scholarly journals How do inter-organisational electronic health records affect hospital physician and pharmacist decisions? A scoping review

2021 ◽  
Author(s):  
Philip Scott ◽  
Haythem Nakkas ◽  
Paul Roderick
Keyword(s):  
Electronic Health Records ◽  
Scoping Review ◽  
Value Chain ◽  
Distributed Cognition ◽  
Clinical Decision Making ◽  
Current Knowledge ◽  
Hidden Variables ◽  
Information Value ◽  
Health Records ◽  
Electronic Health

AbstractObjectiveTo provide an overview of the effects of inter-organisational electronic health records on inpatient diagnosis and treatment decisions by hospital physicians and pharmacists.Materials and MethodsFive-stage scoping review, using distributed cognition and the information value chain as guiding conceptual models. Eligibility criteria: empirical studies addressing how shared health records were used in inpatient clinical decision-making, published 2008-18. Sources: Healthcare Databases Advanced Search, covering nine sources including PubMed. Charting methods: data extraction form completed by one author, with inter-rater reliability assessment at title and abstract review.ResultsQuantitative studies (n=14) often reported relatively low usage of shared records (6.8% to 37.1% of cases). Usage is associated with reduction in diagnostic testing and readmission and variable effects on admissions and overall costs. Qualitative studies (n=6) reported avoidance of duplicate diagnostics, changing clinical decisions, the value of historical laboratory results and optimising the timeliness of care. We found no explicit use of explanatory theoretical models, but there is implicit evidence of an information value chain. We found only one study specifically about pharmacists.DiscussionRelatively low usage is due to clinical judgement whether “extra” data is needed, given current knowledge of the presenting condition and relative complexity. We suggest that extensive EHRs need recommender systems to highlight (sometimes unexpected) relevant content, in parallel with professional guidance on indications for consulting shared records.ConclusionsClinicians only consult shared health records when they must. Mixed effects on process outcomes are due to the hidden variables of patient complexity, clinician judgement and organisational context.

scholarly journals Protocol for a scoping review to understand how interorganisational electronic health records affect hospital physician and pharmacist decisions

BMJ Open ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. e023712 ◽  
Author(s):  
Philip Scott ◽  
Haythem Nakkas ◽  
Paul Roderick
Keyword(s):  
Decision Making ◽  
Electronic Health Records ◽  
Scoping Review ◽  
Distributed Cognition ◽  
Positive Impact ◽  
Ethical Review ◽  
Health Records ◽  
Hospital Physicians ◽  
Electronic Health ◽  
The Uk

IntroductionPatient records are often fragmented across organisations and departments in UK health and care services, often due to substandard information technology. However, although government policy in the UK and internationally is strongly pushing ‘digital transformation’, the evidence for the positive impact of electronic information systems on cost, quality and safety of healthcare is far from clear. In particular, the mechanisms by which information availability is translated into better decision-making are not well understood. We do not know when a full interorganisational record is more useful than a key information summary or an institutional record. In this paper, we describe our scoping review of how interorganisational electronic health records affect decision-making by hospital physicians and pharmacists.Methods and analysisThis scoping review will follow the Arksey and O’Malley (2005) methodology. The review has adopted sociotechnical systems thinking and the notion of distributed cognition as its guiding conceptual models. The UK National Institute for Health and Care Excellence Healthcare Databases Advanced Search will be used, as it incorporates key sources including PubMed, Medline, Embase, HMIC and Health Business Elite. A hand search will be conducted using the reference lists of included studies to identify additional relevant articles. A two-part study selection process will be used: (1) a title and abstract review and (2) full text review. During the first step, two researchers separately will review the citations yielded from the search to determine eligibility based on the defined inclusion and exclusion criteria. Related articles will be included if they are empirical studies that address how interorganisational records affect decision-making by hospital physicians and pharmacists.Ethics and disseminationThe results will be disseminated through stakeholder meetings, conference presentations and peer-reviewed publication. The data used are from publicly available secondary sources, so this study does not require ethical review.

scholarly journals Desiderata for computable representations of electronic health records-driven phenotype algorithms

2015 ◽  
Vol 22 (6) ◽  
pp. 1220-1230 ◽  
Author(s):  
Huan Mo ◽  
William K Thompson ◽  
Luke V Rasmussen ◽  
Jennifer A Pacheco ◽  
Guoqian Jiang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Relational Algebra ◽  
Common Data Model ◽  
Health Records ◽  
Electronic Health ◽  
Value Sets ◽  
Text Searching

Abstract Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

scholarly journals Integrating Genomic Resources with Electronic Health Records using the HL7 Infobutton Standard

2016 ◽  
Vol 07 (03) ◽  
pp. 817-831 ◽  
Author(s):  
Casey Overby ◽  
Guilherme Del Fiol ◽  
Wendy Rubinstein ◽  
Donna Maglott ◽  
Tristan Nelson ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Search Interface ◽  
Health Records ◽  
Knowledge Resources ◽  
Genomic Resources ◽  
Full Compliance ◽  
Genomic Knowledge ◽  
Electronic Health

SummaryThe Clinical Genome Resource (ClinGen) Electronic Health Record (EHR) Workgroup aims to integrate ClinGen resources with EHRs. A promising option to enable this integration is through the Health Level Seven (HL7) Infobutton Standard. EHR systems that are certified according to the US Meaningful Use program provide HL7-compliant infobutton capabilities, which can be leveraged to support clinical decision-making in genomics.To integrate genomic knowledge resources using the HL7 infobutton standard. Two tactics to achieve this objective were: (1) creating an HL7-compliant search interface for ClinGen, and (2) proposing guidance for genomic resources on achieving HL7 Infobutton standard accessibility and compliance.We built a search interface utilizing OpenInfobutton, an open source reference implementation of the HL7 Infobutton standard. ClinGen resources were assessed for readiness towards HL7 compliance. Finally, based upon our experiences we provide recommendations for publishers seeking to achieve HL7 compliance.Eight genomic resources and two sub-resources were integrated with the ClinGen search engine via OpenInfobutton and the HL7 infobutton standard. Resources we assessed have varying levels of readiness towards HL7-compliance. Furthermore, we found that adoption of standard terminologies used by EHR systems is the main gap to achieve compliance.Genomic resources can be integrated with EHR systems via the HL7 Infobutton standard using OpenInfobutton. Full compliance of genomic resources with the Infobutton standard would further enhance interoperability with EHR systems. Citation: Heale BSE, Overby CL, Del Fiol G, Rubinstein WS, Maglott DR, Nelson TH, Milosavljevic A, Martin CL, Goehringer SR, Freimuth RR, Williams MS. Integrating genomic resources with electronic health records using the HL7 Infobutton standard.

scholarly journals Convergence of Blockchain, Autonomous Agents, and Knowledge Graph to Share Electronic Health Records

2021 ◽  
Vol 4 ◽  
Author(s):  
Yao Yao ◽  
Meghana Kshirsagar ◽  
Gauri Vaidya ◽  
Jens Ducrée ◽  
Conor Ryan
Keyword(s):  
Electronic Health Records ◽  
Autonomous Agents ◽  
Knowledge Integration ◽  
Clinical Decision Making ◽  
User Authentication ◽  
Clinical Decision ◽  
Knowledge Graph ◽  
Autonomous Agent ◽  
Health Records ◽  
Electronic Health

In this article, we discuss a data sharing and knowledge integration framework through autonomous agents with blockchain for implementing Electronic Health Records (EHR). This will enable us to augment existing blockchain-based EHR Systems. We discuss how major concerns in the health industry, i.e., trust, security and scalability, can be addressed by transitioning from existing models to convergence of the three technologies – blockchain, agent-based modeling, and knowledge graph in a decentralized ecosystem. Each autonomous agent is responsible for instantiating key processes, such as user authentication and authorization, smart contracts, and knowledge graph generation through data integration among the participating stakeholders in the network. We discuss a layered approach for the design of the proposed system leading to an enhanced, safer clinical decision-making system. This can pave the way toward more informed and engaged patients and citizens by delivering personalized healthcare.

scholarly journals Integrating Biomedical Research and Electronic Health Records to Create Knowledge Based Biologically Meaningful Machine Readable Embeddings

2019 ◽  
Author(s):  
Charlotte A. Nelson ◽  
Atul J. Butte ◽  
Sergio E. Baranzini
Keyword(s):  
Machine Learning ◽  
Electronic Health Records ◽  
Precision Medicine ◽  
Biomedical Research ◽  
Current Knowledge ◽  
Knowledge Network ◽  
Health Records ◽  
Major Barrier ◽  
Learning Platform ◽  
Electronic Health

ABSTRACTIn order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. In an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients were connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm was used to create Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine.

scholarly journals Data-Driven Activities Involving Electronic Health Records: An Activity and Task Analysis Framework for Interactive Visualization Tools

2020 ◽  
Vol 4 (1) ◽  
pp. 7 ◽  
Author(s):  
Neda Rostamzadeh ◽  
Sheikh S. Abdullah ◽  
Kamran Sedig
Keyword(s):  
Electronic Health Records ◽  
Clinical Decision Making ◽  
Interactive Visualization ◽  
Clinical Decision ◽  
Data Driven ◽  
Reference List ◽  
Health Records ◽  
Visualization Tools ◽  
Electronic Health ◽  
Almost All

Electronic health records (EHRs) can be used to make critical decisions, to study the effects of treatments, and to detect hidden patterns in patient histories. In this paper, we present a framework to identify and analyze EHR-data-driven tasks and activities in the context of interactive visualization tools (IVTs)—that is, all the activities, sub-activities, tasks, and sub-tasks that are and can be supported by EHR-based IVTs. A systematic literature survey was conducted to collect the research papers that describe the design, implementation, and/or evaluation of EHR-based IVTs that support clinical decision-making. Databases included PubMed, the ACM Digital Library, the IEEE Library, and Google Scholar. These sources were supplemented by gray literature searching and reference list reviews. Of the 946 initially identified articles, the survey analyzes 19 IVTs described in 24 articles that met the final selection criteria. The survey includes an overview of the goal of each IVT, a brief description of its visualization, and an analysis of how sub-activities, tasks, and sub-tasks blend and combine to accomplish the tool’s main higher-level activities of interpreting, predicting, and monitoring. Our proposed framework shows the gaps in support of higher-level activities supported by existing IVTs. It appears that almost all existing IVTs focus on the activity of interpreting, while only a few of them support predicting and monitoring—this despite the importance of these activities in assisting users in finding patients that are at high risk and tracking patients’ status after treatment.

scholarly journals Closing the Loop: Optimizing Diabetes Care in the Hospital by Addressing Dispersed Information in Electronic Health Records and Using Clinical Decision Support

2018 ◽  
Vol 13 (4) ◽  
pp. 783-789 ◽  
Author(s):  
Ariana R. Pichardo-Lowden ◽  
Paul M. Haidet
Keyword(s):  
Decision Making ◽  
Electronic Health Records ◽  
Clinical Decision Making ◽  
Data Retrieval ◽  
Clinical Decision ◽  
Care Providers ◽  
Health Records ◽  
Data Points ◽  
Closing The Loop ◽  
Electronic Health

Multiple factors hinder the management of diabetes in hospitals. Amid the demands of practice, health care providers must collect, collate, and analyze multiple data points to optimally interpret glucose control and manage insulin dosing. Such data points are commonly dispersed in different sections of electronic health records (EHR), and the system for data display and physician interaction with the EHR are often poorly conducive to seamless clinical decision making. In this perspective article, we examine challenges in the process of EHR data retrieval, interpretation and decision making, using glucose management as an exemplar. We propose a conceptual, systems-based design for closing the loop between data gathering, analysis and decision making in the management of inpatient diabetes. This concept capitalizes on attributes of the EHR that can enable automated recognition of cases and provision of clinical recommendations.

scholarly journals Utilisation of Electronic Health Records for Public Health in Asia: A Review of Success Factors and Potential Challenges

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Lesley Dornan ◽  
Kanokporn Pinyopornpanish ◽  
Wichuda Jiraporncharoen ◽  
Ahmar Hashmi ◽  
Nisachol Dejkriengkraikul ◽  
...  
Keyword(s):  
Public Health ◽  
Electronic Health Records ◽  
Success Factors ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Direct Patient Care ◽  
Health Records ◽  
Middle Income ◽  
Implementation Challenges ◽  
Electronic Health

Introduction. Electronic health records offer a valuable resource to improve health surveillance and evaluation as well as informing clinical decision making. They have been introduced in many different settings, including low- and middle-income countries, yet little is known of the progress and effectiveness of similar information systems within Asia. This study examines the implementation of EHR systems for use at a population health level in Asia and to identify their current role within public health, key success factors, and potential barriers in implementation. Material and Methods. A systematic search process was implemented. Five databases were searched with MeSH key terms and Boolean phrases. Articles selected for this review were based on hospital provider electronic records with a component of implementation, utilisation, or evaluation for health systems or at least beyond direct patient care. A proposed analytic framework considered three interactive components: the content, the process, and the context. Results. Thirty-two articles were included in the review. Evidence suggests that benefits are significant but identifying and addressing potential challenges are critical for success. A comprehensive preparation process is necessary to implement an effective and flexible system. Discussion. Electronic health records implemented for public health can allow the identification of disease patterns, seasonality, and global trends as well as risks to vulnerable populations. Addressing implementation challenges will facilitate the development and efficacy of public health initiatives in Asia to identify current health needs and mitigate future risks.

scholarly journals Electronic Health Records’ Support for Primary Care Physicians’ Situation Awareness: A Metanarrative Review

2021 ◽  
pp. 001872082110143
Author(s):  
April Savoy ◽  
Himalaya Patel ◽  
Daniel R. Murphy ◽  
Ashley N. D. Meyer ◽  
Jennifer Herout ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Making ◽  
Electronic Health Records ◽  
Situation Awareness ◽  
Primary Care Physicians ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Health Records ◽  
Care Plans ◽  
Electronic Health

Objective Situation awareness (SA) refers to people’s perception and understanding of their dynamic environment. In primary care, reduced SA among physicians increases errors in clinical decision-making and, correspondingly, patients’ risk of experiencing adverse outcomes. Our objective was to understand the extent to which electronic health records (EHRs) support primary care physicians (PCPs)’ SA during clinical decision-making. Method We conducted a metanarrative review of papers in selected academic databases, including CINAHL and MEDLINE. Eligible studies included original peer-reviewed research published between January 2012 and August 2020 on PCP–EHR interactions. We iteratively queried, screened, and summarized literature focused on EHRs supporting PCPs’ clinical decision-making and care management for adults. Then, we mapped findings to an established SA framework to classify external factors (individual, task, and system) affecting PCPs’ levels of SA (1–Perception, 2–Comprehension, and 3–Projection) and identified SA barriers. Results From 1504 articles identified, we included and synthesized 19 studies. Study designs were largely noninterventional. Studies described EHR workflow misalignments, usability issues, and communication challenges. EHR information, including lab results and care plans, was characterized as incomplete, untimely, or irrelevant. Unmet information needs made it difficult for PCPs to obtain even basic SA, Level 1 SA. Prevalent barriers to PCPs developing SA with EHRs were errant mental models, attentional tunneling, and data overload. Conclusion Based on our review, EHRs do not support the development of higher levels of SA among PCPs. Review findings suggest SA-oriented design processes for health information technology could improve PCPs’ SA, satisfaction, and decision-making.

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