Why parents seek medical evaluations for their children with mild acute illnesses

Facing the Impact of the COVID-19 Pandemic: How Can We Allocate Outpatient Doctor Resources More Effectively?

The COVID-19 pandemic caused significant damage to global healthcare systems. Previous studies regarding COVID-19’s impact on outpatient numbers focused only on a specific department, lacking research data for multiple departments in general hospitals. We assessed differences in COVID-19’s impact on outpatient numbers for different departments to help hospital managers allocate outpatient doctor resources more effectively during the pandemic. We compared the outpatient numbers of 24 departments in a general hospital in Beijing in 2019 and 2020. We also examined an indicator not mentioned in previous studies, monthly departmental patient reservation rates. The results show that, compared with 2019, 2020 outpatient numbers decreased overall by 33.36%. Ten departments’ outpatient numbers decreased >33.36%; however, outpatient numbers increased in two departments. In 2020, the overall patient reservation rate in 24 departments was 82.22% of the 2019 reservation rate; the rates in 14 departments were <82.22%. Moreover, patient reservation rates varied across different months. Our research shows that COVID-19’s impact on different departments also varied. Additionally, our research suggests that well-known departments will be less affected by COVID-19, as will departments related to tumor treatment, where there may also be an increase in patient numbers. Patient reservation rates are an indicator worthy of attention. We suggest that hospital managers classify departments according to changes in outpatient numbers and patient reservation rates and adopt accurate, dynamic, and humanized management strategies to allocate outpatient doctor resources.

Automated chart review utilizing natural language processing algorithm for asthma predictive index

BACKGROUND

Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language processing (NLP) algorithm to identify patients that meet API criteria.

METHODS

This is a cross-sectional study nested in a birth cohort study in Olmsted County, MN. Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was developed on a training cohort (n = 87) and validated on a test cohort (n = 427). Criterion validity was measured by sensitivity, specificity, positive predictive value and negative predictive value of the NLP algorithm against manual chart review for asthma status. Construct validity was determined by associations of asthma status defined by NLP-API with known risk factors for asthma.

RESULTS

Among the eligible 427 subjects of the test cohort, 48% were males and 74% were White. Median age was 5.3 years (interquartile range 3.6-6.8). 35 (8%) had a history of asthma by NLP-API vs. 36 (8%) by abstractor with 31 by both approaches. NLP-API predicted asthma status with sensitivity 86%, specificity 98%, positive predictive value 88%, negative predictive value 98%. Asthma status by both NLP and manual chart review were significantly associated with the known asthma risk factors, such as history of allergic rhinitis, eczema, family history of asthma, and maternal history of smoking during pregnancy (p value < 0.05). Maternal smoking [odds ratio: 4.4, 95% confidence interval 1.8-10.7] was associated with asthma status determined by NLP-API and abstractor, and the effect sizes were similar between the reviews with 4.4 vs 4.2 respectively.

CONCLUSION

NLP-API was able to ascertain asthma status in children mining from EHR and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria.

Development and initial testing of Asthma Predictive Index for a retrospective study: an exploratory study

OBJECTIVE

Asthma Predictive Index (API) has been used for predicting asthma in prospective or cross-sectional studies, not for a retrospective study. We aim to develop and validate API for a retrospective study.

METHODS

This is a cross-sectional study based on a convenience sample of children who participated in a previous retrospective cohort study. API was operationalized by two or more wheezing episodes in a year during the first 3 years of life PLUS one of the major or two of the minor criteria of the original API. We assessed validity of retrospective API against Predetermined Asthma Criteria (PAC) which has been extensively used in clinical studies for asthma. We assessed criterion validity by measuring kappa and agreement rate between API and PAC and construct validity by determining associations of API with known risk factors for asthma.

RESULTS

Of the eligible 105 children, 55 (52.4%) were male, 90 (85.7%) Caucasians, and the mean age (±SD) was 5.8 years (±1.5). API criteria was met by 15 (14.3%), compared to 33 (31.4%) by PAC, respectively. The agreement rate and kappa between API and definite asthma of PAC were 89.5% and 0.66 (p < 0.01). Atopic conditions, lower parental education, no history of breastfeeding and family history of asthma were significantly associated with risk of asthma by API.

CONCLUSIONS

Application of API to a retrospective study for ascertaining asthma status is suitable. Our study findings need to be replicated by future studies with a larger sample size.

Emergency department and urgent care for children excluded from child care

BACKGROUND

Children in child care are frequently unnecessarily excluded for illness. We investigated parental use of urgent medical evaluation for sick children unable to attend child care.

METHODS

In May 2012, authors conducted a nationally representative survey of parents, who completed online questions regarding child illness causing absence from child care and their medical care-seeking behavior. Main outcome was parents' use of emergency department or urgent care (ED/UC).

RESULTS

Overall survey participation rate was 62%. Of participating parent cohort with children 0 to 5 years old, 57% (n = 357) required child care, of which 84% (n = 303) required out-of-home child care. Over 88% of parents sought acute medical care for their sick children unable to attend child care. Approximately one-third of parents needed a doctor's note for employers and/or child care. Parents sought medical evaluation (>1 option possible) from primary care (81%), UC (26%), or ED (25%). ED/UC use was most common for rash (21%) and fever (15%). Logistic regression indicated ED/UC use was significantly higher among single/divorced parents (odds ratio [OR] = 4.3; 95% confidence interval [CI]: 2.5-13.5); African American parents (OR = 4.2; 95% CI: 1.2-14.6); parents needing a doctor's note (OR = 4.2; 95% CI: 1.5-11.7); and those with job concerns (OR = 3.4; 95% CI: 1.2-9.7).

CONCLUSIONS

A substantial proportion of parents whose sick children cannot attend child care seek care in ED/UC. Training child care professionals regarding appropriate illness exclusions may decrease ED/UC visits by lowering child care exclusions.

Automated chart review for asthma cohort identification using natural language processing: an exploratory study

BACKGROUND

A significant proportion of children with asthma have delayed diagnosis of asthma by health care providers. Manual chart review according to established criteria is more accurate than directly using diagnosis codes, which tend to under-identify asthmatics, but chart reviews are more costly and less timely.

OBJECTIVE

To evaluate the accuracy of a computational approach to asthma ascertainment, characterizing its utility and feasibility toward large-scale deployment in electronic medical records.

METHODS

A natural language processing (NLP) system was developed for extracting predetermined criteria for asthma from unstructured text in electronic medical records and then inferring asthma status based on these criteria. Using manual chart reviews as a gold standard, asthma status (yes vs no) and identification date (first date of a "yes" asthma status) were determined by the NLP system.

RESULTS

Patients were a group of children (n = 112, 84% Caucasian, 49% girls) younger than 4 years (mean 2.0 years, standard deviation 1.03 years) who participated in previous studies. The NLP approach to asthma ascertainment showed sensitivity, specificity, positive predictive value, negative predictive value, and median delay in diagnosis of 84.6%, 96.5%, 88.0%, 95.4%, and 0 months, respectively; this compared favorably with diagnosis codes, at 30.8%, 93.2%, 57.1%, 82.2%, and 2.3 months, respectively.

CONCLUSION

Automated asthma ascertainment from electronic medical records using NLP is feasible and more accurate than traditional approaches such as diagnosis codes. Considering the difficulty of labor-intensive manual record review, NLP approaches for asthma ascertainment should be considered for improving clinical care and research, especially in large-scale efforts.

Is my child sick? Parents' management of signs of illness and experiences of the medical encounter: parents of recurrently sick children urge for more cooperation

OBJECTIVES

Parents of sick children frequently visit their general practitioners (GPs). The aim was to explore parents' interpretation of their child's incipient signs and symptoms when falling ill and their subsequent unsatisfactory experience with the GP in order to make suggestions for improvements in the medical encounter.

DESIGN

Semi-structured interviews.

SETTING AND SUBJECTS

Twenty strategically selected families with a child from a birth cohort in Frederiksborg County, Denmark were interviewed.

RESULTS

Parents wanted to consult their GP at the right time, i.e. neither too early nor too late. Well-educated parents experienced a discrepancy between their knowledge about their child, the information they had sought about the illness and the consultation with the GP, when they were dismissed with phrases such as "it will disappear" or "it is just a virus". The parents went along with the GP's advice if the child only occasionally became sick. However, parents of children with recurrent illnesses seemed very frustrated. During the course of several consultations with their GP, they started to question the GP's competence as the child did not regain health.

CONCLUSIONS

Parents want to be acknowledged as competent collaborators. The GP's failure to acknowledge the parents' knowledge of their child's current illness, and the parents' attempt to identify what is wrong with the child and make the child feel better before the encounter may have consequences for the GP's credibility. It is therefore recommended that parents of children with recurrent illnesses receive extra attention and information.