Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records

Abstract Objective Geriatric syndromes such as functional disability and lack of social support are often not encoded in electronic health records (EHRs), thus obscuring the identification of vulnerable older adults in need of additional medical and social services. In this study, we automatically identify vulnerable older adult patients with geriatric syndrome based on clinical notes extracted from an EHR system, and demonstrate how contextual information can improve the process. Materials and Methods We propose a novel end-to-end neural architecture to identify sentences that contain geriatric syndromes. Our model learns a representation of the sentence and augments it with contextual information: surrounding sentences, the entire clinical document, and the diagnosis codes associated with the document. We trained our system on annotated notes from 85 patients, tuned the model on another 50 patients, and evaluated its performance on the rest, 50 patients. Results Contextual information improved classification, with the most effective context coming from the surrounding sentences. At sentence level, our best performing model achieved a micro-F1 of 0.605, significantly outperforming context-free baselines. At patient level, our best model achieved a micro-F1 of 0.843. Discussion Our solution can be used to expand the identification of vulnerable older adults with geriatric syndromes. Since functional and social factors are often not captured by diagnosis codes in EHRs, the automatic identification of the geriatric syndrome can reduce disparities by ensuring consistent care across the older adult population. Conclusion EHR free-text can be used to identify vulnerable older adults with a range of geriatric syndromes.

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Development of algorithm for classification smoking status from unstructured bilingual electronic health records based on natural language processing (Preprint)

10.2196/preprints.26978 ◽

2021 ◽

Author(s):

Ye Seul Bae ◽

Kyung Hwan Kim ◽

Han Kyul Kim ◽

Sae Won Choi ◽

Taehoon Ko ◽

...

Keyword(s):

Natural Language Processing ◽

Electronic Health Records ◽

Natural Language ◽

Language Processing ◽

Smoking Status ◽

Svm Classifier ◽

Keyword Extraction ◽

Health Records ◽

Clinical Notes ◽

Electronic Health

BACKGROUND Smoking is a major risk factor and important variable for clinical research, but there are few studies regarding automatic obtainment of smoking classification from unstructured bilingual electronic health records (EHR). OBJECTIVE We aim to develop an algorithm to classify smoking status based on unstructured EHRs using natural language processing (NLP). METHODS With acronym replacement and Python package Soynlp, we normalize 4,711 bilingual clinical notes. Each EHR notes was classified into 4 categories: current smokers, past smokers, never smokers, and unknown. Subsequently, SPPMI (Shifted Positive Point Mutual Information) is used to vectorize words in the notes. By calculating cosine similarity between these word vectors, keywords denoting the same smoking status are identified. RESULTS Compared to other keyword extraction methods (word co-occurrence-, PMI-, and NPMI-based methods), our proposed approach improves keyword extraction precision by as much as 20.0%. These extracted keywords are used in classifying 4 smoking statuses from our bilingual clinical notes. Given an identical SVM classifier, the extracted keywords improve the F1 score by as much as 1.8% compared to those of the unigram and bigram Bag of Words. CONCLUSIONS Our study shows the potential of SPPMI in classifying smoking status from bilingual, unstructured EHRs. Our current findings show how smoking information can be easily acquired and used for clinical practice and research.

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The Postencounter Form System: Viewpoint on Efficient Data Collection Within Electronic Health Records (Preprint)

10.2196/preprints.17429 ◽

2019 ◽

Author(s):

Philip Held ◽

Randy A Boley ◽

Walter G Faig ◽

John A O'Toole ◽

Imran Desai ◽

...

Keyword(s):

Electronic Health Records ◽

Medical Center ◽

Health Records ◽

Clinical Notes ◽

The Road ◽

Clinical Encounters ◽

Efficient Data ◽

Data Points ◽

Electronic Health ◽

Using Data

UNSTRUCTURED Electronic health records (EHRs) offer opportunities for research and improvements in patient care. However, challenges exist in using data from EHRs due to the volume of information existing within clinical notes, which can be labor intensive and costly to transform into usable data with existing strategies. This case report details the collaborative development and implementation of the postencounter form (PEF) system into the EHR at the Road Home Program at Rush University Medical Center in Chicago, IL to address these concerns with limited burden to clinical workflows. The PEF system proved to be an effective tool with over 98% of all clinical encounters including a completed PEF within 5 months of implementation. In addition, the system has generated over 325,188 unique, readily-accessible data points in under 4 years of use. The PEF system has since been deployed to other settings demonstrating that the system may have broader clinical utility.

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Safer Prescribing for Hospitalized Older Adults with an Electronic Health Records‐Based Prescribing Context

Journal of the American Geriatrics Society ◽

10.1111/jgs.16640 ◽

2020 ◽

Vol 68 (9) ◽

pp. 2123-2127 ◽

Cited By ~ 1

Author(s):

Kathleen Drago ◽

Jackie Sharpe ◽

Bryanna De Lima ◽

Abdulaziz Alhomod ◽

Elizabeth Eckstrom

Keyword(s):

Older Adults ◽

Electronic Health Records ◽

Health Records ◽

Electronic Health ◽

Hospitalized Older Adults

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Secure Exchange of Medical Data Using a Novel Real-Time Biometric-Based Protection and Recognition Method

Electronics ◽

10.3390/electronics9122013 ◽

2020 ◽

Vol 9 (12) ◽

pp. 2013

Author(s):

Shams Ud Din ◽

Zahoor Jan ◽

Muhammad Sajjad ◽

Maqbool Hussain ◽

Rahman Ali ◽

...

Keyword(s):

Electronic Health Records ◽

Real Time ◽

Signal To Noise Ratio ◽

Security And Privacy ◽

The Internet ◽

Healthcare Organizations ◽

Health Records ◽

Clinical Notes ◽

Electronic Health ◽

Value Decomposition

Security and privacy are essential requirements, and their fulfillment is considered one of the most challenging tasks for healthcare organizations to manage patient data using electronic health records. Electronic health records (clinical notes, images, and documents) become more vulnerable to breaching patients’ privacy when shared with an external organization in the current arena of the internet of medical things (IoMT). Various watermarking techniques were introduced in the medical field to secure patients’ data. Most of the existing techniques focus on an image or document’s imperceptibility without considering the watermark(logo). In this research, a novel technique of watermarking is introduced, which supersedes the shortcomings of existing approaches. It guarantees the imperceptibility of the image/document and takes care of watermark(biometric), which is further passed through a process of recognition for claiming ownership. It extracts suitable frequencies from the transform domain using specialized filters to increase the robustness level. The extracted frequencies are modified by adding the biomedical information while considering the strength factor according to the human visual system. The watermarked frequencies are further decomposed through a singular value decomposition technique to increase payload capacity up to (256 × 256). Experimental results over a variety of medical and official images demonstrate the average peak signal-to-noise ratio (PSNR 54.43), and the normal correlation (N.C.) value is 1. PSNR and N.C. of the watermark were calculated after attacks. The proposed technique is working in real-time for embedding, extraction, and recognition of biometrics over the internet, and its uses can be realized in various platforms of IoMT technologies.

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Putting the “why” in “EHR”: capturing and coding clinical cognition

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocz125 ◽

2019 ◽

Vol 26 (11) ◽

pp. 1379-1384 ◽

Cited By ~ 5

Author(s):

James J Cimino

Keyword(s):

Electronic Health Records ◽

Information Overload ◽

Data Retrieval ◽

Formal Structure ◽

Health Records ◽

Clinical Notes ◽

Electronic Health ◽

Physical Findings ◽

Substantial Effort

Abstract Complaints about electronic health records, including information overload, note bloat, and alert fatigue, are frequent topics of discussion. Despite substantial effort by researchers and industry, complaints continue noting serious adverse effects on patient safety and clinician quality of life. I believe solutions are possible if we can add information to the record that explains the “why” of a patient’s care, such as relationships between symptoms, physical findings, diagnostic results, differential diagnoses, therapeutic plans, and goals. While this information may be present in clinical notes, I propose that we modify electronic health records to support explicit representation of this information using formal structure and controlled vocabularies. Such information could foster development of more situation-aware tools for data retrieval and synthesis. Informatics research is needed to understand what should be represented, how to capture it, and how to benefit those providing the information so that their workload is reduced.

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Mining Interpretable and Predictive Diagnosis Codes from Multi-source Electronic Health Records

Proceedings of the 2014 SIAM International Conference on Data Mining ◽

10.1137/1.9781611973440.120 ◽

2014 ◽

Cited By ~ 3

Author(s):

Sanjoy Dey ◽

Gyorgy Simon ◽

Bonnie Westra ◽

Michael Steinbach ◽

Vipin Kumar

Keyword(s):

Electronic Health Records ◽

Predictive Diagnosis ◽

Health Records ◽

Diagnosis Codes ◽

Electronic Health

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Patient Portal Use Among Older Adults: What Is Really Happening Nationwide?

Journal of Applied Gerontology ◽

10.1177/0733464818776125 ◽

2018 ◽

Vol 39 (4) ◽

pp. 442-450 ◽

Cited By ~ 8

Author(s):

Eun-Shim Nahm ◽

Shijun Zhu ◽

Michele Bellantoni ◽

Linda Keldsen ◽

Kathleen Charters ◽

...

Keyword(s):

Older Adults ◽

Electronic Health Records ◽

Older Adult ◽

Chronic Conditions ◽

Patient Portal ◽

Perceived Benefits ◽

Community Settings ◽

Patient Portals ◽

Health Records ◽

Current State

Patient portals (PPs), secure websites that allow patients to access their electronic health records and other health tools, can benefit older adults managing chronic conditions. However, studies have shown a lack of PP use in older adults. Little is known about the way they use PPs in community settings and specific challenges they encounter. The aim of this study was to examine the current state of PP use in older adults, employing baseline data (quantitative and qualitative) from an ongoing nationwide online trial. The dataset includes 272 older adults (mean age, 70.0 years [50-92]) with chronic conditions. Findings showed that the majority of participants (71.3%) were using one or more PPs, but in limited ways. Their comments revealed practical difficulties with managing PPs, perceived benefits, and suggestions for improvement. Further studies with different older adult groups (e.g., clinic patients) will help develop and disseminate more usable PPs for these individuals.

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Medical device surveillance with electronic health records

npj Digital Medicine ◽

10.1038/s41746-019-0168-z ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 2

Author(s):

Alison Callahan ◽

Jason A. Fries ◽

Christopher Ré ◽

James I. Huddleston ◽

Nicholas J. Giori ◽

...

Keyword(s):

Electronic Health Records ◽

Medical Device ◽

Patient Outcomes ◽

Revision Surgery ◽

Hip Pain ◽

Training Data ◽

Learning Methods ◽

Health Records ◽

Clinical Notes ◽

Electronic Health

Abstract Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements—one of the most common implantable devices—as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8–53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t = 5.14; p < 0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

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Use of Diagnosis Codes to Find Blood Transfusion Adverse Events in Electronic Health Records

10.1101/2020.12.30.20218610 ◽

2021 ◽

Cited By ~ 1

Author(s):

Roselie A. Bright ◽

Susan J. Bright-Ponte ◽

Lee Anne Palmer ◽

Summer K. Rankin ◽

Sergey Blok

Keyword(s):

Adverse Events ◽

Electronic Health Records ◽

Study Data ◽

Medical Product ◽

Health Records ◽

Packed Red Blood Cells ◽

Diagnosis Codes ◽

Major Teaching Hospital ◽

Major Teaching ◽

Electronic Health

ABSTRACTBackgroundElectronic health records (EHRs) and big data tools offer the opportunity for surveillance of adverse events (patient harm associated with medical care). We chose the case of transfusion adverse events (TAEs) and potential TAEs (PTAEs) because 1.) real dates were obscured in the study data, and 2.) there was emerging recognition of new types during the study data period.ObjectiveWe aimed to use the structured data in electronic health records (EHRs) to find TAEs and PTAEs among adults.MethodsWe used 49,331 adult admissions involving critical care at a major teaching hospital, 2001-2012, in the MIMIC-III EHRs database. We formed a T (defined as packed red blood cells, platelets, or plasma) group of 21,443 admissions vs. 25,468 comparison (C) admissions. The ICD-9-CM diagnosis codes were compared for T vs. C, described, and tested with statistical tools.ResultsTAEs such as transfusion associated circulatory overload (TACO; 12 T cases; rate ratio (RR) 15.61; 95% CI 2.49 to 98) were found. There were also PTAEs similar to TAEs, such as fluid overload disorder (361 T admissions; RR 2.24; 95% CI 1.88 to 2.65), similar to TACO. Some diagnoses could have been sequelae of TAEs, including nontraumatic compartment syndrome of abdomen (52 T cases; RR 6.76; 95% CI 3.40 to 14.9) possibly being a consequence of TACO.ConclusionsSurveillance for diagnosis codes that could be TAE sequelae or unrecognized TAE might be useful supplements to existing medical product adverse event programs.

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