scholarly journals Reconsidering What is Vital about Vital Signs in Electronic Health Records: Comment on Matthews et al. (2016)

2017 ◽  
Author(s):  
David M Condon ◽  
Sara J Weston ◽  
Patrick Hill
Keyword(s):  
Electronic Health Records ◽  
Patient Care ◽  
Vital Signs ◽  
Clinical Settings ◽  
Institute Of Medicine ◽  
Psychosocial Variables ◽  
Inclusion Criteria ◽  
Social Psychological ◽  
Health Records ◽  
Electronic Health

The inclusion of psychosocial variables into electronic health records provides a unique opportunity for the translation of findings from social, psychological, and behavioral domains into patient care. This commentary is a response to the recommendations of a committee convened by the Institute of Medicine to address this opportunity (Matthews et al., 2016). We concur with the committee that the inclusion of psychosocial variables in electronic health records will broadly benefit researchers, practitioners, and patients and that there is clear need for a recommended panel of psychosocial measures that is ready for implementation in clinical settings. In fact, it seems likely that these recommendations will have lasting consequences. Given this, our response highlights several concerns about the recommendations and criteria. We suggest further clarification of the audience for these recommendations, reconsideration of the overly restrictive inclusion criteria, and more extensive engagement of psychosocial researchers in order to achieve broader consensus.

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.

scholarly journals Latent-Based Imputation of Laboratory Measures from Electronic Health Records: Case for Complex Diseases

2018 ◽  
Author(s):  
V. Abedi ◽  
M.K. Shivakumar ◽  
P. Lu ◽  
R. Hontecillas ◽  
A. Leber ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Input Data ◽  
Clinical Settings ◽  
Complex Phenotype ◽  
Future Time ◽  
Health Records ◽  
Inflammatory Bowel ◽  
Electronic Health ◽  
Unclear Etiology ◽  
Downstream Analysis

AbstractImputation is a key step in Electronic Health Records-mining as it can significantly affect the conclusions derived from the downstream analysis. There are three main categories that explain the missingness in clinical settings–incompleteness, inconsistency, and inaccuracy–and these can capture a variety of situations: the patient did not seek treatment, the health care provider did not enter the information, etc. We used EHR data from patients diagnosed with Inflammatory Bowel Disease from Geisinger Health System to design a novel imputation that focuses on a complex phenotype. Our approach is based on latent-based analysis integrated with clustering to group patients based on their comorbidities before imputation. IBD is a chronic illness of unclear etiology and without a complete cure. We have taken advantage of the complexity of IBD to pre-process the EHR data of 10,498 IBD patients and show that imputation can be improved using shared latent comorbidities. The R code and sample simulated input data will be available at a future time.

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 Application of artificial intelligence to electronic health records for early detection and diagnosis of cancer in primary care: a Systematic Review (Preprint)

2020 ◽  
Author(s):  
Owain Tudor Jones ◽  
Natalia Calanzani ◽  
Smiji Saji ◽  
Stephen W Duffy ◽  
Jon Emery ◽  
...  
Keyword(s):  
Primary Care ◽  
Early Detection ◽  
Electronic Health Records ◽  
Inclusion Criteria ◽  
Implementation Barriers ◽  
Health Records ◽  
Primary Care Settings ◽  
Primary Care Data ◽  
Diagnosis Of Cancer ◽  
Electronic Health

BACKGROUND More than 17 million people worldwide, including 360,000 people in the UK, were diagnosed with cancer in 2018. Cancer prognosis and disease burden is highly dependent on disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection, and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of healthcare. OBJECTIVE We aimed to systematically review AI technologies based on electronic health record (EHR) data that may facilitate the earlier diagnosis of cancer in primary care settings. We evaluated the quality of the evidence, the phase of development the AI technologies have reached, the gaps that exist in the evidence, and the potential for use in primary care. METHODS We searched Medline, Embase, SCOPUS, and Web of Science databases from 1st January 2000 to 11th June 2019 (PROSPERO ID CRD42020176674), and included all studies providing evidence for accuracy or effectiveness of applying AI technologies to early detection of cancer using electronic health records. We included all study designs, in all settings and all languages. We extended these searches through a scoping review of commercial AI technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer. RESULTS We identified 10,456 studies: 16 met the inclusion criteria, representing the data of 3,862,910 patients. 13 studies described the initial development and testing of AI algorithms and three studies described the validation of an AI technology in independent datasets. One study was based on prospectively collected data; only three studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk-of-bias assessment highlighted a wide range in study quality. The additional scoping review of commercial AI tools identified 21 technologies, only one meeting our inclusion criteria. Meta-analysis was not undertaken due to heterogeneity of AI modalities, dataset characteristics and outcome measures. CONCLUSIONS Applying AI technologies to electronic health records for early detection of cancer in primary care is at an early stage of maturity. Further evidence is needed on performance using primary care data, implementation barriers and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended. This study was supported by funding from the NIHR Cancer Policy Research Programme and Cancer Research UK.

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