A Pragmatic Randomized-controlled Trial of a Multi-pronged Electronic Health Record-based Clinical Decision Support Tool to Reduce Low-value Antipsychotic Prescriptions Among Older Adults With Alzheimer's and Related Dementias
Phase: NA
Conditions: Alzheimer Disease, Dementia Alzheimers, Dementia of Alzheimer Type
Sponsor: University of California, Los Angeles

Title: A Pragmatic Randomized-controlled 
Trial of a Multi-pronged Electronic Health 
Record-based Clinical Decision Support Tool 
to Reduce Low-value Antipsychotic 
Prescriptions Among Older Adults With 
Alzheimer's and Related Dementias 
 
NCT Number: NCT04851691 
Document Date: August 3, 2021 
 

Study Protocol 
Study Aims 
The goal of this study is to design, implement, and test the impact of a quality improvement (QI) 
intervention that uses an EHR CDS tool among physicians newly ordering an antipsychotic medication 
for adults with ADRD to increase guideline-concordant prescribing. The study team hypothesizes that 
the intervention will reduce participating clinicians’ pill days per patient prescribed. 
Study Design and Setting 
This study design is a pragmatic parallel arm randomized-controlled trial. The study team will randomize 
eligible physicians at UCLA Health, a large academic health system in Los Angeles, California, to be either 
exposed to the EHR CDS tool (intervention) or not (control) in a 1:1 allocation ratio over a 12-month 
period when they initiate a prescription for a new antipsychotic medication during a visit with a patient 
with ADRD 
Ethical Considerations 
The Institutional Review Board (IRB) of the University of California, Los Angeles (UCLA) approved this 
protocol on 01/27/2020 (IRB#19–002122). The IRB and National Institute on Aging (NIA) Data Safety 
Monitoring Board (DSMB) approved a waiver of informed consent because this trial is a part of ongoing 
quality and patient safety improvement efforts. In addition, the Principal Investigator will notify all of 
the study participants that this project is an NIH-funded clinical trial and provide them with the trial 
registration link after the study has been completed. This study has been registered in ClinicalTrials.gov, 
with trial registration identifier: NCT04851691. 
The study data containing patient health information will be safeguarded with firewalls and encryption 
following UCLA IRB guidelines, as well as a unique study ID created to protect the identities of patients. 
If successful, UCLA plans to adopt full integration of the EHR CDS tool for all eligible clinicians. 
Methods 
Inclusion Criteria 
Eligible physicians for the EHR CDS tool intervention include physicians who provide ambulatory care in 
the UCLA health system and have generated a new antipsychotic prescription (e.g., Quetiapine, 
Olanzapine, Risperidone, Aripiprazole, Haloperidol, Clozapine) for eligible patients (described below) in 
the UCLA health system at least once between 1/1/2019-4/30/2021 (n = 149). Based on current 
prescribing patterns at UCLA, we estimate that the vast majority of physicians in the study (>75%) will 
only see this EHR CDS tool 1–2 times over a year, suggesting little impact on clinical workflow and 
lessening opportunities for contamination. Eligible physicians will be enrolled in the study during their 
first encounter with one of the patients during which a new antipsychotic medication order is initiated. 
Inclusion criteria for patients will include: 1) having an assigned primary care physician (PCP) and/or 
assignment to an Accountable Care Organization (ACO) at UCLA Health, and 2) being part of the health 
system’s EHR-based dementia registry. The health system’s dementia registry uses a select group of ICD-
10 codes included in the patient’s problem list to identify patients with ADRD, without age restrictions 
(Table 1). This ICD-based approach was implemented by the health system as an update to a previously 
published approach 1. To measure the specificity of this method for correctly classifying patients as 
having ADRD, physicians on the study team (board-certified geriatrician, palliative care specialist, and 

primary care general internist) conducted an implicit review (e.g., expert clinician diagnostic judgment) 2 
on a random sample of 30 registry patients; 29 out of 30 (97%) had ADRD.  
Table 1: Dementia international classification of diseases 9th and 10th revision (ICD-9 and ICD-10) codes. 
 
ICD-9 codes 
ICD-10 codes 
At least one code from a 
problem list 
42, 46.11, 46.19, 46.79, 49.9, 
94.9, 199.1, 277.39, 290, 290.1, 
290.11, 290.12, 290.13, 290.2, 
290.21, 290.3, 290.4, 290.41, 
290.42, 290.43, 290.8, 290.9, 
291.2, 292.82, 293, 294.1, 
294.11, 294.2, 294.21, 294.8, 
294.9, 296.9, 297.9, 298.9, 
305.9, 310.2, 311, 319, 323.9, 
330.1, 330.8, 331, 331.1, 
331.11, 331.19, 331.4, 331.5, 
331.6, 331.82, 331.83, 331.9, 
332, 332.1, 333, 333.4, 340, 
345.9, 348.1, 349.9, 369.9, 437, 
440.9, 459.9, 781, 781.3, 784.3, 
797, 907, 999.9, E980.5, V17.2, 
V40.31 
A52.17, A81.00, A81.01, A81.9, 
A86, B20, C80.1, E75.6, E85.4, 
F01, F01.5, F01.50, F01.51, 
F02.80, F02.81, F03, F03.9, 
F03.90, F03.91, F05, F06.8, 
F07.81, F10.27, F10.97, F13.27, 
F13.97, F18.17, F18.97, F19.17, 
F19.97, F22, F32.9, F39, F84.2, 
G10, G20, G21.8, G23.1, G30, 
G30.0, G30.1, G30.8, G30.9, 
G31.01, G31.09, G31.83, 
G31.84, G31.85, G31.9, G35, 
G40.909, G91.2, G91.9, G93.1, 
G98.8, H54.7, I67.2, I67.3, 
I67.850, I68.0, I70.90, IMO0002, 
R25.2, R27.0, R41.0, R41.81, 
R47.01, S06.9X9S, T80.89XA, 
Z91.83 
 
Consistent with prior research 3, 4, patients will be excluded from eligibility if they have diagnosis codes 
for schizophrenic disorders, delusion disorders, bipolar disorders, or other non-organic psychoses on 
their problem list (Table 2). Patients with Parkinson’s disease on their problem list will also be excluded 
because quetiapine is clinically indicated to decrease hallucinations induced by dopaminergic 
medications in patients with Parkinson’s disease 5. Additionally, because the target for this intervention 
will be new prescriptions, if patients have been prescribed antipsychotics in the prior 12 months, they 
will not be eligible. Eligible encounters will include ambulatory office visits and scheduled telephone and 
video telehealth visits. Emergency department visits, observational stays, and inpatient hospitalizations 
will not be eligible for the CDS to fire. 
Table 2: ICD-9 and ICD-10 codes for exclusion criteria reflecting severe mental illness. 
Diagnosis Code Type 
Diagnosis Code 
ICD-9-CM 
295 
ICD-9-CM 
295.1 
ICD-9-CM 
295.2 
ICD-9-CM 
295.3 
ICD-9-CM 
295.4 
ICD-9-CM 
295.5 
ICD-9-CM 
295.6 
ICD-9-CM 
295.7 
ICD-9-CM 
295.8 
ICD-9-CM 
295.9 

ICD-9-CM 
296 
ICD-9-CM 
296.1 
ICD-9-CM 
296.4 
ICD-9-CM 
296.5 
ICD-9-CM 
296.6 
ICD-9-CM 
296.7 
ICD-9-CM 
296.8 
ICD-9-CM 
297.1 
ICD-9-CM 
297.2 
ICD-9-CM 
297.3 
ICD-9-CM 
297.8 
ICD-9-CM 
298.3 
ICD-9-CM 
298.4 
ICD-10-CM 
F31.9 
ICD-10-CM 
F22 
ICD-10-CM 
F24 
ICD-10-CM 
F23 
ICD-10-CM 
F52.8 
ICD-10-CM 
F31.89 
ICD-10-CM 
F34.0 
ICD-10-CM 
F09 
ICD-10-CM 
F06.1 
ICD-10-CM 
F31.70 
ICD-10-CM 
F43.0 
ICD-10-CM 
G30.9 
ICD-10-CM 
F02.80 
 
Intervention 
We applied theoretically grounded behavioral economic methods to design an EHR CDS to reduce low-
value antipsychotic prescriptions in adults with ADRD at UCLA Health. The EHR CDS tool was designed 
with input from a multidisciplinary team including a geriatrician, a palliative care specialist, a primary 
care general internist, a quality officer, informatics specialists, and two behavioral economists. The study 
team previously presented the proposed intervention in an iterative fashion to several stakeholder 
teams including physicians, informaticists, and health system leaders. The final intervention was 
approved by the UCLA Health System Primary Care Council (which includes patient representation), 
Alert Committee, and the Ambulatory Operations Advisory Group. 
The intervention was designed based on a growing body of scientific evidence that multicomponent 
interventions are more likely to succeed than single-component interventions to reduce low-value care6. 
The intervention is an EHR CDS that will include three components: 1) evidence from Choosing WiselyTM 
guidelines that encourage the clinician to avoid prescribing antipsychotics by highlighting increasing 
patient mortality risk and thereby appealing to the physician’s desire for non-malfeasance; 2) prompts 
to incorporate IDEA! strategy resources on how caregivers can best manage a patient’s behavioral 
disturbance non-pharmacologically7, which will be available in the EHR to include in the patient’s after 

visit summary; and 3) for physicians who do not cancel their prescription order, an automatic default to 
a low dosing and low number of pill days prescribed in the order set (30 pill-days). The study team 
consulted a pharmacist expert (GC) on appropriate defaults for antipsychotics in terms of dosing, 
frequency, and number of pills supplied. For example, currently in the UCLA EHR quetiapine defaults to 
25 mg by mouth once daily 90 tabs x 3 refills, totaling 360 pill-days. The study team will change the 
default to 25 mg by mouth once daily 30 tabs with no refills, totaling 30 pill-days. Physicians will be free 
to increase amounts as they desire and thus this EHR CDS will not include a “hard stop.” The study team 
opted against the hard stop in acknowledgement of the clinical complexity of this vulnerable patient 
population and in recognition of the fact that an outright ban on such prescriptions may have 
unintended consequences including but not limited to endangerment of caretakers or cohabitants. The 
EHR CDS tool will fire when the eligible physician places a new antipsychotic order for an eligible patient. 
Once the CDS tool fires and the physician places the order for either the original dose or a lower dose, 
the patient becomes no longer eligible due to having an active antipsychotic order. Therefore, it will not 
fire for prescription renewals. 
Intervention Implementation 
We designed and pilot tested the EHR CDS in the informatics laboratory, which is led by the Chief 
Medical Informatics Officer. The CDS tool was also pilot tested among the research team, to assess 
correct activation and smooth functionality. The study team specifically designed the intervention to 
seamlessly fit within the EHR to minimize workflow disruption by only adding one additional click if 
accepting the intervention to avoid prescribing, and two clicks if rejecting the intervention in order to 
place the order. All UCLA Health clinicians will be alerted via email (through the usual mechanism to 
highlight new EHR tools at UCLA Health System) to the availability of the IDEA! handout that can be used 
for post-visit instructions to support non-pharmaceutical approaches to ADRD behavioral symptoms. 
The study team will pilot test the intervention to run silently in the EHR background just prior to 
activation in order to ensure proper activation of the EHR CDS tool. Just before the onset of the EHR 
intervention, the Principal Investigator (CS, geriatrician) will send a notification email to all physicians in 
the intervention arm to “prime” the physicians by notifying them they prescribed antipsychotics for 
patients with dementia in the past year, informing them of the upcoming EHR CDS rollout, and 
highlighting non-pharmacologic alternatives including the IDEA! handout 7. 
Data Collection Methods 
Information sources will include: 1) administrative data found in the UCLA EHR; 2) survey data collected 
from clinicians in the intervention group; and 3) manual medical record review for patients with ED, 
falls, or death within 90 days of intervention exposure to assess for potential unintended consequences 
Statistical Analysis Plan 
Randomization & Blinding 
A statistician not involved in data collection will randomize each eligible clinician to the intervention or 
control group; physicians will remain in the same arm of the study throughout the entire study period. 
The randomization of clinicians will be stratified by the number of new antipsychotic prescriptions 
during the baseline year: 1 vs. 2–3 vs. 4 or more new prescriptions. The randomization of the clinicians 
was incorporated into the EHR, tagging the providers as either EHR Intervention or EHR Control in a list 
created for this intervention. The study team will randomize physicians rather than patients to minimize 

contamination. Contamination occurs when a physician sees a patient in the intervention arm at one 
time point, learns from the intervention that ordering antipsychotic medications is wrong and/or 
difficult, and then avoids prescribing these medications in the future, even when seeing a patient in the 
control arm. Because physicians will on average receive the intervention 1–2 times during the study 
period, inter-physician contamination will be minimal. Moreover, the CDS tool will only trigger for a new 
antipsychotic order, defined as an antipsychotic prescription for a patient who has not had an active 
antipsychotic prescription within the previous 12 months. This intervention will be implemented as part 
of a quality improvement effort; clinicians will not be blinded to receiving the intervention. 
Primary Outcome 
The primary outcome will be the cumulative total of new antipsychotic prescription days supplied by 
clinicians per eligible patient in the 12 months after the intervention rollout date compared to the prior 
12-months, in the intervention versus control groups regardless of the number of visits the provider 
receives. 
Secondary Outcomes 
Secondary outcome measures will be compared between the intervention and control groups and will 
include the rate of new initiation of antipsychotic medications, patient receipt of the non-pharmacologic 
IDEA! Strategy handout in post-visit patient instruction, emergency department visits, hospitalizations 
(including psychiatric hospitalizations), and death within 90 days after the encounter. If patients are 
seen in the ED or hospitalized for falls within 90 days of being exposed to the intervention, the study 
team will conduct an implicit medical record review to determine whether these outcomes were 
unintended consequences of the intervention, considering that patients may have seen other physicians 
either outside the study or in a different study arm after the initial encounter. The study team will 
examine whether the intervention led to any unintended consequences such as substitution of other 
psychotropic medications or reduced time living at home (due to residence in a nursing home or other 
non-home institution). The study team will also survey clinicians’ perceptions of changes in workflow, 
autonomy, satisfaction, and quality of care. 
Statistical Analyses 
The study protocol follows SPIRIT international guidelines and will follow CONSORT guidelines when 
reporting the trial results. The complete trial protocol can be accessed in the supporting information 
files. Specifically, the study team will report descriptive statistics to characterize the sample of patients 
and clinicians included in the study. The study team will review patient charts to display the time series 
of new antipsychotic prescriptions during the 12 months before intervention rollout, and the 12 months 
after rollout. Each patient will be monitored for 90 days after their encounter with a physician in either 
the intervention or control arm, so the follow up period could last up to 90 days after the completion of 
the trial. The analysis of the primary outcome–total prescription pill-days–will utilize a physician-level 
linear regression model, with heteroscedasticity-robust standard errors, following prior published 
approaches 8. The model will include a fixed effects study arm (intervention vs. control) and baseline 
number of new prescriptions, adjusting for physician-level characteristics including physician gender and 
specialty. Analysis of secondary outcomes will proceed similarly, with the sensitivity analyses using 
different distributions and link functions as appropriate (e.g., a Bernoulli distribution with a logit link for 
the death outcome). To ensure equal consideration of outcomes among both groups, the study team 
will include 90-day follow-up for each outcome measure. A p-value of less than 0.05 will be considered 

statistically significant. All analyses will follow the intention-to-treat principle and will be performed 
using SAS v. 9.4 (SAS Institute Inc., Cary, NC). 
Sample Size 
Power was evaluated using a simulation study. In each simulation, a data set was constructed, 
combining actual prescribing data from mid-2019 through mid-2020 with simulated data from the 
subsequent year. The number of total pill days per clinician in the subsequent year was simulated to 
preserve the mean and standard deviation across clinicians from the prior year, with an assumed year-
to-year correlation within clinicians of 0.90. We have previously observed that a small number of 
providers do a large amount of prescribing, and a large number of prescribers do very little prescribing. 
The small number of physicians who are likely to continue their high levels of prescribing, leading to high 
correlation. Clinicians were then randomized into intervention and control arms, with the intervention 
arm’s number of pill days reduced by a constant amount. A linear regression of year 2’s number of pill 
days was fitted with study arm and year 1’s number of pill days as predictors. Heteroskedasticity-robust 
standard errors were used to compute p-values. Significance was defined as a p-value less than 0.05 
evaluating the study arm effect in the model, and power was estimated as the percentage of simulations 
in which a significant difference was obtained. We performed 10,000 simulations to estimate power. 
Based on historical data, we estimate that about 65% of physicians included in our randomization list 
will see an eligible patient during the study period. We thus expect our analytic sample to include data 
from 96 physicians. With 48 physicians randomized to each arm, we will have 80% power to detect a 
mean reduction of 108 pill days, which is feasible. This simulation power analysis was performed using R 
v. 3.6.2 (http://www.r-project.org/). 
 

References 
1. Reuben DB, Hackbarth AS, Wenger NS, Tan ZS, Jennings LA. An Automated Approach to 
Identifying Patients with Dementia Using Electronic Medical Records. J Am Geriatr Soc. 
2017;65(3):658–9. doi: 10.1111/jgs.14744 
2. Owen RR, Thrush CR, Hudson TJ, Mallory SR, Fischer EP, Clardy JA, et al. Using an explicit 
guideline-based criterion and implicit review to assess antipsychotic dosing performance for 
schizophrenia. Int J Qual Health Care. 2002;14(3):199–206. doi: 
10.1093/oxfordjournals.intqhc.a002611 
3. Schpero WL, Morden NE, Sequist TD, Rosenthal MB, Gottlieb DJ, Colla CH. For Selected Services, 
Blacks And Hispanics More Likely To Receive Low-Value Care Than Whites. Health Aff 
(Millwood). 2017;36(6):1065–9. doi: 10.1377/hlthaff.2016.1416 
4. Colla CH, Morden NE, Sequist TD, Schpero WL, Rosenthal MB. Choosing wisely: prevalence and 
correlates of low-value health care services in the United States. J Gen Intern Med. 
2015;30(2):221–8. doi: 10.1007/s11606-014-3070-z 
5. Seppi K, Ray Chaudhuri K, Coelho M, Fox SH, Katzenschlager R, Perez Lloret S, et al. Update on 
treatments for nonmotor symptoms of Parkinson’s disease-an evidence-based medicine review. 
Mov Disord. 2019;34(2):180–98. doi: 10.1002/mds.27602 
6. Colla CH, Mainor AJ, Hargreaves C, Sequist T, Morden N. Interventions Aimed at Reducing Use of 
Low-Value Health Services: A Systematic Review. Med Care Res Rev. 2017;74(5):507–50. doi: 
10.1177/1077558716656970 
7. IDEA! Strategy. Alzheimer’s Association of Greater Los Angeles [Available from: 
https://www.alzheimersla.org/wp-content/uploads/2016/01/Dementia-Care-Toolkit-IDEA-
strategy.pdf] 
8. Sacarny A, Barnett ML, Le J, Tetkoski F, Yokum D, Agrawal S. Effect of Peer Comparison Letters 
for High-Volume Primary Care Prescribers of Quetiapine in Older and Disabled Adults: A 
Randomized Clinical Trial. JAMA Psychiatry. 2018;75(10):1003–11. doi: 
10.1001/jamapsychiatry.2018.1867