Can the Use of Bayesian Analysis Methods Correct for Incompleteness in Electronic Health Records Diagnosis Data? Development of a Novel Method Using Simulated and Real-Life Clinical Data

Summary Background: Numerous projects, initiatives, and programs are dedicated to the development of Electronic Health Records (EHR) worldwide. Increasingly more of these plans have recently been brought from a scientific environment to real life applications. In this context, quality is a crucial factor with regard to the acceptance and utility of Electronic Health Records. However, the dissemination of the existing quality approaches is often rather limited. Objectives: The present paper aims at the description and comparison of the current major quality certification approaches to EHRs. Methods: A literature analysis was carried out in order to identify the relevant publications with regard to EHR quality certification. PubMed, ACM Digital Library, IEEExplore, CiteSeer, and Google (Scholar) were used to collect relevant sources. The documents that were obtained were analyzed using techniques of qualitative content analysis. Results: The analysis discusses and compares the quality approaches of CCHIT, EuroRec, IHE, openEHR, and EN13606. These approaches differ with regard to their focus, support of service-oriented EHRs, process of (re-)certification and testing, number of systems certified and tested, supporting organizations, and regional relevance. Discussion: The analyzed approaches show differences with regard to their structure and processes. System vendors can exploit these approaches in order to improve and certify their information systems. Health care organizations can use these approaches to support selection processes or to assess the quality of their own information systems. Citation: Hoerbst A, Ammenwerth E. Quality and certification of electronic health records – An overview of current approaches from the US and Europe. Appl Clin Inf 2010; 1: 149–164 http://dx.doi.org/10.4338/ACI-2010-02-R-0009

Download Full-text

Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-018-0586-x ◽

2018 ◽

Vol 18 (1) ◽

Cited By ~ 8

Author(s):

Emmanuelle Sylvestre ◽

Guillaume Bouzillé ◽

Emmanuel Chazard ◽

Cécil His-Mahier ◽

Christine Riou ◽

...

Keyword(s):

Electronic Health Records ◽

Data Warehouse ◽

Clinical Data ◽

Health Records ◽

Clinical Data Warehouse ◽

Electronic Health ◽

Combining Information

Download Full-text

Facilitating the ethical use of health data for the benefit of society: electronic health records, consent and the duty of easy rescue

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2016.0130 ◽

2016 ◽

Vol 374 (2083) ◽

pp. 20160130 ◽

Cited By ~ 29

Author(s):

Sebastian Porsdam Mann ◽

Julian Savulescu ◽

Barbara J. Sahakian

Keyword(s):

Informed Consent ◽

Electronic Health Records ◽

Data Science ◽

Real Life ◽

Health Data ◽

Sensitive Information ◽

Minimal Risk ◽

Sensitive Data ◽

Health Records ◽

Electronic Health

Advances in data science allow for sophisticated analysis of increasingly large datasets. In the medical context, large volumes of data collected for healthcare purposes are contained in electronic health records (EHRs). The real-life character and sheer amount of data contained in them make EHRs an attractive resource for public health and biomedical research. However, medical records contain sensitive information that could be misused by third parties. Medical confidentiality and respect for patients' privacy and autonomy protect patient data, barring access to health records unless consent is given by the data subject. This creates a situation in which much of the beneficial records-based research is prevented from being used or is seriously undermined, because the refusal of consent by some patients introduces a systematic deviation, known as selection bias, from a representative sample of the general population, thus distorting research findings. Although research exemptions for the requirement of informed consent exist, they are rarely used in practice due to concerns over liability and a general culture of caution. In this paper, we argue that the problem of research access to sensitive data can be understood as a tension between the medical duties of confidentiality and beneficence. We attempt to show that the requirement of informed consent is not appropriate for all kinds of records-based research by distinguishing studies involving minimal risk from those that feature moderate or greater risks. We argue that the duty of easy rescue—the principle that persons should benefit others when this can be done at no or minimal risk to themselves—grounds the removal of consent requirements for minimally risky records-based research. Drawing on this discussion, we propose a risk-adapted framework for the facilitation of ethical uses of health data for the benefit of society. This article is part of the themed issue ‘The ethical impact of data science’.

Download Full-text

A Proposed Electronic Health Record Content Structure Based on Clinical Organizations Survey

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v15i13.5283 ◽

2014 ◽

Vol 15 (13) ◽

pp. 5233-5246 ◽

Cited By ~ 1

Author(s):

Dr. Ayman E. Khedr ◽

Fahad Kamal Alsheref

Keyword(s):

Data Structure ◽

Electronic Health Records ◽

Electronic Health Record ◽

Clinical Data ◽

Data Exchange ◽

Medical Information System ◽

Health Record ◽

Medical Organization ◽

Health Records ◽

Electronic Health

Computer systems and communication technologies made a strong and influential presence in the different fields of medicine. The cornerstone of a functional medical information system is the Electronic Health Records (EHR) management system. Several electronic health records systems were implemented in different states with different clinical data structures that prevent data exchange between systems even in the same state. This leads to the important barrier in implementing EHR system which is the lack of standards of EHR clinical data structure. In this paper we made a survey on several in international and Egyptian medical organization for implementing electronic health record systems for finding the best electronic health record clinical data structure that contains all patientâ€™s medical data. We proposed an electronic health record system with a standard clinical data structure based on the international and Egyptian medical organization survey and with avoiding the limitations in the other electronic health record that exists in the survey.

Download Full-text

FIBER: enabling flexible retrieval of electronic health records data for clinical predictive modeling

JAMIA Open ◽

10.1093/jamiaopen/ooab048 ◽

2021 ◽

Vol 4 (3) ◽

Author(s):

Suparno Datta ◽

Jan Philipp Sachs ◽

Harry FreitasDa Cruz ◽

Tom Martensen ◽

Philipp Bode ◽

...

Keyword(s):

Machine Learning ◽

Electronic Health Records ◽

Clinical Data ◽

Heart Surgery ◽

Data Warehouses ◽

Learning Models ◽

Health Records ◽

Star Schema ◽

Electronic Health ◽

Machine Learning Models

Abstract Objectives The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames. Materials and Methods FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER’s capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models. Results Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case. Conclusion FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process.

Download Full-text

Conceptual Design, Implementation, and Evaluation of Generic and Standard-Compliant Data Transfer into Electronic Health Records

Applied Clinical Informatics ◽

10.1055/s-0040-1710023 ◽

2020 ◽

Vol 11 (03) ◽

pp. 374-386 ◽

Cited By ~ 5

Author(s):

Rogério Blitz ◽

Martin Dugas

Keyword(s):

Clinical Practice ◽

Electronic Health Records ◽

Conceptual Design ◽

Clinical Data ◽

Data Transfer ◽

Care Hospital ◽

Single Source ◽

Everyday Clinical Practice ◽

Health Records ◽

Electronic Health

Abstract Objectives The objective of this study is the conceptual design, implementation and evaluation of a system for generic, standard-compliant data transfer into electronic health records (EHRs). This includes patient data from clinical research and medical care that has been semantically annotated and enhanced with metadata. The implementation is based on the single-source approach. Technical and clinical feasibilities, as well as cost-benefit efficiency, were investigated in everyday clinical practice. Methods Münster University Hospital is a tertiary care hospital with 1,457 beds and 10,823 staff who treated 548,110 patients in 2018. Single-source metadata architecture transformation (SMA:T) was implemented as an extension to the EHR system. This architecture uses Model Driven Software Development (MDSD) to generate documentation forms according to the Clinical Data Interchange Standards Consortium (CDISC) operational data model (ODM). Clinical data are stored in ODM format in the EHR system database. Documentation forms are based on Google's Material Design Standard. SMA:T was used at a total of five clinics and one administrative department in the period from March 1, 2018 until March 31, 2019 in everyday clinical practice. Results The technical and clinical feasibility of SMA:T was demonstrated in the course of the study. Seventeen documentation forms including 373 data items were created with SMA:T. Those were created for 2,484 patients by 283 users in everyday clinical practice. A total of 121 documentation forms were examined retrospectively. The Constructive cost model (COCOMO II) was used to calculate cost and time savings. The form development mean time was reduced by 83.4% from 3,357 to 557 hours. Average costs per form went down from EUR 953 to 158. Conclusion Automated generic transfer of standard-compliant data and metadata into EHRs is technically and clinically feasible, cost efficient, and a useful method to establish comprehensive and semantically annotated clinical documentation. Savings of time and personnel resources are possible.

Download Full-text

Process mining routinely collected electronic health records to define real-life clinical pathways during chemotherapy

International Journal of Medical Informatics ◽

10.1016/j.ijmedinf.2017.03.011 ◽

2017 ◽

Vol 103 ◽

pp. 32-41 ◽

Cited By ~ 30

Author(s):

Karl Baker ◽

Elaine Dunwoodie ◽

Richard G. Jones ◽

Alex Newsham ◽

Owen Johnson ◽

...

Keyword(s):

Electronic Health Records ◽

Process Mining ◽

Clinical Pathways ◽

Real Life ◽

Health Records ◽

Electronic Health

Download Full-text

Sharing of clinical data in a maternity setting: How do paper hand-held records and electronic health records compare for completeness?

BMC Health Services Research ◽

10.1186/s12913-014-0650-x ◽

2014 ◽

Vol 14 (1) ◽

Cited By ~ 12

Author(s):

Glenda Hawley ◽

Claire Jackson ◽

Julie Hepworth ◽

Shelley A Wilkinson

Keyword(s):

Electronic Health Records ◽

Clinical Data ◽

Health Records ◽

Electronic Health

Download Full-text

Visualizing Semantic Structure of a Clinical Text Document

European Journal of Electrical Engineering and Computer Science ◽

10.24018/ejece.2020.4.6.256 ◽

2020 ◽

Vol 4 (6) ◽

Author(s):

Jonah Kenei ◽

Elisha Opiyo ◽

Robert Oboko

Keyword(s):

Electronic Health Records ◽

Clinical Data ◽

Semantic Structure ◽

Cognitive Workload ◽

Health Records ◽

Clinical Text ◽

Clinical Notes ◽

Healthcare Data ◽

Text Document ◽

Electronic Health

The increasing use of Electronic Health Records (EHRs) in healthcare delivery settings has led to increase availability of electronic clinical data. They generate a lot of patients’ clinical data each day, requiring physicians to review them to find clinically relevant information of different patients during care episodes. The availability of electronically collected healthcare data has created the need of computational tools to analyze them. One of the types of data which doctors have access to is clinical notes that resides in electronic health records. These notes are useful as they provide comprehensive information about patients’ health histories with many practical uses. For example, doctors always review these notes during care episodes to appraise themselves about the health history of a patient. These reviews are currently manual where a doctor reads a patient’s chart while looking for specific clinical information. Without the proper support, this manual process leads to information overload and increases physician cognitive workload. Current electronic health records (EHRs) do not provide support to help physicians reduce cognitive workload when completing clinical tasks. This is especially true for long clinical documents which require quick review at the point of care. The growing amount of clinical documentation available in EHRs has arose the need of tools that support synthesize of information in EHRs. The use of visual analytics to explore healthcare data is one such research direction to address this problem. However, existing visualization techniques are mainly based on structured electronic health record and rarely support therapeutic activities. Therefore, visualization of unstructured clinical records to support clinical practice is required. In this paper we propose a unique approach for graphically representing and visualizing the semantic structure of a clinical text document to aid doctors in reviewing electronic clinical notes. A user evaluation demonstrates that the proposed method for visualizing and navigating a document’s semantic structure facilitates a user’s document information exploration.

Download Full-text