Clinical trial NCT02865967: Developing and Implementing Asthma-Guidance and Prediction System (a-GPS) for Better Asthma Management
Official title: Enhancing Asthma Care and Outcome Through the Implementation of Asthma-Guidance and Prediction System (a-GPS) on Asthma Management Program: A Randomized Block Design
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Asthma
Keywords: Decision Support Techniques
Delayed Diagnosis
Natural Language Processing
Geographic Information Systems
Lead sponsor: Mayo Clinic
Sponsor class: OTHER
First posted: 2016-08-15
Enrollment: 185
Sex: ALL
Maximum age (years): 17
Standard ages: CHILD
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SUPPORTIVE_CARE

Brief summary: Asthma is the most common chronic condition in children and one of the five most burdensome diseases in the United States. Despite this, research and care for childhood asthma are limited by inefficient utilization of electronic medical records (EMRs) to facilitate large-scale studies and care.

The primary goal of this clinical trial is to implement the asthma-Guidance and Prediction System (a-GPS) on the Asthma Management Program (AMP, a current care coordination program for asthma care of children aged 5-17 years at Mayo Clinic). Primary hypothesis: The implementation of a-GPS in the current care is logistically feasible.

Detailed description: Despite the availability of evidence-based guidelines for asthma management and effective asthma therapies, asthma continues to cause a significant morbidity and burden to our society. Growing deployments of Electronic Health Records (EHRs) systems have established large practice-based longitudinal datasets, which allow for the identification of patient cohorts for epidemiological investigations and population-based management. Natural Language Processing (NLP; automated chart review using computer program) has received great attention and has played a critical role in secondary use of EHRs for clinical care and translational research. For example, we recently developed an NLP algorithm for the Predetermined Asthma Criteria (PAC) that can ascertain asthma status without manual chart review.

The primary goals of this proposed clinical trial are 1) to implement the asthma-Guidance and Prediction System (a-GPS) on Asthma Management Program (AMP, a current care coordination program for asthma care of children aged 5-17 years at Mayo Clinic) and 2) assess the impact of a-GPS on the primary and secondary end points for a one-year study period. These goals will be accomplished by conducting a randomized clinical trial with block design for three groups of children as the groups (blocks) of children are significantly heterogeneous in terms of receiving asthma care.

The a-GPS program includes 1) natural language processing (NLP) capabilities (i.e., automated EHR review to identify asthma status (yes vs. no) and monitor asthma activity (onset, remission, and relapse) in real time), 2) temporal and geospatial trends analysis of asthma outcome and care, and 3) asthma care optimization through predictive analytics.

The primary end points include asthma outcome using quarterly measured age-appropriate asthma control questionnaire (ie, Asthma Control Test (ACT; validated for children aged ≥ 4 years) scores for children ≥ 4 years: a total duration of ACT scores \> 19, or Test for Respiratory and Asthma Control in Kids (TRACK; validated for children under 5 years) scores for children \<4 years: a total duration of TRACK scores \< 80), care quality (timely care in response to asthma-related events), and costs (total costs per member). For those in Block 3, the rate of a physician diagnosis of asthma during the study will be also compared between the intervention and control groups as a measure for quality care.

Eligibility criteria: Inclusion Criteria for Children in Block 1:

- Must be enrolled in AMP at the time of enrollment.

Inclusion Criteria for Children in Block 2:

* Physician diagnosis of persistent asthma by NLP program for the list of physician diagnoses referring to persistent asthma, and/or
* Persistent asthma equivalent condition by either the Healthcare Effectiveness Data and Information Set (HEDIS); (e.g., ER visit or hospitalization for asthma during the past 12 months) or the National Asthma Education and Prevention Program (NAEPP); (e.g., ≥2 exacerbations requiring oral systemic corticosteroids in the past 6 months for children aged 0-4 years and 12 months for those aged ≥5 years), and/or
* Physician diagnosis of asthma with controller medication (e.g., inhaled corticosteroid) documented in the past 12 months, but they were not enrolled in AMP at the time of enrollment or during run-in period.

Inclusion Criteria for Children in Block 3:

- Children must meet the criteria for asthma delineated in Table 1 in protocol for asthma and recurrent asthma-like symptoms, but do not have a documentation of a diagnosis of asthma in medical records aged 0-17 years.

Exclusion Criteria (All Blocks):

* Non-Olmsted County residents
* Children who are not enrolled in Mayo Clinic downtown pediatric practice
* No research authorization for using medical records for research
* Immunosuppressive therapy
* Conditions making asthma ascertainment difficult for Block 3 (pulmonary function tests that showed forced expiratory volume at one second (FEV1) to be consistently below 50% predicted or diminished diffusion capacity, tracheobronchial foreign body at or about the incidence date of asthma, wheezing occurring only in response to anesthesia or medications, bullous emphysema or pulmonary fibrosis on chest radiograph, homozygous alpha 1-protease inhibitor deficiency (PiZZ) alpha1-antitrypsin, cystic fibrosis, other major chest disease such as severe kyphoscoliosis or bronchiectasis)
* Children and their caregivers who decline to participate in the study

Primary outcomes:
measure: Asthma exacerbation
timeFrame: up to one year
Secondary outcomes:
measure: Clinicians' workload
timeFrame: When collecting data listed in A-GPS with and without A-GPS
measure: Health care cost
timeFrame: T0 (baseline) and T4 (when the study ends, approximately up to one year)
measure: Asthma control status
timeFrame: up to one year
measure: Timeliness of asthma follow-up care after asthma exacerbation
timeFrame: T0 (baseline) and T4 (when the study ends, approximately up to one year)
Other outcomes:
Locations:
city: Rochester
country: United States
facility: Mayo Clinic in Rochester
state: Minnesota
Study officials:
affiliation: Mayo Clinic
name: Young J Juhn
role: PRINCIPAL_INVESTIGATOR
References:
citation: Seol HY, Shrestha P, Muth JF, Wi CI, Sohn S, Ryu E, Park M, Ihrke K, Moon S, King K, Wheeler P, Borah B, Moriarty J, Rosedahl J, Liu H, McWilliams DB, Juhn YJ. Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial. PLoS One. 2021 Aug 2;16(8):e0255261. doi: 10.1371/journal.pone.0255261. eCollection 2021.