Abstract Background To understand how suicide management occurs within the primary care setting in terms of follow-up assessments and referral practices. Methods At an initial primary care visit, adolescents (aged 12–20 years old) completed electronic screening. Data were focused on youth who endorsed a suicidal risk item while completing screening at two Midwestern primary care clinics. Data were collected through retrospective chart reviews to analyze actions taken by the primary care physician at the youth’s initial visit and follow-up visit within the next 12 months. Results At initial visits 200 adolescents endorsed a suicidal risk item and 39 (19.5%) were considered to be concerning by their primary care physician. The average age was 14.7 years old (SD ± 2.0). Seventy-two percent (n = 144) were female, and 65% (n = 129) identified as Black. At initial visits, significant differences between suicidal concern groups were found in reporting active suicidal ideation, past suicide attempts, those who were referred to behavioral health counseling, and those who had a diagnosis of depression. Interestingly, only 13% (n = 25) of all patients who endorsed the suicide item were asked whether or not there were weapons in their home and primary care providers asked only 7% (n = 13) of all patients whether they had a safety plan. Conclusions There was inconsistent follow-up for adolescents with a history of suicide concerns. At this time, national guidelines do not exist regarding primary care follow-up of youth with suicide concerns. Guidelines are a necessary precursor for practice improvement. Trial Registration Clinical Trials Registry: NCT02244138 . Registration date, September 1, 2014.

Electronic health records (EHRs) were originally developed for clinical care and billing. As such, the data are not collected, organized, and curated in a fashion that is optimized for secondary use to support the Learning Health System. Population health registries provide tools to support quality improvement. These tools are generally integrated with the live EHR, are intended to use a minimum of computing resources, and may not be appropriate for some research projects. Researchers may require different electronic phenotypes and variable definitions from those typically used for population health, and these definitions may vary from study to study. Establishing a formal registry that is mapped to the Observation Medical Outcomes Partnership common data model provides an opportunity to add custom mappings and more easily share these with other institutions. Performing preprocessing tasks such as data cleaning, calculation of risk scores, time-to-event analysis, imputation, and transforming data into a format for statistical analyses will improve efficiency and make the data easier to use for investigators. Research registries that are maintained outside the EHR also have the luxury of using significant computational resources without jeopardizing clinical care data. This paper describes a virtual Diabetes Registry at Atrium Health Wake Forest Baptist and the plan for its continued development.

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BackgroundThis study explored the rewards and difficulties of raising an adolescent and investigated parents' level of interest in receiving guidance from healthcare providers on parenting and adolescent health topics. Additionally, this study investigated whether parents were interested in parenting programs in primary care and explored methods in which parents want to receive guidance.MethodsParents of adolescents (ages 12-18) who attended an outpatient pediatric clinic with their adolescent were contacted by telephone and completed a short telephone survey. Parents were asked open-ended questions regarding the rewards and difficulties of parenting and rated how important it was to receive guidance from a healthcare provider on certain parenting and health topics. Additionally, parents reported their level of interest in a parenting program in primary care and rated how they would like to receive guidance.ResultsOur final sample included 104 parents, 87% of whom were interested in a parenting program within primary care. A variety of parenting rewards and difficulties were associated with raising an adolescent. From the list of parenting topics, communication was rated very important to receive guidance on (65%), followed by conflict management (50%). Of health topics, parents were primarily interested in receiving guidance on sex (77%), mental health (75%), and alcohol and drugs (74%). Parents in the study wanted to receive guidance from a pediatrician or through written literature.ConclusionsThe current study finds that parents identify several rewarding and difficult aspects associated with raising an adolescent and are open to receiving guidance on a range of parenting topics in a variety of formats through primary care settings. Incorporating such education into healthcare visits could improve parents' knowledge. Healthcare providers are encouraged to consider how best to provide parenting support during this important developmental time period.

Background The act of diagnosis is one which precipitates semiotic closure, the complex integration of signs and symptoms through cognitive perspectives to ultimately activate causal reasoning and calibrate the assignment of a disease entity to the patient. In writing about this act, physicians encode both structured and unstructured information into the medical record. Unstructured information contains a latent structure which entwines both the cognitive components of the diagnostic act and the linguistic patterns associated with clinical documentation. Existing models of clinical language primarily use a physical or dialogic model of information as their basis, and do not adequately account for the complexity inherent in the diagnostic act. Methods Framing the diagnostic information collected in clinical care as a narrative, we developed a model representative of said information, accounting for its content and structure, as well as the inherent complexity therein. Using an exemplar text, we present the use of known predication and semantic relations from ontological (the Unified Medical Language System) and linguistic theory (Rhetorical Structure Theory) to facilitate the operationalization of the model, and analyze the result. Results The resulting model is demonstrated to be complex, representative of the clinical narrative text, and is fundamentally aligned with the clinical acts of both documentation and diagnosis. We find the model’s representation of the cognitive aspects of narrative consistent with models of reading, as well as an adequate model of information as presented by clinical medicine and the clinical sub-language. Conclusions We present a model to represent diagnostic information in the physician’s note which accounts for the clinical and textual narrative precipitated by the cognition involved in encoding said information into the unstructured medical record. This model prepends the development of (computational) linguistic models of the clinical sublanguage within the physician’s note as it relates to diagnosis, beyond the information level of the lexical unit. Such analysis would facilitate better reflection on the structure and meaning of the clinical note, offering improvements to medical education and care.

In this issue of Pediatrics, Liang et al1 apply the concept of interval likelihood ratios (ILRs) to the diagnosis of urinary tract infections (UTIs) in children under the age of 2 years. The authors conducted a cross-sectional study of a cohort of children <2 years of age who had a urine culture and urine analysis (UA) simultaneously obtained from their emergency department. Their analysis, in contrast to most previous reports, derives ILRs for the scaled results of the UA relative to the diagnosis of UTIs.Most readers will be familiar with the terms sensitivity and specificity. ILRs are closely related to sensitivity and specificity but have at least 2 advantages. First, a separate ILR can be derived for each value of a test result when there can be more than just positive or negative results. For example, a urine dipslide can quantify the presence of leukocyte esterase (LE) as trace, 1+, 2+, or 3+. Liang et al1 showed 3+ LE increases the probability a child has a UTI compared with an LE of 1+ or 2+. This result may not be surprising, but by describing the relationship between LE and UTIs in ILR terms, the diagnostician can better refine her estimate of the probability of UTI, adjusting the probability more for 3+ LE than for 1+ LE.2A second advantage of ILR is that the diagnostician can use relatively simple arithmetic to estimate the probability of a diagnosis (eg, UTI) given a laboratory result (eg, 3+ LE). Starting with a previous probability of disease, she converts this to the odds of disease (probability/1 − probability) and multiplies it by the appropriate ILR (eg, 38 for 3+ LE) to generate the posterior odds. She can then convert the posterior odds back to a probability (probability = posterior odds/[1 + odds]). If the pretest probability of UTI is 5%,3 the odds are 0.05/(1 − 0.05) or (rounding) 0.05. Next, 0.05 × 38 is ∼2 (I rounded the ILR to 40). Finally, the posttest probability is 2/(2 + 1) or 0.67. With a little rounding (and practice), this calculation is easily done in your head.Of course, the probability estimate resulting from this calculation depends on the probability you start with. Liang et al1 found a pretest probability of UTI of 9.2%, which is somewhat higher than that in other large studies of UTI risk in febrile infants,3 probably as a result of some selection bias. That is why the posttest probability reported by Liang et al1 after a 3+ LE is 79%, not 67%. However, ILRs are relatively resistant to selection bias. For this reason, it behooves the diagnostician to know how to apply ILRs to her own estimates of pretest probability to calculate a posttest probability.It is tempting to take this posttest probability of UTI and apply another ILR because, after all, the UA gives us many results. For example, if microscopy showed there were many bacteria in the urine, we might take the posttest odds (2) after the LE and apply the ILR for many bacteria (14), generating a new posttest odds of 28. The corresponding probability would be 28 of 29 or over 96%! This approach of multiplying all of the applicable ILR together by the pretest odds is a technique called sequential Bayes’, and it has been used in computer-based diagnostic systems for decades.4 But the approach is fraught with risk because it assumes the ILRs are independent of one another, given the presence of UTI. We know this cannot always be true. Surely, the probability of seeing white cells on microscopy is not independent of the likelihood of seeing LE on a dipslide.Accommodating these interdependencies among ILRs requires more sophisticated statistical modeling, measuring the associations among different tests, and calculating the diagnostic value of different combinations of results.5 Liang et al1 have not conducted these analyses, but truthfully, these approaches generally require larger sample sizes than were available in this study (198 cases of UTI).Nonetheless, Liang et al1 have taken a step toward helping us be more quantitative in our diagnostic reasoning. This is one important strategy to reduce diagnostic errors, which represent “a major public health problem likely to affect every one of us at least once in our lifetime, sometimes with devastating consequences.”6 If we can learn to apply basic quantitative diagnostic tools like ILR to our medical practice, we are likely to reduce important causes of diagnostic errors (faulty data collection or interpretation, flawed reasoning, or incomplete knowledge) and their downstream impact on our patients.7

Background Sudden unexpected death in epilepsy (SUDEP) is a rare but fatal risk that patients, parents, and professional societies clearly recommend discussing with patients and families. However, this conversation does not routinely happen. Objectives This pilot study aimed to demonstrate whether computerized decision support could increase patient communication about SUDEP. Methods A prospective before-and-after study of the effect of computerized decision support on delivery of SUDEP counseling. The intervention was a screening, alerting, education, and follow-up SUDEP module for an existing computerized decision support system (the Child Health Improvement through Computer Automation [CHICA]) in five urban pediatric primary care clinics. Families of children with epilepsy were contacted by telephone before and after implementation to assess if the clinician discussed SUDEP at their respective encounters. Results The CHICA-SUDEP module screened 7,154 children age 0 to 21 years for seizures over 7 months; 108 (1.5%) reported epilepsy. We interviewed 101 families after primary care encounters (75 before and 26 after implementation) over 9 months. After starting CHICA-SUDEP, the number of caregivers who reported discussing SUDEP with their child's clinician more than doubled from 21% (16/75) to 46% (12/26; p =0.03), and when the parent recalled who brought up the topic, 80% of the time it was the clinician. The differences between timing and sampling methodologies of before and after intervention cohorts could have led to potential sampling and recall bias. Conclusion Clinician-family discussions about SUDEP significantly increased in pediatric primary care clinics after introducing a systematic, computerized screening and decision support module. These tools demonstrate potential for increasing patient-centered education about SUDEP, as well as incorporating other guideline-recommended algorithms into primary and subspecialty cares. Clinical Trial Registration clinicaltrials.gov, NCT03502759.

OBJECTIVE:To examine screening strategies for identifying problematic sleep in a diverse sample of infants. METHODS:Parents of infants (5-19 months; N = 3,271) presenting for a primary care visit responded to five screening items and the Infant Sleep Questionnaire (ISQ), a validated measure of problematic infant sleep. If parents responded affirmatively to any screening item, primary care providers received a prompt to evaluate. For each of the screening questions, we examined differences in item endorsement and criterion related validity with the ISQ. Using conceptual composites of night waking and sleep difficulty, prevalence, criterion-related validity, and concurrent demographic correlates were analyzed. RESULTS:Infants were primarily of Black race (50.1%) or Hispanic ethnicity (31.7%), with the majority (63.3%) living in economically distressed communities. Rates of problematic sleep ranged from 7.4%, for a single item assessing parental perception of an infant having a sleep problem, to 74.0%, for a single item assessing night wakings requiring adult intervention. Items assessing sleep difficulty had high (95.0-97.8%) agreement with the ISQ in identifying infants without problematic sleep, but low agreement (24.9-34.0%) in identifying those with problematic sleep. The opposite was true for items assessing night waking, which identified 91.0-94.6% of those with sleep problems but only 31.8-46.9% of those without. CONCLUSIONS:Screening strategies for identifying problematic infant sleep yielded highly variable prevalence rates and associated factors, depending on whether the strategy emphasized parent-perceived sleep difficulty or night wakings. The strategy that is most appropriate will depend on the system's goals.

Introduction Parents who make decisions about hypospadias repair for their child may seek information from online platforms such as YouTube. Objective The purpose of this study is to evaluate the health literacy demand of hypospadias videos on YouTube using the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V). Study design We performed a YouTube search using the term "hypospadias," limiting results to the first 100 videos. We excluded videos that were <1 min or >20 min and videos that were not in English or did not include subtitles. Two evaluators independently examined videos and determined PEMAT-A/V scores for understandability and actionability (i.e., ability to identify actions the viewer can take). Videos with scores >70% are understandable or actionable. The inter-rater reliability (kappa) and intraclass correlation coefficient (ICC) of PEMAT scores were calculated. Bivariate and multivariable linear regression models assessed the association of video characteristics with respective scores. Results Of the 100 videos that were identified on YouTube, 47 (47%) were excluded leaving 53 for analysis: 14 were >20 min, 14 were <1 min, 9 had no audio or subtitles, 7 were not in English, 1 was a duplicate, 1 was unrelated to hypospadias, and 1 was deleted at the time of data analysis. Three (5.6%) were understandable (mean score 54.5%, standard deviation (SD) 14.9) and eight (15.1%) were actionable (mean score 21.8%, SD 16.6) (Extended Summary Figure). Kappa values ranged from 0.4 to 1. The ICC's were 0.55 and 0.33 for understandability and actionability, respectively. In the bivariate analysis, mean understandability scores were significantly higher for English language videos (p = 0.04), videos with animation (p = 0.002), and those produced by industry (p = 0.02). In the multivariable analysis, mean understandability scores were significantly higher for "expert testimonial" or "other" video types after adjusting for graphics type and overall tone (p = 0.04). Mean understandability scores were also significantly higher for videos with animation after adjusting for video type and overall tone (p = 0.01). Mean actionability scores were significantly higher for videos with a negative tone (p = 0.01). Discussion The vast majority of hypospadias-related YouTube content is not appropriate for users with low health literacy although certain types of videos, such those with animation and expert testimonials, scored higher on understandability than other types. Conclusion Due to the lack of sufficient online informational content regarding hypospadias, we plan to engage parents of sons with hypospadias in the development of high-quality patient educational materials about hypospadias. [GRAPHICS]

Objective With the increasing prevalence of type 2 diabetes (T2D) in youth, primary care providers must identify patients at high risk and implement evidence-based screening promptly. Clinical decision support systems (CDSSs) provide clinicians with personalized reminders according to best evidence. One example is the Child Health Improvement through Computer Automation (CHICA) system, which, as we have previously shown, significantly improves screening for T2D. Given that the long-term success of any CDSS depends on its acceptability and its users' perceptions, we examined what clinicians think of the CHICA diabetes module. Methods CHICA users completed an annual quality improvement and satisfaction questionnaire. Between May and August of 2015 and 2016, the survey included two statements related to the T2D-module: (1) "CHICA improves my ability to identify patients who might benefit from screening for T2D" and (2) "CHICA makes it easier to get the lab tests necessary to identify patients who have diabetes or prediabetes." Answers were scored using a 5-point Likert scale and were later converted to a 2-point scale: agree and disagree. The Pearson chi-square test was used to assess the relationship between responses and the respondents. Answers per cohort were compared using the Mann-Whitney U -test. Results The majority of respondents ( N = 60) agreed that CHICA improved their ability to identify patients who might benefit from screening but disagreed as to whether it helped them get the necessary laboratories. Scores were comparable across both years. Conclusion CHICA was endorsed as being effective for T2D screening. Research is needed to improve satisfaction for getting laboratories with CHICA.

Introduction: Clinical decision support (CDS) integrating patient and family generated data with screening tools and the electronic medical record (EMR) can aid in safe, timely, effective, efficient and patient-centered care. However, pediatric specific CDS tools are not always activated in EMRs. We present a successful integration of a validated web-based pediatric clinical decision support tool with a vendor EMR product. Method: Child Health Improvement through Computer Automation (CHICA) is a pediatric clinical decision support system developed and implemented in other EMRs as previously reported; however, it had …

BACKGROUND:Approximately, 3,500 infants die annually from sleep-related infant deaths in the United States. We sought to improve pediatricians' counseling on safe sleep from birth through 6 months of age through a virtual quality improvement learning collaborative (QILC). Our aim was appropriate screening, counseling, and documentation of safe sleep advice in 75% of eligible patient encounters after the QILC.METHODS:We formed a 9-month QILC for inpatient and outpatient pediatricians. Pediatricians collected data on safe sleep documentation in a newborn discharge or well-child visit note. Data were submitted at baseline and in 9 subsequent phases. Participants met monthly via a webinar, which included a QI presentation, data review, and facilitated discussion among participants. Practices were contacted 12 months after the conclusion of the QILC to assess sustainment.RESULTS:Thirty-four pediatricians from 4 inpatient and 9 outpatient practices participated in the QILC. At baseline, documentation of safe sleep practices varied greatly (0%-98%). However, by the end of the QILC, all participating practices were documenting safe sleep guidance in over 75% of patient encounters. Aggregate practice data show a significant, sustained improvement. The 12-month follow-up data were submitted from 62% of practices, with sustainment of improvement in 75% of practices.CONCLUSION:A facilitated, virtual QILC is an effective methodology to improve safe sleep counseling among a diverse group of pediatric practices. It is one step in improving consistent messaging around safe sleep by healthcare providers as pediatricians work to decrease sleep-related infant deaths.

Purpose: With the increasing prevalence of T2D in youth, it is imperative for primary care providers to identify patients at high risk and implement evidence-based screening protocols promptly. CDSS are tools implemented to provide clinicians with personalized reminders tailored to their patients according to best evidence and guidelines. One example of CDSS is the Child Health Improvement through Computer Automation system (CHICA). We previously showed that primary care providers using CHICA to automate the identification and screening of pediatric patients at high risk for T2D were 4.6 times more likely …

Purpose The objective of this study was to assess caregiver comfort regarding adolescent completion of computerized health screening questionnaires created for adolescents. Methods We conducted a mixed-method, cross-sectional survey of caregivers of adolescent patients (n = 104) aged 12–18 years who had a medical visit between June 2017 and August 2017. Topics assessed included who completed the questionnaire, caregiver comfort and concern regarding questionnaire data, and caregiver reasons for involvement in completing the questionnaire. A one-way analysis of variance was used to compare the age of the adolescent and caregiver involvement in the questionnaire. Results The majority of adolescents (64%) reported independent completion of the questionnaire. Thirteen percent of caregivers completed the questionnaire with no involvement of the adolescent and 23% reported that caregivers and adolescents completed the questionnaire in tandem. The majority of caregivers (84%) were comfortable with adolescents completing the questionnaire. A variety of reasons were identified for caregivers completing the questionnaire (time constraints, 22%; adolescent requested caregiver help, 19%; caregiver desired to answer questions, 14%; caregiver did not realize that the questionnaire was intended for the adolescent, 11%; caregiver believed that the adolescent was too young to respond alone, 11%). Caregiver comfort with adolescent completing the questionnaire increase with age. Conclusion We found the reason most caregivers gave for completing the questionnaires were related to clinic processes (e.g. time constraints). Caregivers were more likely to complete the questionnaire with younger adolescents. Thus, pediatricians should consider how to best prepare families for initial questionnaires in primary care.