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نتیجه جستجو - پزشکی الکترونیکی

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 An empirical study of the antecedents of data completeness in electronic medical records
یک مطالعه تجربی از پیشینیان کامل بودن داده ها در پرونده پزشکی الکترونیکی-2020
There is a body of research that highlights the role of data management to improve the quality of data, which in return improves organizational performance. The literature in data management has indicated the five theoretical constructs used to understand the factors influencing data quality, including top management support, capability on the regulation and process management, business-IT alignment, staff participation, and integration of information systems. However, it is unclear how these theoretical constructs can be utilized to understand the antecedents of data completeness as a dimension of data quality. Following that stream of research, the current paper examines the factors influencing data completeness in electronic medical records (EMR). The scope of this study is by only surveying medical professionals at healthcare settings in northern Nevada. The empirical results reveal that resources should be added as one of the antecedents of data completeness in EMR.
Keywords: Data quality | Data completeness | Electronic medical records
مقاله انگلیسی
2 Patient Clustering Improves Efficiency of Federated Machine Learning to Predict Mortality and Hospital Stay Time Using Distributed Electronic Medical Records
وشه بندی بیمار باعث افزایش کارآیی یادگیری ماشین فدرال برای پیش بینی مرگ و میر و مدت زمان ماندن بیمارستان با استفاده از سوابق پزشکی الکترونیکی توزیع شده-2019
Electronic medical records (EMRs) support the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But so far most algorithms have been centralized, taking little account of the decentralized, non-identically independently distributed (non-IID), and privacy-sensitive characteristics of EMRs that can complicate data collection, sharing and learning. To address this challenge, we introduced a community-based federated machine learning (CBFL) algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered the distributed data into clinically meaningful communities that captured similar diagnoses and geographical locations, and learnt one model for each community. Throughout the learning process, the data was kept local at hospitals, while locally-computed results were aggregated on a server. Evaluation results show that CBFL outperformed the baseline federated machine learning (FL) algorithm in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC), Area Under the Precision-Recall Curve (PR AUC), and communication cost between hospitals and the server. Furthermore, communities’ performance difference could be explained by how dissimilar one community was to others.
Keywords: distributed clustering | autoencoder | federated machine learning | non-IID | critical care
مقاله انگلیسی
3 Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer
بررسی عوارض جانبی دارویی در خلاصه تخلیه سوابق پزشکی الکترونیکی با استفاده از Readpeer-2019
Background: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to find cases of drug-adverse event (AE) relations. Purpose: The objective of this paper is to develop a natural language processing (NLP) framework to detect drug- AE relations from unstructured hospital discharge summaries. Basic procedures: An NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE) terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert review of discharge summaries from a tertiary hospital in Singapore in 2011. Main findings: A total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries) by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600 records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations. Principal conclusions: Adverse drug reactions are a significant contributor to health care costs and utilization. Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting the results of the algorithm as part of an iterative machine learning process, is an important step towards use of hospital discharge summaries for an active pharmacovigilance program
Keywords: Pharmacovigilance | Text mining | Electronic medical records | Expert system | Adverse drug reaction
مقاله انگلیسی
4 Using electronic medical records to create big data and to communicate with patients: Is there room for both?
استفاده از سوابق پزشکی الکترونیکی برای ایجاد داده های بزرگ و ارتباط با بیماران: آیا برای هر دو اتاق وجود دارد؟-2018
Globally, electronic medical records (EMRs), which facilitate the systematic collection and storage of patient information, are increasingly implemented in diverse health care settings. EMRs provide a rich source of comprehensive data on patients’ med ical history, pathology and medical imaging orders and results, prescriptions, service use and medical and surgical procedures (Juvé-Udina, 2013). Extensive interest exists in the utilisation of these data among clinicians (including nurses, doctors and pharma cists), researchers and policy makers. When aggregated, these data have come to be referred to as ‘big data’ (Lee & Yoon, 2017). Big data comprise extremely large datasets that can be manipulated and analysed to identify patterns, trends and associations. Aside from the EMR, there are many sources of patient data including adminis trative claim records, clinical registries, government biometric data through fingerprints and facial images, patient-reported outcome data, medical imaging, and biomarker data. With the availability of voluminous sources of data, there is much excitement about the endless possibilities of new knowledge that can be generated through analysing the links between different sources of patient data (Vayena, Dzenowagis, Brownstein, & Sheikh, 2018).
مقاله انگلیسی
5 A privacy preserving framework for RFID based healthcare systems
چارچوبی برای حفظ حریم خصوصی سیستم های مراقبت های بهداشتی مبتنی بر RFID-2017
RFID (Radio Frequency IDentification) is anticipated to be a core technology that will be used in many practical applications of our life in near future. It has received considerable attention within the healthcare for almost a decade now. The technology’s promise to efficiently track hospital supplies, medical equipment, medications and patients is an attractive proposition to the healthcare industry. However, the prospect of wide spread use of RFID tags in the healthcare area has also triggered discussions regarding privacy, particularly because RFID data in transit may easily be intercepted and can be send to track its user (owner). In a nutshell, this technology has not really seen its true potential in healthcare industry since privacy concerns raised by the tag bearers are not properly addressed by existing identification techniques. There are two major types of privacy preservation techniques that are required in an RFID based healthcare system—(1) a privacy preserving authentication protocol is required while sensing RFID tags for different identification and monitoring purposes, and (2) a privacy preserving access control mechanism is required to restrict unauthorized access of private information while providing healthcare services using the tag ID. In this paper, we propose a framework (PriSens-HSAC) that makes an effort to address the above mentioned two privacy issues. To the best of our knowledge, it is the first framework to provide increased privacy in RFID based healthcare systems, using RFID authentication along with access control technique.
Keywords: RFID | Privacy | Healthcare | Electronic Medical Record | Security
مقاله انگلیسی
6 An improved strategic information management plan for medical institutes
بهبود برنامه مدیریت اطلاعات استراتژیک برای مؤسسات درمان های پزشکی-2016
The driving force behind software development of the Electronic Medical Record (EMR) has been gradually changing. Heterogeneous software requirements have emerged, so how to correctly carry out development pro- ject has become a complex task. This paper adopts the knowledge engineering and management mechanism, i.e., CommonKADS, and software quality engineering to improve existing strategic information management (SIM) plan as a design methodology to help software implementation for medical institutes. We evaluate the adopting performance by a real case that examines the maturity level of the architecture alignment between the target so- lution in the proposed SIM plan and the built medical system.© 2015 Elsevier B.V. All rights reserved.
Keywords: Strategic information management plan | Electronic medical record | Software development | Knowledge engineering and management | Software quality engineering
مقاله انگلیسی
7 An assessment of data quality in a multi-site electronic medical record system in Haiti
ارزیابی کیفیت داده در سیستم مدارک پزشکی الکترونیکی چند سایتی در هائیتی-2016
Objectives: Strong data quality (DQ) is a precursor to strong data use. In resource limited settings, routine DQ assessment (DQA) within electronic medical record (EMR) systems can be resource-intensive using manual methods such as audit and chart review; automated queries offer an efficient alternative. This DQA focused on Haiti’s national EMR – iSanté – and included longitudinal data for over 100,000 persons living with HIV (PLHIV) enrolled in HIV care and treatment services at 95 health care facilities (HCF).
Methods: This mixed-methods evaluation used a qualitative Delphi process to identify DQ priorities among local stakeholders, followed by a quantitative DQA on these priority areas. The quantitative DQA examined 13 indicators of completeness, accuracy, and timeliness of retrospective data collected from 2005 to 2013. We described levels of DQ for each indicator over time, and examined the consistency of within-HCF performance and associations between DQ and HCF and EMR system characteristics.
Results: Over all iSanté data, age was incomplete in <1% of cases, while height, pregnancy status, TB status, and ART eligibility were more incomplete (approximately 20–40%). Suspicious data flags were present for <3% of cases of male sex, ART dispenses, CD4 values, and visit dates, but for 26% of cases of age. Discontinuation forms were available for about half of all patients without visits for 180 or more days, and >60% of encounter forms were entered late. For most indicators, DQ tended to improve over time. DQ was highly variable across HCF, and within HCFs DQ was variable across indicators. In adjusted analyses, HCF and system factors with generally favorable and statistically significant associations with DQ were University hospital category, private sector governance, presence of local iSante server, greater HCF experience with the EMR, greater maturity of the EMR itself, and having more system users but fewer new users. In qualitative feedback, local stakeholders emphasized lack of stable power supply as a key challenge to data quality and use of the iSanté EMR.
Conclusions: Variable performance on key DQ indicators across HCF suggests that excellent DQ is achievable in Haiti, but further effort is needed to systematize and routinize DQ approaches within HCFs. A dynamic, interactive “DQ dashboard” within iSanté could bring transparency and motivate improvement. While the results of the study are specific to Haiti’s iSanté data system, the study’s methods and thematic lessons learned holdgeneralized relevance for other large-scale EMR systems in resource-limited countries.
Keywords: Data quality assessment | Electronic medical record | Health information system | Haiti
مقاله انگلیسی
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