Horizons and Perspectives eHealth

EHealth platform represents the combined use of IT technologies and electronic communications in the health field, using data (electronically transmitted, stored and accessed) with a clinical, educational and administrative purpose, both locally and distantly. eHealth has the significant capability to increase the movement in the direction of services centered towards citizens, improving the quality of the medical act, integrating the application of Medical Informatics (Medical IT), Telemedicine, Health Telematics, Telehealth, Biomedical engineering and Bioinformatics. Supporting the creation, development and recognition of a specific eHealth zone, the European Union policies develop through its programs FP6 and FP7, European-scale projects in the medical information technologies (the electronic health cards, online medical care, medical web portals, trans-European nets for medical information, biotechnology, generic instruments and medical technologies for health, ICT mobile systems for remote monitoring). The medical applications like electronic health cards ePrescription, eServices, medical eLearning, eSupervision, eAdministration are integral part of what is the new medical branch-eHealth, being in a continuous expansion due to the support from the global political, financial and medical organizations; the degree of implementation of the eHealth platform varying according to the development level of the communication infrastructure, allocated funds, intensive political priorities and governmental organizations opened to the new IT challenges.eHealth, telemedicine, telehealth, bioinformatics, telematics

De-identification of primary care electronic medical records free-text data in Ontario, Canada

<p>Abstract</p> <p>Background</p> <p>Electronic medical records (EMRs) represent a potentially rich source of health information for research but the free-text in EMRs often contains identifying information. While de-identification tools have been developed for free-text, none have been developed or tested for the full range of primary care EMR data</p> <p>Methods</p> <p>We used <it>deid </it>open source de-identification software and modified it for an Ontario context for use on primary care EMR data. We developed the modified program on a training set of 1000 free-text records from one group practice and then tested it on two validation sets from a random sample of 700 free-text EMR records from 17 different physicians from 7 different practices in 5 different cities and 500 free-text records from a group practice that was in a different city than the group practice that was used for the training set. We measured the sensitivity/recall, precision, specificity, accuracy and F-measure of the modified tool against manually tagged free-text records to remove patient and physician names, locations, addresses, medical record, health card and telephone numbers.</p> <p>Results</p> <p>We found that the modified training program performed with a sensitivity of 88.3%, specificity of 91.4%, precision of 91.3%, accuracy of 89.9% and F-measure of 0.90. The validations sets had sensitivities of 86.7% and 80.2%, specificities of 91.4% and 87.7%, precisions of 91.1% and 87.4%, accuracies of 89.0% and 83.8% and F-measures of 0.89 and 0.84 for the first and second validation sets respectively.</p> <p>Conclusion</p> <p>The <it>deid </it>program can be modified to reasonably accurately de-identify free-text primary care EMR records while preserving clinical content.</p

Standardization Needs for Effective Interoperability

The Electronic Health Record (EHR) has brought unique challenges in the effort to share information. How data is captured varies from institution to institution. In order for data to be well understood, data should have a definition that is consistent and comprehensively understood by all users of the data. Standardization of how data is captured is critical to allow the production and export of data needed to support quality assessment, decision support, exchange of data for patients with multiple health care providers and public health surveillance. Patient safety and quality improvement are dependent upon embedded clinical guidelines that promote standardized, evidence-based practices. Unless we can achieve standardization with terminology, technologies, apps and devices, the goals of EHR implementation won’t be realized

Can We Rely on Electronic Medical Record Systems to Reduce Medication Errors?

Expectations to Electronic Medical Record (EMR) systems in healthcare are high when it comes to reducing medication errors and increasing security in the medication process. Studies show that certain types of medication errors are eliminated when introducing EMRs; however, such systems also entail new types of errors. Based on a study in an orthopedic surgical ward in a medium-sized Danish hospital, we investigate what previous types of errors can be reduced by using the EMRs but also what new types of errors may appear. We zoom in on the process of medicine prescription and focus on what new types of errors appear in the interaction between the doctors and the technology. Identifying and understanding the nature of errors that emerge when doctors use EMRs may enable system developers and implementers to better manage implementation and maintenance of future EMR projects and accordingly set up appropriate strategies to prevent medication errors

Electronic Medical Records as a Research Tool: Evaluating Topiramate Use at a Headache Center.

Background.—Electronic medical records (EMRs) are used in large healthcare centers to increase efficiency and accuracy of documentation. These databases may be utilized for clinical research or to describe clinical practices such as medication usage. Methods.—We conducted a retrospective analysis of EMR data from a headache clinic to evaluate clinician prescription use and dosing patterns of topiramate. The study cohort comprised 4833 unique de-identified records, which were used to determine topiramate dose and persistence of treatment. Results.—Within the cohort, migraine was the most common headache diagnosis (n = 3753, 77.7%), followed by tension-type headache (n = 338, 7.0%) and cluster or trigeminal autonomic cephalalgias (n = 287, 5.9%). Physicians prescribed topiramate more often for subjects with migraine and idiopathic intracranial hypertension (P \u3c .0001) than for those with other conditions, and more often for subjects with coexisting conditions including obesity, bipolar disorder, and depression. The most common maintenance dose of topiramate was 100 mg/day; however, approximately 15% of subjects received either less than 100 mg/day or more than 200 mg/day. More than a third of subjects were prescribed topiramate for more than 1 year, and subjects with a diagnosis of migraine were prescribed topiramate for a longer period of time than those without migraine. Conclusions.—Findings from our study using EMR demonstrate that physicians use topiramate at many different doses and for many off-label indications. This analysis provided important insight into our patient populations and treatment patterns

Arizona Health Information Exchange

abstract: Arizona strives to be the national role model for the secure, interoperable health information exchange to facilitate safe, secure, high quality and cost effective health care. The purpose of the Health Information Exchange in Arizona is to improve the quality, safety and efficiency of wellness in the Arizona population by securely connecting patients and health care providers so that relevant and understandable information is available anytime, anywhere

A Core Reference Hierarchical Primitive Ontology for Electronic Medical Records Semantics Interoperability

Currently, electronic medical records (EMR) cannot be exchanged among hospitals, clinics, laboratories, pharmacies, and insurance providers or made available to patients outside of local networks. Hospital, laboratory, pharmacy, and insurance provider legacy databases can share medical data within a respective network and limited data with patients. The lack of interoperability has its roots in the historical development of electronic medical records. Two issues contribute to interoperability failure. The first is that legacy medical record databases and expert systems were designed with semantics that support only internal information exchange. The second is ontological commitment to the semantics of a particular knowledge representation language formalism. This research seeks to address these interoperability failures through demonstration of the capability of a core reference, hierarchical primitive ontological architecture with concept primitive attributes definitions to integrate and resolve non-interoperable semantics among and extend coverage across existing clinical, drug, and hospital ontologies and terminologies