Top PDF Combining population-based administrative health records and electronic medical records for disease surveillance

Combining population-based administrative health records and electronic medical records for disease surveillance

Combining population-based administrative health records and electronic medical records for disease surveillance

The computer simulation generated data from two sources using a model in which multiple disease markers are associated with the probability of disease presence/ absence [32]. Specifically, we used copulas to generate multiple binary disease markers [33] for each data source. Copulas are constructed by specifying the joint distribution of correlated random variables that follow a standardized uniform distribution. The disease markers were assumed to be error-free with complete infor- mation. True disease status for each member of the population was generated from a Bernoulli distribution via a logistic regression model. To obtain the specified preva- lence estimates, values of the regression coefficients and marker prevalence were selected based on previous epidemiological studies about hypertension [34, 35].
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Medical databases in studies of drug teratogenicity: methodological issues

Medical databases in studies of drug teratogenicity: methodological issues

Abstract: More than half of all pregnant women take prescription medications, raising con- cerns about fetal safety. Medical databases routinely collecting data from large populations are potentially valuable resources for cohort studies addressing teratogenicity of drugs. These include electronic medical records, administrative databases, population health registries, and teratogenicity information services. Medical databases allow estimation of prevalences of birth defects with enhanced precision, but systematic error remains a potentially serious problem. In this review, we first provide a brief description of types of North American and European medical databases suitable for studying teratogenicity of drugs and then discuss manifestation of system- atic errors in teratogenicity studies based on such databases. Selection bias stems primarily from the inability to ascertain all reproductive outcomes. Information bias (misclassification) may be caused by paucity of recorded clinical details or incomplete documentation of medication use. Confounding, particularly confounding by indication, can rarely be ruled out. Bias that either masks teratogenicity or creates false appearance thereof, may have adverse consequences for the health of the child and the mother. Biases should be quantified and their potential impact on the study results should be assessed. Both theory and software are available for such estimation. Provided that methodological problems are understood and effectively handled, computerized medical databases are a valuable source of data for studies of teratogenicity of drugs.
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Data discipline in electronic medical records

Data discipline in electronic medical records

Evaluating data within an EMR at the clinic or practice levels can be challenging, as many vendors provide a user-friendly interface but are unable to easily capture and evaluate aggregate data. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) enables a practice or clinic to improve data quality and manage the health of its patients at the population level. It extracts data from EMRs and aggregates them into a national database. Data within CPCSSN can be used to drive strategies to improve electronic record measurement fields that are missing or outdated, particularly risk factor fields such as type of smoker. This field is particularly important because cigarette smoking is recognized as one of the most important risk factors for many chronic diseases, including lung cancer, hypertension, Alzheimer disease, and chronic obstructive pulmonary disease. 5-9 High-quality data are essential to managing
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Addendum to Informatics for Health 2017 : advancing both science and practice

Addendum to Informatics for Health 2017 : advancing both science and practice

Discussion Identified features included: patient access to the medical record identity management, high levels of information security, secure messaging between patient and clinician, electronic consultations, robust audit trails, accessibility at the point of care device, independence resilience and availability to high standards of Service Level Agreement consent management and standard messaging (e.g. Health Level 7 (HL7), HL7 Fast Healthcare Interoperability Resources (FHIR)) integration with clinical systems and limited decision support. These requirements could be accommodated with some development by con- temporary PHR technologies. However, a central question emerged about how much an individual should steward of their own data. Participants all voiced the need for integrity of the record via the control of access, with editing rights being granted only to authorised clinicians. The ability to filter parts of the medical record, especially clinicians’ notes and episodic data such as test results in certain circumstances, was desired. Augmentation of the record by individuals was seen as acceptable.
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A Review On Leveraging Big Data Analytics In Health Care Sector For Enhanced Diagnosis And Competent Patient Health Care Monitoring System

A Review On Leveraging Big Data Analytics In Health Care Sector For Enhanced Diagnosis And Competent Patient Health Care Monitoring System

This is one of the most ubiquitous applications in healthcare sector. Every individual (like patients) have their digitalized record. This record includes the patient medical history, allergies and symptoms, laboratory test reports, etc[9]. The evidence-based tools are available to healthcare providers from both public and private sectors and they can use to make decision on patients care. Key features of EHR allows to create and manage health information’s in digitalized format by authorized providers and also information shared securely via secure information systems across many providers (e.g. doctors, laboratories, pharmacy and etc).
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Electronic Health Records (EHR)

Electronic Health Records (EHR)

New technologies also are coming to the forefront and will enable healthcare providers to improve practice work flows and offer the opportunity to gain more efficiency. Bar coding is one technology that is used widely in healthcare today, mostly in larger facilities. The bar coding opportunities and uses will continue to grow. Some future applications include bar codes on patient wrist bands, medications, specimen collections, blood administration, and supplies (Zebra Technologies, 2010). Bar coding will help with patient tracking, will accurately identify the patient with clinical tests, and ensure safety in drug administration. Another technology that will someday come into use in healthcare is RFID (radio-frequency identification frequency identification) (Zebra Technologies, 2010). RFID technology will be used to track supplies and medical equipment within the healthcare setting. It can also be used to track patients as they maneuver through the healthcare setting, seeing physicians and getting testing completed (Zebra Technologies, 2010). RFID technology can also be used in surgical applications to ensure right patient, right procedure, right location, and at the right time (Zebra Technologies, 2010).
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Implementing electronic health records

Implementing electronic health records

We thank the study participants for their contributions. The Thames Valley Family Practice Research Unit is supported by the Ontario Ministry of Health and Long-Term Care. The views expressed in this paper are those of the Thames Valley Family Practice Research Unit and do not necessarily reflect the views of the Ministry of Health and Long-Term Care. Dr Terry was supported by a fellowship in 2007 and 2008 from the Canadian Institutes of Health Research strategic training program Transdisciplinary Understanding and Training on Research—Primary Health Care. Dr Harris holds the CDA Chair in Diabetes Management and the Ian McWhinney Chair of Family Medicine Studies. Dr Thind is Canada Research Chair in Health Services Research. Dr Stewart is funded by the Dr Brian W. Gilbert Canada Research Chair.
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It Is Time! Accelerating the Use of Child Health Information Systems to Improve Child Health

It Is Time! Accelerating the Use of Child Health Information Systems to Improve Child Health

W E HAVE ALL heard it many times, that the information revolution is here. Unfortunately, we also know that the health care industry lags behind all others in its embrace of the vast potential of information systems to improve productivity, quality, and efficiency. This is especially the case for children’s health. It is time, and in many ways past time, to exploit more fully the potential for health information systems to improve child and adolescent health and health care. Articles in this issue show clearly the enormous impact that the use of health information technology (HIT) can have on the quality of health care for children. However, they also point out the challenges that need to be overcome to realize fully the potential of HIT to improve the quality and efficiency of health care more broadly.
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Securing Patient Privacy in E-Health Cloud Using Homomorphic Encryption and Access Control

Securing Patient Privacy in E-Health Cloud Using Homomorphic Encryption and Access Control

need basis in the best possible manner under clouds. The Personal Health Record (PHR) sharing among a wide range of personnel has been identified as an important application in the field of cloud computing. The data outsourced to service providers are largely consumed by wide variety of individuals. Hence the need of security and privacy in personal health records is an important issue. This brings the idea of encrypting the data before outsourcing to the servers. To ensure best policy, it is the patient herself who should encrypt the data and determines which users shall have access in what manner [1].
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Implementation of electronic medical records

Implementation of electronic medical records

Participants found that since they had learned the system, some aspects of the EMR made them more effi- cient. Prescription refills and consultation letters in par- ticular were much quicker. This occurred after an initial decrease in efficiency, once some data entry was com- pleted. Physicians thought that their administrative per- sonnel were more efficient.

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Improving the Use of Electronic Medical Records in Primary Health Care: A Systematic Review and Meta-Analysis

Improving the Use of Electronic Medical Records in Primary Health Care: A Systematic Review and Meta-Analysis

EMRs, as a new software system added into primary health care, require some basic computer skills to operate. Not all primary health care providers or intended users possess those required skills. 51 Therefore, one of the major barriers to use is the skill needed to use basic electronic functions. 50 In addition to basic computer skills, the knowledge of available EMR functions was also found to be lacking in intended users. 20 An important component to increasing EMR use is a good understanding of its features and advanced functions. 51,52 EMRs can assist users in performing the required procedures to allow for the smooth flow of information through primary health care and between health care sectors. 31,38 To allow for the proper use of those features, basic computer skills need to be coupled with knowledge about the availability of those features and guides on how to use them. Concerns have also been raised about the time required to acquire those new skills for those health care providers who are not technologically inclined. 51 Other barriers to EMR use in primary health care include time interruptions and time delays in everyday processes due to the use of EMRs. 52 Therefore, technical barriers to EMR use include: lack of computer skills, time to acquire those skills and, added time to incorporate EMRs into daily functions of primary health care.
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DATA MINING TECHNIQUES IN HEALTH CARE APPLICATION:  A REVIEW OF SURVEY

DATA MINING TECHNIQUES IN HEALTH CARE APPLICATION: A REVIEW OF SURVEY

Information technologies in healthcare have enabled the creation of electronic patient records obtained from monitoring of the patient visits (Dursun Delen et al. 2009). This information includes patient demographics, records on the treatment progress, details of examination, prescribed drugs, previous medical history, lab results, etc. Information system simplifies and automates the workflow of health care institution (Sumana Sharma et al. 2009). Privacy of documentation and ethical use of information about patients is a major obstacle for data mining in medicine. According to (Motilal C. Tayade et al. 2013) data mining to be more exact, it is necessary to make a considerable amount of documentation. Health records are private information, yet the use of these private documents may help in treating deadly diseases (V. Krishnaiah et al. 2013). According to (Gloria Phillips-Wren et al. 2008) before data mining process can begin, healthcare organizations must formulate a clear policy concerning privacy and security of patient records. This policy must be fully implemented in order to ensure patient privacy. According to (Ishtake S. H et al. 2012) health institutions are able to use data mining applications for a variety of areas, such as doctors who use patterns by measuring clinical indicators, quality indicators, customer satisfaction and economic indicators, performance of physicians from multiple perspectives to optimize use of resources, cost efficiency and decision making based on evidence, identifying high-risk patients and intervene proactively, optimize health care, etc.
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A Portrait of Electronic Medical Record Use in Primary Care Across Canada

A Portrait of Electronic Medical Record Use in Primary Care Across Canada

Many countries have established national initiatives to implement information technologies to improve patient safety and the quality and efficiency of health care services, and Canada has been a part of this global trend (Schoen et al. 2012). Many Canadian provinces have implemented health information technologies, such as electronic medical records (EMRs) in primary care in Alberta, population drug information in British Columbia and regional inter-operable health networks in Sault Ste. Marie, Ontario (Rozenblum et al. 2011). The use of electronic health records (EHRs) in clinical settings in Canada is considered to be pivotal to an integrated health care delivery system and assumed to be central to achieving true patient-centred care (The College of Family Physicians of Canada 2011). EHRs, which have the potential to bring together patient health information from multiple health care settings, such as hospitals, community-based primary care clinics, and community pharma- cies, have reportedly contributed to improved efficiency, safety and quality of care (Delpierre et al. 2004; Joos et al. 2006). Whether EHR or EMR can save costs remains uncertain however (Hillestad et al. 2005; Wang et al. 2003). Even though they could benefit, a relatively large number of physicians and clinics in Canada are still hesitant to adopt EMR or EHR within their practices. Indeed, according to a 2012 international survey, 44% of Canadian primary care practices overall do not use EMR (Schoen et al. 2012); the adoption rate varies widely by jurisdiction.
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Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients: the ONCO-CODES study protocol for a randomized controlled trial

Implementing an evidence-based computerized decision support system linked to electronic health records to improve care for cancer patients: the ONCO-CODES study protocol for a randomized controlled trial

The implementation of the intervention is a key aspect of this RCT: the results will be valid if physicians will use MediDSS in their clinical activities. Healthcare ser- vice studies on CDSSs, however, suggested that the mere provision of such technology does not guarantee its up- take. In fact, even if a CDSS is readily available within a hospital, there is a strong tendency to ignore its recom- mendations, not trusting the majority of its alerts [38]. Our RCT is informed by qualitative interviews aimed to detect how the CDSS fits in into clinical practice by diverse health professionals involved in oncology patient care. The interviews are a part of a larger cross-sectional study, which involves three Italian hospitals [39]. The interviews will explore barriers and facilitators that may hinder the use of a CDSS in specialty settings, including technical (e.g., poor usability or knowledge of system), individual (e.g., negative perception of CDSS or EBM, lack of motivation), group or organizational (e.g., struc- tural or administrative constraints), and cultural factors (e.g., adverse social norms).
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Leveraging Medical Literature for Section Prediction in Electronic Health Records

Leveraging Medical Literature for Section Prediction in Electronic Health Records

when a limited amount of in-domain training data is available. We present a new dataset for EHR section prediction from Medical Literature, of which the Wikipedia part is available to the pub- lic for research purposes. We show that com- bining a very small amount of in-domain EHR data with a large amount of automatically la- beled, out-of-domain, out-of-genre Medical Lit- erature data can perform as well as using a large amount of in-domain EHR data at the section and sentence level. We also show that combin- ing out-of-domain, out-of-genre Medical Litera- ture data with out-of-domain EHRs can provide significant improvement over using just out-of- domain EHRs at the section and sentence level, depending on training data size. These results in- dicate that even though the data in Medical Lit- erature is very different in style, the content can bridge between the domain-specific vocabularies of different EHR systems. We show that our ap- proach can be used to achieve good results on new unseen EHR datasets with minimal or even no training data. In the future we would also like to explore using both i2b2 and ClvC together to see if a multi-task learning approach would provide ad- ditional improvements. In addition to BERT, we also briefly explored BioBERT (Lee et al., 2019), a BERT model pre-trained on a medical corpus. In our initial experiments BioBERT performed worse than BERT, but we would like to explore this fur- ther.
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Implementation of electronic medical records

Implementation of electronic medical records

We followed 2 cohorts of physicians: a group of 18 phy- sicians implementing EMRs and a group of 9 physicians using paper records (the non-EMR cohort). Physicians in both cohorts were community-based, were affiliated with a local hospital, and were located in the Toronto area. All were members of the same local after-hours clinic. Physicians were signed out to the clinic and took turns providing walk-in care at a single location on evenings and weekends. No preventive services were offered at the clinic and no clinic data were included in the study.
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Bidirectional RNN for Medical Event Detection in Electronic Health Records

Bidirectional RNN for Medical Event Detection in Electronic Health Records

surrounding but not quite immediate neighborhood. Therefore, the feature vectors have to be explicitly modeled to include the surrounding contextual in- formation. Traditionally, bag of words representa- tion of surrounding context has shown reasonably good performance. However, the information con- tained in the bag of words vector is very sensitive to context window size. If the context window is too short, it will not include all the information. On the other hand if the context window is too large, it will compress the vital information with other irrel- evant words. Usually a way to tackle this problem is to try different context window sizes and use the one that gives the highest validation performance. However, this method cannot be easily applied to our task, because different medical events like med- ication, diagnosis or adverse drug reaction require different context window sizes. For example, while a medication can be determined by a context of two or three words containing the drug name, an adverse drug reaction would require the context of the entire sentence. As an example, this is a sentence from one of the EHRs, “The follow-up needle biopsy results were consistent with bronchiolitis obliterans, which was likely due to the Bleomycin component of his ABVD chemo”. In this sentence, the true labels are Adverse Drug Event(ADE) for “bronchiolitis oblit- erans” and Drugname for “ABVD chemo”. However the ADE , “bronchiolitis obliterans” could be miss- labeled as just another disease or symptom, if the entire sentence is not taken into context.
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Adopting electronic medical records

Adopting electronic medical records

Practice reporting. Practice reporting is the process of internally reviewing one’s own practice (ie, across patient populations) to better understand the nature and needs of the practice and then implementing qual- ity improvement activities. Electronic medical records can be used to develop patient recall lists, report on practice-performance metrics, monitor chronic diseases, and ensure adherence to guidelines. Two common rea- sons were found for not using EMR practice-reporting tools: lack of awareness and lack of time. One excep- tion was in a clinic involved in the Physician Integrated Network quality improvement program. 18 The partic-
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Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network

Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network

Our study design at the Group Health Research Insti- tute (GH) was different from most other eMERGE sites. Most sites’ biobanks, like ours, lacked CLIA compli- ant samples and/or consent to return genetic results and needed to resample and/or consent participants. In our case, rather than redrawing all participants in a CLIA laboratory prior to running the PRGNSeq, we found it more efficient to sequence 900 existing non- CLIA samples from ∼ 6300 eligible biobanked participants at GH, and then recollect 450 participants of interest. As such, our goal was to prioritize our 900 sequenced participants based on potential impact of actionable results to help make choices around re-sampling and re-consenting. Here we describe the algorithm we devel- oped to select participants with the greatest poten- tial for actionable variants (the “selection algorithm,”) and the algorithm we developed to rank variants with highest impact (the “ranking algorithm”). The selection algorithm was designed to enrich for participants of non- European ancestry with conditions likely to be due to vari- ants in the pharmacogenetic (PGx) genes that the ranking algorithm identified as most likely to be clinically action- able. The system we developed to deploy these algorithms will serve as a foundation for identification of potentially actionable variants and EHR integration. These data will inform pathogenicity of specific variants and practices for EHR integration of genomic data.
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Electronic Health Records, Electronic Prescribing and Medication Errors: A Systematic Review of Literature, 2000-2014

Electronic Health Records, Electronic Prescribing and Medication Errors: A Systematic Review of Literature, 2000-2014

In a systematic review of CPOE systems, researchers observed mixed results, including reduction in MEs but increases in the rate of duplicate orders and failures to discontinue drugs, often attributed to inappropriate selection from a dropdown list or to an inability to view all active medication orders concurrently [59]. The investigators recommended that future studies include larger samples and multiple sites, have controlled study designs and standardized error and severity reporting, and discuss the role of CDSSs in minimizing severe prescribing errors. In qualitative research employing the focus group method involving 70 participants and eight focus groups and a semi-structured questionnaire, Devine and colleagues [63] explored prescriber (n = 17) and staff (n = 53) perceptions of a CPOE system and identified ten themes – including prescribing efficiencies, safer care, time efficiencies, enhanced communication with patients and pharmacists, and positive attitudes – that facilitated adoption. Their findings support the results of other researchers on EHR and EP [61]. Other researchers have reported that transitioning from older EHR systems to newer commercial EHR systems with CDSSs and EP is extremely difficult, too complex, and reduces physician efficiency [11]. In yet another study, both provider and patient satisfaction with EP was very high, with a reduction in total after- hours calls, despite a paradoxical increase in medication-related calls [65]. The researchers suggested further study is warranted to document other evidence-based outcomes of EP.
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