Where is Patient in EHR Project?

In this paper, we do a literature review on electronic health records (EHR) and patient involvement. It seems that patients are not included as much as one would presume. After our analysis of both literature and ethical nature, we suggest that research on why this is so and whether they should be included needs to be done.</p

A standard-driven communication protocol for disconnected clinics in rural areas

The importance of the Electronic Health Record (EHR), which stores all healthcare-related data belonging to a patient, has been recognized in recent years by governments, institutions, and industry. Initiatives like Integrating the Healthcare Enterprise (IHE) have been developed for the definition of standard methodologies for secure and interoperable EHR exchanges among clinics and hospitals. Using the requisites specified by these initiatives, many large-scale projects have been set up to enable healthcare professionals to handle patients' EHRs. Applications deployed in these settings are often considered safety-critical, thus ensuring such security properties as confidentiality, authentication, and authorization is crucial for their success. In this paper, we propose a communication protocol, based on the IHE specifications, for authenticating healthcare professionals and assuring patients' safety in settings where no network connection is available, such as in rural areas of some developing countries. We define a specific threat model, driven by the experience of use cases covered by international projects, and prove that an intruder cannot cause damages to the safety of patients and their data by performing any of the attacks falling within this threat model. To demonstrate the feasibility and effectiveness of our protocol, we have fully implemented it

Machine Learning Methods To Identify Hidden Phenotypes In The Electronic Health Record

The widespread adoption of Electronic Health Records (EHRs) means an unprecedented amount of patient treatment and outcome data is available to researchers. Research is a tertiary priority in the EHR, where the priorities are patient care and billing. Because of this, the data is not standardized or formatted in a manner easily adapted to machine learning approaches. Data may be missing for a large variety of reasons ranging from individual input styles to differences in clinical decision making, for example, which lab tests to issue. Few patients are annotated at a research quality, limiting sample size and presenting a moving gold standard. Patient progression over time is key to understanding many diseases but many machine learning algorithms require a snapshot, at a single time point, to create a usable vector form. In this dissertation, we develop new machine learning methods and computational workflows to extract hidden phenotypes from the Electronic Health Record (EHR). In Part 1, we use a semi-supervised deep learning approach to compensate for the low number of research quality labels present in the EHR. In Part 2, we examine and provide recommendations for characterizing and managing the large amount of missing data inherent to EHR data. In Part 3, we present an adversarial approach to generate synthetic data that closely resembles the original data while protecting subject privacy. We also introduce a workflow to enable reproducible research even when data cannot be shared. In Part 4, we introduce a novel strategy to first extract sequential data from the EHR and then demonstrate the ability to model these sequences with deep learning

Electronic Health Records: Cure-all or Chronic Condition?

Computer-based information systems feature in almost every aspect of our lives, and yet most of us receive handwritten prescriptions when we visit our doctors and rely on paper-based medical records in our healthcare. Although electronic health record (EHR) systems have long been promoted as a cost-effective and efficient alternative to this situation, clear-cut evidence of their success has not been forthcoming. An examination of some of the underlying problems that prevent EHR systems from delivering the benefits that their proponents tout identifies four broad objectives - reducing cost, reducing errors, improving coordination and improving adherence to standards - and shows that they are not always met. The three possible causes for this failure to deliver involve problems with the codification of knowledge, group and tacit knowledge, and coordination and communication. There is, however, reason to be optimistic that EHR systems can fulfil a healthy part, if not all, of their potential

Comparison of Open-Source Electronic Health Record Systems Based on Functional and User Performance Criteria

Objectives: Open-source Electronic Health Record (EHR) systems have gained importance. The main aim of our research is to guide organizational choice by comparing the features, functionality, and user-facing system performance of the five most popular open-source EHR systems. Methods: We performed qualitative content analysis with a directed approach on recently published literature (2012-2017) to develop an integrated set of criteria to compare the EHR systems. The functional criteria are an integration of the literature, meaningful use criteria, and the Institute of Medicine's functional requirements of EHR, whereas the user-facing system performance is based on the time required to perform basic tasks within the EHR system. Results: Based on the Alexa web ranking and Google Trends, the five most popular EHR systems at the time of our study were OSHERA VistA, GNU Health, the Open Medical Record System (OpenMRS), Open Electronic Medical Record (OpenEMR), and OpenEHR. We also found the trends in popularity of the EHR systems and the locations where they were more popular than others. OpenEMR met all the 32 functional criteria, OSHERA VistA met 28, OpenMRS met 12 fully and 11 partially, OpenEHR-based EHR met 10 fully and 3 partially, and GNU Health met the least with only 10 criteria fully and 2 partially. Conclusions: Based on our functional criteria, OpenEMR is the most promising EHR system, closely followed by VistA. With regards to user-facing system performance, OpenMRS has superior performance in comparison to OpenEMR

EHR requirements

Published requirements for the EHR are principally available via ISO 18308. They are statements defining the generic features necessary in any Electronic Health Record for it to be communicable and complete, retain integrity across systems, countries and time, and be a useful and effective ethico-legal record of care. Examples of requirements are provided in four themes: -EHR functional requirements; Ethical, legal, and security requirements; Clinical requirements; Technical requirements. The main logical building blocks of an EHR are described using the terminology of CEN TC251 ENV13606. Examples are given of the placement of attributes to satisfy contextual and other requirements at the level of specific building blocks. A worked example of the use of the building blocks is given for the request-report cycle for an imaging investigation

Semantic processing of EHR data for clinical research

There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data.Comment: Accepted for publication in Journal of Biomedical Informatics, 2015, preprint versio

The Promise of Health Information Technology: Ensuring that Florida's Children Benefit

Substantial policy interest in supporting the adoption of Health Information Technology (HIT) by the public and private sectors over the last 5 -- 7 years, was spurred in particular by the release of multiple Institute of Medicine reports documenting the widespread occurrence of medical errors and poor quality of care (Institute of Medicine, 1999 & 2001). However, efforts to focus on issues unique to children's health have been left out of many of initiatives. The purpose of this report is to identify strategies that can be taken by public and private entities to promote the use of HIT among providers who serve children in Florida

Marshfield Clinic: Health Information Technology Paves the Way for Population Health Management

Highlights Fund-defined attributes of an ideal care delivery system and best practices, including an internal electronic health record, primary care teams, physician quality metrics and mentors, and standardized care processes for chronic care management