scholarly journals Electronic Health Records (EHR) of the Emergency Service of the Hospital de Clínicas: Case Study

2021 ◽  
Vol 24 (2) ◽  
Author(s):  
Garcia-Diaz Maria-Elena ◽  
León Silvano Alvaro ◽  
Pinto-Roa Diego ◽  
Ocampos N. David ◽  
Pederzani Marcelo
Keyword(s):  
Electronic Health Records ◽  
Patient Care ◽  
Emergency Service ◽  
International Health ◽  
Online Information ◽  
Health Records ◽  
Care Processes ◽  
History Of ◽  
Electronic Health ◽  
First Time

The automation of the management in the Emergency Service of the Hospital de Clinicas of the Universidad Nacional de Asuncion is a problem addressed in this work since the care of the patient in critical condition must be accurate, appropiate and efficient. The development of a management automation tool will generate benefits for both the patient and the hospital's target staff. With the automatization of the processes, which are still done manually, it will be possible to achieve: (a) more time to devote to a better patient care, (b) doctors will be able to spend part of their time to analyze the statistics and information, which can be generated through the application to carry out investigations in the area of emergency care, and (c) as well as to optimize the resources of both the staff and the logistics used in the Hospital. To achieve the proposed objective, in this work a complete and Electronic Health Records System management based on international health standards has been developed, as well as good practices in the care processes in an area as sensitive as the Emergency Service. After more than 100 years of history of the Hospital, for the first time it is intended to automate processes and generate online information quickly and efficiently from this modern tool optimizing patient care in the emergency area.

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Electronic Health Records ◽  
Patient Care ◽  
Early Identification ◽  
Health Data ◽  
Patient Specific ◽  
Patient Records ◽  
Health Records ◽  
Electronic Health

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

Handoff Communication and Electronic Health Records

2014 ◽  
Vol 3 (1) ◽  
pp. 162-169 ◽  
Author(s):  
Julie Apker ◽  
Christopher Beach ◽  
Kevin O’Leary ◽  
Jennifer Ptacek ◽  
Dickson Cheung ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Patient Care ◽  
Healthcare Delivery ◽  
Communication Style ◽  
Health Records ◽  
Practical Applications ◽  
Sensemaking Theory ◽  
Making Sense ◽  
Study Results ◽  
Electronic Health

When transferring patient care responsibilities across the healthcare continuum, clinicians strive to communicate safely and effectively, but communication failures exist that threaten patient safety. Although researchers are making great strides in understanding and solving intraservice handoff problems, inter-service transition communication remains underexplored. Further, electronic health records (EHRs) figure prominently in healthcare delivery, but less is known about how EHRs contribute to inter-service handoffs. This descriptive, qualitative study uses Sensemaking Theory to explore EHR-facilitated, inter-service handoffs occurring between emergency medicine and internal/hospitalist medicine physicians. The researchers conducted six focus groups with 16 attending physicians and medical residents at a major Midwestern academic hospital. Findings suggest clinicians hold varied expectations for information content and relational communication/style. Their expectations contribute to making sense of uncertain handoff situations and communication best practices. Participants generally perceive EHRs as tools that, when used appropriately, can enhance handoffs and patient care continuity. Ideas for practical applications are offered based on study results.

Electronic health record use among cancer patients: Insights from the Health Information National Trends Survey

2017 ◽  
Vol 25 (1) ◽  
pp. 83-90 ◽  
Author(s):  
Yulia A Strekalova
Keyword(s):  
Electronic Health Records ◽  
Electronic Health Record ◽  
Cancer Patients ◽  
Health Information ◽  
Information Seeking ◽  
Health Record ◽  
Online Information ◽  
Health Records ◽  
Electronic Health ◽  
National Trends

Over 90% of US hospitals provide patients with access to e-copy of their health records, but the utilization of electronic health records by the US consumers remains low. Guided by the comprehensive information-seeking model, this study used data from the National Cancer Institute’s Health Information National Trends Survey 4 (Cycle 4) and examined the factors that explain the level of electronic health record use by cancer patients. Consistent with the model, individual information-seeking factors and perceptions of security and utility were associated with the frequency of electronic health record access. Specifically, higher income, prior online information seeking, interest in accessing health information online, and normative beliefs were predictive of electronic health record access. Conversely, poorer general health status and lack of health care provider encouragement to use electronic health records were associated with lower utilization rates. The current findings provide theory-based evidence that contributes to the understanding of the explanatory factors of electronic health record use and suggest future directions for research and practice.

scholarly journals Quality improvement of prescribing safety: a pilot study in primary care using UK electronic health records

2019 ◽  
Vol 69 (686) ◽  
pp. e605-e611 ◽  
Author(s):  
Helen P Booth ◽  
Arlene M Gallagher ◽  
David Mullett ◽  
Lucy Carty ◽  
Shivani Padmanabhan ◽  
...  
Keyword(s):  
Primary Care ◽  
Quality Improvement ◽  
Electronic Health Records ◽  
Patient Care ◽  
Scale Up ◽  
Pilot Phase ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Patient Level ◽  
Electronic Health

BackgroundQuality improvement (QI) is a priority for general practice, and GPs are expected to participate in and provide evidence of QI activity. There is growing interest in harnessing the potential of electronic health records (EHR) to improve patient care by supporting practices to find cases that could benefit from a medicines review.AimTo develop scalable and reproducible prescribing safety reports using patient-level EHR data.Design and settingUK general practices that contribute de-identified patient data to the Clinical Practice Research Datalink (CPRD).MethodA scoping phase used stakeholder consultations to identify primary care QI needs and potential indicators. QI reports containing real data were sent to 12 pilot practices that used Vision GP software and had expressed interest. The scale-up phase involved automating production and distribution of reports to all contributing practices that used both Vision and EMIS software systems. Benchmarking reports with patient-level case review lists for two prescribing safety indicators were sent to 457 practices in December 2017 following the initial scale-up (Figure 2).ResultsTwo indicators were selected from the Royal College of General Practitioners Patient Safety Toolkit following stakeholder consultations for the pilot phase involving 12 GP practices. Pilot phase interviews showed that reports were used to review individual patient care, implement wider QI actions in the practice, and for appraisal and revalidation.ConclusionElectronic health record data can be used to provide standardised, reproducible reports that can be delivered at scale with minimal resource requirements. These can be used in a national QI initiative that impacts directly on patient care.

ANALYZING THE SCOPE OF INTEGRATING ELECTRONIC MEDICAL RECORDS (EMR) AND ELECTRONIC HEALTH RECORDS (EHR) WITH MULTIMODAL HUMAN COMPUTER INTERACTION (MMHCI)

2016 ◽  
Vol 78 (6) ◽  
Author(s):  
Vairamuthu S. ◽  
Margret Anouncia
Keyword(s):  
Electronic Health Records ◽  
Human Computer Interaction ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Complex Data ◽  
Medical Systems ◽  
Health Records ◽  
History Of ◽  
Electronic Health ◽  
Computer Interaction

Developing applications using Multi Modal Human Computer Interaction (MMHCI) remains a great challenge due to the advancement of technologies. Enhanced interaction applications and tools employed in medical records will help to improve the quality of patients’ healthcare and it opens a variety of research challenges. Replacing a difficult system to store complex data related to medical history of patients through Electronic Medical Records (EMR)/Electronic Health Records (EHR) would offer several advantages that include confidentiality and patient details reliability along with the mechanisms for quick and flexible retrieval of data/information. The task of designing MMHCI applications for real time environment for EMR/EHR is thus complex. As the inputs to medical systems are heterogeneous, the associated issues grow up with the need for new system since the existing frameworks have many gaps and drawbacks. This paper attempts to discuss the possible guidelines, standards, tools and techniques involved in integrating MMHCI with EMR/EHR.

scholarly journals Case Report Medical Eponyms

2012 ◽  
Vol 03 (03) ◽  
pp. 349-355 ◽  
Author(s):  
L.N. Guptha Munugoor Baskaran ◽  
P.J. Greco ◽  
D.C. Kaelber
Keyword(s):  
Case Report ◽  
Electronic Health Records ◽  
Patient Care ◽  
Improve Patient Care ◽  
Clinical Informatics ◽  
Health Records ◽  
Electronic Health ◽  
Informatics Tools ◽  
Improve Patient

SummaryMedical eponyms are medical words derived from people’s names. Eponyms, especially similar sounding eponyms, may be confusing to people trying to use them because the terms themselves do not contain physiologically descriptive words about the condition they refer to. Through the use of electronic health records (EHRs), embedded applied clinical informatics tools including synonyms and pick lists that include physiologically descriptive terms associated with any eponym appearing in the EHR can significantly enhance the correct use of medical eponyms. Here we describe a case example of two similar sounding medical eponyms – Wegener’s disease and Wegner’s disease – which were confused in our EHR. We describe our solution to address this specific example and our suggestions and accomplishments developing more generalized approaches to dealing with medical eponyms in EHRs. Integrating brief physiologically descriptive terms with medical eponyms provides an applied clinical informatics opportunity to improve patient care.

scholarly journals Electronic health records and clinician burnout: A story of three eras

2020 ◽  
Author(s):  
Kevin B Johnson ◽  
Michael J Neuss ◽  
Don Eugene Detmer
Keyword(s):  
Electronic Health Records ◽  
Technology Policy ◽  
Healthcare Delivery ◽  
Medical Documentation ◽  
Health Records ◽  
Behavioral Determinants ◽  
System A ◽  
History Of ◽  
Electronic Health ◽  
Time Required

Abstract Objective The study sought to provide physicians, informaticians, and institutional policymakers with an introductory tutorial about the history of medical documentation, sources of clinician burnout, and opportunities to improve electronic health records (EHRs). We now have unprecedented opportunities in health care, with the promise of new cures, improved equity, greater sensitivity to social and behavioral determinants of health, and data-driven precision medicine all on the horizon. EHRs have succeeded in making many aspects of care safer and more reliable. Unfortunately, current limitations in EHR usability and problems with clinician burnout distract from these successes. A complex interplay of technology, policy, and healthcare delivery has contributed to our current frustrations with EHRs. Fortunately, there are opportunities to improve the EHR and health system. A stronger emphasis on improving the clinician’s experience through close collaboration by informaticians, clinicians, and vendors can combine with specific policy changes to address the causes of burnout. Target audience This tutorial is intended for clinicians, informaticians, policymakers, and regulators, who are essential participants in discussions focused on improving clinician burnout. Learners in biomedicine, regardless of clinical discipline, also may benefit from this primer and review. Scope We include (1) an overview of medical documentation from a historical perspective; (2) a summary of the forces converging over the past 20 years to develop and disseminate the modern EHR; and (3) future opportunities to improve EHR structure, function, user base, and time required to collect and extract information.

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