Vital Signs: Percentage of IMGs in Primary Care Increasing

2006 ◽  
Vol 36 (11) ◽  
pp. 1
Keyword(s):  
Primary Care ◽  
Vital Signs

scholarly journals Cohort Design and Natural Language Processing to Reduce Bias in Electronic Health Records Research: The Community Care Cohort Project

2021 ◽  
Author(s):  
Shaan Khurshid ◽  
Christopher Reeder ◽  
Lia X Harrington ◽  
Pulkit Singh ◽  
Gopal Sarma ◽  
...  
Keyword(s):  
Primary Care ◽  
Natural Language Processing ◽  
Missing Data ◽  
Electronic Health Records ◽  
Ambulatory Care ◽  
Language Processing ◽  
Community Care ◽  
Vital Signs ◽  
Tabular Data ◽  
Health Records

Background: Electronic health records (EHRs) promise to enable broad-ranging discovery with power exceeding that of conventional research cohort studies. However, research using EHR datasets may be subject to selection bias, which can be compounded by missing data, limiting the generalizability of derived insights. Methods: Mass General Brigham (MGB) is a large New England-based healthcare network comprising seven tertiary care and community hospitals with associated outpatient practices. Within an MGB-based EHR warehouse of >3.5 million individuals with at least one ambulatory care visit, we approximated a community-based cohort study by selectively sampling individuals longitudinally attending primary care practices between 2001-2018 (n=520,868), which we named the Community Care Cohort Project (C3PO). We also utilized pre-trained deep natural language processing (NLP) models to recover vital signs (i.e., height, weight, and blood pressure) from unstructured notes in the EHR. We assessed the validity of C3PO by deploying established risk models including the Pooled Cohort Equations (PCE) and the Cohorts for Aging and Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) score, and compared model performance in C3PO to that observed within typical EHR Convenience Samples which included all individuals from the same parent EHR with sufficient data to calculate each score but without a requirement for longitudinal primary care. All analyses were facilitated by the JEDI Extractive Data Infrastructure pipeline which we designed to efficiently aggregate EHR data within a unified framework conducive to regular updates. Results: C3PO includes 520,868 individuals (mean age 48 years, 61% women, median follow-up 7.2 years, median primary care visits per individual 13). Estimated using reports, C3PO contains over 2.9 million electrocardiograms, 450,000 echocardiograms, 12,000 cardiac magnetic resonance images, and 75 million narrative notes. Using tabular data alone, 286,009 individuals (54.9%) had all vital signs available at baseline, which increased to 358,411 (68.8%) after NLP recovery (31% reduction in missingness). Among individuals with both NLP and tabular data available, NLP-extracted and tabular vital signs obtained on the same day were highly correlated (e.g., Pearson r range 0.95-0.99, p<0.01 for all). Both the PCE models (c-index range 0.724-0.770) and CHARGE-AF (c-index 0.782, 95% 0.777-0.787) demonstrated good discrimination. As compared to the Convenience Samples, AF and MI/stroke incidence rates in C3PO were lower and calibration error was smaller for both PCE (integrated calibration index range 0.012-0.030 vs. 0.028-0.046) and CHARGE-AF (0.028 vs. 0.036). Conclusions: Intentional sampling of individuals receiving regular ambulatory care and use of NLP to recover missing data have the potential to reduce bias in EHR research and maximize generalizability of insights.

Primary Care: The Actual Intelligence Required for Artificial Intelligence to Advance Health Care and Improve Health (Preprint)

2021 ◽  
Author(s):  
Winston R. Liaw ◽  
John M Westfall ◽  
Tyler S Williamson ◽  
Yalda Jabbarpour ◽  
Andrew Bazemore
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Vital Signs ◽  
Patient Privacy ◽  
Conversational Agents ◽  
Care Needs ◽  
Remote Sensors ◽  
Delivery Platform ◽  
And Training ◽  
Improve Health

UNSTRUCTURED With conversational agents triaging symptoms, cameras aiding diagnoses, and remote sensors monitoring vital signs, the use of artificial intelligence (AI) outside of hospitals has the potential to improve health, according to a recently released report from the National Academy of Medicine. Despite this promise, AI’s success is not guaranteed, and stakeholders need to be involved with its development to ensure that the resulting tools can be easily used by clinicians, protect patient privacy, and enhance the value of the care delivered. A crucial stakeholder group missing from the conversation is primary care. As the nation’s largest delivery platform, primary care will have a powerful impact on whether AI is adopted and subsequently exacerbates health disparities. To leverage these benefits, primary care needs to serve as a medical home for AI, broaden its teams and training, and build on government initiatives and funding.

scholarly journals Towards Consensus on Essential Components of Physical Examination in Primary Care-based Memory Clinics*

2018 ◽  
Vol 21 (2) ◽  
pp. 143-151 ◽  
Author(s):  
George A. Heckman ◽  
Bryan B. Franco ◽  
Linda Lee ◽  
Loretta Hillier ◽  
Veronique Boscart ◽  
...  
Keyword(s):  
Primary Care ◽  
Physical Examination ◽  
Overall Response Rate ◽  
Response Rate ◽  
Vital Signs ◽  
Online Surveys ◽  
Essential Components ◽  
Memory Clinics ◽  
Memory Concerns ◽  
With Memory

BackgroundPrimary care-based memory clinics were established to meet the needs of persons with memory concerns. We aimed to identify: 1) physical examination maneuvers required to assess persons with possible dementia in specialist-supported primary care-based memory clinics, and 2) the best-suited clinicians to perform these maneuvers in this setting.MethodsWe distributed in-person and online surveys of clinicians in a network of 67 primary care-based memory clinics in Ontario, Canada.Results90 surveys were completed for an overall response rate of 66.7%. Assessments of vital signs, gait, and for features of Parkinsonism were identified as essential by most respondents. There was little consensus on which clinician should be responsible for specific physical examination maneuvers.ConclusionsWhile we identified specific physical examination maneuvers deemed by providers to be both necessary and feasible to perform in the context of primary care-based memory clinics, further research is needed to clarify interprofessional roles related to the examination.

scholarly journals Relationship between documentation method and quality of chronic disease visit notes

2014 ◽  
Vol 05 (02) ◽  
pp. 480-490 ◽  
Author(s):  
L.A. Volk ◽  
S. Samaha ◽  
S.E. Pollard ◽  
D.H. Williams ◽  
J.M. Fiskio ◽  
...  
Keyword(s):  
Primary Care ◽  
Chronic Disease ◽  
Vital Signs ◽  
Primary Care Providers ◽  
Subjective Quality ◽  
Free Form ◽  
Care Providers ◽  
Quality Ratings ◽  
Significant Difference

SummaryObjective: To assses the relationship between methods of documenting visit notes and note quality for primary care providers (PCPs) and specialists, and to determine the factors that contribute to higher quality notes for two chronic diseases.Methods: Retrospective chart review of visit notes at two academic medical centers. Two physicians rated the subjective quality of content areas of the note (vital signs, medications, lifestyle, labs, symptoms, assessment & plan), overall quality, and completed the 9 item Physician Documentation Quality Instrument (PDQI-9). We evaluated quality ratings in relation to the primary method of documentation (templates, free-form or dictation) for both PCPs and specialists. A one factor analysis of variance test was used to examine differences in mean quality scores among the methods.Results: A total of 112 physicians, 71 primary care physicians (PCP) and 41 specialists, wrote 240 notes. For specialists, templated notes had the highest overall quality scores (p≤0.001) while for PCPs, there was no statistically significant difference in overall quality score. For PCPs, free form received higher quality ratings on vital signs (p = 0.01), labs (p = 0.002), and lifestyle (p = 0.002) than other methods; templated notes had a higher rating on medications (p≤0.001). For specialists, templated notes received higher ratings on vital signs, labs, lifestyle and medications (p = 0.001).Discussion: There was no significant difference in subjective quality of visit notes written using free-form documentation, dictation or templates for PCPs. The subjective quality rating of templated notes was higher than that of dictated notes for specialists.Conclusion: As there is wide variation in physician documentation methods, and no significant difference in note quality between methods, recommending one approach for all physicians may not deliver optimal results.Citation: Neri PM, Volk LA, Samaha S, Pollard SE, Williams DH, Fiskio JM, Burdick E, Edwards ST, Ramelson H, Schiff GD, Bates DW. Relationship between documentation method and quality of chronic disease visit notes. Appl Clin Inf 2014; 5: 480–490 http://dx.doi.org/10.4338/ACI-2014-01-RA-0007

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