Extracting Patient-Centered Outcomes from Clinical Notes in Electronic Health Records: Assessment of Urinary Incontinence After Radical Prostatectomy

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 Patient-Centered Medical Home, Electronic Health Records, and Quality of Care

Annals of Internal Medicine ◽

10.7326/m13-1798 ◽

2014 ◽

Vol 160 (11) ◽

pp. 741 ◽

Cited By ~ 44

Author(s):

Lisa M. Kern ◽

Alison Edwards ◽

Rainu Kaushal

Keyword(s):

Quality Of Care ◽

Electronic Health Records ◽

Medical Home ◽

Patient Centered Medical Home ◽

Patient Centered ◽

Health Records ◽

Electronic Health

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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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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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Meaningful Use of Electronic Health Records for Physician Collaboration: A Patient Centered Health Care Perspective

2014 47th Hawaii International Conference on System Sciences ◽

10.1109/hicss.2014.87 ◽

2014 ◽

Cited By ~ 4

Author(s):

Cherie Bakker Noteboom ◽

Sergey P. Motorny ◽

Sajda Qureshi ◽

Surendra Sarnikar

Keyword(s):

Health Care ◽

Electronic Health Records ◽

Meaningful Use ◽

Patient Centered ◽

Health Records ◽

Electronic Health

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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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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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Acronym Disambiguation in Clinical Notes from Electronic Health Records

10.1101/2020.11.25.20221648 ◽

2020 ◽

Author(s):

Nicholas B. Link ◽

Selena Huang ◽

Tianrun Cai ◽

Zeling He ◽

Jiehuan Sun ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Machine Learning ◽

Electronic Health Records ◽

Language Processing ◽

Learning Approaches ◽

Health Records ◽

Hospital System ◽

Clinical Notes ◽

Unsupervised Method ◽

Electronic Health

ABSTRACTObjectiveThe use of electronic health records (EHR) systems has grown over the past decade, and with it, the need to extract information from unstructured clinical narratives. Clinical notes, however, frequently contain acronyms with several potential senses (meanings) and traditional natural language processing (NLP) techniques cannot differentiate between these senses. In this study we introduce an unsupervised method for acronym disambiguation, the task of classifying the correct sense of acronyms in the clinical EHR notes.MethodsWe developed an unsupervised ensemble machine learning (CASEml) algorithm to automatically classify acronyms by leveraging semantic embeddings, visit-level text and billing information. The algorithm was validated using note data from the Veterans Affairs hospital system to classify the meaning of three acronyms: RA, MS, and MI. We compared the performance of CASEml against another standard unsupervised method and a baseline metric selecting the most frequent acronym sense. We additionally evaluated the effects of RA disambiguation on NLP-driven phenotyping of rheumatoid arthritis.ResultsCASEml achieved accuracies of 0.947, 0.911, and 0.706 for RA, MS, and MI, respectively, higher than a standard baseline metric and (on average) higher than a state-of-the-art unsupervised method. As well, we demonstrated that applying CASEml to medical notes improves the AUC of a phenotype algorithm for rheumatoid arthritis.ConclusionCASEml is a novel method that accurately disambiguates acronyms in clinical notes and has advantages over commonly used supervised and unsupervised machine learning approaches. In addition, CASEml improves the performance of NLP tasks that rely on ambiguous acronyms, such as phenotyping.

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Visualizing Semantic Structure of a Clinical Text Document

European Journal of Electrical Engineering and Computer Science ◽

10.24018/ejece.2020.4.6.256 ◽

2020 ◽

Vol 4 (6) ◽

Author(s):

Jonah Kenei ◽

Elisha Opiyo ◽

Robert Oboko

Keyword(s):

Electronic Health Records ◽

Clinical Data ◽

Semantic Structure ◽

Cognitive Workload ◽

Health Records ◽

Clinical Text ◽

Clinical Notes ◽

Healthcare Data ◽

Text Document ◽

Electronic Health

The increasing use of Electronic Health Records (EHRs) in healthcare delivery settings has led to increase availability of electronic clinical data. They generate a lot of patients’ clinical data each day, requiring physicians to review them to find clinically relevant information of different patients during care episodes. The availability of electronically collected healthcare data has created the need of computational tools to analyze them. One of the types of data which doctors have access to is clinical notes that resides in electronic health records. These notes are useful as they provide comprehensive information about patients’ health histories with many practical uses. For example, doctors always review these notes during care episodes to appraise themselves about the health history of a patient. These reviews are currently manual where a doctor reads a patient’s chart while looking for specific clinical information. Without the proper support, this manual process leads to information overload and increases physician cognitive workload. Current electronic health records (EHRs) do not provide support to help physicians reduce cognitive workload when completing clinical tasks. This is especially true for long clinical documents which require quick review at the point of care. The growing amount of clinical documentation available in EHRs has arose the need of tools that support synthesize of information in EHRs. The use of visual analytics to explore healthcare data is one such research direction to address this problem. However, existing visualization techniques are mainly based on structured electronic health record and rarely support therapeutic activities. Therefore, visualization of unstructured clinical records to support clinical practice is required. In this paper we propose a unique approach for graphically representing and visualizing the semantic structure of a clinical text document to aid doctors in reviewing electronic clinical notes. A user evaluation demonstrates that the proposed method for visualizing and navigating a document’s semantic structure facilitates a user’s document information exploration.

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

JMIR Formative Research ◽

10.2196/17429 ◽

2020 ◽

Vol 4 (4) ◽

pp. e17429

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

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.

Download Full-text