Pulmonary Specialist-Health Coach Consult (PuSHCon) Model to Improve Access to Specialist Consultation and Receipt of Recommended Evidence-based Care for Vulnerable Patients With Chronic Obstructive Pulmonary Disease (COPD) and Asthma
Phase: NA
Conditions: COPD Asthma
Sponsor: University of California, San Francisco

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Study title: Pulmonary Specialist-Health Coach Consult Model Study 
Document type: Study protocol 
NCT number: NCT03695276 
Document date: September 30, 2025 
 

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Study Protocol 
 
The PuSHCon study was a randomized controlled trial of health coaching-facilitated pulmonary 
specialist consultations added to usual care (HC arm) versus usual care alone (UC arm) for 
patients with a diagnosis of asthma and/or COPD.  The protocol is available at clinicaltrials.gov 
(NCT03695276).  The study was approved by the UCSF Institutional Review Board (18-26335). 
Written informed consent was obtained from all study participants. 
 
Setting 
The PuSHCon study was conducted between October 2020 and January 2025 at ten urban 
Federally Qualiﬁed Health Centers (FQHCs) or “FQHC look-alikes” by the Health Services 
Resources and Services Administration. Eight participating study clinics were in the community 
and two were located at the county hospital medical campus.  
 
Participants 
Patients were eligible for enrollment if they had been seen in primary care at least once but had 
not been seen by pulmonary specialty care in the past 12 months, spoke Spanish or English, and 
had a diagnosis of COPD and/or asthma. To be eligible, they also had to report a high level of 
symptoms as measured by the COPD Assessment Test1 or the Asthma Control Test,2 or to have 
had two exacerbations or one hospitalization due to an exacerbation within the last year. While 
this was a pragmatic trial with eligibility based on presence of a diagnosis in the medical record, 
diagnoses were reviewed by a study physician (MK) and pulmonologist (GS) blinded to allocation 
arm, who reviewed spirometric and imaging data as well as other clinical criteria.   
 
Recruitment, enrollment, and randomization within clinics 
Potentially eligible patients were identified primarily through review of electronic medical records 
(EMR) from the previous 12 months for the 10 clinics and the county hospital and emergency 
department (ED).  Patients with a COPD- or asthma-related diagnosis (ICD-10 parent codes J41 – 
J45) were assessed by review of electronic health records and their primary care provider (Figure 
1). A research assistant (RA) attempted to contact the patients not excluded by telephone and 
mail to describe the study and conduct additional eligibility screening. Eligible patients interested 
in the study met with the RA remotely or at their primary care clinic for informed consent and 
study enrollment. Within each clinic, patients were randomized 1:1 to HC or UC, in blocks of 10, 
using a random generator (Microsoft Excel 360).  Randomization by the individual, rather than by 

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clinic, was chosen when a pilot phase of the study found a higher ICC than anticipated, such that 
clinic-level randomization would require a sample size nearly 80% larger than individual-level 
randomization. In addition, individual level randomization enhanced recruitment of clinics by 
enabling the study team to offer coaching for some patients at each participating clinic. After 
obtaining baseline survey measurements, the RA activated a lookup feature to reveal the 
participant’s study arm assignment. 
 
Blinding 
Blinding of patients and clinical teams was not feasible due to the nature of the intervention. 
While RAs were trained to gather unbiased data, it was not possible to completely blind them to 
study arm, as we could not prevent patients from revealing they worked with a health coach.  
 
Health coaching (HC) training and ﬁdelity 
We used a health coaching model developed by the Center for Excellence in Primary Care3 to train 
unlicensed health workers to support patient self-management using evidence-based patient-
centered techniques such as motivational interviewing and action planning.4, 5 The study Health 
Coach was a high school graduate with a diploma from a medical assistant program, bilingual in 
Spanish and English without medical licensure. The coach received approximately 30 hours of 
training over four weeks. COPD- and asthma-speciﬁc training was delivered to HC by two 
pulmonary specialists and covered the physiology of the conditions, related comorbidities, 
international disease management guidelines, and self-management skills that the HC would 
teach the patients. The coach shadowed the Pulmonary Specialist Nurse Practitioner (PSNP) in 
her specialty clinic and took part in existing educational classes on obstructive lung disease. The 
PSNP met weekly with the health coach to review cases, reinforce skills, and troubleshoot 
interventional activities. 
 
Usual care (UC) arm 
Patients randomized to usual care continued to have visits with their primary care provider over 
the course of their 4-month intervention period. They had access to any resources their provider 
and their clinic offered as part of standard care, including access to asthma or COPD educators, 
breathing education classes, pulmonary rehabilitation, assessment for co-morbidities, smoking 
cessation classes and pulmonary specialist referrals by the primary care clinician.   
 
Pulmonary-specialist - Health coach Consultation (HC) arm  

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Patients randomized to the intervention arm continued to have access to all resources available 
through usual care. In addition, they received a health coach-facilitated consultation and support 
for 16 weeks, as our protocol previously described.6 Health coaches ﬁrst reviewed the EMR and 
met with the patient to complete the Pulmonary Specialist Consultation Form, a tool to organize 
patient information for the PSNP.  HCs recorded patient medical history and co-morbidities, 
smoking history, risk and symptom assessment, COPD and asthma medications and treatment 
history, environmental triggers and screens for symptoms of sleep apnea, from review of the EMR 
and information supplied by the patient. The HC then presented the patient’s information to the 
PSNP who could gather additional information from the medical record if needed. The PSNP 
created a set of recommendations for changes in care using the GOLD/GINA guidelines without 
needing to see the patient.   
The health coach was assigned 165 patients over the course of the study with a maximum 
caseload of 30 patients at any given time. The HC was expected to conduct at least one in-depth 
consultation with the study PSNP, ideally within the ﬁrst 2-3 weeks of randomization, and to attend 
medical visits between the patient and their primary care provider (PCP) throughout the 
intervention period, whenever possible. Health coaches were expected to meet in person with the 
participant at least 2 additional times over the course of the study and to conduct a phone check-
in call at least every 3 weeks, including within two weeks after each medical visit, resulting in at 
least 5 points of contact over 16 weeks. Additional contacts were guided by patient needs and 
preferences. Health coaching focused on helping patients identify and achieve self-care goals for 
their COPD using techniques from motivational interviewing and adult learning models. Speciﬁc 
content included asthma or COPD education, action planning for exacerbations, practicing 
optimal inhaler use, and facilitating consultation with a PSNP. The Health Coach documented 
interactions in a study-speciﬁc database. 
 
Protocol Modiﬁcations  
The original PuSHCon protocol planned for at least three in-person meetings between the health 
coach and participant during the 16-week intervention, with the health coach present at one or 
more primary care visits to facilitate implementation of recommendations. Due to COVID-19–
related restrictions beginning in March 2020, the intervention was adapted to rely primarily on 
telephone and video visits. Health coaches received additional training to support patients with 
limited digital literacy in accessing video visits, although uptake remained low. When video was 
not feasible, education and skills training (e.g., inhaler technique review) were conducted via 
telephone using teach-back methods, supplemented with mailed or electronic educational 
materials and links to demonstration videos. The pandemic also reduced the availability of in-
person primary care visits, limiting opportunities for the health coach to coordinate directly with 

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the patient’s clinician during visits. To address this, health coaches used secure messaging within 
the electronic health record to communicate recommendations to primary care providers and to 
request approval for referrals, prescriptions, and other orders. These adaptations were 
documented in the study database, and ﬁdelity monitoring accounted for both planned and 
adapted delivery modes. 
 
Data Safety and Monitoring 
A Data Safety and Monitoring Board (DSMB) was established prior to recruitment. The DSMB 
consisted of a pulmonary nurse specialist, a primary care physician and an epidemiologist.  As 
directed by the DSMB, the research team reported ED visits and hospitalizations twice yearly and 
participant deaths within 30 days over the course of the study.  
 
Data Collection 
The study survey was administered remotely or in person by an RA at baseline and again at 16 
weeks. Medical records were reviewed for receipt of guideline-based care (e.g., recommended 
medications, smoking cessation support as needed, immunizations, screening or treatment for 
comorbidities). Patients received a $40 gift card or cash for completion of measures at baseline 
and 16 weeks, for a total of up to $80.  
 
Outcome Measures 
Primary outcomes, as deﬁned in ClinicalTrials.gov (NCT03695276) prior to the study launch, were 
the receipt of guideline-based care and the receipt of recommended medications. For Receipt of 
guideline-based care, we identiﬁed 13 guideline-based care recommendations (Table E1) based 
on international GOLD and GINA guidelines. For each participant, we identiﬁed the items for 
which the participant had an unmet recommendation (“care gap”) at baseline. The outcome was 
the proportion of those care gaps that were addressed by 16 weeks, as documented in the 
electronic health record.  
Table E1. Recommended care deﬁned for Speciﬁc Aim 1 
Recommended care 
Condition 
Gap closure deﬁned as 
Vaccines 
Inﬂuenza 
All 
No inﬂuenza vaccine within last year 
COVID-19 
All 
No COVID-19 vaccine within last year 

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Recommended care 
Condition 
Gap closure deﬁned as 
Pneumococcal 
All 
No pneumococcal vaccine OR 
No second dose if older than 65 and at least 5 years 
since ﬁrst 
Medications 
Rescue inhaler 
All 
No short acting beta agonist OR 
No ICS (e.g. budesonide) for asthma patients  
Inhaled corticosteroid 
(ICS) 
Asthma 
No ICS 
No high dose ICS for Moderate to Severe persistent 
asthma 
Long-acting beta agonist 
(LABA)/ Long-acting 
muscarinic agent (LAMA) 
COPD 
Moderate to severe 
asthma 
Medications based on severity* 
Self-management support 
Chronic lung disease  
(CLD) education 
All patients at baseline  
Referral to any CLD class or asthma home visit 
Patient-reported education on exacerbation 
management, breathing techniques, staying active 
Smoking cessation support 
Smoking at baseline 
Smoking cessation or reduction OR  
Prescription of smoking cessation medications OR  
Patient-reported receipt of cessation support 
Assessment or management of condition/co-morbidities 
Pulmonary function testing 
(PFTs) 
No record of PFT  
Had received PFT at follow up  
Allergies 
Positive screen on 
Respiratory Allergy 
Prediction Test† 
Any of: New diagnosis, referral for 
consultation/testing, new allergy medication, 
patient-reported allergy management support  
Gastroesophageal reﬂux 
disease (GERD)‡ 
Positive screen on 
GERDQ (score of >=8) 
Any of: New diagnosis, new medication, patient-
reported support for lifestyle management, referral 
for assessment 
Sleep apnea 
Positive screen on STOP-
BANG (score of >=3) 
Any of: New diagnosis, referral for testing or 
treatment, education received on impact of sleep 
apnea or use of positive airway pressure device 
Any evidence of diagnosis 
clariﬁcation 
Identiﬁed by pulmonary 
specialist 
Any of: Change in asthma or COPD diagnosis, new 
testing (e.g., Chest CT, exertional oximetry, 
nocturnal oximetry), referral cardiology 
* Medications based on severity based on GOLD or GINA guidelines.  
COPD GOLD Group B: LAMA or LABA 
Group D: LAMA, LAMA + LABA, or ICS + LABA 
From 2024 onward: Groups B/E: LABA + LAMA 
Moderate to severe asthma patients: LABA 
† The Respiratory Allergy Prediction Test, a single item question associated with allergic rhinitis: “Have you ever been 
told by a doctor or nurse that you have allergies that may affect your breathing?” Source: Galimberti M, Passalacqua 

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G, Incorvaia C, Castella V, Costantino MT, Cucchi B, Gangemi S, Nardi G, Raviolo P, Rottoli P, Scichilone N. Catching 
allergy by a simple questionnaire. World Allergy Organization Journal. 2015 Dec;8(1):1-7. 
‡ The GERD-Q  is a 6-item screening tool validated for identiﬁcation of gastro-esophageal reﬂux disease. Source: Jones 
R, Junghard O, Dent J, Vakil N, Halling K, Wernersson B, Lind T. Development of the GerdQ, a tool for the diagnosis 
and management of gastro-oesophageal reﬂux disease in primary care. Alimentary pharmacology & therapeutics. 
2009 Nov;30(10):1030-8. 
 
For Receipt of recommended medications, we compared received medications to the minimal 
medications recommended based on their GOLD or GINA classiﬁcation and coded each 
participant’s care (0=Not receiving all recommended medications; 1= Receiving all recommended 
medications) based on previously described methods,7 at enrollment and at 16 weeks (Table E2). 
For example, a patient with moderate persistent asthma who was not on at least a low dose 
inhaled corticosteroid at baseline was considered not to be receiving recommended medications; 
if they received the medication during the 16 weeks post baseline, they would be considered to 
have received recommended medications at 16 weeks.  
Table E2. Deﬁning receipt of guideline-based medications  
Condition 
Classiﬁcation  
Guideline for minimal medications  
COPD 
Prior to 2024 
Class A 
 
Class B 
 
 
Class C 
 
 
Class D  
 
 
 
 
After 2024:  
 
Class B 
 
 
Class E (formerly C/D) 
  
 
Rescue inhaler: Short acting beta agonist (SABA) 
 
Rescue: SABA AND 
Controller: Long-acting beta agonist (LABA) OR  
Long-acting muscarinic agent (LAMA) 
 
Rescue: SABA AND 
Controller: Long-acting muscarinic agent (LAMA) 
 
Rescue: Short acting beta agonist AND  
Controller: Long-acting muscarinic agent (LAMA) OR  
LAMA + LABA OR  
ICS + LABA 
 
 
 
Rescue: SABA AND  
Controller: LAMA AND LABA 
 
Rescue: SABA AND  
Controller: LABA AND LAMA 

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Asthma 
Steps 1-2 (formerly 
intermittent or mild 
persistent asthma) 
 
Step 3 (formerly moderate 
persistent asthma) 
 
Steps 4 – 5 (formerly severe 
persistent asthma) 
SMART Therapy: Inhaled corticosteroid (ICS) + LABA 
as needed OR  
Rescue: ICS + SABA together  
 
SMART Therapy with low dose ICS + LABA  
 
 
SMART Therapy with moderate dose ICS for  
 
Pre-speciﬁed secondary outcomes included the proportion of patients in each arm who received 
chronic lung disease education, deﬁned as referral to any class or one-on-one education on 
chronic lung conditions or patient-reported receipt of education on identifying and responding to 
signs of exacerbations, staying active, or breathing training. In addition, we measured several 
patient-reported outcomes, including disease-speciﬁc quality of life, as measured by the St. 
George Respiratory Questionnaire (SGRQ)8, 9 and symptoms, as measured by the symptom 
subscale of the SGRQ. Patient-reported quality of care was measured using the short form of the 
Patient Assessment of Chronic Illness Care (PACIC) (mean item score 1-8).10, 11  Self-reported 
adherence to inhaler therapy was deﬁned as the proportion of patients taking all doses of 
controller medications as prescribed in at least ﬁve of the last seven days.12 For the intervention 
arm, provider acceptance of recommended care was deﬁned as the proportion of PSNP 
recommendations for which the provider took action, including placing of referrals, orders for new 
prescriptions, or approval for the health coach to follow up.  
Additional outcomes not pre-speciﬁed in ClinicalTrials.gov included exacerbations and utilization 
of acute and primary care services were identiﬁed through the electronic medical records from 
participating clinics, the county hospital, and outside facilities for ED visits and hospitalizations 
that were viewable through the Care Everywhere feature in Epic. A study physician (MK) blinded to 
study arm reviewed cases to identify exacerbations with blinded consultation from the study 
pulmonologist (GS) as needed. 
 
Analysis Methods  
We summarize by arm the demographic and clinical characteristics of participants in the 
PushCon study using descriptive statistics but, following convention, do not compare 
characteristics between arms using statistical tests. To examine differential dropout by arm, 
affecting Week-16 survey outcomes only, we compare characteristics of participants with and 
without missing outcomes using chi-square or Wilcoxon rank-sum tests.    

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To analyze study outcomes, we used generalized estimating equation (GEE) models to estimate 
the mean (95% CI) ratio of arm-speciﬁc rates (for binomial or count outcomes) or geometric 
means (for continuous outcomes) and to generate 2-sided p-values, while accounting for 
correlated outcomes within study clinics. For longitudinal outcomes, assessed at baseline (after 
consent but before randomization; Week 0) and at the end of the intervention period (Week 16), 
we ﬁt time and time*arm as ﬁxed effects, and nest correlated outcomes within study clinics; for 
cross-sectional outcomes we exclude the time effect from the model. In the setting of RCTs, the 
constrained longitudinal data analysis (cLDA) approach, used here, estimates a common mean 
across arms at baseline; in contrast, the LDA approach would include a ﬁxed effect for arm to 
estimate distinct mean outcomes at baseline. The cLDA model is equivalent to the ANCOVA 
model when no data are missing; however, participants with some missing values, for example, at 
follow-up only, contribute to the cLDA model but not the ANCOVA model, increasing the cLDA’s 
relative power.    
Receipt of guideline-recommended care was calculated as a single proportion that can be 
modeled cross-sectionally. Each participant’s outcome arises from a ratio of two counts, p_care = 
x/d, where d is the number of gaps in care noted over the 12-month pre-baseline (denominator, 
see Appendix A), and x is the number of these gaps that were noted as addressed during the 16-
week intervention (numerator). To estimate the rate ratio (RR) (95% CI) we modeled x as a function 
of arm, speciﬁed a negative binomial distribution for x and used a log link with log(d) as an offset.  
Receipt of all guideline-recommended medications is a binary longitudinal outcome. All 
medications recommended by GOLD/GINA guidelines for each participant were identiﬁed prior to 
the start of the study. The outcomes at Week 0 (using EHR data the 12 months prior to 
randomization) and at Week 16 (using EHR data from the 16-week intervention period) are scored 
y=1, if all were prescribed, and y=0, otherwise. Using a constrained longitudinal data analysis 
(cLDA) approach, which estimates equal rates by arm at baseline, we modeled y as a function of 
time and the time*arm interaction,13 speciﬁed that y is binomial, and used a log link to estimate 
RRs at Week 16. Other binomial outcomes modeled analogously.  
The Saint George Respiratory Questionnaire, a patient-reported outcome, is also measured 
longitudinally. It consists of 51 items in three domains: symptoms, activity, and impact, and has a 
total score ranging from 0 to 100, with higher scores indicating greater disability. The analysis 
method is the same for the overall score and its domains. Using the cLDA approach, we modeled z 
as a function of time and time*arm, used a log link, and speciﬁed that y has a negative binomial 
distribution. The model estimates geometric means (95% CI) and estimates the relative mean 
(RM) (95% CI) to compare the HC and UC arms at Week 16. Another outcome using this model is 
PACIC.   
 

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