Browsing by Author "Grout, Randall W."
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Item Assessing the use of a clinical decision support tool for pain management in primary care(Oxford University Press, 2022-09-15) Apathy, Nate C.; Sanner, Lindsey; Adams, Meredith C.B.; Mamlin, Burke W.; Grout, Randall W.; Fortin, Saura; Hillstrom, Jennifer; Saha, Amit; Teal, Evgenia; Vest, Joshua R.; Menachemi, Nir; Hurley, Robert W.; Harle, Christopher A.; Mazurenko, Olena; Health Policy and Management, School of Public HealthObjective: 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.Item Caregiver Comfort in Adolescents Independently Completing Screening Tablet-Based Questionnaires at Primary Care Visits(Elsevier, 2019-09-12) Ferrin, Stephanie N.; Grout, Randall W.; Gilbert, Amy Lewis; Wilkinson, Tracey A.; Cheng, Erika R.; Downs, Stephen M.; Aalsma, Matthew C.; Pediatrics, School of MedicineObjectives The objective of this study was to assess caregiver comfort regarding adolescent completion of computerized health screening questionnaires created for adolescents. Design We conducted a mixed method, cross-sectional survey of caregivers of adolescent patients (n=104) ages 12–18 years that had a medical visit between June and August of 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 ANOVA was used to compare 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.Item Clinician Perceptions of a Computerized Decision Support System for Pediatric Type 2 Diabetes Screening(Thieme, 2020-03) El Mikati, Hala K.; Yazel-Smith, Lisa; Grout, Randall W.; Downs, Stephen M.; Carroll, Aaron E.; Hannon, Tamara S.; Pediatrics, School of MedicineObjective: 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.Item Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED)(BMC, 2021-04-03) Grout, Randall W.; Hui, Siu L.; Imler, Timothy D.; El‑Azab, Sarah; Sands, George H.; Ateya, Mohammad; Pike, Francis; Pediatrics, School of MedicineBackground: 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.Item 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(MDPI, 2022-10-11) Kleiman, Michael J.; Plewes, Abbi D.; Owora, Arthur; Grout, Randall W.; Dexter, Paul Richard; Fowler, Nicole R.; Galvin, James E.; Ben Miled, Zina; Boustani, Malaz; Medicine, School of MedicineBackground: 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.Item Feature engineering from medical notes: A case study of dementia detection(Elsevier, 2023-03-18) Ben Miled, Zina; Dexter, Paul R.; Grout, Randall W.; Boustani, Malaz; Electrical and Computer Engineering, School of Engineering and TechnologyBackground 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.Item How can healthcare professionals provide guidance and support to parents of adolescents? Results from a primary care-based study(BMC, 2021-03-20) Jones, Lindsey D.; Grout, Randall W.; Gilbert, Amy L.; Wilkinson, Tracey A.; Garbuz, Tamila; Downs, Stephen M.; Aalsma, Matthew C.; Psychology, School of ScienceBackground: 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.Item Improving Patient-Centered Communication about Sudden Unexpected Death in Epilepsy through Computerized Clinical Decision Support(Thieme, 2021) Grout, Randall W.; Buchhalter, Jeffrey; Patel, Anup D.; Brin, Amy; Clark, Ann A.; Holmay, Mary; Story, Tyler J.; Downs, Stephen M.; Pediatrics, School of MedicineBackground: 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.Item Let them speak for themselves: Improving adolescent self-report rate on pre-visit screening(Elsevier, 2019) Grout, Randall W.; Cheng, Erika R.; Aalsma, Matthew C.; Downs, Stephen M.; Pediatrics, School of MedicineBackground Adolescent pre-visit screening on patient-generated health data is a common and efficient practice to guide clinical decision making. However, proxy informants (e.g., 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. Pre-intervention rates were low, and we created and implemented two 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 post-intervention periods using interrupted time series analysis. Results Patients or caregivers completed 2,670 qualifying pre-visit screenings across 19 pre-intervention, 7 intervention, and 44 post-intervention weeks. Self-reporting by younger adolescents nearly doubled with a significant increase of 19.3 percentage points (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 -7.0-25.3). There were no significant pre- or post-intervention 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.Item Leveraging artificial intelligence to summarize abstracts in lay language for increasing research accessibility and transparency(Oxford University Press, 2024) Shyr, Cathy; Grout, Randall W.; Kennedy, Nan; Akdas, Yasemin; Tischbein, Maeve; Milford, Joshua; Tan, Jason; Quarles, Kaysi; Edwards, Terri L.; Novak, Laurie L.; White, Jules; Wilkins, Consuelo H.; Harris, Paul A.; Pediatrics, School of MedicineObjective: Returning aggregate study results is an important ethical responsibility to promote trust and inform decision making, but the practice of providing results to a lay audience is not widely adopted. Barriers include significant cost and time required to develop lay summaries and scarce infrastructure necessary for returning them to the public. Our study aims to generate, evaluate, and implement ChatGPT 4 lay summaries of scientific abstracts on a national clinical study recruitment platform, ResearchMatch, to facilitate timely and cost-effective return of study results at scale. Materials and methods: We engineered prompts to summarize abstracts at a literacy level accessible to the public, prioritizing succinctness, clarity, and practical relevance. Researchers and volunteers assessed ChatGPT-generated lay summaries across five dimensions: accuracy, relevance, accessibility, transparency, and harmfulness. We used precision analysis and adaptive random sampling to determine the optimal number of summaries for evaluation, ensuring high statistical precision. Results: ChatGPT achieved 95.9% (95% CI, 92.1-97.9) accuracy and 96.2% (92.4-98.1) relevance across 192 summary sentences from 33 abstracts based on researcher review. 85.3% (69.9-93.6) of 34 volunteers perceived ChatGPT-generated summaries as more accessible and 73.5% (56.9-85.4) more transparent than the original abstract. None of the summaries were deemed harmful. We expanded ResearchMatch's technical infrastructure to automatically generate and display lay summaries for over 750 published studies that resulted from the platform's recruitment mechanism. Discussion and conclusion: Implementing AI-generated lay summaries on ResearchMatch demonstrates the potential of a scalable framework generalizable to broader platforms for enhancing research accessibility and transparency.