scholarly journals Use of Electronic Health Record Tools to Facilitate and Audit Infliximab Prescribing

2018 ◽  
Vol 23 (1) ◽  
pp. 18-25
Author(s):  
Bethany R. Sharpless ◽  
Fernando del Rosario ◽  
Zarela Molle-Rios ◽  
Elora Hilmas
Keyword(s):  
Literature Review ◽  
Electronic Health Record ◽  
National Survey ◽  
Free Text ◽  
Health Record ◽  
Order Information ◽  
Text Data ◽  
Review Analysis ◽  
Electronic Health ◽  
Implementation Data

OBJECTIVES The objective of this project was to assess a pediatric institution's use of infliximab and develop and evaluate electronic health record tools to improve safety and efficiency of infliximab ordering through auditing and improved communication. METHODS Best use of infliximab was defined through a literature review, analysis of baseline use of infliximab at our institution, and distribution and analysis of a national survey. Auditing and order communication were optimized through implementation of mandatory indications in the infliximab orderable and creation of an interactive flowsheet that collects discrete and free-text data. The value of the implemented electronic health record tools was assessed at the conclusion of the project. RESULTS Baseline analysis determined that 93.8% of orders were dosed appropriately according to the findings of a literature review. After implementation of the flowsheet and indications, the time to perform an audit of use was reduced from 60 minutes to 5 minutes per month. Four months post implementation, data were entered by 60% of the pediatric gastroenterologists at our institution on 15.3% of all encounters for infliximab. Users were surveyed on the value of the tools, with 100% planning to continue using the workflow, and 82% stating the tools frequently improve the efficiency and safety of infliximab prescribing. CONCLUSIONS Creation of a standard workflow by using an interactive flowsheet has improved auditing ability and facilitated the communication of important order information surrounding infliximab. Providers and pharmacists feel these tools improve the safety and efficiency of infliximab ordering, and auditing data reveal that the tools are being used.

scholarly journals HAI-Proactive: Development of an Automated Surveillance System for Healthcare-Associated Infections in Sweden

2020 ◽  
Vol 41 (S1) ◽  
pp. s39-s39
Author(s):  
Pontus Naucler ◽  
Suzanne D. van der Werff ◽  
John Valik ◽  
Logan Ward ◽  
Anders Ternhag ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Electronic Health Record ◽  
Predictive Value ◽  
Surveillance System ◽  
Free Text ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Healthcare Associated

Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections.Funding: Sweden’s Innovation Agency and Stockholm County CouncilDisclosures: None

Enhancing goals of care communication by oncologists using a pathway-based intervention.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 324-324
Author(s):  
Isaac S. Chua ◽  
Elise Tarbi ◽  
Jocelyn H. Siegel ◽  
Kate Sciacca ◽  
Anne Kwok ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Advanced Cancer ◽  
Language Processing ◽  
Clinical Pathways ◽  
Free Text ◽  
Health Record ◽  
Goals Of Care ◽  
Progress Notes ◽  
Electronic Health ◽  
Pilot Sample

324 Background: Delivering goal-concordant care to patients with advanced cancer requires identifying eligible patients who would benefit from goals of care (GOC) conversations; training clinicians how to have these conversations; conducting conversations in a timely manner; and documenting GOC conversations that can be readily accessed by care teams. We used an existing, locally developed electronic cancer care clinical pathways system to guide oncologists toward these conversations. Methods: To identify eligible patients, pathways directors from 12 oncology disease centers identified therapeutic decision nodes for each pathway that corresponded to a predicted life expectancy of ≤1 year. When oncologists selected one of these pre-identified pathways nodes, the decision was captured in a relational database. From these patients, we sought evidence of GOC documentation within the electronic health record by extracting coded data from the advance care planning (ACP) module—a designated area within the electronic health record for clinicians to document GOC conversations. We also used rule-based natural language processing (NLP) to capture free text GOC documentation within these same patients’ progress notes. A domain expert reviewed all progress notes identified by NLP to confirm the presence of GOC documentation. Results: In a pilot sample obtained between March 20 and September 25, 2020, we identified a total of 21 pathway nodes conveying a poor prognosis, which represented 91 unique patients with advanced cancer. Among these patients, the mean age was 62 (SD 13.8) years old; 55 (60.4%) patients were female, and 69 (75.8%) were non-Hispanic White. The cancers most represented were thoracic (32 [35.2%]), breast (31 [34.1%]), and head and neck (13 [14.3%]). Within the 3 months leading up to the pathways decision date, a total 62 (68.1%) patients had any GOC documentation. Twenty-one (23.1%) patients had documentation in both the ACP module and NLP-identified progress notes; 5 (5.5%) had documentation in the ACP module only; and 36 (39.6%) had documentation in progress notes only. Twenty-two unique clinicians utilized the ACP module, of which 1 (4.5%) was an oncologist and 21 (95.5%) were palliative care clinicians. Conclusions: Approximately two thirds of patients had any GOC documentation. A total of 26 (28.6%) patients had any GOC documentation in the ACP module, and only 1 oncologist documented using the ACP module, where care teams can most easily retrieve GOC information. These findings provide an important baseline for future quality improvement efforts (e.g., implementing serious illness communications training, increasing support around ACP module utilization, and incorporating behavioral nudges) to enhance oncologists’ ability to conduct and to document timely, high quality GOC conversations.

Transitioning from one Electronic Health Record (EHR) to Another: A Narrative Literature Review

2018 ◽  
Vol 62 (1) ◽  
pp. 489-493 ◽  
Author(s):  
Jason J. Saleem ◽  
Jennifer Herout
Keyword(s):  
Health Care ◽  
Literature Review ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Health Care Organization ◽  
Data Migration ◽  
Health Record ◽  
Care Organization ◽  
Healthcare Organizations ◽  
Electronic Health

This paper reports the results of a literature review of health care organizations that have transitioned from one electronic health record (EHR) to another. Ten different EHR to EHR transitions are documented in the academic literature. In eight of the 10 transitions, the health care organization transitioned to Epic, a commercial EHR which is dominating the market for large and medium hospitals and health care systems. The focus of the articles reviewed falls into two main categories: (1) data migration from the old to new EHR and (2) implementation of the new EHR as it relates to patient safety, provider satisfaction, and other measures pre-and post-transition. Several conclusions and recommendations are derived from this review of the literature, which may be informative for healthcare organizations preparing to replace an existing EHR. These recommendations are likely broadly relevant to EHR to EHR transitions, regardless of the new EHR vendor.

scholarly journals Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

2019 ◽  
Author(s):  
Daniel M. Bean ◽  
James Teo ◽  
Honghan Wu ◽  
Ricardo Oliveira ◽  
Raj Patel ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Natural Language Processing ◽  
Natural Language ◽  
Electronic Health Record ◽  
Open Source ◽  
Language Processing ◽  
Risk Scores ◽  
Free Text ◽  
Health Record ◽  
Electronic Health

AbstractAtrial fibrillation (AF) is the most common arrhythmia and significantly increases stroke risk. This risk is effectively managed by oral anticoagulation. Recent studies using national registry data indicate increased use of anticoagulation resulting from changes in guidelines and the availability of newer drugs.The aim of this study is to develop and validate an open source risk scoring pipeline for free-text electronic health record data using natural language processing.AF patients discharged from 1st January 2011 to 1st October 2017 were identified from discharge summaries (N=10,030, 64.6% male, average age 75.3 ± 12.3 years). A natural language processing pipeline was developed to identify risk factors in clinical text and calculate risk for ischaemic stroke (CHA2DS2-VASc) and bleeding (HAS-BLED). Scores were validated vs two independent experts for 40 patients.Automatic risk scores were in strong agreement with the two independent experts for CHA2DS2-VASc (average kappa 0.78 vs experts, compared to 0.85 between experts). Agreement was lower for HAS-BLED (average kappa 0.54 vs experts, compared to 0.74 between experts).In high-risk patients (CHA2DS2-VASc ≥2) OAC use has increased significantly over the last 7 years, driven by the availability of DOACs and the transitioning of patients from AP medication alone to OAC. Factors independently associated with OAC use included components of the CHA2DS2-VASc and HAS-BLED scores as well as discharging specialty and frailty. OAC use was highest in patients discharged under cardiology (69%).Electronic health record text can be used for automatic calculation of clinical risk scores at scale. Open source tools are available today for this task but require further validation. Analysis of routinely-collected EHR data can replicate findings from large-scale curated registries.

scholarly journals A dynamic reaction picklist for improving allergy reaction documentation in the electronic health record

2020 ◽  
Vol 27 (6) ◽  
pp. 917-923
Author(s):  
Liqin Wang ◽  
Suzanne V Blackley ◽  
Kimberly G Blumenthal ◽  
Sharmitha Yerneni ◽  
Foster R Goss ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Language Processing ◽  
Clinical Decision ◽  
Free Text ◽  
Health Record ◽  
Dynamic Reaction ◽  
Value Set ◽  
Expert Review ◽  
Electronic Health ◽  
Development And Validation

Abstract Objective Incomplete and static reaction picklists in the allergy module led to free-text and missing entries that inhibit the clinical decision support intended to prevent adverse drug reactions. We developed a novel, data-driven, “dynamic” reaction picklist to improve allergy documentation in the electronic health record (EHR). Materials and Methods We split 3 decades of allergy entries in the EHR of a large Massachusetts healthcare system into development and validation datasets. We consolidated duplicate allergens and those with the same ingredients or allergen groups. We created a reaction value set via expert review of a previously developed value set and then applied natural language processing to reconcile reactions from structured and free-text entries. Three association rule-mining measures were used to develop a comprehensive reaction picklist dynamically ranked by allergen. The dynamic picklist was assessed using recall at top k suggested reactions, comparing performance to the static picklist. Results The modified reaction value set contained 490 reaction concepts. Among 4 234 327 allergy entries collected, 7463 unique consolidated allergens and 469 unique reactions were identified. Of the 3 dynamic reaction picklists developed, the 1 with the optimal ranking achieved recalls of 0.632, 0.763, and 0.822 at the top 5, 10, and 15, respectively, significantly outperforming the static reaction picklist ranked by reaction frequency. Conclusion The dynamic reaction picklist developed using EHR data and a statistical measure was superior to the static picklist and suggested proper reactions for allergy documentation. Further studies might evaluate the usability and impact on allergy documentation in the EHR.

scholarly journals Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study (Preprint)

2020 ◽  
Author(s):  
Tjardo D Maarseveen ◽  
Timo Meinderink ◽  
Marcel J T Reinders ◽  
Johannes Knitza ◽  
Tom W J Huizinga ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Electronic Health Record ◽  
Support Vector ◽  
Free Text ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Data Set ◽  
Record Data ◽  
Electronic Health

BACKGROUND Financial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries. OBJECTIVE The aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records. METHODS Two electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation. RESULTS For the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97). CONCLUSIONS We demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

scholarly journals Improving Electronic Health Record Note Comprehension With NoteAid: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Crowdsourced Workers (Preprint)

2018 ◽  
Author(s):  
John P Lalor ◽  
Beverly Woolf ◽  
Hong Yu
Keyword(s):  
Electronic Health Record ◽  
Assessment Tool ◽  
Amazon Mechanical Turk ◽  
Free Text ◽  
Health Record ◽  
Patient Portals ◽  
Significant Difference ◽  
Baseline Group ◽  
Electronic Health ◽  
Medical Concepts

BACKGROUND Patient portals are becoming more common, and with them, the ability of patients to access their personal electronic health records (EHRs). EHRs, in particular the free-text EHR notes, often contain medical jargon and terms that are difficult for laypersons to understand. There are many Web-based resources for learning more about particular diseases or conditions, including systems that directly link to lay definitions or educational materials for medical concepts. OBJECTIVE Our goal is to determine whether use of one such tool, NoteAid, leads to higher EHR note comprehension ability. We use a new EHR note comprehension assessment tool instead of patient self-reported scores. METHODS In this work, we compare a passive, self-service educational resource (MedlinePlus) with an active resource (NoteAid) where definitions are provided to the user for medical concepts that the system identifies. We use Amazon Mechanical Turk (AMT) to recruit individuals to complete ComprehENotes, a new test of EHR note comprehension. RESULTS Mean scores for individuals with access to NoteAid are significantly higher than the mean baseline scores, both for raw scores (P=.008) and estimated ability (P=.02). CONCLUSIONS In our experiments, we show that the active intervention leads to significantly higher scores on the comprehension test as compared with a baseline group with no resources provided. In contrast, there is no significant difference between the group that was provided with the passive intervention and the baseline group. Finally, we analyze the demographics of the individuals who participated in our AMT task and show differences between groups that align with the current understanding of health literacy between populations. This is the first work to show improvements in comprehension using tools such as NoteAid as measured by an EHR note comprehension assessment tool as opposed to patient self-reported scores.

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