APA advocates for patient care, privacy issues at hearing on electronic health records

2005 ◽  
2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  

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


Author(s):  
Julie Apker ◽  
Christopher Beach ◽  
Kevin O’Leary ◽  
Jennifer Ptacek ◽  
Dickson Cheung ◽  
...  

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.


2019 ◽  
Vol 69 (686) ◽  
pp. e605-e611 ◽  
Author(s):  
Helen P Booth ◽  
Arlene M Gallagher ◽  
David Mullett ◽  
Lucy Carty ◽  
Shivani Padmanabhan ◽  
...  

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.


2012 ◽  
Vol 03 (03) ◽  
pp. 349-355 ◽  
Author(s):  
L.N. Guptha Munugoor Baskaran ◽  
P.J. Greco ◽  
D.C. Kaelber

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.


JAMIA Open ◽  
2019 ◽  
Vol 2 (3) ◽  
pp. 282-290 ◽  
Author(s):  
Elizabeth T Toll ◽  
Maria A Alkureishi ◽  
Wei Wei Lee ◽  
Stewart F Babbott ◽  
Philip A Bain ◽  
...  

AbstractWe present findings of an international conference of diverse participants exploring the influence of electronic health records (EHRs) on the patient–practitioner relationship. Attendees united around a belief in the primacy of this relationship and the importance of undistracted attention. They explored administrative, regulatory, and financial requirements that have guided United States (US) EHR design and challenged patient-care documentation, usability, user satisfaction, interconnectivity, and data sharing. The United States experience was contrasted with those of other nations, many of which have prioritized patient-care documentation rather than billing requirements and experienced high user satisfaction. Conference participants examined educational methods to teach diverse learners effective patient-centered EHR use, including alternative models of care delivery and documentation, and explored novel ways to involve patients as healthcare partners like health-data uploading, chart co-creation, shared practitioner notes, applications, and telehealth. Future best practices must preserve human relationships, while building an effective patient–practitioner (or team)-EHR triad.


2021 ◽  
Vol 24 (2) ◽  
Author(s):  
Garcia-Diaz Maria-Elena ◽  
León Silvano Alvaro ◽  
Pinto-Roa Diego ◽  
Ocampos N. David ◽  
Pederzani Marcelo

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.


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