialized patient assessment, diagnosis, and management skills. This study aimed to evaluate the demand for APNs in rural community hospitals, focusing on the specific educational needs and clinical competencies required to improve healthcare outcomes in these settings. Method Employing a mixed-methods approach, this research combined qualitative insights from stakeholder interviews with quantitative data analysis of electronic health records (EHRs) at Unnan City Hospital. This sequential exploratory design aimed to capture comprehensive educational needs and outcomes, integrating the depth of qualitative data with the breadth of quantitative evidence to tailor a curriculum for APNs in rural healthcare contexts. Results The study revealed a critical demand for APNs skilled in managing common medical issues in rural settings, such as infections, circulatory failures, and respiratory problems. Stakeholder interviews highlighted the necessity for a curriculum that enhances clinical competencies and emphasizes soft skills like communication and leadership. An analysis of EHRs identified 21 specific diseases across six categories, underlining the importance of targeted education on these prevalent conditions. Conclusion The findings underscore the urgent need for specialized education programs for APNs in rural hospitals to address these communities' unique healthcare challenges. Developing a curriculum that focuses on clinical and soft skills essential for rural healthcare delivery can significantly enhance the quality of care. This study advocates for implementing such tailored educational programs to empower APNs, thereby contributing to healthcare equity and improving patient outcomes in rural settings.

Clinical risk prediction using language models: benefits and considerations.

The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means toincorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks. We propose two novel LM-based methods, namely "LLaMA2-EHR" and "Sent-e-Med." Our focus is on utilizing the textual descriptions within structured EHRs to make risk predictions about future diagnoses. We conduct a comprehensive comparison with previous approaches across various data types and sizes. Experiments across 6 different methods and 3 separate risk prediction tasks reveal that employing LMs to represent structured EHRs, such as diagnostic histories, results in significant performance improvements when evaluated using standard metrics such as area under the receiver operating characteristic (ROC) curve and precision-recall (PR) curve. Additionally, they offer benefits such as few-shot learning, the ability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. However, it is noteworthy that outcomes may exhibit sensitivity to a specific prompt. LMs encompass extensive embedded knowledge, making them valuable for the analysis of EHRs in the context of risk prediction. Nevertheless, it is important to exercise caution in their application, as ongoing safety concerns related to LMs persist and require continuous consideration.

Value of Electronic Health Records Measured Using Financial and Clinical Outcomes: Quantitative Study.

The Health Information Technology for Economic and Clinical Health Act of 2009 was legislated to reduce health care costs, improve quality, and increase patient safety. Providers and organizations were incentivized to exhibit meaningful use of certified electronic health record (EHR) systems in order to achieve this objective. EHR adoption is an expensive investment, given the resources and capital that are invested. Due to the cost of the investment, a return on the EHR adoption investment is expected. This study performed a value analysis of EHRs. The objective of this study was to investigate the relationship between EHR adoption levels and financial and clinical outcomes by combining both financial and clinical outcomes into one conceptual model. We examined the multivariate relationships between different levels of EHR adoption and financial and clinical outcomes, along with the time variant control variables, using moderation analysis with a longitudinal fixed effects model. Since it is unknown as to when hospitals begin experiencing improvements in financial outcomes, additional analysis was conducted using a 1- or 2-year lag for profit margin ratios. A total of 5768 hospital-year observations were analyzed over the course of 4 years. According to the results of the moderation analysis, as the readmission rate increases by 1 unit, the effect of a 1-unit increase in EHR adoption level on the operating margin decreases by 5.38%. Hospitals with higher readmission payment adjustment factors have lower penalties. This study fills the gap in the literature by evaluating individual relationships between EHR adoption levels and financial and clinical outcomes, in addition to evaluating the relationship between EHR adoption level and financial outcomes, with clinical outcomes as moderators. This study provided statistically significant evidence (P<.05), indicating that there is a relationship between EHR adoption level and operating margins when this relationship is moderated by readmission rates, meaning hospitals that have adopted EHRs could see a reduction in their readmission rates and an increase in operating margins. This finding could further be supported by evaluating more recent data to analyze whether hospitals increasing their level of EHR adoption would decrease readmission rates, resulting in an increase in operating margins. Hospitals would incur lower penalties as a result of improved readmission rates, which would contribute toward improved operating margins.

EHR Innovations: Shedding Light on Anemia in the Healthcare Paradigm

This study introduces a novel approach to Electronic Health Record (EHR) analysis, extending the use of phenotyping with machine learning (ML) models to enhance the recognition and treatment of anemia. It first examines the healthcare scenario in India and suggests potential improvements through data-driven personalized care. Using the MIMIC-III dataset, the research involves extensive data preprocessing and analysis to uncover key insights into anemia’s prevalence, gender distribution, comorbidities, and Intensive Care Unit (ICU) stays. Partitioning clustering algorithms like K-Means, K-medoids, Fuzzy C-means, and hierarchical clustering algorithms such as Agglomerative Clustering, DIANA, and HDBSCAN are used to identify groups of patients with similar medical profiles. The distance metrics employed are Levenshtein and Euclidean distances combined with TF-IDF Vectorization. The effectiveness of these algorithms is evaluated based on Length of Stay (LoS) estimation, a critical parameter in EHR studies. To predict a new patient’s LoS, the patient is at first classified into an existing cluster, which shows the highest support to the patient’s clinical activities. A decision tree regressor is then trained using data from the selected cluster to predict the new patient’s LoS, significantly improving predictive accuracy and reliability. Notably, the HDBSCAN algorithm, applied to the Tf-Idf Vectorizer object, achieves a 50.82% reduction in Root Mean Squared Error (RMSE) compared to baseline model. The novelty of this study lies in proposing an efficient approach for EHR analysis, specifically for predicting ICU patients’ LoS, and identifying the most effective clustering algorithm to improve healthcare delivery for anemic patients in healthcare scenario of India.

Lung cancer registry to promote person-centered care.&amp;nbsp;

Introduction: Lung cancer is a leading cause of death and major burden on healthcare systems. Patient-centered outcome measurements allow the optimization of care organization and quality improvement. The aim of this project was to outline a set of variables and outcomes to evaluate the quality of care provided to lung cancer patients. &#x0D; Methods: This project took place at Hospital Italiano de Buenos Aires, a community-based tertiary care hospital in Argentina; which provides thoracic surgery, pneumology, oncology, pathology, radiology and radiotherapy services. It also has a Lung Cancer Multidisciplinary Board. All patient information is stored in a Data Repository fed by the electronic health record (EHR).&#x0D; We established a team of experts in management, IT, thoracic surgery, pneumology, oncology, and internal medicine. We reviewed care processes and patient trajectory along the usual pathway, through a retrospective EHR analysis of a consecutive sample of lung cancer patients. &#x0D; We performed a literature review to outline possible variables and health outcomes to be collected, instruments, and frequency of measurement. We determined needs, usefulness, and feasibility of collecting variables and outcomes in group meetings. &#x0D; Results: We reviewed 15 cases of lung cancer, 53% male, median age 68 years old (range: 29-85). We detected heterogeneity in the time elapsed between critical points of management (eg first visit to diagnosis 21 to 642 days; biopsy report to chemotherapy 19 to 163). The proportion of use of the healthcare system was also variable (eg: 87% consulted pneumonolory or oncology, only 13% had pain management visits and 7% received respiratory kinesic therapy). Patient-reported outcomes were not systematically evaluated.&#x0D; The selected baseline variables were: age, sex, weight loss, smoking status, comorbidities, patient-reported health status (EORTC European Organization for Research and treatment of cancer, QLC-LC30 Core quality of life questionnaire, QLC-LC13 Lung cancer-specific quality of life questionnaire), performance status (ECOG Eastern Cooperative Oncology Group), pulmonary function (VEF1), histology, clinical and pathological stage (AJCC-TNM 8th Ed), molecular tests and treatment intent. The outcomes (collected annually): clinical staging performed before treatment, time from diagnosis to treatment, performance status, patient-reported health status, major surgical complications, major chemotherapy/radiotherapy complications, time from diagnosis to treatment, completed treatment, overall survival, cause of death, treatment-related mortality, place of death, time spent in hospital at the end of life (last 30 days).&#x0D; Discussion: Cancer registries comprise a set of systematically and prospectively collected data. Quality measurements performed in the context of cancer registries could assist in monitoring and evaluating quality of care. &#x0D; Conclusions: The project determined essential variables and outcomes to be measured, considering their relevance and feasibility.&#x0D; Lessons learnt: Teamwork is essential when developing a registry, as different stakeholders' opinions are reflected. Integrated care is a useful solution to overcome fragmentation.&#x0D; Limitations: Patients did not participate directly in this development. &#x0D; Suggestions for future research: These results will assist in outlining a care pathway for patients with lung cancer, which is currently under development. Evaluation before and after the implementation of care pathways may evidence quality improvement.

Longitudinal Study of Comorbidities and Clinical Outcomes in Persons with Gallstone Disease Using Electronic Health Records

BackgroundGallstone disease (GSD) is common and leads to significant morbidity, mortality, and health care utilization in the USA. We examined comorbidities and clinical outcomes among persons with GSD using electronic health records (EHR). MethodsIn this retrospective study of 1,381,004 adults, GSD was defined by ICD-9 code 574 or ICD-10 code K80 using Optum® longitudinal EHR from January 2007 to March 2021. We obtained diagnosis, procedure, prescription, and vital sign records and evaluated associations between demographics, comorbidities, and medications with cholecystectomy, digestive cancers, and mortality. ResultsAmong persons with GSD, 30% had a cholecystectomy and were more likely to be women, White, and younger, and less likely to have comorbidities, except for obesity, gastroesophageal reflux disease (GERD), abdominal pain, hyperlipidemia, and pancreatitis. Among persons with GSD, 2.2% had a non-colorectal digestive cancer diagnosis during follow-up and risk was 40% lower among persons with a cholecystectomy. Non-colorectal digestive cancer predictors included older age, male sex, non-White race-ethnicity, lower BMI, other cancers, diabetes, chronic liver disease, pancreatitis, GERD, and abdominal pain. Among persons with GSD, mortality was 15.1% compared with 9.7% for the whole EHR sample. Persons with a cholecystectomy had 40% lower mortality risk and mortality predictors included older age, male sex, Black race, lower BMI, and most comorbidities. ConclusionsIn this EHR analysis of persons with GSD, 30% had a cholecystectomy. Mortality was higher compared with the whole EHR sample. Persons with cholecystectomy were less likely to have non-colorectal digestive cancer or to die.

Inequalities in Communicating about Nutrition Information during Prenatal Care: The After-Visit Summary

ABSTRACT Background Though prenatal nutrition information is critical, it is not known whether information is shared equitably by patient race, financial security, or English proficiency. Purpose To evaluate whether delivery or receipt of ChooseYourFish.org nutrition information in the first prenatal visit differed by patient demographics. Methods Analysis of clinician-document electronic health record (EHR) or patient-reported surveys compared delivery and receipt of fish-related nutrition information in the first prenatal visit. Inferential statistics were used to compare delivery or receipt and race, ethnicity, payor, or interpreter use. Results EHR analysis (n = 2,329) revealed Medicaid patients who used an interpreter were half as likely to have the fish nutrition message in their after-visit summary compared to those with Medicaid who did not use an interpreter (OR = 0.54, 95% CL: 0.35–0.84). The same was not true for non-Medicaid patients. Survey analysis (n = 52) showed respondents identifying as Black or African American were 25% less likely to report receiving the after-visit summary compared to respondents who identified as white (p < .01). Discussion The results presented here illustrate how nutrition communication in the prenatal period can differ by patient race, financial security, and language. Translation to Health Education Practice: Culturally humble efforts to understand drivers of healthcare communication are needed to eliminate inequalities. A AJHE Self-Study quiz is online for this article via the SHAPE America Online Institute (SAOI) http://portal.shapeamerica.org/trn-Webinars

Using Natural Language Processing and Machine Learning to Identify Opioids in Electronic Health Record Data.

This study evaluates the utility of machine learning (ML) and natural language processing (NLP) in the processing and initial analysis of data within the electronic health record (EHR). We present and evaluate a method to classify medication names as either opioids or non-opioids using ML and NLP. A total of 4216 distinct medication entries were obtained from the EHR and were initially labeled by human reviewers as opioid or non-opioid medications. An approach incorporating bag-of-words NLP and supervised ML classification was implemented in MATLAB and used to automatically classify medications. The automated method was trained on 60% of the input data, evaluated on the remaining 40%, and compared to manual classification results. A total of 3991 medication strings were classified as non-opioid medications (94.7%), and 225 were classified as opioid medications by the human reviewers (5.3%). The algorithm achieved a 99.6% accuracy, 97.8% sensitivity, 94.6% positive predictive value, F1 value of 0.96, and a receiver operating characteristic (ROC) curve with 0.998 area under the curve (AUC). A secondary analysis indicated that approximately 15-20 opioids (and 80-100 non-opioids) were needed to achieve accuracy, sensitivity, and AUC values of above 90-95%. The automated approach achieved excellent performance in classifying opioids or non-opioids, even with a practical number of human reviewed training examples. This will allow a significant reduction in manual chart review and improve data structuring for retrospective analyses in pain studies. The approach may also be adapted to further analysis and predictive analytics of EHR and other "big data" studies.

A Deep-Learning Neural Network Approach for Secure Wireless Communication in the Surveillance of Electronic Health Records

The electronic health record (EHR) surveillance process relies on wireless security administered in application technology, such as the Internet of Things (IoT). Automated supervision with cutting-edge data analysis methods may be a viable strategy to enhance treatment in light of the increasing accessibility of medical narratives in the electronic health record. EHR analysis structured data structure code was used to obtain data on initial fatality risk, infection rate, and hazard ratio of death from EHRs for prediction of unexpected deaths. Patients utilizing EHRs in general must keep in mind the significance of security. With the rise of the IoT and sensor-based Healthcare 4.0, cyber-resilience has emerged as a need for the safekeeping of patient information across all connected devices. Security for access, amendment, and storage is cumulatively managed using the common paradigm. For improving the security of surveillance in the aforementioned services, this article introduces an endorsed joint security scheme (EJSS). This scheme recognizes the EHR utilization based on the aforementioned processes. For each process, different security measures are administered for sustainable security. Access control and storage modification require relative security administered using mutual key sharing between the accessing user and the EHR database. In this process, the learning identifies the variations in different processes for reducing adversarial interruption. The federated learning paradigm employed in this scheme identifies concurrent adversaries in the different processes initiated at the same time. Differentiating the adversaries under each process strengthens mutual authentication using individual attributes. Therefore, individual surveillance efficiency through log inspection and adversary detection is improved for heterogeneous and large-scale EHR databases.