Dataset Information

Characteristics associated with pediatric growth measurement collection in electronic medical records: a retrospective observational study.

ABSTRACT: BACKGROUND:Complete growth measurements are an essential part of pediatric care providing a proxy for a child's overall health. This study describes the frequency of well-child visits, documented growth measurements, and clinic and provider factors associated with measurement. METHODS:Retrospective cross-sectional study utilizing electronic medical records (EMRs) from primary care clinics between 2015 and 2017 in Manitoba, Canada. This study assessed the presence of recorded height, weight and head circumference among children (0-24?months) who visited one of 212 providers participating in the Manitoba Primary Care Research Network. Descriptive and multivariable logistic regression analyses assessed clinic, provider, and patient factors associated with children having complete growth measurements. RESULTS:Our sample included 4369 children. The most frequent growth measure recorded was weight (79.2% n?=?3460) followed by height (70.8% n?=?3093) and head circumference (51.4% n?=?2246). 67.5% of children (n?=?2947) had at least one complete growth measurement recorded (i.e. weight, height and head circumference) and 13.7% (n?=?599) had complete growth measurements at all well-child intervals attended. Pediatricians had 2.7 higher odds of documenting complete growth measures within well-child intervals compared to family physicians (95% CI 1.8-3.8). Additionally, urban located clinics (OR 1.7, 95% CI 1.2-2.5), Canadian trained providers (OR 2.3, 95% CI 1.4-3.7), small practice size (OR 1.6, 95% CI 1.2-2.2) and salaried providers (OR 3.4, 95% CI 2.2-5.2) had higher odds of documented growth measures. CONCLUSIONS:Growth measurements are recorded in EMRs but documentation is variable based on clinic and provider factors. Pediatric growth measures at primary care appointments can improve primary prevention and surveillance of child health outcomes.

SUBMITTER: Kosowan L

PROVIDER: S-EPMC7490864 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characteristics associated with pediatric growth measurement collection in electronic medical records: a retrospective observational study.

Kosowan Leanne L Page John J Protudjer Jennifer J Williamson Tyler T Queenan John J Singer Alexander A

BMC family practice 20200915 1

<h4>Background</h4>Complete growth measurements are an essential part of pediatric care providing a proxy for a child's overall health. This study describes the frequency of well-child visits, documented growth measurements, and clinic and provider factors associated with measurement.<h4>Methods</h4>Retrospective cross-sectional study utilizing electronic medical records (EMRs) from primary care clinics between 2015 and 2017 in Manitoba, Canada. This study assessed the presence of recorded heigh ...[more]

PMID: 32933471

Similar Datasets

Project description:OBJECTIVE:To assess association of clinical features on COVID-19 patient outcomes. DESIGN:Retrospective observational study using electronic medical record data. SETTING:Five member hospitals from the Mount Sinai Health System in New York City (NYC). PARTICIPANTS:28?336 patients tested for SARS-CoV-2 from 24 February 2020 to 15 April 2020, including 6158 laboratory-confirmed COVID-19 cases. MAIN OUTCOMES AND MEASURES:Positive test rates and in-hospital mortality were assessed for different racial groups. Among positive cases admitted to the hospital (N=3273), we estimated HR for both discharge and death across various explanatory variables, including patient demographics, hospital site and unit, smoking status, vital signs, lab results and comorbidities. RESULTS:Hispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to their representation in the overall NYC population (p<0.05); however, no differences in mortality rates were observed in hospitalised patients based on race. Outcomes differed significantly between hospitals (Gray's T=248.9; p<0.05), reflecting differences in average baseline age and underlying comorbidities. Significant risk factors for mortality included age (HR 1.05, 95% CI 1.04 to 1.06; p=1.15e-32), oxygen saturation (HR 0.985, 95% CI 0.982 to 0.988; p=1.57e-17), care in intensive care unit areas (HR 1.58, 95% CI 1.29 to 1.92; p=7.81e-6) and elevated creatinine (HR 1.75, 95% CI 1.47 to 2.10; p=7.48e-10), white cell count (HR 1.02, 95% CI 1.01 to 1.04; p=8.4e-3) and body mass index (BMI) (HR 1.02, 95% CI 1.00 to 1.03; p=1.09e-2). Deceased patients were more likely to have elevated markers of inflammation. CONCLUSIONS:While race was associated with higher risk of infection, we did not find racial disparities in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. In addition, we identified key clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk of a severe infection response and predict survival.

Project description:BackgroundDrug safety in children is a major concern; however, there is still a lack of methods for quantitatively measuring, let alone to improving, drug safety in children under different clinical conditions. To assess pediatric drug safety under different clinical conditions, a computational method based on Electronic Medical Record (EMR) datasets was proposed.MethodsIn this study, a computational method was designed to extract the significant drug-diagnosis associations (based on a Bonferroni-adjusted hypergeometric P-value <?0.05) among drug and diagnosis co-occurrence in EMR datasets. This allows for differences between pediatric and adult drug use to be compared based on different EMR datasets. The drug-diagnosis associations were further used to generate drug clusters under specific clinical conditions using unsupervised clustering. A 5-layer quantitative pediatric drug safety level was proposed based on the drug safety statement of the pediatric labeling of each drug. Therefore, the drug safety levels under different pediatric clinical conditions were calculated. Two EMR datasets from a 1900-bed children's hospital and a 2000-bed general hospital were used to test this method.ResultsThe comparison between the children's hospital and the general hospital showed unique features of pediatric drug use and identified the drug treatment gap between children and adults. In total, 591 drugs were used in the children's hospital; 18 drug clusters that were associated with certain clinical conditions were generated based on our method; and the quantitative drug safety levels of each drug cluster (under different clinical conditions) were calculated, analyzed, and visualized.ConclusionWith this method, quantitative drug safety levels under certain clinical conditions in pediatric patients can be evaluated and compared. If there are longitudinal data, improvements can also be measured. This method has the potential to be used in many population-level, health data-based drug safety studies.

Dataset Information

Characteristics associated with pediatric growth measurement collection in electronic medical records: a retrospective observational study.

Publications

Characteristics associated with pediatric growth measurement collection in electronic medical records: a retrospective observational study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets