Maintaining Implementation Through Dynamic Adaptations (MIDAS) (QUE 20-025)
Phase: NA
Conditions: Polypharmacy, Insomnia, Anticoagulants
Sponsor: VA Office of Research and Development

 
MIDAS Quality Enhancement Research Initiative 
Center for Clinical Management Research 
2215 Fuller Rd (152) 
Ann Arbor, MI 48109 
(734) 845-3502 
 
 
 
 
 
Official Title of Project: MIDAS Cluster Randomized Controlled Trial of Implementation Strategies to 
Optimize Use of Medications in VA Clinical Settings (MIDAS cRCT) 
ClinicalTrials.gov ID: NCT05065502 
Date: 09/24/2025 
 
Please find the Study Protocol and Statistical Analysis Plan below. Since the creation of this document, 
the following changes have been made to the analysis plan:  
• 
Data was collected through 18 months post-baseline rather than through 36 months due to time 
constraints. 
• 
Instead of a patient-level dichotomous response over time, the differences between arms were 
modelled as a single outcome adjusting for the evidence-based practice (EBP) and the level of 
potentially inappropriate medications (PIMs) in the pre-period to address the large difference in 
PIMs between EBPs. 
• 
After conducting additional testing of our algorithm for detecting CBTI sessions, we found the 
algorithm to be adequately sensitive to detecting *any* CBTI but not accurate for detecting the 
number of CBTI sessions (e.g., CBTI could often be mentioned in the context of a referral or 
some other ongoing treatment). Thus, the secondary outcome “Change in Mean Cognitive 
Behavioral Therapy for Insomnia (CBTI) Sessions Completed” could not be analyzed. 
• 
We had originally planned for all CBTI sites to provide information regarding where in the 
medical records we could identify referrals. However, prior to launching the trial, we 
determined several of the sites' processes for referrals were not distinguishable as referrals vs. 
other forms of communication (e.g., adding a therapist as a cosigner to a note), making 
collection of this data impractical. Thus, we did not have sites collect referral criteria and the 
secondary outcome “Change in the Monthly Percentage of Patients Referred to Cognitive 
Behavioral Therapy for Insomnia (CBTI) Across Facilities” could not be analyzed. 
A protocol paper for this project, titled “Maintaining Implementation through Dynamic Adaptations 
(MIDAS): protocol for a cluster-randomized trial of implementation strategies to optimize and sustain 
use of evidence-based practices in Veteran Health Administration (VHA) patients,” was published in May 
2022. 
Jeremy Sussman, MD, MS 
Principal Investigator 

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Aims 
Mixed methods analyses will be used to evaluate the following three aims: 
1. To compare the effectiveness of two implementation strategies (LEAP Quality Improvement 
Learning Program + Academic Detailing (AD) vs. AD alone) on potentially inappropriate 
medication use, using a pooled analysis of effects across three trials at 18 months, two 
years, and three years post-baseline at the clinic-level, based on monthly assessed data 
from 13-36 months; 
2. To compare the effectiveness of the two implementation strategies on secondary outcomes 
specific to each trial at 18 months, two years, and three years post-baseline, based on 
monthly assessed data from 13 to 36 months; and 
3. To explore the effects of implementation, provider behaviors and experiences, and context, 
on sustained improvements in potentially inappropriate medication use.  
Methods 
Clinic Randomization 
Within each trial, clinics will be randomized after assenting to participate (equivalent to 
enrollment). Clinics will be assigned to one of two arms by a statistician, stratified further by clinic type 
(medical center, community clinic, or Community Living Center) if needed to ensure partial balance 
between arms with respect to potential confounders associated with culture and complexities 
associated with clinic location [1,2].  
Outcomes and Analyses 
As part of a pooled analysis, we will compare the same two implementation strategies across all three 
EBPs and take a unified approach to implementation and evaluation across the trials. Table 1 shows 
MIDAS measures, data collection timeframe, and data sources. While a unified dichotomous outcome, 
i.e., potentially inappropriate medications, was identified for each trial to allow for the pooled analysis, 
each trial will also be analyzed individually (see Table 1). 
Table 1: MIDAS Measures showing Data Sources and Timepoints by Aim 
Aim 
Type 
Source 
Aim 1: Primary outcome 
 
 
Service Outcomes 
 
 
Proportion of potentially inappropriate care 
Quantitative 
CDW administrative data 
Aim 2: Secondary outcomes 
 
 
Service Outcomes 
 
 
VIONE Trial 
 
 
Potentially inappropriate use of proton pump 
inhibitors (PPIs), aspirin, and CNS active 
medications (e.g., muscle relaxants), analyzed one 
at a time 
Quantitative 
CDW administrative data 
Monthly medication costs for all drugs 
Quantitative 
CDW administrative data 
Number of pharmacist medication reviews 
Quantitative 
CDW administrative data 

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Aim 
Type 
Source 
DOAC Trial 
 
 
Dashboard flags including potential mis-dosing, 
potential medication interactions, or concern for 
nonadherence 
Quantitative 
CDW administrative data 
CBTI Trial 
 
 
Prevalence of any CBTI receipt 
Quantitative 
CBTI note templates completed by 
CBTI therapists 
Patient Outcomes 
 
 
VIONE Trial 
 
 
Number of inappropriate medications by patient 
 
CDW administrative data 
Aim 3: Exploratory outcomes 
 
 
Employee Behavioral Outcomes  
 
 
Continuous Quality Improvement Assessment  
Quantitative 
LEAP and AD participants 
Workgroup Cohesion & Engagement Scale  
Quantitative 
LEAP and AD participants 
QI skill application  
Quantitative 
LEAP participants 
Employee Experience 
 
 
Burnout 
Quantitative 
LEAP and AD participants 
Best Places to Work 
Quantitative 
LEAP and AD participants 
Process Evaluation 
 
 
Pragmatic Context Assessment Tool  
Qualitative/ 
Quantitative 
LEAP participants 
Semi-structured interviews 
Qualitative 
Purposive sample of LEAP and AD 
participants 
How often the provider uses the dashboard 
Quantitative 
CDW administrative data 
Rates of new DOAC starts compared to warfarin 
starts 
Quantitative 
CDW administrative data 
Fidelity 
Qualitative/ 
Quantitative 
Academic Detailer and 
Champion/LEAP Coaches 
Provider satisfaction  
Qualitative/ 
Quantitative 
LEAP and AD participants 
Intentions 
Qualitative/ 
Quantitative 
LEAP participants 
Detailing visit documentation 
Qualitative/ 
Quantitative 
Academic Detailer and Champion 
Coaching documentation 
Qualitative/ 
Quantitative 
LEAP Coaches 
Semi-structured interviews 
Qualitative 
Purposive sample of LEAP and AD 
participants 
Aim 1: Primary Outcomes and Pooled Analysis.  
Although each trial will be conducted as an independent study, our primary aim is to compare 
across trials the effectiveness between the two implementation strategy arms in reducing potentially 
inappropriate medications during post-implementation period. To this end, we defined a unified primary 
outcome to allow us to combine the results across the three trials. The unified primary outcome will be 
operationalized based on a patient-level dichotomous response indicating potentially inappropriate 

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medication use (yes/no) among patients at-risk of potentially inappropriate medications, i.e., among 
those who may benefit from the specific EBP each month. The monthly patient-level potentially 
inappropriate medication use response will be summarized to clinic-level month-by-month percentage 
of potentially inappropriate use using administrative data from baseline to 36 months, with months 13-
36 as the post-implementation follow-up period. Each trial-specific monthly data will be cross-sectional, 
i.e., different patients may be included in each month.  
For inappropriate polypharmacy, the clinic-month outcome will be the proportion of patients 
who had medication possession (based on VA pharmacy fill data) of one or more medications from the 
AGS Beers criteria that are included on the VIONE PIMs dashboard [3] (numerator) among patients age 
65 or older, not receiving palliative care, and followed by the clinic (denominator). For each drug 
included on the PIMS dashboard, there are associated business rules that define when medication use is 
flagged as potentially inappropriate; these same criteria will be applied in this trial. For example, use of a 
first or second generation anti-psychotic drug is flagged as potentially inappropriate unless there is a 
diagnosis of schizophrenia or bipolar disorder. These criteria had previously been determined by 
VIONE’s Subject Matter Expert group, which provides VIONE with guidance on translating deprescribing 
criteria into the most practical and appropriate rules for use on the dashboard. Altogether, the following 
AGS Beers medications from the PIMS dashboard will be included in the analysis: anticholinergics, 
antipsychotics, aspirin, benzodiazepines, long-acting sulfonylureas, muscle relaxants, NSAIDs, proton 
pump inhibitors (PPIs), sliding scale insulin, and Z-drugs.  
For DOAC safety, the outcome will be the proportion of patients with potentially inappropriate 
prescribing out of those using DOACs, as measured by “flags” (e.g., potential mis-dosing based on renal 
function and other indicators) on the DOAC dashboard. The DOAC flagging system is based on FDA 
indications and has been in clinical use since 2018. Components of the outcome include inappropriate 
dosing for the given indication and use of DOACs in contraindicated settings (such as valve 
replacements).  
For first-line treatment for insomnia, the outcome will be the proportion of patients with a new 
prescription for a sedative-hypnotic medication who have not had CBTI in the prior 12 months out of all 
primary care patients actively following with the clinic and are not in hospice/palliative care.  
For all three trials, medication use (yes/no) and possession of active prescription for each month 
will be determined using exposure days based on supply days, and use will be determined by the 
exposure status on day 1 of each month. We will also do sensitivity analyses based on the criteria of use 
anytime during the month as well as PIMs defined to medications used chronically, for example, greater 
than 90 of the 180 prior days.  
For each trial we will first compare demographic characteristics (age, sex, and race) of patients 
at risk of potentially inappropriate medications in the first month of implementation between the two 
arms. We will then obtain, for each trial by arm, crude monthly percentages (along with the 
corresponding 95% confidence intervals) of potentially inappropriate medications, averaged across 
clinics randomized to each arm and weighted by clinic-month size. For each trial, we will plot the 
monthly clinic level percentages over the follow-up 13-36 months to graphically assess if the difference 
between the two arms can be meaningfully summarized across the three trials with the unified 
outcome. If we find, for example, that trends between-arms over post-implementation months differ 
notably across the three trials, unified results comparing AD+LEAP vs. AD arm across trials may not be 
meaningful, and we will only conduct analyses separately by each trial.  

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For comparison between arms, we will use generalized estimating equations (GEE) with clinic 
level monthly percent of potentially inappropriate medications among patients at risk during post 
implementation period (months 13 to 36) as the dependent variable. The model will include indicators 
of two trials with one trial as the referent category to account for differing underlying levels of 
inappropriate medication use across trials. The model will also include follow-up time in months and the 
LEAP+AD arm indicator with AD as the referent category and will adjust for serial correlation within 
clinic over time. We will also include time by arm interaction to assess if the magnitude of the difference 
between LEAP+AD vs. AD changes over time. If the interaction is significant, we will estimate between-
arm difference at 18 months as well as at 2- and 3-years separately based on the model with the 
interaction term. On the other hand, if the interaction is not significant, this would indicate between-
arm difference not to differ at the three follow-up times of interest (18, 24, and 36 months), and thus 
we will drop the interaction term and the parameter estimate of the LEAP+AD arm indicator will be used 
to estimate the time-averaged difference in percentage of patients with inappropriate medications 
during post-implementation period in clinics randomized to LEAP+AD compared to clinics randomized to 
AD.  
If we find notable baseline demographic difference between arms within trials, we will use a 
generalized linear mixed model (GLMM) with logit link to estimate the between-arm difference while 
adjusting for baseline age, sex, and race difference with monthly person level response (yes/no) data 
from post-implementation period of months 13 to 36. In addition to time, AD+LEAP indicator and trial 
type indicators as predictors, the GLMM model using patient level data will include patient age, sex, 
race, and random intercepts for patients nested within clinic to adjust for potential correlation within 
clinics and serial correlation over time. The parameter estimate for the LEAP+AD arm indicator will be 
used to estimate the time-averaged odds of inappropriate medication use during the post-
implementation period for patients in clinics randomized to LEAP+AD compared to the odds of the same 
patients if their clinics were randomized to AD. Although the GEE and GLMM models give different 
summary estimates with different interpretations, the GLMM model allows for adjusting for patient 
characteristics, and a consistent substantive conclusion will assure us of the evidence for the effect of 
LEAP when added to AD. Similar to the GEE model, we will test if the odds ratio of LEAP+AD vs. AD 
changes over time by including time by arm interaction term, and if the interaction term is significant, 
we will obtain adjusted ORs associated with LEAP+AD compared to AD at 18 months, 2-years, and 3-
years.  
For each trial we will also compare AD and AD+LEAP to usual care controls. To do this, we will 
perform a non-randomized secondary analysis for each trial. The analysis will have the same primary 
outcome variable and use the same generalized linear mixed model (GLMM) with logit link. The primary 
control group will be all non-participating sites. We will also use a secondary analysis, where for each 
intervention site we will have two control sites that are matched on clinic size (within 50%), pre-
intervention outcome rate (within 30 rankings of all sites), and region of the country. These analyses will 
adjust for the clinic-level variables clinic size, intervention outcome rate, region of the country and the 
patient-level variables age, sex, and race.  
Aim 2: Secondary Outcomes and Analyses 
Secondary outcomes for VIONE will be the prevalence of potentially inappropriate use of PPIs; 
the prevalence of potentially inappropriate use of aspirin; and the prevalence of potentially 
inappropriate use of CNS active medications (muscle relaxants, anti-psychotics, Z-drugs, and 

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benzodiazepines) or anticholinergic drugs; number of inappropriate medications at a patient level; 
monthly medication costs for all drugs, without regard to appropriateness; and number of pharmacist 
medication reviews. 
Secondary outcomes for the DOAC trial will be the sub-components of the “flags” on the 
dashboard. These include potential mis-dosing, potential medication interactions, or concern for 
nonadherence. This follows the organizational structure of both the presentation of the flags on the 
dashboard and the key messages provided to the academic detailing and LEAP teams. Process outcomes 
will be how often the provider uses the dashboard and rates of new DOAC starts compared to warfarin 
starts. These outcomes will be kept in alignment with our other work using the dashboard [4]. 
In stand-alone analyses of the CBTI trial, the primary outcome will be the prevalence of any CBTI 
receipt among primary care patients actively following with the clinic who are not in hospice/palliative 
care.  
Analyses of secondary outcomes such as percent of potentially inappropriate use of PPI or mean 
number of CBTI sessions at each clinic month will be similar to that of the primary outcome using the 
GEE model accounting for correlation over time. We will also conduct separate analyses by trial with the 
dependent variables that are unique to each trial. For example, for the polypharmacy trial, secondary 
outcome of interest is count of medications flagged as inappropriate based on Beers’ criteria [3]. We will 
compare monthly rates of Beers’ list medication use between implementation strategies using 
generalized linear mixed models with log link.  
Aim 3: Exploration of Potential Predictors of Clinical Outcomes 
Our process evaluation will follow a multi-phase concurrent nested mixed methods design [5]. 
This design has three purposes: 1) help prepare all stakeholders and participants prior to the start of 
each trial; 2) monitor progress of implementation; and 3) explain summative findings. Overall priority is 
placed on quantitative methods that guide the trials, while qualitative methods are embedded or 
“nested” within conduct of the trials.  
Employee behavior and experience measures will be collected via five scales as listed in Table 1. 
Surveys will be administered via online link within invitation emails; administration will occur at baseline 
and 12-months post-baseline; satisfaction will be elicited at the end of each intervention (upon 
completion of the 6-month “core” LEAP program for LEAP team members and at the end of each AD visit 
for AD participants). Descriptive statistics will be generated and tests for differences across 
implementation strategy arms will be conducted using mixed models to account for within-clinic 
correlation.  
Qualitative data will be collected prior to and 18-months following baseline via semi-structured 
interviews (virtual by telephone or conferencing software (e.g., MS Teams platform)). A purposive 
sample of key people (clinic leaders, supervisors, providers, and staff) at each clinic will be invited to 
participate so we can better understand the context in which the implementation strategies are/were 
deployed. The interview guides and qualitative analyses will be guided by the CFIR to identify potential 
and actual barriers and facilitators [6–8]. Principles embedded within the DSF will guide exploration of 
the degree of engagement in quality improvement and teamwork [9]. Prior to implementation, this 
information will help inform the work of the academic detailers and LEAP Coaches; post-

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implementation, this information will help to explain quantitative findings within and across the trials. 
Interviews will be audio recorded and transcribed verbatim. Pre-implementation, interviews will focus 
on collecting practical information using a rapid analysis approach [10,11] to help tailor and adapt 
implementation for each participating clinic. Post-implementation, qualitative analyses will seek insights 
on what kinds of improvements were made, barriers and facilitators to making improvements, 
reflections on/satisfaction with participation in AD/LEAP and explore relationships between 
determinants, participants, and key stakeholders and how these may lead to building coalitions of 
support [12,13]. We will combine qualitative findings with quantitative measures from Aims 1 and 3 to 
help explain changes (or lack of) over time.  
Our process evaluation will rely on quantitative and qualitative data sources. Fidelity to each 
implementation strategy will be tracked by interventionists (the detailers and LEAP coaches) completing 
a mixed-methods self-assessment tool after each interaction (a coaching session for LEAP, detailing 
contact for AD). These assessments will be used to guide coach-supervisor and peer reflections on 
improvements, problem-solving, and mitigating barriers and amplifying facilitators of improvement 
efforts. We will also track participation by participants (individuals scheduled for detailing and/or LEAP 
team members) and completed assignments by LEAP teams. The Academic Detailer and LEAP coaches 
will enter notes for each interaction into a tracking system for each strategy. This data will be combined 
with pre-implementation and 18-month semi-structured interview data for further insights into barriers, 
facilitators, and problem-solving approaches used by LEAP coaches and detailers. Quantitative and 
qualitative data will be combined at the analysis or interpretation phase.  
Economic Evaluation 
We will use a micro-costing method [14,15] to determine the costs to deliver LEAP and academic 
detailing (AD). The LEAP coaches and academic detailers developed a list of the activities they will 
perform for each participating site. Depending on the specific activity, they determined the best way to 
record the time spent on each activity—e.g., logging the start and stop time each time the activity takes 
place vs. setting an estimated average time for activities that take approximately the same amount of 
time for each incidence (such as recurring meetings, responding to quick queries via e-mail, meeting 
preparation, etc.). In the latter case, the coaches and detailers simply record the occurrence of the 
activity, which is then assigned the estimated time. The coaches and detailers will log times for each 
activity, categorized by participating site, in a time tracking database. Using data from this database, we 
will calculate the average time required for each activity and apply this to the number of times it takes 
place over the course of performing the implementation strategy at a site. These data can then be used 
to determine an estimated total time required to perform the implementation strategy (LEAP or AD) at a 
site, which, combined with the hourly cost of the LEAP coach or detailer, can be used to calculate the 
total cost of employing the strategy at a site. 
List of Abbreviations 
Academic detailing (AD) 
American Geriatrics Society (AGS) 
Cognitive Behavioral Therapy for Insomnia (CBTI) 
Department of Defense (DoD) 
Direct-acting anticoagulant medications (DOACs) 

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Dynamic Sustainability Framework (DSF) 
Evidence-based practices (EBPs) 
Generalized estimating equations (GEE) 
Generalized linear mixed model (GLMM) 
Learn. Engage. Act. Process. (LEAP) 
Maintaining Implementation through Dynamic Adaptations (MIDAS) 
Massive-Open Online Course (MOOC) 
Office of Mental Health Services and Suicide Prevention (OMHSP) 
Plan-Do-Study-Act (PDSA) 
Quality Enhancement Research Initiative (QUERI) 
Quality improvement (QI) 
VA Center for Medication Safety (VA MedSAFE) 
Veteran Health Administration (VHA) 
Veterans Affairs (VA) 
 
 

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