How can healthcare professionals provide guidance and support to parents of adolescents? Results from a primary care-based study.

BACKGROUND: This 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. METHODS: Parents 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. RESULTS: Our 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. CONCLUSIONS: The 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.

Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED).

BACKGROUND: Many patients with atrial fibrillation (AF) remain undiagnosed despite availability of interventions to reduce stroke risk. Predictive models to date are limited by data requirements and theoretical usage. We aimed to develop a model for predicting the 2-year probability of AF diagnosis and implement it as proof-of-concept (POC) in a production electronic health record (EHR). METHODS: We used a nested case-control design using data from the Indiana Network for Patient Care. The development cohort came from 2016 to 2017 (outcome period) and 2014 to 2015 (baseline). A separate validation cohort used outcome and baseline periods shifted 2 years before respective development cohort times. Machine learning approaches were used to build predictive model. Patients ≥ 18 years, later restricted to age ≥ 40 years, with at least two encounters and no AF during baseline, were included. In the 6-week EHR prospective pilot, the model was silently implemented in the production system at a large safety-net urban hospital. Three new and two previous logistic regression models were evaluated using receiver-operating characteristics. Number, characteristics, and CHA2DS2-VASc scores of patients identified by the model in the pilot are presented. RESULTS: After restricting age to ≥ 40 years, 31,474 AF cases (mean age, 71.5 years; female 49%) and 22,078 controls (mean age, 59.5 years; female 61%) comprised the development cohort. A 10-variable model using age, acute heart disease, albumin, body mass index, chronic obstructive pulmonary disease, gender, heart failure, insurance, kidney disease, and shock yielded the best performance (C-statistic, 0.80 [95% CI 0.79-0.80]). The model performed well in the validation cohort (C-statistic, 0.81 [95% CI 0.8-0.81]). In the EHR pilot, 7916/22,272 (35.5%; mean age, 66 years; female 50%) were identified as higher risk for AF; 5582 (70%) had CHA2DS2-VASc score ≥ 2. CONCLUSIONS: Using variables commonly available in the EHR, we created a predictive model to identify 2-year risk of developing AF in those previously without diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk.

Prevalence of pain reports in pediatric primary care and association with demographics, body mass index, and exam findings: a cross-sectional study.

BACKGROUND: Pediatric pain is associated to patient weight and demographics in specialized settings, but pain prevalence and its associated patient attributes in general pediatric outpatient care are unknown. Our objective was to determine the rate of positive pain screenings in pediatric primary care and evaluate the relationship between reported pain and obesity, demographics, and exam findings during routine pediatric encounters. METHODS: Cross-sectional observational study of 26,180 patients ages 2 to 19 seen in five urban pediatric primary care clinics between 2009 and 2016. Data were collected from systematic screening using a computerized clinical decision support system. Multivariable logistic regressions were used to analyze the association between pain reporting and obesity (body mass index), age, sex, race, season, insurance status, clinic site, prior pain reporting, pain reporting method, and exam findings. RESULTS: Pain was reported by the patient or caregiver in 14.9% of visits. In adjusted models, pain reporting was associated with obesity (Odds Ratio (OR) 1.23, 95% Confidence Intervals (CI) 1.11-1.35) and severe obesity (OR 1.32, CI 1.17-1.49); adolescents (OR 1.47, CI 1.33-1.61); and females (OR 1.21, CI 1.12-1.29). Pain reported at the preceding visit increased odds of pain reporting 2.67 times (CI 2.42-2.95). Abnormal abdominal, extremity, ear, nose, throat, and lymph node exams were associated with pain reporting. Pain reporting increased in minority races within clinics that predominantly saw a concordant race. CONCLUSIONS: Pain is common in general pediatric encounters, and occurs more frequently in obese children and those who previously reported pain. Pain reporting may be influenced by seasonal variation and clinic factors. Future pediatric pain screening may be guided by associated risk factors to improve identification and targeted healthcare interventions.

High Blood Pressure in Children Aged 3 to 12 Years Old With Overweight or Obesity.

Objective: (1) To describe the prevalence of high blood pressure (BP) and the association with BMI in young children with overweight/obesity; (2) to evaluate the accuracy of a single high BP to diagnose sustained hypertension over three visits. Methods: We used pre-intervention data from the Improving Pediatric Obesity Practice Using Prompts (iPOP-UP) trial. We included children aged 3-12 years with BMI ≥85th percentile at well-visits in 2019-2021 at 84 primary care practices in 3 US health systems in the Northeast, Midwest, and South. BP percentiles were calculated from the first visit with BP recorded during the study period. Hypertensive-range BP was defined by the 2017 American Academy of Pediatrics guideline. We tested the association between BMI classification and hypertensive BP using multivariable logistic regression. Results: Of 78,280 children with BMI ≥85th percentile, 76,214 (97%) had BP recorded during the study period (mean 7.4 years, 48% female, 53% with overweight, and 13% with severe obesity). The prevalence of elevated or hypertensive BP was 31%, including 27% in children with overweight and 33%, 39%, and 49% with class I, II, and III obesity, respectively. Higher obesity severity was associated with higher odds of hypertensive BP in the multivariable model. Stage 2 hypertensive BP at the initial visit had specificity of 99.1% (95% confidence interval 98.9-99.3) for detecting sustained hypertension over ≥3 visits. Conclusions: High BP is common in 3- to 12-year-olds with overweight/obesity, with higher obesity severity associated with greater hypertension. Children with overweight/obesity and stage 2 BP are likely to have sustained hypertension and should be prioritized for evaluation. Trial Registration: ClinicalTrials.gov Identifier: NCT05627011.

Considerations for Alternative Decision-Making When Transitioning to Adulthood for Youth With Intellectual and Developmental Disabilities: Policy Statement.

With advances in medical care, more youth with intellectual and/or developmental disabilities (IDD) are transitioning into adulthood. Patient- and family-centered, integrated care is warranted around this time of transition. Support teams (including the youth, caregivers, teachers, and pediatricians) should engage in transition planning, ideally starting between 12 and 14 years of age, to identify and develop resources to support the maturing youth's capacity for independent decision-making. Care teams should consider the varied levels of alternative decision-making support, which may include supported decision-making, medical proxy decision-making, power of attorney, and/or establishment of legal guardianship arrangements, to support the youth's health and well-being optimally. Ultimately, if independent decision-making is not appropriate, the goal for youth with IDD should be the least restrictive alternative, while preserving human rights and human dignity and promoting their autonomy. These considerations review alternative decision-making support, concepts, and legal requirements available for youth with IDD and their care teams. Pediatricians can support youth with IDD and their families in the transition process and decision-making autonomy by actively engaging the youth in care decisions, supporting needs for augmentative communication, fostering their expression of preferences and understanding of care decisions, and linking them to resources such as the medical-legal partnership model.

Better Guidelines and Policies: AAP's Partnership for Policy Implementation.

OBJECTIVES: In 2005, the American Academy of Pediatrics founded the Partnership for Policy Implementation (PPI). The PPI has collaborated with authors to improve the quality of clinical guidelines, technical reports, and policies that standardize care delivery, improve care quality and patient outcomes, and reduce variation and costs. METHODS: In this article, we describe how the PPI trained informaticians apply a variety of tools and techniques to these guidance documents, eliminating ambiguity in clinical recommendations and allowing guideline recommendations to be implemented by practicing clinicians and electronic health record (EHR) developers more easily. RESULTS: Since its inception, the PPI has participated in the development of 45 published and 27 in-progress clinical practice guidelines, policy statements, technical and clinical reports, and other projects endorsed by the American Academy of Pediatrics. The partnership has trained informaticians to apply a variety of tools and techniques to eliminate ambiguity or lack of decidability and can be implemented by practicing clinicians and EHR developers. CONCLUSIONS: With the increasing use of EHRs in pediatrics, the need for medical societies to improve the clarity, decidability, and actionability of their guidelines has become more important than ever.

Feature engineering from medical notes: A case study of dementia detection.

Background and objectives: Medical notes are narratives that describe the health of the patient in free text format. These notes can be more informative than structured data such as the history of medications or disease conditions. They are routinely collected and can be used to evaluate the patient's risk for developing chronic diseases such as dementia. This study investigates different methodologies for transforming routine care notes into dementia risk classifiers and evaluates the generalizability of these classifiers to new patients and new health care institutions. Methods: The notes collected over the relevant history of the patient are lengthy. In this study, TF-ICF is used to select keywords with the highest discriminative ability between at risk dementia patients and healthy controls. The medical notes are then summarized in the form of occurrences of the selected keywords. Two different encodings of the summary are compared. The first encoding consists of the average of the vector embedding of each keyword occurrence as produced by the BERT or Clinical BERT pre-trained language models. The second encoding aggregates the keywords according to UMLS concepts and uses each concept as an exposure variable. For both encodings, misspellings of the selected keywords are also considered in an effort to improve the predictive performance of the classifiers. A neural network is developed over the first encoding and a gradient boosted trees model is applied to the second encoding. Patients from a single health care institution are used to develop all the classifiers which are then evaluated on held-out patients from the same health care institution as well as test patients from two other health care institutions. Results: The results indicate that it is possible to identify patients at risk for dementia one year ahead of the onset of the disease using medical notes with an AUC of 75% when a gradient boosted trees model is used in conjunction with exposure variables derived from UMLS concepts. However, this performance is not maintained with an embedded feature space and when the classifier is applied to patients from other health care institutions. Moreover, an analysis of the top predictors of the gradient boosted trees model indicates that different features inform the classification depending on whether or not spelling variants of the keywords are included. Conclusion: The present study demonstrates that medical notes can enable risk prediction models for complex chronic diseases such as dementia. However, additional research efforts are needed to improve the generalizability of these models. These efforts should take into consideration the length and localization of the medical notes; the availability of sufficient training data for each disease condition; and the variabilities resulting from different feature engineering techniques.

Selecting EHR-driven recruitment strategies: An evidence-based decision guide.

Participant recruitment for research is a persistent bottleneck that can be improved by leveraging electronic health records (EHRs). Despite emerging evidence for various EHR-driven approaches, guidance for those attempting to select and use such approaches is limited. The national Recruitment Innovation Center established the EHR Recruitment Consult Resource (ERCR) service line to support multisite studies through implementation of EHR-driven recruitment strategies. As the ERCR, we evolved a guide through 17 consultations over 3 years with multisite studies recruiting in diverse biomedical research domains. We assessed literature and engaged domain experts to identify five key EHR-driven recruitment strategies: direct to patient messages, candidate lists for mailings/calls, direct to research alerts, point of care alerts, and participant registries. Differentiating factors were grouped into factors of study population, study protocol and recruitment workflows, and recruitment site capabilities. The decision matrix indicates acceptable or preferred strategies based on the differentiating factors. Across the ERCR consultations, candidate lists for mailing or calls were most common, participant registries were least frequently recommended, and for some studies no EHR-driven recruitment was recommended. Comparative effectiveness research is needed to refine further evidence for these and potentially new strategies to come.

Poverty, Comorbidity, and Ethnicity: COVID-19 Outcomes in a Safety Net Health System.

Objective: To determine if race-ethnicity is correlated with case-fatality rates among low-income patients hospitalized for COVID-19. Research Design: Observational cohort study using electronic health record data. Patients: All patients assessed for COVID-19 from March 2020 to January 2021 at one safety net health system. Measures: Patient demographic and clinical characteristics, and hospital care processes and outcomes. Results: Among 25,253 patients assessed for COVID-19, 6,357 (25.2%) were COVID-19 positive: 1,480 (23.3%) hospitalized; 334 (22.6%) required intensive care; and 106 (7.3%) died. More Hispanic patients tested positive (51.8%) than non-Hispanic Black (31.4%) and White patients (16.7%, P<.001]. Hospitalized Hispanic patients were younger, more often uninsured, and less likely to have comorbid conditions. Non-Hispanic Black patients had significantly more diabetes, hypertension, obesity, chronic kidney disease, and asthma (P<.05). Non-Hispanic White patients were older and had more cigarette smoking history, COPD, and cancer. Non-Hispanic White patients were more likely to receive intensive care (29.6% vs 21.1% vs 20.8%, P=.007) and more likely to die (12% vs 7.3% vs 3.5%, P<.001) compared with non-Hispanic Black and Hispanic patients, respectively. Length of stay was similar for all groups. In logistic regression models, Medicaid insurance status independently correlated with hospitalization (OR 3.67, P<.001) while only age (OR 1.076, P<.001) and cerebrovascular disease independently correlated with in-hospital mortality (OR 2.887, P=.002). Conclusions: Observed COVID-19 in-hospital mortality rate was lower than most published rates. Age, but not race-ethnicity, was independently correlated with in-hospital mortality. Safety net health systems are foundational in the care of vulnerable patients suffering from COVID-19, including patients from under-represented and low-income groups.

Digital detection of dementia (D3): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems.

BACKGROUND: Early detection of Alzheimer's disease and related dementias (ADRD) in a primary care setting is challenging due to time constraints and stigma. The implementation of scalable, sustainable, and patient-driven processes may improve early detection of ADRD; however, there are competing approaches; information may be obtained either directly from a patient (e.g., through a questionnaire) or passively using electronic health record (EHR) data. In this study, we aim to identify the benefit of a combined approach using a pragmatic cluster-randomized clinical trial. METHODS: We have developed a Passive Digital Marker (PDM), based on machine learning algorithms applied to EHR data, and paired it with a patient-reported outcome (the Quick Dementia Rating Scale or QDRS) to rapidly share an identified risk of impairment to a patient's physician. Clinics in both south Florida and Indiana will be randomly assigned to one of three study arms: 1200 patients in each of the two populations will be administered either the PDM, the PDM with the QDRS, or neither, for a total of 7200 patients across all clinics and populations. Both incidence of ADRD diagnosis and acceptance into ADRD diagnostic work-up regimens is hypothesized to increase when patients are administered both the PDM and QDRS. Physicians performing the work-up regimens will be blind to the study arm of the patient. DISCUSSION: This study aims to test the accuracy and effectiveness of the two scalable approaches (PDM and QDRS) for the early detection of ADRD among older adults attending primary care practices. The data obtained in this study may lead to national early detection and management program for ADRD as an efficient and beneficial method of reducing the current and future burden of ADRD, as well as improving the annual rate of newly documented ADRD in primary care practices. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05231954 . Registered February 9, 2022.

Assessing the use of a clinical decision support tool for pain management in primary care.

Objective: Given time constraints, poorly organized information, and complex patients, primary care providers (PCPs) can benefit from clinical decision support (CDS) tools that aggregate and synthesize problem-specific patient information. First, this article describes the design and functionality of a CDS tool for chronic noncancer pain in primary care. Second, we report on the retrospective analysis of real-world usage of the tool in the context of a pragmatic trial. Materials and methods: The tool known as OneSheet was developed using user-centered principles and built in the Epic electronic health record (EHR) of 2 health systems. For each relevant patient, OneSheet presents pertinent information in a single EHR view to assist PCPs in completing guideline-recommended opioid risk mitigation tasks, review previous and current patient treatments, view patient-reported pain, physical function, and pain-related goals. Results: Overall, 69 PCPs accessed OneSheet 2411 times (since November 2020). PCP use of OneSheet varied significantly by provider and was highly skewed (site 1: median accesses per provider: 17 [interquartile range (IQR) 9-32]; site 2: median: 8 [IQR 5-16]). Seven "power users" accounted for 70% of the overall access instances across both sites. OneSheet has been accessed an average of 20 times weekly between the 2 sites. Discussion: Modest OneSheet use was observed relative to the number of eligible patients seen with chronic pain. Conclusions: Organizations implementing CDS tools are likely to see considerable provider-level variation in usage, suggesting that CDS tools may vary in their utility across PCPs, even for the same condition, because of differences in provider and care team workflows.

Improving Patient-Centered Communication about Sudden Unexpected Death in Epilepsy through Computerized Clinical Decision Support.

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.

Transforming primary medical research knowledge into clinical decision.

While the utility of computerized clinical decision support (CCDS) for multiple select clinical domains has been clearly demonstrated, much less is known about the full breadth of domains to which CCDS approaches could be productively applied. To explore the applicability of CCDS to general medical knowledge, we sampled a total of 500 primary research articles from 4 high-impact medical journals. Employing rule-based templates, we created high-level CCDS rules for 72% (361/500) of primary medical research articles. We subsequently identified data sources needed to implement those rules. Ourfindings suggest that CCDS approaches, perhaps in the form of non-interruptive infobuttons, could be much more broadly applied. In addition, our analytic methods appear to provide a means of prioritizing and quantitating the relative utility of available data sources for purposes of CCDS.

Clinician Perceptions of a Computerized Decision Support System for Pediatric Type 2 Diabetes Screening.

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.

Let Them Speak for Themselves: Improving Adolescent Self-Report Rate on Pre-Visit Screening.

BACKGROUND: Adolescent pre-visit screening on patient-generated health data is a common and efficient practice to guide clinical decision making. However, proxy informants (eg, parents or caregivers) often complete these forms, which may lead to incorrect information or lack of confidentiality. Our objective was to improve the adolescent self-report rate on pre-visit screening. METHODS: We conducted an interventional study using an interrupted time series design to compare adolescent self-report rates (percent of adolescents ages 12-18 years completing their own pre-visit screening) over 16 months in general pediatric ambulatory clinics. We collected data using a computerized clinical decision support system with waiting room electronic tablet screening. Preintervention rates were low, and we created and implemented 2 electronic workflow alerts, one each to the patient/caregiver and clinical staff, reminding them that the adolescent should answer the questions independently. We included the first encounter from each adolescent and evaluated changes in adolescent self-reporting between pre- and postintervention periods using interrupted time series analysis. RESULTS: Patients or caregivers completed 2670 qualifying pre-visit screenings across 19 preintervention, 7 intervention, and 44 postintervention weeks. Self-reporting by younger adolescents nearly doubled, with a significant increase of 19.3 percentage points (confidence interval [CI], 9.1-29.5) from the baseline 20.5%. Among older adolescents, the stable baseline rate of 53.6% increased by 9.2 absolute percentage points (CI, -7.0 to 25.3). There were no significant pre- or postintervention secular trends. CONCLUSIONS: Two automated alerts directing clinic personnel and families to have adolescents self-report significantly and sustainably improved younger adolescent self-reporting on electronic patient-generated health data instruments.

Caregiver Comfort in Adolescents Independently Completing Screening Tablet-Based Questionnaires at Primary Care Visits.

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.

A six-year repeated evaluation of computerized clinical decision support system user acceptability.

OBJECTIVE: Long-term acceptability among computerized clinical decision support system (CDSS) users in pediatrics is unknown. We examine user acceptance patterns over six years of our continuous computerized CDSS integration and updates. MATERIALS AND METHODS: Users of Child Health Improvement through Computer Automation (CHICA), a CDSS integrated into clinical workflows and used in several urban pediatric community clinics, completed annual surveys including 11 questions covering user acceptability. We compared responses across years within a single healthcare system and between two healthcare systems. We used logistic regression to assess the odds of a favorable response to each question by survey year, clinic role, part-time status, and frequency of CHICA use. RESULTS: Data came from 380 completed surveys between 2011 and 2016. Responses were significantly more favorable for all but one measure by 2016 (OR range 2.90-12.17, all p < 0.01). Increasing system maturity was associated with improved perceived function of CHICA (OR range 4.24-7.58, p < 0.03). User familiarity was positively associated with perceived CDSS function (OR range 3.44-8.17, p < 0.05) and usability (OR range 9.71-15.89, p < 0.01) opinions. CONCLUSION: We present a long-term, repeated follow-up of user acceptability of a CDSS. Favorable opinions of the CDSS were more likely in frequent users, physicians and advanced practitioners, and full-time workers. CHICA acceptability increased as it matured and users become more familiar with it. System quality improvement, user support, and patience are important in achieving wide-ranging, sustainable acceptance of CDSS.

Repeatability and Sample Size Assessment Associated with Computed Tomography-Based Lung Density Metrics.

RATIONALE AND OBJECTIVES: Density-based metrics assess severity of lung disease but vary with lung inflation and method of scanning. The aim of this study was to evaluate the repeatability of single center, CT-based density metrics of the lung in a normal population and assess study sample sizes needed to detect meaningful changes in lung density metrics when scan parameters and volumes are tightly controlled. MATERIALS AND METHODS: Thirty-seven subjects (normal smokers and non-smokers) gave consent to have randomly assigned repeated, breath-held scans at either inspiration (90% vital capacity: TLC) or expiration (20% vital capacity: FRC). Repeated scans were analyzed for: mean lung density (MLD), 15th percentile point of the density histogram (P15), low attenuation areas (LAA) and alpha (fractal measure of hole size distribution). Using inter-subject differences and previously reported bias, sample size was estimated from month or yearly change in density metrics obtained from published literature (i.e. meaningful change). RESULTS: Inter-scan difference measurements were small for density metrics (ICC > 0.80) and average ICCs for whole lung alpha-910 and alpha-950 were 0.57 and 0.64, respectively. Power analyses demonstrated that, under the control conditions with minimal extrinsic variation, population sizes needed to detect meaningful changes in density measures for TLC or FRC repeated scans ranged from a few (20-40) to a few hundred subjects, respectively. CONCLUSION: A meaningful sample size was predicted from this study using volume-controlled normal subjects in a controlled imaging environment. Under proper breath-hold conditions, high repeatability was obtained in cohorts of normal smokers and non-smokers.