scholarly journals Sharing Clinical Notes and Electronic Health Records with People Affected by Mental Health Conditions: Scoping Review (Preprint)

10.2196/34170 ◽  
2021 ◽  
Author(s):  
Julian Schwarz ◽  
Annika Bärkås ◽  
Charlotte Blease ◽  
Lorna Collins ◽  
Maria Hägglund ◽  
...  
Keyword(s):  
Mental Health ◽  
Electronic Health Records ◽  
Scoping Review ◽  
Health Conditions ◽  
Health Records ◽  
Clinical Notes ◽  
Mental Health Conditions ◽  
Electronic Health

Sharing Clinical Notes and Electronic Health Records with People Affected by Mental Health Conditions: Scoping Review (Preprint)

2021 ◽  
Author(s):  
Julian Schwarz ◽  
Annika Bärkås ◽  
Charlotte Blease ◽  
Lorna Collins ◽  
Maria Hägglund ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Electronic Health Records ◽  
Scoping Review ◽  
General Health ◽  
Health Conditions ◽  
Service Users ◽  
Narrative Synthesis ◽  
Health Records ◽  
Clinical Notes

BACKGROUND Electronic health records (EHRs) are increasingly implemented internationally, whereas digital sharing of EHRs with service users (SUs) is a relatively new practice. First studies conducted in general health settings show promising results of Patient-accessible EHRs (PAEHRs) which are also often referred to as “open notes”. However, studies carried out in Mental Health Care (MHC) settings highlight several challenges which require further exploration. OBJECTIVE This scoping review aimed to map the available evidence on PAEHRs in MHC. We seek to relate findings with research from other health contexts, to compare different stakeholders’ perspectives, expectations, actual experiences with PAEHRs, and identify potential research gaps. METHODS A systematic scoping review was carried out for six electronic databases. Studies were included focusing on digital sharing of clinical notes or EHRs with people affected by a mental health condition up to September 2021. The Mixed Methods Appraisal Tool was used to assess the quality of the studies. The Preferred Reporting Items for Systematic Reviews and Meta-analyzes extension for Scoping Reviews guided the narrative synthesis and reporting of findings. RESULTS Of the 1.032 papers screened, 29 were included in this review. The studies used mostly qualitative methods or surveys and were predominantly published after 2018 in the US. PAEHRs were examined in outpatient (n=27) and inpatient settings (n=9), while a third of all research was conducted in Veterans Affairs Mental Health. There was no focus on specific psychiatric diagnoses among SUs. Narrative synthesis allowed to integrate findings according to the different stakeholders: (1) Service users reported mainly positive experiences with PAEHRs such as increased trust in their clinician, health literacy and empowerment. Negative experiences were related to inaccurate notes, disrespectful language used, or the uncovering of undiscussed diagnoses. (2) For health care professionals (HCPs), concerns outweighed the benefits of sharing EHRs, including an increased clinical burden due to more documentation efforts and possible harm triggered by reading the notes. (3) Relatives gained a better understanding of their family members' mental problems and were able to better support them when having access to their EHR. (4) Policy stakeholders and experts addressed ethical challenges and recommended the development of guidelines and trainings to better prepare both clinicians and SUs on how to write and read notes. CONCLUSIONS PAEHRs in MHC may strengthen user involvement, patients' autonomy and shift medical treatment to a co-produced process. Acceptance issues among HCPs align with findings from general health settings and may change over time. Notably, however, the corpus of evidence on digital sharing of EHRs with people affected by mental health conditions is limited. Above all, further research is needed to examine clinical effectiveness, efficiency and implementation of this socio-technical intervention.

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

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.

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

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.

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

Electronics ◽  
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.

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

2019 ◽  
Vol 26 (11) ◽  
pp. 1379-1384 ◽  
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.

scholarly journals Maximizing the use of social and behavioural information from secondary care mental health electronic health records

2020 ◽  
Vol 107 ◽  
pp. 103429
Author(s):  
S.M. Goodday ◽  
A. Kormilitzin ◽  
N. Vaci ◽  
Q. Liu ◽  
A. Cipriani ◽  
...  
Keyword(s):  
Mental Health ◽  
Electronic Health Records ◽  
Secondary Care ◽  
Health Records ◽  
Electronic Health

scholarly journals Medical device surveillance with electronic health records

2019 ◽  
Vol 2 (1) ◽  
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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