ASCEND: ApproacheS to CHC ImplEmeNtation of SDH Data Collection and Action
Conditions: Diabetes Mellitus
Sponsor: Kaiser Permanente

 
 
 
ASCEND STUDY PROTOCOL 
ASCEND: ApproacheS to CHC ImplEmeNtation of SDH 
Data Collection and Action 
NCT03607617 
Version: September 27, 2021  
This Version Approved by Kaiser Permanente – Northwest IRB on September 30, 2021 

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1. Protocol Title 
ASCEND: ApproacheS to CHC implEmeNtation of SDH data collection and action 
2. Objectives 
ASCEND will test strategies for helping community health centers (CHCs) systematically identify and take 
action on social determinants of health (SDH). This work builds on our NIDDK-funded pilot study 
(R18DK105463), in which we developed a set of electronic health record (EHR) tools for SDH data collection 
and summary. These tools were activated in 440 primary care CHCs nationwide in June 2016. We propose to 
act on this unique opportunity to study: (i) SDH data collection / action in CHCs in 19 states; and (ii) the 
effectiveness of a set of implementation strategies at supporting CHCs in integrating SDH data collection and 
action into workflows and DM risk management care plans.  
Note: The SDH intervention includes strategies to help CHC’s systematically (i) collect SDH data in standard 
workflows using EHR tools and (ii) integrate SDH data from flowsheets into care plans (e.g., making 
referrals to social services; adapting treatment plans) for adults with / at risk for DM. The study team will 
provide intensive implementation support (in the form of technical assistance and training materials) to 
study CHCs. 
Focusing on DM risk management and obesity prevention outcomes in adult patients, we will: 
Aim 1. Conduct a mixed methods formative evaluation of SDH data collection uptake among CHCs that 
had SDH data tools activated in their EHR in June 2016. (Quantitative data will come from all 440 CHCs’ 
shared EHR, qualitative data from up to 12 CHCs purposively recruited from this pool). Identify patterns of 
SDH data collection in these diverse CHCs, and clinic-level factors associated with variation in SDH data 
collection rates. Use results to fine-tune the SDH Action Plan intervention’s strategies for helping CHCs 
systematically: (i) collect SDH data in standard workflows, and (ii) integrate SDH data into care plans (e.g., 
making referrals to social services; adapting treatment plans) for adults with / at risk for DM.  
Aim 2. Conduct a pragmatic, stepped-wedge, cluster-randomized trial in 30 CHCs. The CHCs will be 
randomized to one of six wedges, with staggered timing. CHCs in each wedge will receive intensive 
implementation support (the SDH Action Plan). This scalable intervention includes both comprehensive 
technical assistance and training materials designed to help CHCs plan for and implement SDH data 
collection / action, and six months of remote access to an ‘SDH Implementation Team’ that will tailor 
implementation support to each CHC’s needs, with an emphasis on the Building Blocks of Primary Care. 
(The SDH Action Plan will be fine-tuned based on Aim 1 results, and a baseline survey of the study CHCs.)  
Aim 3: Conduct a process evaluation of whether and how the SDH Action Plan intervention improves: (i) 
SDH data collection in CHC workflows; (ii) integration of SDH data into DM risk management care; and (iii) 
clinical measures associated with effective DM risk management (controlled blood pressure, HbA1c, BMI, 
lipids, etc.; up-to-date preventive care). H3a: Intervention CHCs will have significantly greater increases in 
(i) SDH data collection, and (ii) actions taken to address SDH needs, compared to control CHCs. H3b: 
Patients at intervention CHCs for whom SDH data are collected will have significant improvements in DM / 
obesity risk management / receipt of related preventive care, compared to those at control CHCs. 
3. Background 
Research is needed on how to help community health centers (CHCs) start collecting and taking action on 
patient-reported SDH data. CHCs serve our nation’s most vulnerable patients, whose DM prevalence and 
risk (notably, obesity rates) are higher than the general population’s, and whose health is particularly 
impacted by SDH. At present, CHC staff may seek to identify whether SDH are impacting a patient’s ability to 
act on care recommendations for DM prevention / management, and / or whether DM care plans should be 
modified to address these SDH. However, such efforts are typically ad-hoc, and rarely involve structured EHR 
data as recommended by public health leaders. There is a pressing need to identify barriers to CHCs 

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systematically screening for and acting on SDH using EHR-based tools, and to determine whether 
implementation strategies that support other practice changes can effectively address these barriers. 
We propose to test a SDH intervention (also referred to as a set of strategies) for helping CHCs routinely 
identify and take action on the SDH-related needs of patients with / at risk for DM. The proposed work 
builds on our pilot study called Assess & Do (R18DK105463), which used a stakeholder-driven process to 
develop EHR-based SDH data collection / summary tools for CHCs. The ASESSS&DO study is Pro00005883 
within the IRB system. 
Please refer the end of this document for a bibliography. 
4. Study Design 
ASCEND is a pragmatic trial which assesses whether / how an implementation support intervention impacts 
SDH data collection / action and DM risk management, in 30 CHCs. We will tailor the support provided to 
meet each CHC’s needs, while ensuring that any support could be replicated in usual care, so that study 
findings are useful to healthcare decision-makers. Our intervention, delivered in routine care settings, 
addresses recent calls to study methods for adapting change management strategies to meet setting-
specific needs.  
We propose a mixed methods, stepped-wedge, cluster-randomized trial to assess the effectiveness of a set 
of evidence-based, pragmatic approaches to helping CHCs implement SDH data collection / action, in care 
for adults with / at risk for DM / obesity.  
We hypothesize that intervention CHCs will have significantly greater increases in (i) SDH data collection, 
and (ii) actions taken to address SDH needs, compared to control CHCs. We also hypothesize that patients at 
intervention CHCs for whom SDH data are collected will have significant improvements in DM / obesity risk 
management / receipt of related preventive care, compared to those at control CHCs. 
5. Study Population 
a. Number of Subjects 
First, we will pull data on CHCs from the OCHIN membership database; OCHIN has agreements with 
their member CHCs for such data extraction and analysis.  
Then, we will recruit CHCs from this pool based on high adoption rates at baseline (N=no more than 12). 
From these 10-12 CHCs, we will interview up to 6 CHC staff (total N=72) as part of formative data 
collection efforts.  
Next, we will recruit up to 31 CHCs, and conduct block randomization to one of six study wedges for 
staggered receipt of the SDH Action Plan (N= up to 31). Thus, a maximum of 43 CHCs will participate in 
the study; although some CHCs that participate in the formative interviews may also opt to be included 
in the study phase, meaning we may not recruit 42 unique CHCs.  
We will ask CHC champions in the 31 CHCs to oversee completion of a baseline survey before the SDH 
Action Plan intervention is implemented.  The subsequent process evaluation will include a card study 
with selected providers at each clinic, and content analysis of interactions between the implementation 
support team and CHC staff. 
Total maximum participants for each aspect of the study 
CHCs: up to 43 total 
CHC staff: 
• 
Phone /in-person interviews: 72 (cross-section of staff) 
• 
Baseline surveys: ~ up to 50 (project champions at each clinic) 
• 
Card study: up to 62 (primary care providers) 

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• 
Content analysis of OCHIN’s SDH implementation team interactions with study CHC staff (text 
data from recorded webinars, emails, online discussions between OCHIN implementation team 
and CHCs) 
b. Inclusion and Exclusion Criteria 
Quantitative Data 
Inclusion Criteria: 
• 
Any patient who had an encounter at an OCHIN member facility between June 24, 2016 
(when tools went live) and December 31, 2022.   
• 
Pregnant women 
• 
Children 
• 
May include some subjects with mental health conditions of various types; however, it is 
important to systematically address high DM /obesity risk in this population, because such 
patients may be at risk for elevated high DM /obesity risk and have often been excluded or 
underrepresented in previous research studies. 
• 
Decisionally/cognitively impaired 
• 
Economically/educationally disadvantaged 
• 
Non-English Speakers 
• 
Elderly  
Note: Social determinants of health (SDH) information may be incidentally collected using the 
tools of this project at the discretion of the clinics.  The project is designed to allow them to use 
the SDH tools in a way that best fits the individual clinics’ needs.  This study is looking specifically 
at the impact of SDHs on diabetes, however SDHs are important factors for many aspects of 
clinical care.  Based on previous work around SDHs, clinics may collect SDH information for 
children or pregnant women in their practice as a tool to guide their care.  As a result, the 
inclusion of SDH data for all patients that have been screen for SDH needs is important for the 
project to understand the extent of the tool’s use across their entire patient population.  The 
inclusion of pregnant women will be limited by the extent to which the participating clinics 
administer the tool to their patient population but will not be specifically targeted as a 
participation criterion.  However, aggregated descriptive analyses will include an indicator for 
pregnancy to understand the frequency and utility for use of these tools in this population. All 
data will be housed securely in the EMR and only reported at an aggregate level.  They will be 
part of the limited dataset for analyses and protected according to the data security procedures 
described in this protocol. 
Exclusion Criteria: 
• 
Neonates of uncertain viability or nonviable neonates (up to 28 days post birth) 
• 
Prisoners 
Note: The investigators are not enrolling patients for this clinic-randomized study, but rather 
studying the uptake and impact of a set of EHR-based clinical decision support tools into regular 
care at the participating clinics. In this clinic-randomized trial, the intervention / randomization 
occurs at the clinic level. The intervention targets clinic processes that are part of the regular 
care patients receive and will not require special visits.  
Qualitative Data 
Inclusion Criteria: 

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• 
CHC staff: Employees who are specifically and intentionally targeted to be included in this 
study population because of their workplace and/or employee status 
Exclusion Criteria: 
• 
None 
 
c. Vulnerable Populations 
As mentioned above, we will exclude the following vulnerable populations from the study: 
• 
Neonates of uncertain viability or nonviable neonates (up to 28 days post birth) 
• 
Prisoners: Prisoners are excluded or not anticipated to become study subjects 
d. Setting 
OCHIN, Inc., is a non-profit, community-based health center-controlled network. Its members (>480 
CHCs, in 19 states) share characteristics of other CHCs, so results will be generalizable to many CHCs. As 
the nation’s largest CHC network with a single EHR system, OCHIN pioneered the development of EHR 
tools for CHCs. OCHIN’s member CHCs share a single Epic© EHR, which is unduplicated, centrally 
maintained, and network-wide. Data are standardized and quality-checked: thus, validated data are 
already linked between all study sites.  
OCHIN will rely on CHR’s IRB for IRB review. As with previous studies, OCHIN will submit their cede letter 
after we are funded. We anticipate that OHSU (Dr. Miguel Marino; lead biostatistician with expertise in 
complex biostatistics, cluster-randomized, stepped-wedge design) and University of Massachusetts 
Medical School (Dr. Arvin Garg; co-investigator with expertise in SDH data collection in pediatric care) 
will cede IRB approval as well.  
e. Recruitment Methods 
This study has no formal patient recruitment, as ‘participants’ will be the CHCs that volunteer to take 
part in the study. Patients will interact with clinicians as they normally would; the intervention will give 
clinicians tools for managing patient data during encounters and panel management. We will collect 
qualitative data from CHC staff as described in the proposal. Below is a summary of recruitment. 
CHCs will be recruited by an OCHIN research associate. Materials used for recruitment of clinics will 
include a participation information sheet, a slide deck with information about study participation, and a 
recruitment card with a brief description of what we are offering clinics. 
CHC staff: 
• 
Phone / in-person interviews: up to 72 (cross-section of staff) will be recruited by OCHIN staff, in 
partnership with CHR’s qualitative team 
• 
Baseline surveys: ~50 (project champions at each clinic) 
• 
Card study: up to 62 (primary care providers) 
• 
Content analysis of OCHIN’s SDH implementation team interactions with study CHC staff (text 
data from recorded webinars, emails, online discussions between OCHIN implementation team 
and CHCs) 
f. Consent Process 
We are requesting a waiver of informed consent for all data collection activities; details below. ASCEND 
is a pragmatic trial and obtaining signed consent would unnaturally restrict our study sample, 
diminishing the external validity of our findings. The project is promoting standard clinical care and 
quality improvement in the CHC setting. We believe that a waiver of informed consent will not adversely 

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impact clinic staff. For this study, we are simply overseeing CHC interactions with OCHIN about their 
workflow implementation. We are not asking for any personal information from staff or patients.  It 
would be impractical to ask for consent as anybody in the clinic could ask for OCHIN support over these 
6 months, and we’d inevitably miss someone. No data will be collected from or about patients. 
 
Formative: Semi-structured phone or in-person interviews with CHC staff: We will ask for verbal 
consent prior to conducting the interviews. We have submitted a copy of the ASCEND Study Fact Sheet 
and Interview guide with this IRB application. This Fact Sheet includes the key elements of consent. 
6. Study Procedures 
Aim 1 
Clinic Recruitment 
• 
Up to 12 CHCs will be recruited from OCHIN’s pool of clinics to participate in semi-structured 
interviews.  
Mixed Methods Formative Evaluation 
• 
The qualitative team will conduct semi-structured interview to explore barriers / facilitators to 
EHR-based SDH data collection / use in DM-related care, with a focus on contextual factors. At 
each CHC, we will conduct ≈6 phone / web interviews with a cross-section of staff including, as 
appropriate, medical directors, primary care providers (MD, PA, NP) / team members (MA, RN), 
and staff members responsible for QI (clinic managers, care managers) / ad-hoc SDH 
management (e.g., social workers). We will ask how SDH information is now used to inform 
care, challenges to collecting / using SDH data, experiences with the EHR’s SDH data tools, and 
how to facilitate SDH data collection / action. Verbal consent will be obtained pre-interview.  
 
• 
The quantitative team will collect baseline data from 6/24/16-12/31/17 for the 440 CHCs that 
share OCHIN’s EHR. We will measure clinic-level SDH data collection rates as percentage of adult 
patients seen in 6/24/16-12/31/17 for whom patient-reported SDH data from the IOM’s list are 
recorded, on a monthly basis. We will assess variation in rates of SDH data collection and 
identify clinic-level factors associated with this variation, using standard statistical methods. The 
distribution of the outcomes of interest and eligible population will be examined before 
selecting an analysis model; specific models will be refined iteratively, guided by the hypotheses 
and conceptual model.  
 
Fine-tune the SDH Action Plan 
• 
Results from the formative evaluation (semi-structured interview and baseline data analyses) 
will be used to fine-tune the SDH Action Plan. 
 
Aim 2 
Clinic Recruitment and Randomization 
• 
CHCs will be recruited in two separate waves for the pragmatic trial. Wave 1 (wedges 1, 2, and 
3) will be recruited prior to the start of wedge 1. Wave two (wedges 4,5,6) will be recruited 
roughly 1 year later, prior to the start of wedge 4. Each wave of CHCs will be block randomized 
by baseline rates of SDH data collection.  
 
 

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Pragmatic, Stepped-wedge Trial 
• 
The implementation support team will provide tailored implementation support for SDH data 
collection / action to each CHC’s needs for six months (one wedge at a time).  
• 
CHC Staff Baseline Survey: Using the ‘Building Blocks of Primary Care’ (BBPC) model, we will 
assess the study CHCs’ baseline status of four foundational components/’building blocks’: (1) 
Engaged leadership; (2) Data-driven improvement using EHRs; (3) Empanelment; and (4) Team-
based care. The survey will be completed by project champion at each clinic. We will send a 
survey asking about the CHCs’ structural characteristics (payment, staffing, etc.), external 
policies and incentives that might have an impact, and other potentially impactful organizational 
characteristics not identifiable in EHR data, OCHIN member records, or the other baseline data. 
This information will help us understand the impact adoption of SDH data collection / action.  
• 
Baseline EHR data: Aim 1 baseline data will be used to identify baseline SDH data collection / 
action in the trial CHCs, and to indicate which of these CHCs may need targeted assistance. 
These data will be pulled in the month prior to wedge implementation.  
• 
Card study (short provider survey):  Two providers at each CHC will be asked to complete a brief 
survey (“card”) within the EHR following selected patient encounters. Patient encounters will be 
selected based on the clinic’s specified target population for SDH screening. These card studies 
will be administered over approximately 3 weeks in month 5 of each study wedge. OCHIN will 
identify two providers that see the majority of their clinics’ specified target population and work 
with the clinic project champion to recruit the providers to participate in this survey. If clinics do 
not identify a target population, then OCHIN or the study CHC will identify the two providers 
who screen the most patients for SDH or identify two providers who see the most patients. Each 
provider will be asked to complete one card immediately after an office and / or telehealth 
encounter with a patient. PCPs will access the card study questions via a link built into the EHR. 
Responses will be pulled from the Clarity database by the OCHIN analyst. Each “card” will be 
associated with the following characteristics: responses from provider for card study questions, 
SDH screening reported, SDH need(s) documented, reason for visit, patient demographics, and 
provider and site information. 
The card study questions will ask questions such as: (1) Whether / how SDH data informed 
clinical decisions / actions; (2) How the SDH data was obtained (e.g., via the EHR tools); (3) 
Whether any desired SDH data were unavailable; and (4) Estimated time spent looking up SDH 
data. Answers will be fixed-choice with an “other” option and space for free text. No PHI will be 
captured, and provider answers will not be stored in patients’ charts. 
The qualitative team will present a card study information sheet to the clinics in month 4 of each 
wedge.  
• 
Member checking survey: The qualitative team will prepare clinic specific paragraphs of our 
learnings, barriers, and facilitators. We will ask up to 2 clinic champions per clinic to review this 
paragraph and answer questions about their SDH screening post-end wedge. The survey will be 
sent out via email within the body of the email or using a survey platform link. The study team 
will send a reminder email within one week of sending out the survey.  
• 
Content analysis of CHC / implementation support team interactions: Prior to study 
participation, we will notify CHC staff that their exchanges with the implementation support 
team will be analyzed for research purposes. Staff participation will serve as implied consent. 
There is no contact with patients.  We are observing to identify barriers and facilitators to 
implementation (akin to document review, which we have used in previous studies for email 
exchanges, clinic meeting minutes, etc.).  

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All exchanges with study CHCs will be used in content analysis; calls and webinars will be 
recorded (with permission) and transcribed; emails / online discussions will be collected. The 
OCHIN and CHR teams may incidentally view and record PHI while helping clinics implement the 
SDH EHR tools as part of the webinar sessions. Recording PHI is not intended to contribute to 
the research question and any PHI inadvertently captured would be deleted before the data is 
used in analysis.   
When each wedge ends, the qualitative team will record a debrief session with the SDH 
Implementation Team to capture their understanding of implementation at each CHC in that 
wedge. We will also collect ‘trouble-tickets’ about the SDH tools submitted to OCHIN’s member 
support system.  
• 
Assess the relationship between clinic context and implementation strategy adaptation: We will 
track context and implementation strategies including planned, unplanned, enacted, and 
adapted strategies and justifications for strategy adaptation. We will use a comparative case 
study approach to compare strategies between sites, document adaptations, and explore 
preliminary patterns of association between context, strategies, and outcomes.  
We will synthesize meeting notes, transcripts, and email which document interactions between 
the research team and each clinic, internal planning meetings between research team members, 
and clinics’ use of intervention guidance and decisions tools. 
 
Aim 3 
Mixed methods process evaluation 
• 
We will analyze the quantitative data and qualitative data to understand whether and how the 
SDH Action Plan intervention improves:  
1. SDH data collection in CHC workflows;  
2. integration of SDH data into DM risk management care; and  
3. clinical measures associated with effective DM risk management (controlled blood pressure, 
HbA1c, BMI, lipids, etc.; up-to-date preventive care).  
• 
We will also analyze the quantitative and qualitative data to: 
o  Examine the impact of screening domain, patient characteristics, clinical encounter 
characteristics, and clinic factors and on accepting on refusing help to address a social 
need. 
o Characterize variability in food insecurity and other social risk screening implementation 
before and during the COVID-19 pandemic, with an emphasis on screening during 
telehealth encounters, using multilevel data. 
o Identify barriers and facilitators to food insecurity and other social risk screening 
implementation before and during the pandemic, with an emphasis on screening during 
telehealth encounters 
 
Data Collection 
 
Quantitative data points 
 
• 
Aim 1 data points - We will collect baseline data from 6/24/16-12/31/17 for the 440 CHCs that 
share OCHIN’s EHR. We have extracted similar EHR data in prior research. We will measure 

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clinic-level SDH data collection rates as percentage of adult patients seen in 6/24/16-12/31/17 
for whom patient-reported SDH data from the IOM’s list are recorded, on a monthly basis. We 
will assess variation in rates of SDH data collection and identify clinic-level factors associated 
with this variation, using standard statistical methods. The distribution of the outcomes of 
interest and eligible population will be examined before selecting an analysis model; specific 
models will be refined iteratively, guided by the hypotheses and conceptual model. Results from 
these analyses will inform clinic recruitment and fine-tuning of the intervention as described 
below. 
 
• 
Aim 2 & 3 data points -  
 
Monthly rate of targeted patients for whom SDH data are documented in the EHR.  
 
Monthly rate of patients for whom an SDH-related referral is made (exclude if ‘patient 
declined referral’ is documented).  
 
Control of DM risk management biomarkers: (i) BP control (<140/80 mmHg); (ii) A1c 
control (<7.0%); (iii) BMI <30; (iv) LDL control (l<100 mg/dL).  
 
Rates of incident comorbidities, e.g. retinopathy, nephropathy, neuropathy (among 
patients with DM).  
 
Rate of patients up-to-date on DM key tests (e.g. lipid panel annually, HbA1c within 6 
months,.) 
 
Qualitative data points 
 
• 
Aim 1: Formative, semi-structured interviews. 
 
• 
Aim 2 & 3: Baseline survey at study clinics, card study (short provider survey), exchanges between 
CHC staff and implementation support team (can include, but is not limited to: emails, webinar 
recordings, meeting notes, ASCEND SDH Toolkit, etc.), and the member checking survey. 
7. Data Analysis 
a. Analysis Plan 
Quantitative data analysis plan 
All quantitative analyses will be led by co-I Dr. Marino from OHSU. This stepped-wedge cluster 
randomized trial will evaluate the impact of the proposed intervention on our dependent variables 
(see Table 6 in proposal): adoption of SDH data collection / action, and impact of this adoption on 
DM risk management. Outcomes will be measured monthly in all clinics at every period, so that each 
wedge provides data points in both control and intervention conditions. Practices in each wedge will 
be block-randomized by baseline rates of SDH data collection to balance SDH collection adoption 
across wedges. The main goal of this trial is to establish whether the intervention is effective and to 
what degree. Our evaluation includes a rigorous design so we may attribute positive findings to the 
intervention. To compare the effect of the intervention with usual practices on SDH outcome 
measures in a stepped-wedge design, we will utilize Generalized Linear Mixed Models (GLMMs).  
Quantitative data analyses will use elements from The Limited Data Set. Data elements will contain 
the following variables and/or variables that have been derived from these variables (such as 
counts, means, etc.) (“Data Elements”) between June 24, 2016 and December 31, 2022: 
 
 

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DATA ELEMENTS 
 
Category 
Examples include, but are not limited to: 
Demographics 
Patient ID, sex, date of birth, race, ethnicity, primary language, annual 
income, FPL, primary payor, state of residence, urban/rural 
Encounter 
Encounter ID, patient ID, admit date, provider ID, facility location, 
encounter type, facility id, admitting source, primary payor, site ID 
Diagnosis 
Diagnosis ID, patient ID, encounter ID, encounter type, admit date, provide 
ID, diagnosis, diagnosis type, diagnosis source, site ID, comorbidity index 
score 
Procedures 
Procedures ID, patient ID, encounter ID, encounter type, provider ID, 
procedure date, procedure code, procedure type, procedure source, site ID 
Vital 
Vital ID, patient ID, encounter ID, measure date, measure time, vital source, 
height, weight, diastolic blood pressure, systolic blood pressure, original 
BMI, blood pressure position, smoking, tobacco, tobacco type, site ID 
Lab Result CM 
Lab result cm ID, patient ID, encounter ID, lab name, specimen source, raw 
lab name, lab loinc, result location, lab procedure, lab procedure type, lab 
order date, result date, result quality, result number, result modifier, result 
unit, norm range low, norm range high, norm modifier high, abnormal 
result indicator, site ID 
Condition 
Condition ID, patient ID, encounter ID, report date, onset date, resolve 
date, condition status, condition, condition type, condition source, site ID 
Prescribing 
Prescribing ID, patient ID, encounter ID, ID provider ID, Rx order date, Rx 
start date, Rx end date, Rx quantity, Rx refills, Rx days supply, Rx frequency, 
Rx basis, Rx norm concept identifier, site ID 
Clinic Characteristics 
Patient portal use,  years on EHR, AHC participation 
Provider 
Characteristics 
Sex, credentials, specialty, years practicing at clinic, number of visits, SDH 
tool use 
Social Determinants of 
Health 
SDH need, education, financial resource strain, housing insecurity, food 
insecurity, intimate partner violence, inadequate physical activity, social 
isolation, stress, request for help 
Additional quantitative data analyses will include assessing SDH data collection / action, overall 
and among the subset of clinics that are both OCHIN-member clinics and part of the 
Accountable Health Communities (AHC) study at OHSU, as well as all OCHIN member CHCs 
during a time when federal initiatives are emphasizing this change. We will track SDH data 
collection rates in these CHCs over study years 1-4, in descriptive analyses. 
We will also use a generalized estimating equation (GEE) approach to assess factors associated with 
patients’ acceptance of a resource to address an identified social need (see model specification below). 
Acceptance of help will be calculated using the proportion of patients who screened positive for one or 
more social need who accept a help to address that need. GEE produces a population average effect 
that accounts for group-level correlation and is appropriate for repeated or correlated observations. To 
account for the likely correlation between patients within a clinic, the model will include clustering at 
the clinic level. GEE takes into account the specified correlation structure while selecting the final 
parameter estimates and generally controls for clustering more effectively than using post-processing 
adjustment. We will test exchangeable and unstructured correlations structures and compare models 
using Quasi-Likelihood AIC (QIC) test. We will select the model with the lower QIC value. Using the 

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preferred model, we will assess predictors of positive screening including screening domain, patient 
characteristics, encounter characteristics, and clinic characteristics.   
 
Yi Patient acceptance = 𝛽𝛽0  + 𝛽𝛽1𝑋𝑋𝑖𝑖Screening domain + 𝛽𝛽2𝑋𝑋𝑖𝑖Patient characteristics +  𝛽𝛽3 𝑋𝑋𝑖𝑖Encounter characteristics + 
 𝛽𝛽4𝑋𝑋𝑖𝑖 Clinic characteristics + 𝑢𝑢𝑖𝑖 
 
To characterize variability in food insecurity and other social risk screening implementation 
before and during the COVID-19 pandemic, we will use a multilevel mixed-effects model to 
predict implementation of screening based on site, provider, patient, and visit characteristics. 
Variables will include urbanicity at the site level; profession at the provider level; race/ethnicity, 
age, and number of diet-related chronic conditions at the patient level; and location (in-person 
v. telehealth) at the visit level. 
 
Qualitative data analysis plan 
Data from each case will be ‘merged’ for analysis, then compared within and across clinics to 
confirm, expand on, or challenge each site’s finding.  Data collection and analysis will be parallel 
and iterative; analysis will begin at the end of the first wedge and continue as data from each 
wedge is collected. A grounded theory approach and immersion-crystallization process will be 
used to identify data themes and patterns.  
Qualitative data analyses for the card study will use elements from the below data set. Data 
elements will include, but are not limited to the following variables:  
Category 
Description and Variable Name 
Provider 
Characteristics 
Sex, credentials, specialty, years practicing at clinic, number of visits, SDH 
tool use 
Encounter 
characteristics 
Encounter date, type, reason for visit 
Card study responses  
What factors influenced the care you provided, how did you know the 
patient’s SDH information, How long did it take to look up the patient’s SDH 
information, how much did SDH influence your clinical decisions 
Patient-level SDH 
 SDH need, education, financial resource strain, housing insecurity, food 
insecurity, intimate partner violence, inadequate physical activity, social 
isolation, stress, request for help 
 
Card study response data will be summarized by provider, clinic, and wedge. Analyses will be Ns and 
percentages of each survey item response and descriptive patient demographic data for the patients 
whose encounters were selected for inclusion in the card study (e.g., patient sex, age category, 
race/ethnicity, preferred language, and whether they had any discrete SDH screening data recorded in 
the EHR). 
 
We will assess the relationship between clinic context and implementation strategy adaptation, 
we will code implementation strategies and barriers deductively and using ERIC and CFIR 
respectively and inductively based on themes that emerge from the data. We will incorporate 

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additional categories proposed based on the application of ERIC categories across several large 
interventions and analyze data as comparative case studies.  
 
We will also analyze clinic communications using a constant-comparative approach and merge 
that with quantitative data in joint display tables to characterize variability in food insecurity.  
An implementation framework will be used to develop a model of facilitators and barriers to 
food insecurity screening. 
 
 
b. Sharing of Results with Subjects 
The study results will not be shared with subjects. This study does not carry the risk of incidental 
findings. This study does not involve laboratory results. 
c. Data and Specimen Banking 
Not applicable.  
 
8. Privacy, Confidentiality, and Data Security 
 
Quantitative data management, sharing and protection:  
Datasets will be created with data from OCHIN’s web server and data shared by OHSU for the AHC-
participating clinics, imported into SAS, and securely maintained at OCHIN. Data will be extracted for all 
target patients seen in study CHCs and stored (limited data set) in a secure repository. Data from 
subsequent encounters is stored and linked to the same patient. Identified patient data will be transferred 
from OHSU (ORPRN) for the subset of patients seen in OCHIN+AHC clinic sites; these data will include 
patient identifiers sufficient to match them to their EHR record in the OCHIN system (e.g., name, date of 
birth, medical record number), as well as SDH screening data necessary for analysis. After matching is 
conducted, all PHI will be stripped from these datasets and will not be accessed for analysis or transferred 
back to OHSU as part of Limited Data Sets for analysis.  
 
OCHIN and CHR have expertise in high-quality and secure data management, project operations, and 
multisite research collaborations. Data structures will remove personal identifiers. Analyses will be done in 
SAS® v9 or higher. Limited data sets will be shared between OCHIN, OHSU, UNC, and WSU. Please note that 
OCHIN will send the same final limited dataset to OHSU and WSU. Limited data sets will be password 
protected for transfer and exchanged via a secure data transfer method. Quantitative data will be securely 
stored on Dropbox or on an institutional server. Only aggregate EHR data will be shared outside of the study 
team. 
 
Qualitative data management, sharing and protection: Qualitative data will be securely stored at CHR, 
OCHIN, UNC, and WSU. All data work will be done on password-protected workstations, in a secure 
environment (HIPAA-compliant). Data will be catalogued within 1 week of collection, and field notes within 
24 hours.  
 
• 
Interviews will be recorded and professionally transcribed by an outside vendor (with who KPCHR 
and OCHIN have a Business Agreement with). Recordings will be sent via secure file transfer (SFT) to 
the transcriptionist.  
 
• 
Online meeting software recordings (e.g. Zoom or Microsoft Teams) of the monthly webinars will be 
sent from OCHIN to CHR via SFT, encrypted email, secure site, or Box. Recordings will be sent via 

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secure file transfer to an approved transcriptionist with whom the study team has an established 
BAA. The transcriptionist will be instructed to delete any identifiable patient information in the 
transcript (e.g., [*Name*]) and to delete the recording once the transcription is complete. 
Recordings have the potential to contain PHI as a result of the Epic environment that may be used in 
SDH tool demonstration; transcripts will NOT contain any PHI and PHI will not be used in analysis.  
The transcript will be sent back to the qualitative study team via secure file transfer (access to the 
secure file transfer site will be limited to appropriate members of the research team); recordings 
and transcripts will be stored securely at both CHR, OCHIN, UNC, and WSU. Transcripts will be 
entered into QSR NVivo or Dedoose. Recordings will be deleted at the end of the study.   
 
• 
Card study data will be sent via secure site, encrypted email, Secure File Transfer or Box between 
OCHIN and CHR.  
 
All qualitative data work will be done on password-protected workstations, in a secure environment (HIPAA-
compliant). At CHR, only study team members have access to the saved data. At OCHIN, access to study data 
is limited to the research department. At UNC and WSU, access is limited to study team members. Data files 
will be password protected for transfer; data sharing agreements / protections will be established as 
necessary. 
 
Other qualitative data will be entered into QSR NVivo and Dedoose; data files will be password protected; 
data sharing agreements / protections will be set as necessary; transfers will use a secure website or 
encrypted email. 
 
Confidentiality measures: All research staff at CHR, OCHIN, OHSU, and WSU who handle data sign 
confidentiality pledges and receive IRB and HIPAA training/certification.  
Security measures: Multiple measures are in place to ensure security of PHI. Data transfer to and from the 
EHR (at OCHIN), and the web display (accessed at OCHIN member CHCs) uses a Simple Object Access 
Protocol (SOAP) with Secure Sockets Layer (SSL) encryption over a Hypertext Transfer Protocol Secure 
(HTTPS) computer network.  
9. Provisions to Monitor the Data to Ensure the Safety of Subjects 
 
This study involves data from OCHIN’s network of safety net clinics, 42 of which will be recruited for formal 
study participation. All EHR data from OCHIN clinics are stored at OCHIN. Each clinic has business use 
agreements with OCHIN to handle and manage PHI from their clinical data. EHR data will be linked using 
OCHIN’s unique patient identification codes, in order to create clinic-level aggregate data and a limited data 
set.  
 
All data sources will be linked into a secure relational database at OCHIN. The de-identification process 
replaces the original identifiers on sensitive or confidential fields with arbitrary pseudo-identifiers. Access to 
the “crosswalk” tables that contain mappings of original- to pseudo-identifiers is limited to authorized 
OCHIN staff; crosswalk tables will not be shared with OHSU or CHR. We will de-identify the following fields:  
• 
Patient ID 
• 
Provider ID 
• 
Encounter ID 
 
Data analysis will be conducted at OCHIN, OHSU,  UNC, and WSU. If necessary, to share PHI, we will use a 
secure data transfer method (e.g., sFTP) with access limited to appropriate members of the research team. 
IRB and HIPAA approval will be obtained for all study steps. CHR, OHSU and OCHIN have expertise in high-

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quality data and secure management and project operations and multisite research collaborations. Data 
structures are typically designed to separate personal identifiers from other critical data, further enhancing 
protections.  
 
CHR, OHSU, and OCHIN standards meet or exceed requirements for patient data safety established in the 
federal HIPAA guidelines. Data structures will derive from the data confidentiality, security, and privacy 
standards that CHR, OCHIN, and OHSU have in place to meet or exceed all current HIPAA requirements. 
Quality control will begin with real-time, inter-field checks in the data at OCHIN. Additional back-end checks 
(for missing data and logical inconsistencies) will be conducted to ensure the highest standards of data 
reliability. We will examine the distribution and measurement properties of variables before making final 
decisions about analyses.   
 
10. Risks and Benefits 
 
a. Risks to Subjects 
 
The ASCEND study poses minimal risk. We do not anticipate any physical, psychological, social, legal or 
economic risks.  
 
b. Potential Benefits to Subjects 
Overall benefits: This research has the potential to provide valuable information on how best to 
support uptake of standardized collection of patients’ SDH data using EHR-based tools, and 
integration of SDH data into DM / obesity risk management in CHCs. 
Quantitative: Patients at the study CHCs will have no defined personal benefit from this project. 
Qualitative: Care team members at the study CHCs will have no defined benefits from 
participating in this project. However, the intervention is designed to optimize identification and 
management of adult subjects with elevated DM risk. Some providers exposed to this 
potentially useful CDS may use it to improve their clinical care during the study or after. 
 
11. Costs to Participants 
 
CHC patients: There are no new costs to patients as a result of this study; however they will 
continue to pay for co-pays, routine treatment costs, etc. as part of normal care at their CHC. 
 
CHC staff: There are no costs to staff as a result of this study; other than time spent during 
interviews and observations. 
 
12. Compensation to Participants 

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CHCs: During Year 1, the recruited CHCs (N=up to 12) will receive an impact fee in the amount of $300 for 
participation in formative data collection.  
 
CHC patients:  None 
 
CHC staff: During Years 1-4, CHC staff will receive a gift card in the amount of $25 or $50 for participation in 
the interviews and the card study (short de-identified provider survey). 
 
Resources Available  
 
No special resources or expertise are required to conduct this study. 
 
13. Drugs or Devices  
 
Not applicable.  
 
14. Multi-Site Coordination  
 
CHR will act as the coordinating center for this study. We will ensure that:  
• 
All sites have the most current version of the protocol, consent document, and HIPAA 
authorization. 
• 
All required approvals have been obtained at each site (including approval by the site’s IRB 
of record). 
• 
All modifications have been communicated to sites and approved (including approval by the 
site’s IRB of record) before the modification is implemented. 
• 
All engaged participating sites will safeguard data as required by local information security 
policies. 
• 
All local site investigators conduct the study appropriately. 
• 
All non-compliance with the study protocol or applicable requirements will be reported in 
accordance with local policy. 
• 
Communication of problems, interim results, and study closure. 
 
15. Community-Based Participatory Research  
 
This study has system-level support: OCHIN’s leadership strongly supports SDH data collection in its member 
CHCs, which aligns with OCHIN’s ongoing efforts to improve care and outcomes in CHCs via EHR-based 
strategies. In all study steps, the study team will engage OCHIN’s operational leadership (including co-I Karen 
Parr, a CHC clinician and informaticist). We will also engage OCHIN CHC clinicians via existing communication 
structures. OCHIN has a long history of engaging stakeholders in all system-wide efforts; CHC clinicians serve 
on standing committees that direct all changes made to OCHIN’s EHR.  
 
 
 

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