Use of an Online Clinical Process Support System as an Aid to Identification and Management of Developmental and Mental Health Problems

Developmental Milestone Attainment in US Children Before and During the COVID-19 Pandemic

Importance

Restrictions related to the COVID-19 pandemic disrupted the lives of young children, but the association between the pandemic and any changes in early childhood developmental milestone achievement in the US remains unclear.

Objectives

To determine the association between the COVID-19 pandemic and changes in developmental screening scores among US children aged 0 to 5 years and to investigate whether caregivers self-reported more worries about their children or concerns about children's behavior during the pandemic, regardless of milestone achievement.

Design, Setting, and Participants

This was a cohort study using an interrupted time series analysis comparing prepandemic (March 1, 2018, to February 29, 2020), interruption (March 1 to May 31, 2020), and intrapandemic (June 1, 2020, to May 30, 2022) periods among 50 205 children (randomly sampled from a population of 502 052 children) aged 0 to 5 years whose parents or caregivers completed developmental screening at pediatric visits at US pediatric primary care practices participating in a web-based clinical process support system.

Exposure

COVID-19 pandemic period.

Main Outcomes and Measures

Age-standardized Ages and Stages Questionnaire, Third Edition (ASQ) domain scores (communication, personal-social, problem-solving, gross motor, fine motor), and rate of caregivers' concerns about the child's behavior or worries about the child as measured on the ASQ.

Results

A total of 50 205 children (25 852 [51.5%] male; mean [SD] age, 18.6 [16.0] months) and 134 342 ASQ observations were included. In adjusted models, significant age-specific mean score decreases from prepandemic to intrapandemic were observed in communication (-0.029; 95% CI, -0.041 to -0.017), problem-solving (-0.018; 95% CI, -0.030 to -0.006), and personal-social (-0.016; 95% CI, -0.028 to -0.004) domains. There were no changes in fine or gross motor domains prepandemic to intrapandemic. For infants aged 0 to 12 months, similar effect sizes were observed but only for communication (-0.027; 95% CI, -0.044 to -0.011) and problem-solving (-0.018; 95% CI, -0.035 to -0.001). After accounting for age-standardized ASQ scores, caregiver worries about the child increased slightly in the intrapandemic period compared with the prepandemic period (rate ratio, 1.088; 95% CI, 1.036-1.143), but there were no changes in caregiver concerns about the child's behavior. While changes in developmental screening scores were modest (2%-3%), nationwide, this could translate to more than 1500 additional recommended developmental referrals over baseline each month.

Conclusions and Relevance

Modest changes in developmental screening scores are reassuring in the short term but may tax an already overburdened developmental behavioral pediatrics infrastructure. Continued attention to developmental surveillance is critical since the long-term population- and individual-level implications of these changes are unclear.

Do Autism-Specific and General Developmental Screens Have Complementary Clinical Value?

Prior studies suggest autism-specific and general developmental screens are complementary for identifying both autism and developmental delay (DD). Parents completed autism and developmental screens before 18-month visits. Children with failed screens for autism (n = 167) and age, gender, and practice-matched children passing screens (n = 241) completed diagnostic evaluations for autism and developmental delay. When referral for autism and/or DD was considered, overall false positives from the autism screens were less frequent than for referral for autism alone. Presence of a failed communication subscale in the developmental screen was a red flag for autism and/or DD. An ordinally-scored autism screen had more favorable characteristics when considering autism and/or DD, yet none of the screens achieved recommended standards at 18 months, reinforcing the need for recurrent screening as autism emerges in early development.

Autism screening at 18 months of age: a comparison of the Q-CHAT-10 and M-CHAT screeners

Background

Autism screening is recommended at 18- and 24-month pediatric well visits. The Modified Checklist for Autism in Toddlers—Revised (M-CHAT-R) authors recommend a follow-up interview (M-CHAT-R/F) when positive. M-CHAT-R/F may be less accurate for 18-month-olds than 24-month-olds and accuracy for identification prior to two years is not known in samples that include children screening negative. Since autism symptoms may emerge gradually, ordinally scoring items based on the full range of response options, such as in the 10-item version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), might better capture autism signs than the dichotomous (i.e., yes/no) items in M-CHAT-R or the pass/fail scoring of Q-CHAT-10 items. The aims of this study were to determine and compare the accuracy of the M-CHAT-R/F and the Q-CHAT-10 and to describe the accuracy of the ordinally scored Q-CHAT-10 (Q-CHAT-10-O) for predicting autism in a sample of children who were screened at 18 months.

Methods

This is a community pediatrics validation study with screen positive (n = 167) and age- and practice-matched screen negative children (n = 241) recruited for diagnostic evaluations completed prior to 2 years old.

Clinical diagnosis of autism was based on results of in-person diagnostic autism evaluations by research reliable testers blind to screening results and using the Autism Diagnostic Observation Schedule—Second Edition (ADOS-2) Toddler Module and Mullen Scales of Early Learning (MSEL) per standard guidelines.

Results

While the M-CHAT-R/F had higher specificity and PPV compared to M-CHAT-R, Q-CHAT-10-O showed higher sensitivity than M-CHAT-R/F and Q-CHAT-10.

Limitations

Many parents declined participation and the sample is over-represented by higher educated parents. Results cannot be extended to older ages.

Conclusions

Limitations of the currently recommended two-stage M-CHAT-R/F at the 18-month visit include low sensitivity with minimal balancing benefit of improved PPV from the follow-up interview. Ordinal, rather than dichotomous, scoring of autism screening items appears to be beneficial at this age. The Q-CHAT-10-O with ordinal scoring shows advantages to M-CHAT-R/F with half the number of items, no requirement for a follow-up interview, and improved sensitivity. Yet, Q-CHAT-10-O sensitivity is less than M-CHAT-R (without follow-up) and specificity is less than the two-stage procedure. Such limitations are consistent with recognition that screening needs to recur beyond this age.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-021-00480-4.

Technology-Enabled and Artificial Intelligence Support for Pre-Visit Planning in Ambulatory Care: Findings From an Environmental Scan

PURPOSE

Pre-visit planning (PVP) is believed to improve effectiveness, efficiency, and experience of care, yet numerous implementation barriers exist. There are opportunities for technology-enabled and artificial intelligence (AI) support to augment existing human-driven PVP processes-from appointment reminders and pre-visit questionnaires to pre-visit order sets and care gap closures. This study aimed to explore the current state of PVP, barriers to implementation, evidence of impact, and potential use of non-AI and AI tools to support PVP.

METHODS

We used an environmental scan approach involving: (1) literature review; (2) key informant interviews with PVP experts in ambulatory care; and (3) a search of the public domain for technology-enabled and AI solutions that support PVP. We then synthesized the findings using a qualitative matrix analysis.

RESULTS

We found 26 unique PVP implementations in the literature and conducted 16 key informant interviews. Demonstration of impact is typically limited to process outcomes, with improved patient outcomes remaining elusive. Our key informants reported that many PVP barriers are human effort-related and see potential for non-AI and AI technologies to support certain aspects of PVP. We identified 8 examples of commercially available technology-enabled tools that support PVP, some with AI capabilities; however, few of these have been independently evaluated.

CONCLUSIONS

As health systems transition toward value-based payment models in a world where the coronavirus disease 2019 pandemic has shifted patient care into the virtual space, PVP activities-driven by humans and supported by technology-may become more important and powerful and should be rigorously evaluated.

Recognizing Adolescent Depression with Parent- and Youth-Report Screens in Pediatric Primary Care

OBJECTIVES

To compare the use of the parent-report Pediatric Symptom Checklist (PSC-17P) and youth-report Patient Health Questionnaire-9 Modified for Teens (PHQ-9M) in compliance with recent quality standards for adolescent depression screening.

STUDY DESIGN

Parents of 5411 pediatric outpatients (11.0-17.9 years old) completed the PSC-17P, which contains scales that assign categorical risk for overall (PSC-17P-OVR), internalizing (PSC-17P-INT), externalizing (PSC-17P-EXT), and attention (PSC-17P-ATT) problems. Adolescents completed the PHQ-9M, which assesses depressive symptoms. Both forms were completed online within 24 hours of each other before pediatric well-child visits.

RESULTS

A total of 9.9% of patients (n = 535) were at risk on the PSC-17P-OVR, 14.3% (n = 775) were at risk on the PSC-17P-INT, and 17.0% (n = 992) were at risk on either or both scales (PSC-17P-OVR and/or PSC-17P-INT). Using the PHQ-9M cut-off score of 10 (moderate-very severe depression), an additional 2.4% (n = 131) were classified as at risk, with 66.8% (n = 263) of all PHQ-9M positives (n = 394) also coded as at risk by the PSC-17P-OVR and/or PSC-17P-INT scales. Using a PHQ-9M cut-off score of 15 (severe-very severe depression), only 29 patients (21.8% of the PHQ-9M positives) not identified by the PSC-17P-OVR and/or PSC-17P-INT were classified as being at risk.

CONCLUSIONS

The combined PSC-17P-OVR and/or PSC-17P-INT scales identified 17% of adolescents as at risk for depression, including about two-thirds to three-quarters of adolescents classified as at risk on the PHQ-9M. These findings support using the PSC-17P to meet quality standards for depression as well as overall screening in pediatrics. Primary care clinicians can add the PHQ-9M to identify additional adolescents who may self-report depressive symptoms.

The Future(s) of Pediatric Primary Care

Pediatric primary care (PPC) arose in the early 20th century as the fusion of acute and chronic pediatric illness care with preventive elements borrowed from public and maternal and child health. Well-established and thriving by the 1930s, PPC saw major changes in childhood morbidity and mortality in the latter half of the 20th century with the recognition of the "new morbidity" of school, behavior, and social problems. At the same time, PPC experienced changes in its workforce, which became increasingly female and added nurse practitioners and physician assistants as practitioners. Independent practice, previously the dominant business model, decreased in prominence at the end of the 20th century as health systems bought practices and other sites morphed into federally qualified health centers. In the present century, electronic health records (EHRs) have brought profound changes in PPC workflows and practitioner experience. In addition, disruptive market competition such as retail clinics and corporate telemedicine providers coupled with changes in health insurance from fee-for-service to value-based payment further challenge the care model and economics of PPC. Finally, recognition of family social circumstances as major determinants of children's health presents another challenge to the status quo. As such, although one PPC future may resemble its present state, a more innovative future is likely to include clinics and practices more oriented toward and linked to communities and directed at the social determinants of health. In addition, the rise in physical, behavioral, and social problems in practice call for a growing focus on wellness, including sleep, nutrition, and activity, that promises to reorient the PPC future in productive new directions. The half-way technology of current EHR systems will ideally be spun into electronic hubs that facilitate teamwork between PPC, specialists, and community groups. Research and practice improvement strategies including involvement in "learning health systems" will be critical to making PPC effective in an evolving society. Although threatened by 21st century forces and hard-to-anticipate change, PPC is ideally positioned to build upon its core functions to create multidisciplinary teams that reach into the community, promoting a holistic wellness for children consistent with the broadest definition of health.

What matters to me - a web-based preference elicitation tool for clients in long-term care: a user-centred design

Background

During the process of decision-making for long-term care, clients are often dependent on informal support and available information about quality ratings of care services. However, clients do not take ratings into account when considering preferred care, and need assistance to understand their preferences. A tool to elicit preferences for long-term care could be beneficial. Therefore, the aim of this qualitative descriptive study is to understand the user requirements and develop a web-based preference elicitation tool for clients in need of long-term care.

Methods

We applied a user-centred design in which end-users influence the development of the tool. The included end-users were clients, relatives, and healthcare professionals. Data collection took place between November 2017 and March 2018 by means of meetings with the development team consisting of four users, walkthrough interviews with 21 individual users, video-audio recordings, field notes, and observations during the use of the tool. Data were collected during three phases of iteration: Look and feel, Navigation, and Content. A deductive and inductive content analysis approach was used for data analysis.

Results

The layout was considered accessible and easy during the Look and feel phase, and users asked for neutral images. Users found navigation easy, and expressed the need for concise and shorter text blocks. Users reached consensus about the categories of preferences, wished to adjust the content with propositions about well-being, and discussed linguistic difficulties.

Conclusion

By incorporating the requirements of end-users, the user-centred design proved to be useful in progressing from the prototype to the finalized tool ‘What matters to me’. This tool may assist the elicitation of client’s preferences in their search for long-term care.