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.

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

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.

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.

scholarly journals Protecting healing relationships in the age of electronic health records: report from an international conference

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 ◽  
...  
Keyword(s):  
United States ◽  
Electronic Health Records ◽  
Patient Care ◽  
User Satisfaction ◽  
Care Delivery ◽  
The United States ◽  
Models Of Care ◽  
Health Records ◽  
International Conference ◽  
Electronic Health

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.

scholarly journals Remote symptom monitoring integrated into electronic health records: A systematic review

2020 ◽  
Vol 27 (11) ◽  
pp. 1752-1763
Author(s):  
Julie Gandrup ◽  
Syed Mustafa Ali ◽  
John McBeth ◽  
Sabine N van der Veer ◽  
William G Dixon
Keyword(s):  
Systematic Review ◽  
Electronic Health Records ◽  
Clinical Care ◽  
Long Term Conditions ◽  
Health Records ◽  
Symptom Monitoring ◽  
Study Results ◽  
Patient Reported ◽  
Electronic Health

Abstract Objective People with long-term conditions require serial clinical assessments. Digital patient-reported symptoms collected between visits can inform these, especially if integrated into electronic health records (EHRs) and clinical workflows. This systematic review identified and summarized EHR-integrated systems to remotely collect patient-reported symptoms and examined their anticipated and realized benefits in long-term conditions. Materials and Methods We searched Medline, Web of Science, and Embase. Inclusion criteria were symptom reporting systems in adults with long-term conditions; data integrated into the EHR; data collection outside of clinic; data used in clinical care. We synthesized data thematically. Benefits were assessed against a list of outcome indicators. We critically appraised studies using the Mixed Methods Appraisal Tool. Results We included 12 studies representing 10 systems. Seven were in oncology. Systems were technically and functionally heterogeneous, with the majority being fully integrated (data viewable in the EHR). Half of the systems enabled regular symptom tracking between visits. We identified 3 symptom report-guided clinical workflows: Consultation-only (data used during consultation, n = 5), alert-based (real-time alerts for providers, n = 4) and patient-initiated visits (n = 1). Few author-described anticipated benefits, primarily to improve communication and resultant health outcomes, were realized based on the study results, and were only supported by evidence from early-stage qualitative studies. Studies were primarily feasibility and pilot studies of acceptable quality. Discussion and Conclusions EHR-integrated remote symptom monitoring is possible, but there are few published efforts to inform development of these systems. Currently there is limited evidence that this improves care and outcomes, warranting future robust, quantitative studies of efficacy and effectiveness.

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