scholarly journals Period Prevalence of Rheumatic Heart Disease and the Need for a Centralized Patient Registry in American Samoa, 2016 to 2018

2021 ◽  
Author(s):  
Rebecca C. Woodruff ◽  
Ipuniuesea Eliapo‐Unutoa ◽  
Howard Chiou ◽  
Maria Gayapa ◽  
Sara Noonan ◽  
...  
Keyword(s):  
Heart Disease ◽  
Electronic Health Record ◽  
Rheumatic Heart Disease ◽  
American Samoa ◽  
Clinical Encounter ◽  
Health Record ◽  
Period Prevalence ◽  
Icd 10 ◽  
Electronic Health ◽  
Rheumatic Heart

Background Rheumatic heart disease (RHD) is a severe, chronic complication of acute rheumatic fever, triggered by group A streptococcal pharyngitis. Centralized patient registries are recommended for RHD prevention and control, but none exists in American Samoa. Using existing RHD tracking systems, we estimated RHD period prevalence and the proportion of people with RHD documented in the electronic health record. Methods and Results RHD cases were identified from a centralized electronic health record system, which retrieved clinical encounters with RHD International Classification of Diseases, Tenth Revision, Clinical Modification ( ICD‐10‐CM ) codes, clinical problem lists referencing RHD, and antibiotic prophylaxis administration records; 3 RHD patient tracking spreadsheets; and an all‐cause mortality database. RHD cases had ≥1 clinical encounter with RHD ICD‐10‐CM codes, a diagnostic echocardiogram, or RHD as a cause of death, or were included in RHD patient tracking spreadsheets. Period prevalence per 1000 population among children aged <18 years and adults aged ≥18 years from 2016 to 2018 and the proportion of people with RHD with ≥1 clinical encounter with an RHD ICD‐10‐CM code were estimated. From 2016 to 2018, RHD was documented in 327 people (57.2%: children aged <18 years). Overall RHD period prevalence was 6.3 cases per 1000 and varied by age (10.0 pediatric cases and 4.3 adult cases per 1000). Only 67% of people with RHD had ≥1 clinical encounter with an RHD ICD‐10‐CM code. Conclusions RHD remains a serious public health problem in American Samoa, and the existing electronic health record does not include all cases. A centralized patient registry could improve tracking people with RHD to ensure they receive necessary care.

scholarly journals The effect of number of healthcare visits on study sample selection and prevalence estimates in electronic health record data

2019 ◽  
Author(s):  
Laura J. Rasmussen-Torvik ◽  
Al’ona Furmanchuk ◽  
Alexander J. Stoddard ◽  
Kristen I. Osinski ◽  
John R. Meurer ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Systems ◽  
Sample Selection ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Period Prevalence ◽  
Prevalence Estimates ◽  
Icd 10 ◽  
Number Of Visits ◽  
Electronic Health

AbstractIntroductionFew studies have addressed how to select a study sample when using electronic health record (EHR) data.MethodsYear 2016 EHR data from three health systems was used to examine how alternate definitions of the study sample, based on number of healthcare visits in one year, affected measures of disease period prevalence. Curated collections of ICD-9, ICD-10, and SNOMED codes were used to define three diseases.ResultsAcross all health systems, increasing the minimum required number of visits to be included in the study sample monotonically increased crude period prevalence estimates. The rate at which prevalence estimates increased with number of visits varied across sites and across diseases.ConclusionsWhen using EHR data authors must carefully describe how a study sample is identified and report outcomes for a range of sample definitions, so that others can assess the sensitivity of reported results to sample definition in EHR data.

Echocardiographic Screening of Rheumatic Heart Disease in American Samoa

2017 ◽  
Vol 39 (1) ◽  
pp. 38-44 ◽  
Author(s):  
Jennifer H. Huang ◽  
Michael Favazza ◽  
Arthur Legg ◽  
Kathryn W. Holmes ◽  
Laurie Armsby ◽  
...  
Keyword(s):  
Heart Disease ◽  
Rheumatic Heart Disease ◽  
American Samoa ◽  
Rheumatic Heart

scholarly journals The electronic health record audit file: the patient is waiting

2016 ◽  
Vol 24 (e1) ◽  
pp. e28-e34 ◽  
Author(s):  
Annemarie G Hirsch ◽  
J B Jones ◽  
Virginia R Lerch ◽  
Xiaoqin Tang ◽  
Andrea Berger ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Wait Time ◽  
Clinical Encounter ◽  
Health Record ◽  
Clinical Workflow ◽  
Waiting Room ◽  
Exam Room ◽  
Physician Time ◽  
Nurse Time ◽  
Electronic Health

Objective: We describe how electronic health record (EHR) audit files can be used to understand how time is spent in primary care (PC). Materials/methods: We used audit file data from the Geisinger Clinic to quantify elements of the clinical workflow and to determine how these times vary by patient and encounter factors. We randomly selected audit file records representing 36 437 PC encounters across 26 clinic locations. Audit file data were used to estimate duration and variance of: (1) time in the waiting room, (2) nurse time with the patient, (3) time in the exam room without a nurse or physician, and (4) physician time with the patient. Multivariate modeling was used to test for differences by patient and by encounter features. Results: On average, a PC encounter took 54.6 minutes, with 5 minutes of nurse time, 15.5 minutes of physician time, and the remaining 62% of the time spent waiting to see a clinician or check out. Older age, female sex, and chronic disease were associated with longer wait times and longer time with clinicians. Level of service and numbers of medications, procedures, and lab orders were associated with longer time with clinicians. Late check-in and same-day visits were associated with shorter wait time and clinician time. Conclusions: This study provides insights on uses of audit file data for workflow analysis during PC encounters. Discussion: Scalable ways to quantify clinical encounter workflow elements may provide the means to develop more efficient approaches to care and improve the patient experience.

scholarly journals Patient-specific factors associated with pressure injuries revealed by electronic health record analyses

2019 ◽  
Vol 26 (1) ◽  
pp. 474-485 ◽  
Author(s):  
Megan W Miller ◽  
Rebecca T Emeny ◽  
Jennifer A Snide ◽  
Gary L Freed
Keyword(s):  
Electronic Health Record ◽  
Day Length ◽  
Patient Specific ◽  
Health Record ◽  
Pressure Injury ◽  
Icd 10 ◽  
Specific Factors ◽  
Electronic Health ◽  
Hospital Acquired ◽  
Future Work

Hospital-acquired pressure injuries (HAPIs) are a major source of unintended patient harm and unnecessary costs. The Braden Scale is widely used for risk assessment, yet it lacks specificity and clinical applications. This study used the electronic health record to examine associations between patient-specific factors and pressure injury. Adult patients (age >18) with 3-day length of stay from April 2011 to December 2016 were included. Pressure injuries were identified by ICD-9/ICD-10 codes. Longitudinal multivariate logistic regression was used to evaluate the association between patient-specific factors and HAPIs. This included 57,227 hospital encounters and 241 HAPIs. We observed 2–3 times increased likelihood of acquiring a pressure injury among patients who were malnourished or who had increased intraoperative time. The Braden subscales of nutrition, mobility, and friction showed significant predictive value. Future work is needed to assess the clinical applicability of this work.

scholarly journals The effect of number of healthcare visits on study sample selection in electronic health record data

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Laura Rasmussen-Torvik ◽  
Al'ona Furmanchuk ◽  
Alexander Stoddard ◽  
Kristen Osinski ◽  
John Meurer ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Health Systems ◽  
Health Record ◽  
Case Definitions ◽  
Electronic Health Record Data ◽  
Period Prevalence ◽  
Prevalence Estimates ◽  
Number Of Visits ◽  
Electronic Health

IntroductionFew studies have addressed how to select a study sample when using electronic health record (EHR) data. ObjectiveTo examine how changing criterion for number of visits in EHR data required for inclusion in a study sample would impact one basic epidemiologic measure: estimates of disease period prevalence. MethodsYear 2016 EHR data from three Midwestern health systems (Northwestern Medicine in Illinois, University of Iowa Health Care, and Froedtert & the Medical College of Wisconsin, all regional tertiary health care systems including hospitals and clinics) was used to examine how alternate definitions of the study sample, based on number of healthcare visits in one year, affected measures of disease period prevalence. In 2016, each of these health systems saw between 160,000 and 420,000 unique patients. Curated collections of ICD-9, ICD-10, and SNOMED codes (from CMS-approved electronic clinical quality measures) were used to define three diseases: acute myocardial infarction, asthma, and diabetic nephropathy). ResultsAcross all health systems, increasing the minimum required number of visits to be included in the study sample monotonically increased crude period prevalence estimates. The rate at which prevalence estimates increased with number of visits varied across sites and across diseases. ConclusionsIn addition to providing thorough descriptions of case definitions, when using EHR data authors must carefully describe how a study sample is identified and report data for a range of sample definitions, including minimum number of visits, so that others can assess the sensitivity of reported results to sample definition in EHR data.

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