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American Journal of Physical Medicine &... Aug 2023Stigmatizing language can negatively influence providers' attitudes and care toward patients, but this has not been studied among physiatrists. An online survey was... (Randomized Controlled Trial)
Randomized Controlled Trial
Stigmatizing language can negatively influence providers' attitudes and care toward patients, but this has not been studied among physiatrists. An online survey was created to assess whether stigmatizing language can impact physical medicine and rehabilitation trainees' attitudes toward patients. We hypothesized stigmatizing language would negatively impact trainees' attitudes. Participants were randomized to a stigmatizing or neutral language vignette describing the same hypothetical spinal cord injury patient. Questions were asked about attitudes and assumptions toward the patient, pain management based on the vignette, and general views regarding individuals with disabilities. Between August 2021 and January 2022, 75 US physical medicine and rehabilitation residency trainees participated. Thirty-seven (49.3%) identified as women; 52 (69.3%) were White, and half (50.6%) received the stigmatized vignette. Participants exposed to stigmatizing language scored 4.8 points lower ( P < 0.01) on the provider attitude toward patient scale compared with those exposed to neutral language. There were no significant differences in the disability attitude scores between the two groups ( P = 0.81). These findings may indicate that stigmatizing language in the medical record may negatively affect physical medicine and rehabilitation trainees' attitudes toward patients. Further exploration is needed to identify the best way to educate trainees and reduce the propagation of bias in the medical record.
Topics: Humans; Female; Attitude; Medicine; Surveys and Questionnaires; Physical and Rehabilitation Medicine; Medical Records; Attitude of Health Personnel
PubMed: 36757856
DOI: 10.1097/PHM.0000000000002186 -
Journal of Atherosclerosis and... Sep 2023Recently, the Cerebrovascular and Cardiovascular Disease Control Act was enacted, for which it was necessary to establish a comprehensive and accurate nationwide...
Recently, the Cerebrovascular and Cardiovascular Disease Control Act was enacted, for which it was necessary to establish a comprehensive and accurate nationwide database and promote rational and economical stroke countermeasures in Japan, thus serving the public interest. Among the many studies on stroke registries, the Fukuoka Stroke Registry, a regional cohort, provides highly accurate information, and the Japanese Stroke Data Bank, a nationwide cohort, is highly comprehensive. The findings of these studies have contributed to the construction of evidence and the establishment of guidelines for stroke management. In the Nationwide survey of Acute Stroke care capacity for Proper dEsignation of Comprehensive stroke CenTer in Japan, research on improving the quality of medical care to close the gap between guidelines and clinical practice was performed using electronic medical records. This has enabled the recommendation of medical policies in Japan by visualizing medical care. In the era of healthcare big data and the Internet of Things, plenty of healthcare information is automatically recorded electronically and incorporated into databases. Thus, the establishment of stroke registries with the effective utilization of these electronic records can contribute to the development of stroke care.
Topics: Humans; Japan; Registries; Stroke; Delivery of Health Care; Electronic Health Records
PubMed: 37468262
DOI: 10.5551/jat.RV22008 -
Pediatrics Aug 2023Autism spectrum disorder (ASD) and gender dysphoria (GD) frequently cooccur. However, existing research has primarily used smaller samples, limiting generalizability and...
BACKGROUND AND OBJECTIVES
Autism spectrum disorder (ASD) and gender dysphoria (GD) frequently cooccur. However, existing research has primarily used smaller samples, limiting generalizability and the ability to assess further demographic variation. The purpose of this study was to (1) examine the prevalence of cooccurring ASD and GD diagnoses among US adolescents aged 9 to 18 and (2) identify demographic differences in the prevalence of cooccurring ASD and GD diagnoses.
METHODS
This secondary analysis used data from the PEDSnet learning health system network of 8 pediatric hospital institutions. Analyses included descriptive statistics and adjusted mixed logistic regression testing for associations between ASD and GD diagnoses and interactions between ASD diagnosis and demographic characteristics in the association with GD diagnosis.
RESULTS
Among 919 898 patients, GD diagnosis was more prevalent among youth with an ASD diagnosis compared with youth without an ASD diagnosis (1.1% vs 0.6%), and adjusted regression revealed significantly greater odds of GD diagnosis among youth with an ASD diagnosis (adjusted odds ratio = 3.00, 95% confidence interval: 2.72-3.31). Cooccurring ASD/GD diagnoses were more prevalent among youth whose electronic medical record-reported sex was female and those using private insurance, and less prevalent among youth of color, particularly Black and Asian youth.
CONCLUSIONS
Results indicate that youth whose electronic medical record-reported sex was female and those using private insurance are more likely, and youth of color are less likely, to have cooccurring ASD/GD diagnoses. This represents an important step toward building services and supports that reduce disparities in access to care and improve outcomes for youth with cooccurring ASD/GD and their families.
Topics: Adolescent; Child; Female; Humans; Asian; Autism Spectrum Disorder; Electronic Health Records; Gender Dysphoria; Prevalence; Black or African American
PubMed: 37395084
DOI: 10.1542/peds.2023-061363 -
FP Essentials Feb 2024The association between electronic health record (EHR) documentation and physician burnout is well-known. A combination of insufficient time to complete tasks, clinical...
The association between electronic health record (EHR) documentation and physician burnout is well-known. A combination of insufficient time to complete tasks, clinical documentation burden, and electronic inbox overload comprises the definition of documentation-related burnout. Burnout mitigation strategies related to clinical documentation include use of targeted EHR training for documentation, use of medical scribes, and institutional documentation redesign. Mitigation strategies related to electronic inbox overload include assigning designated administrative time for inbox management, tailoring of message content to decrease length, and a team-based approach to clinical workflows. Best practices for improving the efficiency of clinical documentation in the EHR include use of automation tools (eg, macros, templates), physician note optimization, and use of team-based documentation. Clinical documentation aids such as medical scribes, speech recognition software, and artificial intelligence (AI)-based software are popular and often considered a necessary resource in health care. For most practices, decisions regarding which aid to use will likely be determined by cost. Speech recognition software is the lowest cost option. AI-based software and medical scribes are more costly.
Topics: Humans; Artificial Intelligence; Electronic Health Records; Burnout, Professional; Documentation; Technology
PubMed: 38363362
DOI: No ID Found -
Circulation. Cardiovascular Imaging Dec 2023In addition to the traditional clinical risk factors, an increasing amount of imaging biomarkers have shown value for cardiovascular risk prediction. Clinical and... (Review)
Review
In addition to the traditional clinical risk factors, an increasing amount of imaging biomarkers have shown value for cardiovascular risk prediction. Clinical and imaging data are captured from a variety of data sources during multiple patient encounters and are often analyzed independently. Initial studies showed that fusion of both clinical and imaging features results in superior prognostic performance compared with traditional scores. There are different approaches to fusion modeling, combining multiple data resources to optimize predictions, each with its own advantages and disadvantages. However, manual extraction of clinical and imaging data is time and labor intensive and often not feasible in clinical practice. An automated approach for clinical and imaging data extraction is highly desirable. Convolutional neural networks and natural language processing can be utilized for the extraction of electronic medical record data, imaging studies, and free-text data. This review outlines the current status of cardiovascular risk prediction and fusion modeling; and in addition gives an overview of different artificial intelligence approaches to automatically extract data from images and electronic medical records for this purpose.
Topics: Humans; Artificial Intelligence; Neural Networks, Computer; Electronic Health Records; Natural Language Processing; Diagnostic Imaging
PubMed: 38073535
DOI: 10.1161/CIRCIMAGING.122.014533 -
Expert Review of Medical Devices Mar 2024Medical device (MD)-integrated (I) electronic medical record (EMR) (MDI-EMR) poses cyber threats that undermine patient safety, and thus, they require effective control... (Review)
Review
INTRODUCTION
Medical device (MD)-integrated (I) electronic medical record (EMR) (MDI-EMR) poses cyber threats that undermine patient safety, and thus, they require effective control mechanisms. We reviewed the related literature, including existing EMR and MD risk assessment approaches, to identify MDI-EMR comprehensive evaluation dimensions and measures.
AREAS COVERED
We searched multiple databases, including PubMed, Web of Knowledge, Scopus, ACM, Embase, IEEE and Ingenta. We explored various evaluation aspects of MD and EMR to gain a better understanding of their complex integration. We reviewed numerous risk management and assessment frameworks related to MD and EMR security aspects and mitigation controls and then identified their common evaluation aspects. Our review indicated that previous evaluation frameworks assessed MD and EMR independently. To address this gap, we proposed an evaluation framework based on the sociotechnical dimensions of health information systems and risk assessment approaches for MDs to evaluate MDI-EMR integratively.
EXPERT OPINION
The emergence of MDI-EMR cyber threats requires appropriate evaluation tools to ensure the safe development and application of MDI-EMR. Consequently, our proposed framework will continue to evolve through subsequent validations and refinements. This process aims to establish its applicability in informing stakeholders of the safety level and assessing its effectiveness in mitigating risks for future improvements.
Topics: Humans; Electronic Health Records; Patient Safety; Risk Assessment
PubMed: 38318674
DOI: 10.1080/17434440.2024.2315024 -
FP Essentials Feb 2024Remote patient monitoring (RPM) provides real-time clinical patient data to the medical team. The foundational element of RPM is communication, including data processing...
Remote patient monitoring (RPM) provides real-time clinical patient data to the medical team. The foundational element of RPM is communication, including data processing and integration in the electronic health record and communication of data between patients and clinicians. Patient portals are integral to this communication and their use can result in improved health outcomes and patient safety. Patient portals promote engagement of patients in their care, increase access to the medical team, and integrate RPM system data. RPM systems can monitor a spectrum of parameters related to chronic conditions, from vital signs (eg, heart and respiration rates, blood pressure, blood oxygen and glucose levels) to advanced cardiovascular measures. Some RPM systems are capable of automated monitoring. Health care insurance coverage of RPM systems varies widely, which has health equity implications, particularly for high-risk patients with endocrine and cardiovascular conditions. Additional challenges to widespread adoption of RPM include its contribution to administrative burden for physicians, patient data privacy issues, and variable effectiveness of RPM systems in the management of different chronic conditions.
Topics: Humans; Electronic Health Records; Medicine; Monitoring, Physiologic; Chronic Disease; Technology
PubMed: 38363361
DOI: No ID Found -
Tidsskrift For Den Norske Laegeforening... Sep 2023
Topics: Humans; Periodicals as Topic; Medical Records
PubMed: 37753759
DOI: 10.4045/tidsskr.23.0584 -
Yearbook of Medical Informatics Aug 2023To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022. (Randomized Controlled Trial)
Randomized Controlled Trial
OBJECTIVES
To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2022.
METHOD
A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.
RESULTS
Among the 1,324 papers returned by the search, published in 2022, that were in the scope of the various areas of CRI, the full review process selected four best papers. The first best paper describes the process undertaken in Germany, under the national Medical Informatics Initiative, to define a process and to gain multi-decision-maker acceptance of broad consent for the reuse of health data for research whilst remaining compliant with the European General Data Protection Regulation. The authors of the second-best paper present a federated architecture for the conduct of clinical trial feasibility queries that utilizes HL7 Fast Healthcare Interoperability Resources and an HL7 standard query representation. The third best paper aligns with the overall theme of this Yearbook, the inclusivity of potential participants in clinical trials, with recommendations to ensure greater equity. The fourth proposes a multi-modal modelling approach for large scale phenotyping from electronic health record information. This year's survey paper has also examined equity, along with data bias, and found that the relevant publications in 2022 have focused almost exclusively on the issue of bias in Artificial Intelligence (AI).
CONCLUSIONS
The literature relevant to CRI in 2022 has largely been dominated by publications that seek to maximise the reusability of wide scale and representative electronic health record information for research, either as big data for distributed analysis or as a source of information from which to identify suitable patients accurately and equitably for invitation to participate in clinical trials.
Topics: Humans; Artificial Intelligence; Medical Informatics; Electronic Health Records; Big Data; Peer Review
PubMed: 38147857
DOI: 10.1055/s-0043-1768748 -
Studies in Health Technology and... Jan 2024Unstructured medical records boast an abundance of information that could greatly facilitate medical decision-making and improve patient care. With the development of...
Unstructured medical records boast an abundance of information that could greatly facilitate medical decision-making and improve patient care. With the development of Natural Language Processing (NLP) methodology, the free-text medical data starts to attract more and more research attention. Most existing studies try to leverage the power of such unstructured data using Machine Learning algorithms, which would usually require a relatively large training set, and high computational capacity. However, when faced with a smaller-scale project, opting for an alternative approach may be more effective and practical. This project proposes an efficient and light-weight rule-based approach to categorize dental diagnosis data. It not only fills the void of dental records in the medical free-text processing area, but also demonstrates that with expertly designed research structure and proper implementation, simple method could achieve our study goal very competently.
Topics: Humans; Clinical Decision-Making; Algorithms; Machine Learning; Medical Records; Natural Language Processing
PubMed: 38269884
DOI: 10.3233/SHTI231040