Hospital Electronic Health Record System Research Articles

Data Flow Construction and Quality Evaluation of Electronic Source Data in Clinical Trials: Pilot Study Based on Hospital Electronic Medical Records in China.

The traditional clinical trial data collection process requires a clinical research coordinator who is authorized by the investigators to read from the hospital's electronic medical record. Using electronic source data opens a new path to extract patients' data from electronic health records (EHRs) and transfer them directly to an electronic data capture (EDC) system; this method is often referred to as eSource. eSource technology in a clinical trial data flow can improve data quality without compromising timeliness. At the same time, improved data collection efficiency reduces clinical trial costs. This study aims to explore how to extract clinical trial-related data from hospital EHR systems, transform the data into a format required by the EDC system, and transfer it into sponsors' environments, and to evaluate the transferred data sets to validate the availability, completeness, and accuracy of building an eSource dataflow. A prospective clinical trial study registered on the Drug Clinical Trial Registration and Information Disclosure Platform was selected, and the following data modules were extracted from the structured data of 4 case report forms: demographics, vital signs, local laboratory data, and concomitant medications. The extracted data was mapped and transformed, deidentified, and transferred to the sponsor's environment. Data validation was performed based on availability, completeness, and accuracy. In a secure and controlled data environment, clinical trial data was successfully transferred from a hospital EHR to the sponsor's environment with 100% transcriptional accuracy, but the availability and completeness of the data could be improved. Data availability was low due to some required fields in the EDC system not being available directly in the EHR. Some data is also still in an unstructured or paper-based format. The top-level design of the eSource technology and the construction of hospital electronic data standards should help lay a foundation for a full electronic data flow from EHRs to EDC systems in the future.

Fluorescent Imaging Using Indocyanine Green for Intraoperative Detection of Endometriosis [ID 2683620

INTRODUCTION: Endometriosis is characterized by the presence of endometrial-like tissue outside of the uterus. Definitive diagnosis is made by laparoscopic visualization with confirmatory histopathologic biopsy. In some cases, lesions can be ambiguous or difficult to visualize with traditional white light (WL)-illuminated laparoscopy. The study objective is to evaluate whether the use of indocyanine green (ICG) aids in identification and removal of clear or ambiguous endometriosis lesions, not visualized under traditional WL imaging. METHODS: This is a retrospective chart review of patients with symptoms suspicious for endometriosis where ICG is used during laparoscopy to assist in identification of suspected endometriosis lesions. Data collection includes patient demographics, medical history, surgical history, preoperative symptoms, and localization of lesions. Data will be collected from hospital electronic health record system and from a private practice. RESULTS: In total, 211 suspected lesions were excised, of which 115 had histologically proven endometriosis. 196 lesions were detected using ICG and 112 were confirmed to be endometriosis. These results were statistically significant (P=.024) with a sensitivity of 97.4% and a negative predictive value of 80%, demonstrating that ICG can significantly improve the intraoperative detection of endometriosis lesions. Research is ongoing with more data being collected. Our goal is that the additional data will further strengthen the power of our study. CONCLUSION: Our study shows that ICG fluorescence significantly improves the intraoperative detection and resection of ambiguous or clear endometriosis lesions and improves histopathologic diagnosis when compared to WL imaging. Indocyanine green has the potential to increase the detection of endometriosis and improve patient outcomes.

Using Principles of Agile Leadership in a Frontier Hospital Electronic Health Record System Implementation.

The challenge of implementing a new electronic health record system in any hospital can be daunting. Doing so in a small frontier hospital with limited resources can be monumental. By incorporating fundamental leadership principles, including Agile leadership principles and committing to a set of values, Lincoln Health in Hugo, Colorado, achieved a successful implementation on an accelerated schedule.

Evaluating Effective Therapeutic Options for Inhibitor Eradication in Patients with Acquired Hemophilia a: A 10-Year Single Center Experience

Background Acquired hemophilia A (AHA) is a rare disorder characterized by emergence of neutralizing antibodies or inhibitors to factor VIII. The main treatment strategy for AHA involves control of bleeding with factor VIII bypassing agents and eradicating the autoantibodies using immunosuppressive therapy (IST) for restoration of normal hemostasis. Previous studies have demonstrated the effectiveness of both cyclophosphamide and rituximab in combination with steroids for treating AHA, however, clear guidelines for choosing between these agents for rapid inhibitor eradication are lacking. This retrospective study aimed to compare the effectiveness of cyclophosphamide and rituximab as first-line therapies for rapid antibody elimination and relapse prevention. Methods We queried our hospital electronic health record system using ICD 9/10 code to identify patients >18 years that had at least 2 inpatient or outpatient encounters for the diagnosis of AHA between 1 st July 2013 and 30 th June 2023. Variables collected included initial aPTT value, serial factor VIII activity & Bethesda inhibitor titer and immunosuppressive therapies used. Time to remission was defined when inhibitor titer was <1% along with improving factor VIII activity. At our institute, all patients received 1mg/kg of steroids initially, then either six cycles of 400mg/kg IV Cyclophosphamide every 28 days on day 1 along with 100mg of oral Prednisone days 1-5 or four cycles of IV Rituximab at a weekly dose of 375mg/m2 was used as 1 stline IST for patients with inhibitor titer >10%. Patients that did not achieve remission or those with persistently high inhibitor titers after 3 cycles of Cyclophosphamide were switched to 2 nd line therapy. In few patients oral Cyclophosphamide or Mycophenolate mofetil was used as maintenance therapy after 1 st or 2 nd line therapy. Bleeding control was achieved with use of rpFVIII or rFVII, FEIBA or emicizumab depending on product availability. Descriptive statistics (mean, SD) & openAI.ChatGPT-4 generated correlation coefficient was used for data analysis. Results A total of 11 male and 5 female patients were identified. The mean age at diagnosis was 69 years and the average length of follow up was 672 days. The mean aPTT value at diagnosis was 84.3 seconds. Inhibitor titer ranged from 1.5 to 329% with an average of 63.62%. Patients with low inhibitor titer of <10% received only steroids and all of them achieved remission without relapse. Rituximab as first-line therapy led to remission in 100% of cases, while only 28.5% of patients treated with cyclophosphamide achieved remission without needing second-line therapy. The time to remission was significantly shorter in patients treated with rituximab compared to cyclophosphamide (43.4vs148.7days). Additionally, none of the patients treated with rituximab experienced a relapse after 1 st remission, while 57% of patients treated with cyclophosphamide did and majority needed 2 nd line & maintenance therapy (Figure-1). Initial inhibitor titer had a moderate to strong positive correlation with the time to remission, the incidence of relapse, and the need for second line and maintenance therapy (Figure- 2). Conclusions Rituximab as first line IST appeared to be more effective than cyclophosphamide in achieving rapid remission and reducing incidence of relapse. Our analysis also revealed that initial inhibitor titer may serve as a useful predictor of disease course, potentially guiding therapeutic decision-making and eliminating need for cytotoxic medication or maintenance therapy. Future large prospective studies are needed to confirm these findings and further investigate other factors influencing therapeutic outcomes in patients with AHA.

Causal knowledge graph construction and evaluation for clinical decision support of diabetic nephropathy

BackgroundMany important clinical decisions require causal knowledge (CK) to take action. Although many causal knowledge bases for medicine have been constructed, a comprehensive evaluation based on real-world data and methods for handling potential knowledge noise are still lacking. ObjectiveThe objectives of our study are threefold: (1) propose a framework for the construction of a large-scale and high-quality causal knowledge graph (CKG); (2) design the methods for knowledge noise reduction to improve the quality of the CKG; (3) evaluate the knowledge completeness and accuracy of the CKG using real-world data. Material and methodsWe extracted causal triples from three knowledge sources (SemMedDB, UpToDate and Churchill's Pocketbook of Differential Diagnosis) based on rule methods and language models, performed ontological encoding, and then designed semantic modeling between electronic health record (EHR) data and the CKG to complete knowledge instantiation. We proposed two graph pruning strategies (co-occurrence ratio and causality ratio) to reduce the potential noise introduced by SemMedDB. Finally, the evaluation was carried out by taking the diagnostic decision support (DDS) of diabetic nephropathy (DN) as a real-world case. The data originated from a Chinese hospital EHR system from October 2010 to October 2020. The knowledge completeness and accuracy of the CKG were evaluated based on three state-of-the-art embedding methods (R-GCN, MHGRN and MedPath), the annotated clinical text and the expert review, respectively. ResultsThis graph included 153,289 concepts and 1,719,968 causal triples. A total of 1427 inpatient data were used for evaluation. Better results were achieved by combining three knowledge sources than using only SemMedDB (three models: area under the receiver operating characteristic curve (AUC): p < 0.01, F1: p < 0.01), and the graph covered 93.9 % of the causal relations between diseases and diagnostic evidence recorded in clinical text. Causal relations played a vital role in all relations related to disease progression for DDS of DN (three models: AUC: p > 0.05, F1: p > 0.05), and after pruning, the knowledge accuracy of the CKG was significantly improved (three models: AUC: p < 0.01, F1: p < 0.01; expert review: average accuracy: + 5.5 %). ConclusionsThe results demonstrated that our proposed CKG could completely and accurately capture the abstract CK under the concrete EHR data, and the pruning strategies could improve the knowledge accuracy of our CKG. The CKG has the potential to be applied to the DDS of diseases.

Does illicit amphetamine seizures quantity associated with amphetamine use disorder related admissions in Saudi Arabia?

BackgroundIllicit amphetamine-type stimulants (ATS) trafficking activities have increased substantially in Saudi Arabia over the last 10 years. In the period 2013–2017 Saudi Arabia seized the largest quantities of amphetamine at the global level. The current study examines whether the increased quantity of ATS seizures has an impact on amphetamine use disorder admissions.MethodThis is an ecological study combining two datasets, the first dataset was obtained from United Nations Office on Drugs and Crime (UNODC), and the Al-Amal Hospital Electronic Health Record System in the city of Dammam, Eastern region of Saudi Arabia from 2005 to 2018. The annual incidence of patients diagnosed with amphetamine use was the dependent variable. The independent variable was the annual reported count of seized quantities of ATS in Saudi Arabia. We used a random intercept Negative Binomial model to predict the yearly count of amphetamine use disorder admission rates.ResultsA total of 910 amphetamine disorder admission patients in Al-Amal rehabilitation and addiction center, and the quantity equivalent to 200 tons of ATS was seized from 2005 to 2018. The amphetamine disorder admission rate has increased from 1.33% in 2005 to 18.27% in 2018. For each one-unit increase in the amphetamine confiscated quantities, the amphetamine use disorder admission rate increased by 49 to 88%.ConclusionThe current study found that reported amphetamine seized quantities were significantly and positively associated with the increase of amphetamine use disorder-related admission rates. In 2018, both ATS seized quantities and admission rates significantly increased, nearly doubling from the previous year. Rigorous, and multidisciplinary interventional studies to evaluate factors associated with increasing abuse of ATS should be a priority for policymakers and researchers in Saudi.

The effect of postoperative hepatic fibrosis factors on morbidity in mitral valve replacement surgery: A single center ten years' experience.

Previous studies have shown that hepatic fibrosis indices and rates can be used to predict cardiovascular mortality and morbidity. Our aim with this study was to investigate the effect of aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio and fibrosis-4 (FIB-4) index calculated with ALT, AST, and platelet biomarkers, which are simple, fast, and relatively inexpensive and were used in previous studies to predict cardiovascular disease prognosis, on the prediction of postoperative morbidity and early mortality after mitral valve replacement (MVR) surgery. By scanning the hospital electronic health record system, 116 patients who underwent isolated MVR or MVR + tricuspid valve intervention were identified from 178 patients who underwent MVR with the standard sternotomy procedure between 2011 and 2021. The study was completed with 81 of these patients. Patients were divided into AST/ALT <2 (Group 1) and >2 (Group 2). In addition, the same patients were divided into FIB-4 index <3.25 (Group 3) and >3.25 (Group 4), and a total of four groups were formed. The mean age of Group 2 was significantly higher than Group 1 (P = 0.049). In addition, the mean age of Group 4 was significantly higher than Group 3 (P = 0.003). Postoperative complications did not differ between Groups 1 and 2 (P > 0.05). While noninvasive mechanincal ventilation (NIMV) requirements did not differ between Groups 3 and 4 (P > 0.05), MV duration and intensive care unit stay were significantly longer in Group 4 (P < 0.05). The AST/ALT ratio, which has been shown to be a predictor of cardiovascular mortality in various studies, was not useful in predicting mortality and morbidity in our study. However, a high FIB-4 index, another hepatic fibrosis index, was found to be associated with increased perioperative bleeding, duration of mechanical ventilation, and cardiac intensive care unit stay, which are important criteria in the prediction of morbidity in cardiovascular surgery.

Risk of thrombosis with thrombocytopenia syndrome after COVID‐19 vaccination prior to the recognition of vaccine‐induced thrombocytopenia and thrombosis: A self‐controlled case series study in England

BackgroundSeveral studies have found increased risks of thrombosis with thrombocytopenia syndrome (TTS) following the ChAdOx1 vaccination. However, case ascertainment is often incomplete in large electronic health record (EHR)‐based studies. ObjectivesTo assess for an association between clinically validated TTS and COVID‐19 vaccination. MethodsWe used the self‐controlled case series method to assess the risks of clinically validated acute TTS after a first COVID‐19 vaccine dose (BNT162b2 or ChAdOx1) or severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection. Case ascertainment was performed uninformed of vaccination status via a retrospective clinical review of hospital EHR systems, including active ascertainment of thrombocytopenia. ResultsOne hundred seventy individuals were admitted to the hospital for a TTS event at the study sites between January 1 and March 31, 2021. A significant increased risk (relative incidence [RI], 5.67; 95% confidence interval [CI], 1.02‐31.38) of TTS 4 to 27 days after ChAdOx1 was observed in the youngest age group (18‐ to 39‐year‐olds). No other period had a significant increase, although for ChAdOx1 for all ages combined the RI was >1 in the 4‐ to 27‐ and 28‐ to 41‐day periods (RI, 1.52; 95% CI, 0.88‐2.63; and (RI, 1.70; 95% CI, 0.73‐3.8, respectively). There was no significant increased risk of TTS after BNT162b2 in any period. Increased risks of TTS following a positive SARS‐CoV‐2 test occurred across all age groups and exposure periods. ConclusionsWe demonstrate an increased risk of TTS in the 4 to 27 days following COVID‐19 vaccination, particularly for ChAdOx1. These risks were lower than following SARS‐CoV‐2 infection. An alternative vaccine may be preferable in younger age groups in whom the risk of postvaccine TTS is greatest.

A maintenance hemodialysis mortality prediction model based on anomaly detection using longitudinal hemodialysis data

BackgroundMost end-stage renal disease patients rely on hemodialysis (HD) to maintain their life, and they face a serious financial burden and high risk of mortality. Due to the current situation of the health care system in China, a large number of patients on HD are lost to follow-up, making the identification of patients with high mortality risks an intractable problem. ObjectiveThis paper aims to propose a maintenance HD mortality prediction approach using longitudinal HD data under the situation of data imbalance caused by follow-up losses. MethodsA long short-term memory autoencoder (LSTM AE) based model is proposed to capture the physical condition changes of HD patients and distinguish between surviving and nonsurviving patients. The approach adopts anomaly detection theory, using only the surviving samples in the model training and identifying dead samples based on autoencoder reconstruction errors. The data are from a Chinese hospital electronic health record system between July 30, 2007, and August 25, 2016, and 36/72/108 continuous HD sessions were used to predict mortality within prediction windows of 90/180/365 days. Furthermore, the model performance is compared to that of logistic regression, support vector machine, random forest, LSTM classifier, isolation forest, and stacked autoencoder models. ResultsData for 1200 patients (survival: 1055, death: 145) were used to predict mortality during the next 90 days using 36 continuous HD sessions. The area under the PR curve for the LSTM AE was 0.57, the Recallmacro was 0.86, and the F1-scoremacro was 0.87, outperforming the other models. Upon varying the observation window or prediction window length, LSTM AE continued to outperform the other models. According to the variable importance analysis, the dialysis session length was the feature that contributed the most to the prediction model. ConclusionsThe proposed approach was able to detect patients on maintenance HD with high mortality risk from an imbalanced dataset using anomaly detection theory and leveraging longitudinal HD data.

The Importance of Electronic Health Record Access For Accurately Completing Drug Regimen Reviews.

OBJECTIVE: To challenge the standard of practice by evaluating the identification of medication discrepancies found depending on type of access to an electronic health record (EHR). In other words, is there a difference in the number of discrepancies between a pharmacist with only access to the postacute long-term care (PALTC) EHR (ie, single-access pharmacist [SAP]) compared with a pharmacist with access to both the PALTC and hospital EHRs (ie, dual-access pharmacist DAP) In October 2018, the Improving Medicare PostAcute Care Transformation (IMPACT) Act mandated admission drug review (DRR) upon admission to a postacute, long-term care (PALTC) facility.<br/> SUMMARY:This was a prospective study investigating the occurrence of medication therapy problems (MTP) identified by two different DRR processes; SAP versus DAP. Data were collected in a community hospital and a stand-alone PALTC facility. It was found that the DAP identified more safety-related medication needs and medication omissions than an SAP. There was a significant association between the type of access and whether a MTP (ie, yes or no) was discovered, the type of medication-related need, and MTP category.<br/> CONCLUSION: These results strongly suggest that current standard of practice should change to require access to both hospital and PALTC EHR systems for a pharmacist completing the medication reconciliation. Until the gap in EHR interoperability is closed, the potential breakdown in communication associated with SAP places patients transitioning from hospital to PALTC facilities at increased risk for medication problems and accompanying adverse medication events.

The Importance of Electronic Health Record Access For Accurately Completing Drug Regimen Reviews

OBJECTIVE: To challenge the standard of practice by evaluating the identification of medication discrepancies found depending on type of access to an electronic health record (EHR). In other words, is there a difference in the number of discrepancies between a pharmacist with only access to the postacute long-term care (PALTC) EHR (ie, single-access pharmacist [SAP]) compared with a pharmacist with access to both the PALTC and hospital EHRs (ie, dual-access pharmacist DAP) In October 2018, the Improving Medicare PostAcute Care Transformation (IMPACT) Act mandated admission drug review (DRR) upon admission to a postacute, long-term care (PALTC) facility.&lt;br/&gt; SUMMARY:This was a prospective study investigating the occurrence of medication therapy problems (MTP) identified by two different DRR processes; SAP versus DAP. Data were collected in a community hospital and a stand-alone PALTC facility. It was found that the DAP identified more safety-related medication needs and medication omissions than an SAP. There was a significant association between the type of access and whether a MTP (ie, yes or no) was discovered, the type of medication-related need, and MTP category.&lt;br/&gt; CONCLUSION: These results strongly suggest that current standard of practice should change to require access to both hospital and PALTC EHR systems for a pharmacist completing the medication reconciliation. Until the gap in EHR interoperability is closed, the potential breakdown in communication associated with SAP places patients transitioning from hospital to PALTC facilities at increased risk for medication problems and accompanying adverse medication events.

ACTION-EHR: Patient-Centric Blockchain-Based Electronic Health Record Data Management for Cancer Care.

BackgroundWith increased specialization of health care services and high levels of patient mobility, accessing health care services across multiple hospitals or clinics has become very common for diagnosis and treatment, particularly for patients with chronic diseases such as cancer. With informed knowledge of a patient’s history, physicians can make prompt clinical decisions for smarter, safer, and more efficient care. However, due to the privacy and high sensitivity of electronic health records (EHR), most EHR data sharing still happens through fax or mail due to the lack of systematic infrastructure support for secure, trustable health data sharing, which can also cause major delays in patient care.ObjectiveOur goal was to develop a system that will facilitate secure, trustable management, sharing, and aggregation of EHR data. Our patient-centric system allows patients to manage their own health records across multiple hospitals. The system will ensure patient privacy protection and guarantee security with respect to the requirements for health care data management, including the access control policy specified by the patient.MethodsWe propose a permissioned blockchain-based system for EHR data sharing and integration. Each hospital will provide a blockchain node integrated with its own EHR system to form the blockchain network. A web-based interface will be used for patients and doctors to initiate EHR sharing transactions. We take a hybrid data management approach, where only management metadata will be stored on the chain. Actual EHR data, on the other hand, will be encrypted and stored off-chain in Health Insurance Portability and Accountability Act–compliant cloud-based storage. The system uses public key infrastructure–based asymmetric encryption and digital signatures to secure shared EHR data.ResultsIn collaboration with Stony Brook University Hospital, we developed ACTION-EHR, a system for patient-centric, blockchain-based EHR data sharing and management for patient care, in particular radiation treatment for cancer. The prototype was built on Hyperledger Fabric, an open-source, permissioned blockchain framework. Data sharing transactions were implemented using chaincode and exposed as representational state transfer application programming interfaces used for the web portal for patients and users. The HL7 Fast Healthcare Interoperability Resources standard was adopted to represent shared EHR data, making it easy to interface with hospital EHR systems and integrate a patient’s EHR data. We tested the system in a distributed environment at Stony Brook University using deidentified patient data.ConclusionsWe studied and developed the critical technology components to enable patient-centric, blockchain-based EHR sharing to support cancer care. The prototype demonstrated the feasibility of our approach as well as some of the major challenges. The next step will be a pilot study with health care providers in both the United States and Switzerland. Our work provides an exemplar testbed to build next-generation EHR sharing infrastructures.

National Trends in the Safety Performance of Electronic Health Record Systems From 2009 to 2018

Despite the broad adoption of electronic health record (EHR) systems across the continuum of care, safety problems persist. To measure the safety performance of operational EHRs in hospitals across the country during a 10-year period. This case series included all US adult hospitals nationwide that used the National Quality Forum Health IT Safety Measure EHR computerized physician order entry safety test administered by the Leapfrog Group between 2009 and 2018. Data were analyzed from July 1, 2018 to December 1, 2019. The Health IT Safety Measure test, which uses simulated medication orders that have either injured or killed patients previously to evaluate how well hospital EHRs could identify medication errors with potential for patient harm. Descriptive statistics for performance on the assessment test over time were calculated at the overall test score level, type of decision support category level, and EHR vendor level. Among 8657 hospital-years observed during the study, mean (SD) scores on the overall test increased from 53.9% (18.3%) in 2009 to 65.6% (15.4%) in 2018. Mean (SD) hospital score for the categories representing basic clinical decision support increased from 69.8% (20.8%) in 2009 to 85.6% (14.9%) in 2018. For the categories representing advanced clinical decision support, the mean (SD) score increased from 29.6% (22.4%) in 2009 to 46.1% (21.6%) in 2018. There was considerable variation in test performance by EHR. These findings suggest that despite broad adoption and optimization of EHR systems in hospitals, wide variation in the safety performance of operational EHR systems remains across a large sample of hospitals and EHR vendors. Hospitals using some EHR vendors had significantly higher test scores. Overall, substantial safety risk persists in current hospital EHR systems.

Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records.

We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms. Four years of data were extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycemic episodes (BG ≤3.9 and ≤2.9 mmol/L, respectively). We used patient demographics, administered medications, vital signs, laboratory results, and procedures performed during the hospital stays to inform the model. Two iterations of the data set included the doses of insulin administered and the past history of inpatient hypoglycemia. Eighteen different prediction models were compared using the area under the receiver operating characteristic curve (AUROC) through a 10-fold cross validation. We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, and metformin), and albumin levels. The machine learning model with the best performance was the XGBoost model (AUROC 0.96). This outperformed the logistic regression model, which had an AUROC of 0.75 for the estimation of the risk of clinically significant hypoglycemia. Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycemia.

Electronic health record year and country of birth testing and patient navigation to increase diagnosis of chronic viral hepatitis.

The United States Preventive Services Task Force recommends hepatitis C testing people born from 1945 to 1965, "birth cohort" as well as hepatitis C and hepatitis B testing people from countries of birth with endemic infection risk. We automated the hospital electronic health record system to test birth cohort and those born in countries with endemic infection risk. A script is launched searching the laboratory database upon registration for any hepatitis C antibody, hepatitis C RNA and/or hepatitis B surface antigen result. If no positive result was found, a hepatitis C antibody/reflex RNA and/or hepatitis B surface antigen were ordered. A patient navigator received weekly results and assisted patients with positive serology to schedule an appointment with their primary care provider or treatment specialist. A total of 10726 participants were hepatitis C antibody tested, with 6.9% antibody positive. Monthly hepatitis C testing from January to July 2016 compared to August 2016-August 2017 increased 342% as a result of "birth cohort" testing. Following country of birth testing, monthly hepatitis B and hepatitis C testing increased 91%, and 44%, respectively, during June-August 2017 compared to September 2017-March 2018. 67% of hepatitis C-positive patients were linked to care. If the navigator contacted the patient, 92% were linked to care, and 32% were treated. Of hepatitis B surface antigen-positive patients, 43% were linked to care, 5% were on treatment, and 15% started treatment. Automated electronic health record ordering of hepatitis C and/or hepatitis B testing is feasible and increases testing. In the population tested, much improvement is needed with linkage to care and treatment.

Learning to detect and understand drug discontinuation events from clinical narratives.

Identifying drug discontinuation (DDC) events and understanding their reasons are important for medication management and drug safety surveillance. Structured data resources are often incomplete and lack reason information. In this article, we assessed the ability of natural language processing (NLP) systems to unlock DDC information from clinical narratives automatically. We collected 1867 de-identified providers' notes from the University of Massachusetts Medical School hospital electronic health record system. Then 2 human experts chart reviewed those clinical notes to annotate DDC events and their reasons. Using the annotated data, we developed and evaluated NLP systems to automatically identify drug discontinuations and reasons at the sentence level using a novel semantic enrichment-based vector representation (SEVR) method for enhanced feature representation. Our SEVR-based NLP system achieved the best performance of 0.785 (AUC-ROC) for detecting discontinuation events and 0.745 (AUC-ROC) for identifying reasons when testing this highly imbalanced data, outperforming 2 state-of-the-art non-SEVR-based models. Compared with a rule-based baseline system for discontinuation detection, our system improved the sensitivity significantly (57.75% vs 18.31%, absolute value) while retaining a high specificity of 99.25%, leading to a significant improvement in AUC-ROC by 32.83% (absolute value). Experiments have shown that a high-performance NLP system can be developed to automatically identify DDCs and their reasons from providers' notes. The SEVR model effectively improved the system performance showing better generalization and robustness on unseen test data. Our work is an important step toward identifying reasons for drug discontinuation that will inform drug safety surveillance and pharmacovigilance.

Analysis of Allergy Alerts Registered in a Hospital Electronic Health Record System.

Analysis of Allergy Alerts Registered in a Hospital Electronic Health Record System.

An appraisal of published usability evaluations of electronic health records via systematic review.

In this systematic review, we aimed to evaluate methodological and reporting trends present in the current literature by investigating published usability studies of electronic health records (EHRs). A literature search was conducted for articles published through January 2015 using MEDLINE (Ovid), EMBASE, Scopus, and Web of Science, supplemented by citation and reference list reviews. Studies were included if they tested the usability of hospital and clinic EHR systems in the inpatient, outpatient, emergency department, or operating room setting. A total of 4848 references were identified for title and abstract screening. Full text screening was performed for 197 articles, with 120 meeting the criteria for study inclusion. A review of the literature demonstrates a paucity of quality published studies describing scientifically valid and reproducible usability evaluations at various stages of EHR system development. A lack of formal and standardized reporting of EHR usability evaluation results is a major contributor to this knowledge gap, and efforts to improve this deficiency will be one step of moving the field of usability engineering forward.

Assessing and comparing the usability of Chinese EHRs used in two Peking University hospitals to EHRs used in the US: A method of RUA

ObjectivesThe objective of this study was to investigate the usability level of Chinese hospital Electronic Health Records (EHRs) by assessing the completion times of EHRs for seven “meaningful use (MU)” relevant tasks conducted at two Chinese tertiary hospitals and comparing the results to those of relevant research conducted in US EHRs. MethodsUsing Rapid Usability Assessment (RUA) developed by the National Center for Cognitive Informatics and Decision Making (NCCD), the usability of EHRs from two Peking University hospitals was assessed using a three-step Keystroke Level Model (KLM) in a laboratory environment. Results(1) The total EHR task completion time for 7 MU relevant test tasks showed no significant differences between the two Chinese EHRs and their US counterparts, in which the time for thinking was relatively large and comprised 35.6% of the total time. The time for the electronic physician order was the largest. (2) For specific tasks, the mean completion times of the 2 hospital EHR systems spent on recording, modifying and searching (RMS) the medication orders were similar to those for the RMS radioactive tests; the mean time spent on the RMS laboratory test orders were much less. (3) There were 85 usability problems identified in the 2 hospital EHR systems. DiscussionIn Chinese EHRs, a substantial amount of time is required to complete tasks relevant to MU targets and many preventable usability problems can be discovered. The task completion time of the 2 Chinese EHR systems was a little shorter than in the 5 reported US EHR systems, while the differences in smoking status and CPOE tasks were obvious; one main reason for these differences was the use of structured data entry. ConclusionsThe efficiency of Chinese and US EHRs was not significantly different. The key to improving the efficiency of both systems lies in expediting the Computerized physician order entry (CPOE) task. Many usability problems can be identified using heuristic assessments and improved by corresponding actions.

What Do Electronic Health Record Vendors Reveal About Their Products: An Analysis of Vendor Websites

BackgroundPurchasing electronic health records (EHRs) typically follows a process in which potential adopters actively seek information, compare alternatives, and form attitudes towards the product. A potential source of information on EHRs that can be used in the process is vendor websites. It is unclear how much product information is presented on EHR vendor websites or the extent of its value during EHR purchasing decisions.ObjectiveTo explore what features of EHR systems are presented by vendors in Ontario, Canada, on their websites, and the persuasive means they use to market such systems; to compare the online information available about primary care EHR systems with that about hospital EHR systems, and with data compiled by OntarioMD, a regional certifying agency.MethodsA list of EHR systems available in Ontario was created. The contents of vendor websites were analyzed. A template for data collection and organization was developed and used to collect and organize information on the vendor, website content, and EHR features. First, we mapped information on system features to categories based on a framework from the Institute of Medicine (IOM). Second, we used a grounded theory–like approach to explore information for building consumer confidence in the vendor and product, and the various persuasive strategies employed on vendor websites. All data were first coded by one researcher. A peer reviewer independently analyzed a randomly chosen subset of the websites (10 of 21; 48%) and provided feedback towards a unified coding scheme. All data were then re-coded and categorized into themes. Finally, we compared information from vendor websites and data gathered by OntarioMD.ResultsVendors provided little specific product information on their websites. Only two of five acute care EHR websites (40%) and nine of 16 websites for primary care systems (56%) featured seven or all eight of the IOM components. Several vendor websites included system interface demonstrations: screenshots (six websites), public videos or slideshows (four websites), or for registered viewers only (three websites). Persuasive means used by vendors included testimonials on 14/21 (67%) websites, and directional language. Except for one free system, trial EHR versions were not available. OntarioMD provided more comprehensive information about primary care systems than the vendors’ websites. Of 14 points of comparison, only the inclusion of templates and bilingual interfaces were fully represented in both data sources. For all other categories, the vendor websites were less complete than the OntarioMD site.ConclusionsEHR vendor websites employ various persuasive means, but lack product-specific information and do not provide options for trying systems on a limited basis. This may impede the ability of potential adopters to form perceptions and compare various offerings. Both vendors and clients could benefit from greater transparency and more specific product information on the Web.Trial RegistrationN/A