Medication Accuracy in Electronic Health Records for Microbial Keratitis

The Impact of Documentation Workflow on the Accuracy of the Coded Diagnoses in the Electronic Health Record

Objective

To determine the impact of documentation workflow on the accuracy of coded diagnoses in electronic health records (EHRs).

Design

Cross-sectional study.

Participants

All patients who completed visits at the Casey Eye Institute Retina Division faculty clinic between April 7, 2022 and April 13, 2022.

Main Outcome Measures

Agreement between coded diagnoses and clinical notes.

Methods

We assessed the rate of agreement between the diagnoses in the clinical notes and the coded diagnosis in the EHR using manual review and examined the impact of the documentation workflow on the rate of agreement in an academic retina practice.

Results

In 202 visits by 8 physicians, 78% (range, 22%-100%) had an agreement between the coded diagnoses and the clinical notes. When physicians integrated the diagnosis code entry and note composition, the rate of agreement was 87.9% (range, 62%-100%). For those who entered the diagnosis codes separately from writing notes, the agreement was 44.4% (22%-50%, P < 0.0001).

Conclusion

The visit-specific agreement between the coded diagnosis and the progress note can vary widely by workflow. The workflow and EHR design may be an important part of understanding and improving the quality of EHR data.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models

Understanding the diagnostic goal of medical reports is valuable information for understanding patient flows. This work focuses on extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring. We investigate the performance of domain-dependent and general state-of-the-art language models and their alignment with domain expertise. To this end, eXplainable Artificial Intelligence (XAI) techniques are used to acquire insight into the inner workings of the model, which are verified on their trustworthiness. The verified XAI explanations are then compared with explanations from a domain expert, to indirectly determine the reliability of the model. BERTje, a Dutch Bidirectional Encoder Representations from Transformers (BERT) model, outperforms RobBERT and MedRoBERTa.nl in both accuracy and reliability. The latter model (MedRoBERTa.nl) is a domain-specific model, while BERTje is a generic model, showing that domain-specific models are not always superior. Our validation of BERTje in a small prospective study shows promising results for the potential uptake of the model in a practical setting.

Applications of natural language processing in ophthalmology: present and future

Advances in technology, including novel ophthalmic imaging devices and adoption of the electronic health record (EHR), have resulted in significantly increased data available for both clinical use and research in ophthalmology. While artificial intelligence (AI) algorithms have the potential to utilize these data to transform clinical care, current applications of AI in ophthalmology have focused mostly on image-based deep learning. Unstructured free-text in the EHR represents a tremendous amount of underutilized data in big data analyses and predictive AI. Natural language processing (NLP) is a type of AI involved in processing human language that can be used to develop automated algorithms using these vast quantities of available text data. The purpose of this review was to introduce ophthalmologists to NLP by (1) reviewing current applications of NLP in ophthalmology and (2) exploring potential applications of NLP. We reviewed current literature published in Pubmed and Google Scholar for articles related to NLP and ophthalmology, and used ancestor search to expand our references. Overall, we found 19 published studies of NLP in ophthalmology. The majority of these publications (16) focused on extracting specific text such as visual acuity from free-text notes for the purposes of quantitative analysis. Other applications included: domain embedding, predictive modeling, and topic modeling. Future ophthalmic applications of NLP may also focus on developing search engines for data within free-text notes, cleaning notes, automated question-answering, and translating ophthalmology notes for other specialties or for patients, especially with a growing interest in open notes. As medicine becomes more data-oriented, NLP offers increasing opportunities to augment our ability to harness free-text data and drive innovations in healthcare delivery and treatment of ophthalmic conditions.

Outcomes of Primary Trabeculectomy versus Combined Phacoemulsification-Trabeculectomy Using Automated Electronic Health Record Data Extraction

PURPOSE

Cataract is a known effect of trabeculectomy (TE), but some surgeons are hesitant to perform combined phacoemulsification-TE (PTE) due to a risk of increased TE failure. Herein, we compare intraocular pressure (IOP) lowering between trabeculectomy (TE) and phacoemulsification-TE (PTE) and investigate factors that impact patient outcomes.

METHODS

We performed a retrospective study of adults undergoing primary TE or PTE at our institution from 2010 to 2017. We used Kaplan-Meier survival analysis to investigate time to TE failure, and Cox proportional hazards modeling to investigate predictors of TE failure, defined as undergoing a second glaucoma surgery or using more IOP-lowering medications than pre-operatively.

RESULTS

318 surgeries (218 TE; 100 PTE) from 268 patients were included. Median follow-up time was 753 days. Mean baseline IOP was 21.1 mmHg. There were no significant differences in IOP between TE and PTE groups beyond postoperative year 1, with 28.9-46.5% of TE and 35.5-44.4% of PTE groups achieving IOP ≤10. Final IOP was similar in both groups (p = 0.22): 12.41 (SD 4.18) mmHg in the TE group and 14.05 (SD 5.45) in the PTE group. 84 (26.4%) surgeries met failure criteria. After adjusting for surgery type, sex, age, race, surgeon, and glaucoma diagnosis there were no significant differences in TE failure.

CONCLUSION

This study suggests there is no significant difference in the risk of TE failure in patients receiving TE versus those receiving PTE.

Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients

Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication lists frequently contain errors. In contrast, physicians often pay more attention to the clinical notes and record medication information in them. The medication information in notes may be used for medication reconciliation to improve the medication lists' accuracy. However, accurately extracting patient's current medications from free-text narratives is challenging. In this study, we first explored the discrepancies between medication documentation in medication lists and progress notes for glaucoma patients by manually reviewing patients' charts. Next, we developed and validated a named entity recognition model to identify current medication and adherence from progress notes. Lastly, a prototype tool for medication reconciliation using the developed model was demonstrated. In the future, the model has the potential to be incorporated into the EHR system to help with realtime medication reconciliation.

How Do Different Health Record Systems Affect Home Health Care? A Cross-Sectional Study of Electronic- versus Manual Documentation System

Objective

To investigate electronic health record (EHR) systems compared to manual systems (MS) in home health care and how documentation and reporting activities are impacted regarding time use, variation, and accuracy.

Methods

This is a cross-sectional study of two municipalities (M1 and M2) that use statistical process control charts and interview with caregivers to discuss the issue. Regarding reporting, 309 observations were used for the control charts in M1 and 572 for those in M2. Concerning documentation, 831 observations were used for M1 and 572 for M2. In addition, interviews were conducted with four caregivers from each municipality.

Results

The municipality with EHR system use 3% of their total time for documentation and 7% for reporting. The municipality with the MS uses 7% of their total time in documentation and 12% for reporting. There is less variation in the charts for the municipality with the EHR system, than for the municipality using an MS.

Conclusion

The municipality using the EHR system uses less time for documentation and reporting than the other municipality. This is probably due to the standardization of information in M1, and that M2 needs to record documentation twice. The standardization arising from EHR use system may cause less variation in the process than the MS, but less variation might also negatively affect information accuracy. Reduced time for oral reporting also affects information accuracy.

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in particular, the volume range of data captured in EHR systems has been growing rapidly. Yet making effective secondary use of this EHR data for improving patient care and facilitating clinical decision-making has remained challenging due to the complexity and heterogeneity of these data. Artificial intelligence (AI) techniques present a promising way to analyze these multimodal data sets. While AI techniques have been extensively applied to imaging data, there are a limited number of studies employing AI techniques with clinical data from the EHR. The objective of this review is to provide an overview of different AI methods applied to EHR data in the field of ophthalmology. This literature review highlights that the secondary use of EHR data has focused on glaucoma, diabetic retinopathy, age-related macular degeneration, and cataracts with the use of AI techniques. These techniques have been used to improve ocular disease diagnosis, risk assessment, and progression prediction. Techniques such as supervised machine learning, deep learning, and natural language processing were most commonly used in the articles reviewed.