Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06377033 |
| Other study ID # |
10877980 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
June 10, 2024 |
| Est. completion date |
June 30, 2027 |
Study information
| Verified date |
June 2024 |
| Source |
University of Pennsylvania |
| Contact |
Benita Weathers, MPH |
| Phone |
2155738860 |
| Email |
weathers[@]upenn.edu |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
Given the expansion of indications for genetic testing and our understanding of conditions
for which the results change medical management, it is imperative to consider novel ways to
deliver care beyond the traditional genetic counseling visit, which are both amenable to
large-scale implementation and sustainable. The investigators propose an entirely new
approach for the implementation of genomic medicine, supported by the leadership of Penn
Medicine, investigating the use of non-geneticist clinician and patient nudges in the
delivery of genomic medicine through a pragmatic randomized clinical trial, addressing NHGRI
priorities. Our application is highly conceptually and technically innovative, building upon
expertise and infrastructure already in place.
Innovative qualities of our proposal include: 1) Cutting edge EHR infrastructure already
built to support genomic medicine (e.g., partnering with multiple commercial genetic testing
laboratories for direct test ordering and results reporting in the EHR); 2) Automated
EHR-based direct ordering or referring by specialist clinicians (i.e., use of replicable
modules that enable specialist clinicians to order genetic testing through Epic Smartsets,
including all needed components, such as populated gene lists, smartphrases, genetic testing,
informational websites and acknowledgement e-forms for patient signature); 3) EHR algorithms
for accurate patient identification (i.e., electronic phenotype algorithms to identify
eligible patients, none of which currently have phenotype algorithms present in PheKB; 4)
Behavioral economics-informed implementation science methods: This trial will be the first to
evaluate implementation strategies informed by behavioral economics, directed at clinicians
and/or patients, for increasing the use of genetic testing; further it will be the first
study in this area to test two forms of defaults as a potential local adaptation to
facilitate implementation (ordering vs. referring); and 5) Dissemination: In addition to
standard dissemination modalities,PheKB95, GitHub and Epic Community Library, the
investigators propose to disseminate via AnVIL (NHGRI's Genomic Data Science Analysis,
Visualization, and Informatics Lab-Space). Our results will represent an entirely new
paradigm for the provision of genomic medicine for patients in whom the results of genetic
testing change medical management.
Description:
Overview: Using key stakeholder engagement, this study will refine clinician- and
patient-directed nudges designed to change the status quo bias that too often is relied upon
within the complexity of medical care and decision-making, which reduces the likelihood that
genetic testing will be used in situations where it will change medical management. The
investigatorswill define algorithms to identify patients eligible for genetic testing (Aim
1); conduct a hybrid type 3 cluster-randomized implementation trial to evaluate optimized
patient- and/or clinician-directed nudges for increasing the use of genetic testing to inform
medical management (Aim 2); and engage in dissemination activities to increase the capacity
of other medical settings to adopt both our EHR-based infrastructure and the implementation
strategies designed and evaluated in this trial (Aim 3).
For Aim 1, our Stakeholder Advisory Council will design the nudges to "de-risk" and optimize
implementation strategies by ensuring that the perspectives of end-users are included
initially. In Aim 2, the investigators will test our optimized nudges in a six-arm hybrid
type 3 pragmatic cluster randomized controlled trial (RCT) to evaluate the effectiveness of
nudges to clinicians (referral vs. ordering), nudges to patients, or nudges to both for
increasing genetic testing, vs. generic clinician Best Practice Alert and no nudge. The trial
will include 230 clinicians, who will be the unit of randomization, with randomization
performed on "clusters" of clinicians to control for the potential of contamination that can
arise when clinicians who work closely together are randomized to different arms of the
trial. Once the clusters are randomized by specialty, the trial will follow at least 16,500
patients over 3 years, monitoring fidelity of nudge delivery, use of genetic testing, and
secondary implementation outcomes. Patient, clinician, and system factors will be assessed as
moderators and an effectiveness outcome will be examined.
In Aim 3, Both EHR-based algorithms already established through the PennChart Genomics
Initiative, for which the investigators have received multiple requests, and those developed
through this application, will be shared through PheKB, ANVIL, and Epic Community Library.
Aim 1: To develop clinician- and patient-directed nudges, informed by behavioral economic
theory, within the EHR that will address the barriers to specialist clinician genetic testing
of patients in whom it will change medical management, develop clinician- and
patient-directed informational websites, and refine EHR algorithms to identify patients who
are good candidates for genetic testing. Procedures for refining systems to identify patients
for genetic testing: Although there is a growing number of conditions for which genetic
testing is indicated where the results will change medical management for patients, between
0-90% of patients have genetic testing. Changing this first depends on the use of electronic
phenotyping algorithms to identify eligible patients. Development of electronic phenotype
algorithms involves the integration of ICD-10 diagnosis codes, clinical lab measurements,
vital signs, medications, procedures, and clinical notes into rule-based algorithms to
identify individuals who can be classified with a diagnosis for a given disease phenotype.
The phenotype knowledgebase (PheKB) is a collaborative environment to build, validate, and
share these electronic phenotype algorithms. To perform our RCT, the investigators first need
to identify patients who would benefit from genetic testing. The investigators will work
closely with the clinical experts in neurogenetics, cardiac, and medical genetics to identify
the clinical features from the EHR for defining disease diagnosis for each condition,
including two prior encounters for the diagnosis with first within a year. The investigators
will deploy the algorithm and then perform manual review of 100 charts to estimate the
positive predictive value (PPV) of the algorithm; this is the proportion of patients defined
as cases by the algorithm who have the condition. The investigators will also perform manual
review of 100 charts for individuals who have a diagnosis code in the EHR from two different
encounters for one of the study conditions but are not identified as cases based on the
electronic phenotyping algorithm. This review will give us an estimate of how much
specificity the investigators gain with the electronic phenotype algorithm above and beyond
the diagnosis codes alone. As the patients identified by the algorithm will be enrolled in
the clinical trial, the investigators will aim for 100% PPV. Each patient identified by the
phenotype algorithms will be added to the Diagnosis-specific Epic Registry and the SQL
database. The algorithms will be disseminated through Aim 3.
Procedures for nudge design: To develop a sustainable EHR-based infrastructure, supporting
the provision of genomic medicine and inclusion of specialty clinicians, equally valuable for
other groups nationally, the investigators must consider multiple perspectives on how our
nudges - content, design, and mode of delivery - will impact institutions, payors,
clinicians, and patients. Thus, the investigators have formed a Stakeholder Advisory Council
with diverse representation from genetics and non-genetics clinicians, informaticians,
payors, testing companies, legal experts and ethicists, and patient groups and community
representation, with expertise regarding health disparities and equity. The group will
support the development of and wording in the nudges, genetics and disease-specific website
education for patients and clinicians, and ease of use of the EHR-based infrastructure for
genetic test ordering, results, and referral.
To ensure that our nudge design and delivery consider issues relevant to health disparities
and equity, the investigators have engaged Dr. Rachel Shelton as a consultant. She is an
expert in understanding and addressing existing health inequities and identifying
interventions and strategies to promote greater health equity. She has worked with members of
the research team to develop nudges that improve the quality of cancer care delivery and
consider existing health disparities and evaluate indicators of health disparities as
moderators of nudge effectiveness. The Stakeholder Advisory Council will meet three times in
the first year and then every six months in subsequent years; discussions will be
audio-recorded, and survey questions assessing usability of nudge designs will be
administered.
Patient informational video: The video will be employed as part of the patient-directed
nudge. Patients in patient nudge arms will be provided with a link to the video via text
message. The video will provide general information about genetic testing and patients will
be able to view it multiple times.
Clinician informational website: An informational website, maintained on the Penn Medicine
intranet, for clinicians will be developed; it will be available through the clinician nudges
and when results are returned. The website will include information about genetics and
genetic testing, details about ordering genetic testing through the EHR (with tipsheets),
referral to Penn Medicine genetic clinics and how the results of genetic testing would
influence the patient's medical management for each diagnosis.
Aim 2: To conduct a type 3 hybrid implementation cluster randomized clinical trial to
evaluate the effect of behavioral economic theory clinician nudges and patient nudges
delivered within the EHR on the rate of genetic testing by non-geneticist specialist
clinicians across a diverse health system, compared to generic messages and no
default.Overall design. The investigators will test optimized implementation strategies in a
six-arm factorial hybrid type 3 cluster implementation RCT, testing the effectiveness of
nudges to clinicians (referral vs. order), nudges to patients, or nudges to both for
increasing genetic testing among patients for whom testing would influence medical management
vs. a generic Best Practice Alert/no patient or clinician nudge. The investigators include
two forms of physician nudges - referral and ordering - to consider and test the effects of a
local adaptation to this implementation strategy, which can be an important factor that
influences the effectiveness of implementation strategies. Primary and secondary
implementation outcomes, and contextual factors that shape implementation effectiveness and
clinician census. Our trial adopts a health equity lens, as done in our ongoing trials. To
this end, our preliminary data focused on identifying medical conditions for which genetic
testing may affect outcomes and for which there exists disparities in testing across races.
Second, the investigators have considered important health disparities in the design and
delivery of our nudges. In particular, the investigators examined the rate of access to our
patient portal and, found that there are lower rates of access for racial minority groups,
including delivery of patient nudges via text message as well. Third, the analytic plan
includes an assessment of the impact of our nudges across equity groups. All patient-facing
materials will be available in Spanish.
Participants and randomization. Clinicians within each site will be randomized to six arms
using variable permuted blocks. The researchers will form clusters of clinicians and
randomize clusters using raw data from clinic administrators to identify networks of
interconnected colleagues. A waiver of informed consent will allow for collection of general
census data to characterize the sample of clinicians, EHR data to characterize the sample of
patients, and ascertain data to assess as study moderators. Our clinician sample, drawn from
practicing clinicians within all sites, will 1) be currently in practice at a Penn Medicine
site; 2) have prescribing authority in Pennsylvania (i.e., physician or APP); and 3) have
cared for at least five patients in 30 days prior to recruitment.
Diagnosis-specific Epic Registry: Based on the electronic phenotype algorithms developed in
Aim 1, eligible patients will be filtered into diagnosis-specific Epic Registries. This step
is necessary to identify the patients eligible for observation in the trial. Once identified
and entered into this registry, and when these patients have an appointment with a clinician
within our clinician sample, they are entered into the trial's system within one of the
randomized arms. The registry drives the downstream nudges, e.g. for clinic referral, genetic
test selection, and clinician information.
Nudge to clinicians: The investigators will use the Best Practice Alert (BPA) functionality
within the EHR as our conduit to the point of decision-making about genetic testing with
clinicians. Epic BPA deployment is modifiable to accommodate the inclusion of nudges. When a
patient is scheduled to be seen by a clinician randomized to one of our study arms and
matching the patient eligibility (registry), at the subsequent visit with this clinician, the
clinician BPA will fire. The BPA can be triggered with over 50 multiple potential actions
within the chart, such as entering patient diagnosis or problem, or opening or entering
orders. Resolution of the BPA will be required before the chart is closed. The clinician
nudge will have two forms to account for the need to assess for local adaptation of the
implementation strategy: refer or order. In either case, refer or order are defaulted; the
clinician must toggle to "do not order" or "do not open (Order Set)" and, if so, an
explanation is required. Prior to the launch of the trial, clinicians receive standardized
information about the trial through service line disease team monthly meetings, led by the
study MPIs. These sessions give basic information about the study without disclosing the
hypotheses.
Refer Clinician Nudge: For the refer clinician nudge, if accepted, an order is automatically
placed for a genetic consult with the appropriate genetics program, based on the
diagnosis-specific Epic Registry, either medical/cancer, cardiac or neuro-genetics. The order
will go to the scheduling pool for each program, which will contact the patient for an
appointment (warm hand off). For patients with pheo/pgl seen in medical or cancer genetics,
they will be seen locally, as all hospitals have cancer genetics providers. For patients
referred to cardiac or neurogenetics, patients will be contacted and offered an in-person
visit at either HUP or PAH or a telemedicine visit, based on preference.
Order Clinician Nudge: For the order clinician nudge, the Epic SmartSet function will be used
since it is common and easily transferrable. The SmartSet will include 1) genetic testing
order with a default set of diagnosis-specific genes and a testing lab selected (the BPAs and
resulting Smart Sets will be sensitive to the patient's insurance, so if the patient is
capitated to a certain commercial testing lab or if sponsored testing is available, the
testing will go to that lab); 2) default order to have the genetic testing kit (saliva or
buccal swab) sent to the patient's house; 3) smartphrase to populate the clinician's note;
and 4) an option to send the clinical letter to the testing company. Once the order is placed
through the SmartSet, a linked second BPA will come up that contains a one-page
acknowledgment of genetic testing e-form for patient's signature (every exam room has a
signature pad for e-signature) for the clinician to review with the patient. The after-visit
summary (AVS) that each patient receives at the end of their visit will be automatically
populated with the signed acknowledgment e-form and link to the patient educational website.
Nudge to patients: The patient nudge will be designed to "prime" the patient to discuss the
potential benefits of genetic testing with their clinician ahead of their next appointment.
The patient nudge will be delivered via text message directly to the patient's cell phone.
The patient nudge will be delivered within 72 hours prior to their medical appointment and
will include normalizing language about the potential benefits of genetic testing for their
condition and a clear message of endorsement: The patient nudge will contain a link to the
informational video discussed above.
Generic clinician nudge: To standardize the experience of all clinicians randomized to this
arm (and the patients they see), we will use a generic clinician BPA. The content of the BPA
will indicate that their patient may be a candidate for genetic testing and a link to the
clinician website. No choice architecture will be embedded to facilitate genetic testing
ordering or referring; no patient nudge is provided.
Support for clinicians when genetic testing results are returned: Across all study arms,
discrete results of genetic tests will be returned into the EHR, with an accompanying PDF
with the full results. Along with the results will be a static option with the hyperlink to
the clinician informational website and when they open the results, they will get a BPA with
options to: 1) order a consult to genetics, which will automatically go to the disease
appropriate clinic; or 2) e-Consult genetics, meaning that they can send a question to
genetics (again triaged based on disease type [Epic registry]) without a formal referral.
Insurance coverage for genetic testing: A key point of concern for both clinicians and
patients is insurance coverage for genetic testing. Clinicians will not deal with insurance
coverage directly. The patient's insurance information goes via HL7 with the testing order to
the commercial lab, which deals with the insurance company.
Testing and validation of EHR nudges: To launch the nudges, investigators will use a
two-phase approach from past and ongoing studies. In the first phase, the alert will fire
invisibly in the background without prompting patients or clinicians, followed by an
evaluation of the results to verify accuracy. The algorithm will be refined until it achieves
perfect accuracy. In the second phase, clinician nudges will fire live for several weeks
among a few clinicians. Investigators will then compare the patients for whom it should have
fired to the ones for whom it did and the acceptability of the alert to clinical staff. Nudge
delivery will be monitored in all arms throughout the trial.
