scholarly journals Electronic health record–based disease surveillance systems: A systematic literature review on challenges and solutions

2020 ◽  
Vol 27 (12) ◽  
pp. 1977-1986
Author(s):  
Ali Aliabadi ◽  
Abbas Sheikhtaheri ◽  
Hossein Ansari
Keyword(s):  
Data Quality ◽  
Disease Surveillance ◽  
Data Privacy ◽  
Data Retrieval ◽  
Machine Learning Algorithms ◽  
Unstructured Data ◽  
Cochrane Library ◽  
Surveillance Systems ◽  
Electronic Health ◽  
Implementation Of Ehr

Abstract Objective Disease surveillance systems are expanding using electronic health records (EHRs). However, there are many challenges in this regard. In the present study, the solutions and challenges of implementing EHR-based disease surveillance systems (EHR-DS) have been reviewed. Materials and Methods We searched the related keywords in ProQuest, PubMed, Web of Science, Cochrane Library, Embase, and Scopus. Then, we assessed and selected articles using the inclusion and exclusion criteria and, finally, classified the identified solutions and challenges. Results Finally, 50 studies were included, and 52 unique solutions and 47 challenges were organized into 6 main themes (policy and regulatory, technical, management, standardization, financial, and data quality). The results indicate that due to the multifaceted nature of the challenges, the implementation of EHR-DS is not low cost and easy to implement and requires a variety of interventions. On the one hand, the most common challenges include the need to invest significant time and resources; the poor data quality in EHRs; difficulty in analyzing, cleaning, and accessing unstructured data; data privacy and security; and the lack of interoperability standards. On the other hand, the most common solutions are the use of natural language processing and machine learning algorithms for unstructured data; the use of appropriate technical solutions for data retrieval, extraction, identification, and visualization; the collaboration of health and clinical departments to access data; standardizing EHR content for public health; and using a unique health identifier for individuals. Conclusions EHR systems have an important role in modernizing disease surveillance systems. However, there are many problems and challenges facing the development and implementation of EHR-DS that need to be appropriately addressed.

User Training Evaluation of Surveillance Outbreak Response Management & Analysis System (SORMAS) among Disease Surveillance Notification Officers (DSNOs) in Nigeria 2018 (Preprint)

2019 ◽  
Author(s):  
Daniel Tom-Aba ◽  
Bernard Chawo Silenou ◽  
Chinedu Chukwujekwu Arinze ◽  
Ferdinand Oyiri ◽  
Olawunmi Adeoye ◽  
...  
Keyword(s):  
Data Quality ◽  
Disease Surveillance ◽  
Sub Saharan Africa ◽  
Contact Tracing ◽  
P Value ◽  
Surveillance Systems ◽  
Outbreak Response ◽  
Before And After ◽  
The Mean ◽  
Analysis System

BACKGROUND Electronic health (eHealth) systems increase the efficiency of disease surveillance by reducing delays in the availability of data, usability, improve processing of data and detect outbreaks. Mobile health (mHealth) technology plays a strong role in containing any disease outbreak and eHealth interventions are being used in many of the countries in sub-Saharan Africa to track global progress towards health related outcomes and to help guide clinical decision making and management. The Center for Disease Control and Prevention (CDC) guideline recommends that in evaluating surveillance systems, effectiveness and efficiency of surveillance systems are to be improved by continuous monitoring and evaluation and this cannot be obtained without effective training of health workers. OBJECTIVE The basis of this study is evaluate the knowledge gained before and after Surveillance Outbreak Response Management and Analysis System (SORMAS) training by measuring the following attributes: usefulness, acceptability, data quality and work load time of SORMAS when used by public health officers for their daily tasks. METHODS Our study is a pre/post observational study design which accesses two types of evaluation (pre-evaluation and post-evaluation questionnaires) administered during the very first SORMAS training of the district level officers. We asked the participants to select correct responses out of a 9-multiple choice option what they thought were the functionalities of SORMAS before and after the training. We provided 6/9 correct responses (67%) and 3 incorrect responses (33%). Users were scored based on the correct responses and a proportion score assigned to each user for the pre-training score and the post training score. The outcome of the measurement which was the post training score (percentage) was used to generate a pass/fail score within a 75% dichotomized threshold per user. RESULTS We rejected the null hypothesis that there is no difference between the scores obtained before and after the training by the SORMAS users. The mean score of those who passed was 83% after the training compared to the mean score of 68% before the training. For contact tracing experience, effect was 0.681 (p-value=0.03, OR=1.98, 95%CI [0.069, 1.293]). For participants who stated that they would need same time per case record, effect was 1.771 (p-value=0.001, OR=5.88, 95%CI [0.425, 3.118]). For participants who stated that data quality will improve, the effect was 2.963 (p-value=<0.001, OR=19.34, 95%CI [1.301, 4.624]). For participants who stated that they would recommend SORMAS to their colleagues, the effect was 0.332 (p-value=0,692, OR=1.39, 95%CI [-1.314, 1.979]). CONCLUSIONS Contact tracing experience, data quality, workload and acceptability predictor variables were observed to have a direct effect on the outcome (pass score). The model generated fitted the data and we are 82% accurate that there was indeed knowledge gain comparing before and after the training

scholarly journals Automating Clinical Score Calculation within the Electronic Health Record

2017 ◽  
Vol 08 (02) ◽  
pp. 369-380 ◽  
Author(s):  
Christopher Aakre ◽  
Mikhail Dziadzko ◽  
Mark Keegan ◽  
Vitaly Herasevich
Keyword(s):  
Electronic Health Record ◽  
Clinical Examination ◽  
Data Retrieval ◽  
Clinical Score ◽  
Structured Data ◽  
Unstructured Data ◽  
Health Record ◽  
Clinical Judgments ◽  
Clinical Scores ◽  
Electronic Health

Summary Objectives: Evidence-based clinical scores are used frequently in clinical practice, but data collection and data entry can be time consuming and hinder their use. We investigated the programmability of 168 common clinical calculators for automation within electronic health records. Methods: We manually reviewed and categorized variables from 168 clinical calculators as being extractable from structured data, unstructured data, or both. Advanced data retrieval methods from unstructured data sources were tabulated for diagnoses, non-laboratory test results, clinical history, and examination findings. Results: We identified 534 unique variables, of which 203/534 (37.8%) were extractable from structured data and 269/534 (50.4.7%) were potentially extractable using advanced techniques. Nearly half (265/534, 49.6%) of all variables were not retrievable. Only 26/168 (15.5%) of scores were completely programmable using only structured data and 43/168 (25.6%) could potentially be programmable using widely available advanced information retrieval techniques. Scores relying on clinical examination findings or clinical judgments were most often not completely programmable. Conclusion: Complete automation is not possible for most clinical scores because of the high prevalence of clinical examination findings or clinical judgments – partial automation is the most that can be achieved. The effect of fully or partially automated score calculation on clinical efficiency and clinical guideline adherence requires further study. Citation: Aakre C, Dziadzko M, Keegan MT, Herasevich V. Automating clinical score calculation within the electronic health record: A feasibility assessment. Appl Clin Inform 2017; 8: 369–380 https://doi.org/10.4338/ACI-2016-09-RA-0149

Automated chronic disease surveillance and visualization using electronic health record data

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Michael Klompas ◽  
Chaim Kirby ◽  
Jason McVetta ◽  
Paul Oppedisano ◽  
John Brownstein ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Electronic Health Record ◽  
Disease Surveillance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

scholarly journals Clinical Research Informatics Contributions from 2015

2016 ◽  
Vol 25 (01) ◽  
pp. 219-223
Author(s):  
R. Choquet ◽  
C. Daniel ◽  
Keyword(s):  
Clinical Research ◽  
Data Privacy ◽  
Design Stage ◽  
Editorial Team ◽  
Legal Requirements ◽  
Double Blind ◽  
Blind Review ◽  
Healthcare Data ◽  
Clinical Research Informatics ◽  
Electronic Health

Summary Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2015. Method: A bibliographic search using a combination of MeSH and free terms search over PubMed on Clinical Research Informatics (CRI) was performed followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. Results: Among the 579 returned papers published in the past year in the various areas of Clinical Research Informatics (CRI) - i) methods supporting clinical research, ii) data sharing and interoperability, iii) re-use of healthcare data for research, iv) patient recruitment and engagement, v) data privacy, security and regulatory issues and vi) policy and perspectives - the full review process selected four best papers. The first selected paper evaluates the capability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) to support the representation of case report forms (in both the design stage and with patient level data) during a complete clinical study lifecycle. The second selected paper describes a prototype for secondary use of electronic health records data captured in non-standardized text. The third selected paper presents a privacy preserving electronic health record linkage tool and the last selected paper describes how big data use in US relies on access to health information governed by varying and often misunderstood legal requirements and ethical considerations. Conclusions: A major trend in the 2015 publications is the analysis of observational, “nonexperimental” information and the potential biases and confounding factors hidden in the data that will have to be carefully taken into account to validate new predictive models. In addiction, researchers have to understand complicated and sometimes contradictory legal requirements and to consider ethical obligations in order to balance privacy and promoting discovery.

scholarly journals A Secure Ciphertext Retrieval Scheme against Insider KGAs for Mobile Devices in Cloud Storage

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Run Xie ◽  
Chanlian He ◽  
Dongqing Xie ◽  
Chongzhi Gao ◽  
Xiaojun Zhang
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Privacy ◽  
Security Analysis ◽  
Data Retrieval ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Security Issues ◽  
Efficiency Comparison ◽  
Cloud Servers

With the advent of cloud computing, data privacy has become one of critical security issues and attracted much attention as more and more mobile devices are relying on the services in cloud. To protect data privacy, users usually encrypt their sensitive data before uploading to cloud servers, which renders the data utilization to be difficult. The ciphertext retrieval is able to realize utilization over encrypted data and searchable public key encryption is an effective way in the construction of encrypted data retrieval. However, the previous related works have not paid much attention to the design of ciphertext retrieval schemes that are secure against inside keyword-guessing attacks (KGAs). In this paper, we first construct a new architecture to resist inside KGAs. Moreover we present an efficient ciphertext retrieval instance with a designated tester (dCRKS) based on the architecture. This instance is secure under the inside KGAs. Finally, security analysis and efficiency comparison show that the proposal is effective for the retrieval of encrypted data in cloud computing.

scholarly journals Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives

2014 ◽  
Vol 23 (01) ◽  
pp. 21-26 ◽  
Author(s):  
T. Miron-Shatz ◽  
A. Y. S. Lau ◽  
C. Paton ◽  
M. M. Hansen
Keyword(s):  
Health Care ◽  
Big Data ◽  
Health Care Providers ◽  
Data Privacy ◽  
Recent Literature ◽  
Unstructured Data ◽  
Small Scale ◽  
Small Data ◽  
Quantified Self ◽  
Care Providers

Summary Objectives: As technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges. Methods: A literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale. Results: Scientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to “small data” would also be useful.

scholarly journals Validity of acute cardiovascular outcome diagnoses in European electronic health records: a systematic review protocol

BMJ Open ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. e031373 ◽  
Author(s):  
Jennifer Anne Davidson ◽  
Amitava Banerjee ◽  
Rutendo Muzambi ◽  
Liam Smeeth ◽  
Charlotte Warren-Gash
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Predictive Value ◽  
Grey Literature ◽  
Cochrane Library ◽  
Free Text ◽  
Health Records ◽  
Coronary Syndrome ◽  
Validation Measure ◽  
Electronic Health

IntroductionCardiovascular diseases (CVDs) are among the leading causes of death globally. Electronic health records (EHRs) provide a rich data source for research on CVD risk factors, treatments and outcomes. Researchers must be confident in the validity of diagnoses in EHRs, particularly when diagnosis definitions and use of EHRs change over time. Our systematic review provides an up-to-date appraisal of the validity of stroke, acute coronary syndrome (ACS) and heart failure (HF) diagnoses in European primary and secondary care EHRs.Methods and analysisWe will systematically review the published and grey literature to identify studies validating diagnoses of stroke, ACS and HF in European EHRs. MEDLINE, EMBASE, SCOPUS, Web of Science, Cochrane Library, OpenGrey and EThOS will be searched from the dates of inception to April 2019. A prespecified search strategy of subject headings and free-text terms in the title and abstract will be used. Two reviewers will independently screen titles and abstracts to identify eligible studies, followed by full-text review. We require studies to compare clinical codes with a suitable reference standard. Additionally, at least one validation measure (sensitivity, specificity, positive predictive value or negative predictive value) or raw data, for the calculation of a validation measure, is necessary. We will then extract data from the eligible studies using standardised tables and assess risk of bias in individual studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data will be synthesised into a narrative format and heterogeneity assessed. Meta-analysis will be considered when a sufficient number of homogeneous studies are available. The overall quality of evidence will be assessed using the Grading of Recommendations, Assessment, Development and Evaluation tool.Ethics and disseminationThis is a systematic review, so it does not require ethical approval. Our results will be submitted for peer-review publication.PROSPERO registration numberCRD42019123898

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