Speaker

Harlan M. Krumholz, MD, SM
Harold H. Hines, Jr. Professor of Medicine
Department of Internal Medicine
Section of Cardiovascular Medicine
Yale University School of Medicine
Director, Yale-New Haven Hospital Center for Outcomes Research and Evaluation

Slides

Keywords

Decentralized Trial, Digital Trial, Yale PaxLC Trial, Long COVID

Key Points

• The PaxLC Trial is a decentralized Phase 2, 1:1 randomized double-blind superiority placebo-controlled study on non-hospitalized high symptomatic adult participants with Long Covid to determine the efficacy, safety, and tolerability of 15 days of Nirmatrelvir/Ritonavir compared with a Placebo/Ritonavir.

• This talk describes the PaxLC Trial’s strategies to implement a digital, decentralized, and democratized approaches to multidisciplinary research that address the deficiencies of traditional research studies, which can tend to be hierarchical, siloed, slow, expensive, and inconvenient.

• A key question for the study was how do we put the interest of the participants first and foremost and produce knowledge rapidly? Research can improve by simultaneously leveraging advances in technology and culture. Studies must be convenient, meaningful, respectful, efficient, rapid, and fair.

• The research optimization requires partnership with participants, including involvement in study design and workflow, data collection and analysis, and data and results sharing. Participants should have access to investigators and the results of studies.

• PaxLC brings together many innovations including online screening, digital medical record review, e-consent, home-delivery of medications, local clinical blood draws, home-based biospecimen collection, online diaries and surveys, digital medical record outcomes, and participant-centricity, and return of results. During the presentation, members from across the research team shared how PaxLC implemented all of these innovations through the course of the trial.

Discussion Themes

-What have you experienced on the scalability of the approaches you have taken, such as regulations and IRB? Is this knowledge generalizable to other similar trials? Using the local Yale IRB was an asset. We had to have the right people in the room to initiate communication with collaborators and their IRB representatives. The Yale IRB and Trusted Medical helped facilitate the other states and anything we need to take in account for recruitment. We will have a repository with Trusted Medical for future studies. We had to operate the trial with an institution that would allow zero risk. We had to come up with solutions.

-Can you comment on the decision to make the PROMIS scale the primary outcome? Does it capture most what matters to patients? We spent a lot of time discussing what would be the best primary outcome. Pfizer provided feedback. We wanted to measure a lot of stuff. We were familiar with the PROMIS scale and  felt PROMIS would capture whether people were feeling better. We wanted to say if this is working, people’s general health should improve.

–How do you organize recruitment to ensure you have the diversity you want and how do you explain to people you are turning away? It’s a multipronged strategy. Be welcoming and be authentically committed to health equity and be worthy of the trust. One of the main reasons we turn people away is because they are taking a medication that does not work with Paxlovid. A lot of participants have worked with their physician to stop a drug so they can participate.

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Speakers

Rachael L. Fleurence, PhD, MSc 
Senior Advisor
National Institutes of Health

Joshua M. Sharfstein, MD
Vice Dean for Public Health Practice and Community Engagement
Director, Bloomberg American Health Initiative
Professor of the Practice in Health Policy and Management

Slides

Keywords

Hepatitis C, NIH, PCORnet

Key Points

• The advances in Hepatitis C drugs is one of the greatest successes in clinical research in the last 20 years, yet Hepatitis C is a public health crisis in the U.S. with the rate of reported acute Hepatitis C cases increasing 400% during 2010-2020. Rates are the highest among 20-39-year-olds.

• Untreated, chronic Hepatitis C infection leads to liver damage, liver cancer, and death. There is now a cure for Hepatitis C, but many people are not able to access it. Many people do not know they have Hepatitis C (about 40% of patients). There is a lack of point-of-care diagnostics; it can take up to 3 steps to treatment initiation. There is high cost of treatment and insurance prior-authorization requirements. The treatment is not a routine part of primary care, and there is an underserved and hard-to-reach population.

• Even when diagnosed, only 1 in 3 adults are cured of Hepatitis C in the U.S. Pilots for elimination programs for Hepatitis C have been successful in individual states and other countries.

• To address this crisis, the NIH is embarking on a National Initiative on Hepatitis C. The initiative would bring to the U.S. point of care diagnostic tests, provide broad access to curative Hepatitis C medications with a national subscription model, with Medicare co-pay assistance and commercial insurance coverage.

• The initiative will also empower implementation efforts through a public awareness campaign, expansion of screening strategies and settings, especially for high-risk populations; expansion of the number of providers using innovative telehealth methods such as the ECHO program; and expansion of the number of community health workers who can link people to care.

• There are possible clinical research components that would include research on treatment during pregnancy, vaccine development, and implementation model research.

• The economic benefits of a Hepatitis C elimination program would save lives and have enormous financial benefits to Medicare and Medicaid, paying for the program within 10 years.

• PCORnet has been an important resource by executing a query to identify the volume of HCV tests conducted by participating health systems and the number of co-infections with Hepatitis B virus. A manuscript is under development. PCORnet is engaging with sites to support the ITAP clinical study for de novo clearance of a qualitative POC HCV test-to-treat platform. Discussions are currently underway with 13 partner sites.

• Unless we take action, our system will be spending tens of billions of dollars for Hepatitis C care over the coming decades for people already infected. The current trends of Hepatitis C epidemiology in the U.S. show that a cure is not sufficient to guarantee disease elimination.

Discussion Themes

–When did NIH begin to think a program like this was possible? In early 2022 Dr. Collins was asked to serve as Biden’s acting science advisor, and Dr. Collins wanted to use his position at the White House to help advance health and medical space. He came to the conclusion that Hepatitis C has the most untapped potential to benefit from White House support. We spent 15 months working with the White House to get the program in the president’s budget and now our focus is on the Hill.

-What are the policy considerations to get to easier testing? Our review of the data from other countries is that having same-day tests available for certain settings and populations is a helpful strategy. The hope is that we can have coherent and national organization to roll out this program and get the point of care tests in places where it really matters to have these tests.

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Speaker

Keith Marsolo, PhD
Associate Professor
Department of Population Health Sciences
Duke University School of Medicine

Slides

Keywords

PCORnet, Common Data Model, EHR, Social Determinants of Health

Key Points

• There are many different definitions of social determinants of health. The World Health Organization defines social determinants of health as non-medical factors that influence health outcomes and conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life.

• The PCORnet Common Data Model (CDM) includes data available from Clinical Research Networks. Some data, such as basic clinical data and demographics, are ready for research. Other data, such as immunizations, social determinants of health (SDOH), patient-generated data, and others, may or may not be in the PCORnet Common Data Model and require additional work for use in research.

• In 2021-2022, PCORI contracted with NORC at the University of Chicago to undertake a series of convenings to consider data infrastructure enhancements to PCORnet. A social determinants of health convening built upon efforts of prior PCORnet SDOH workgroup and included survey development, key informant interviews, and public webinars.

• When we talk about patient-level SDOH measures, the CDM has some general purpose tables that can store that data. Adding these data to the CDM generally involves several steps. Identifying whether there are codes to represent the measures in standard terminologies. Partners then must find the relevant measures within their EHRs and harmonizing them to the appropriate code. In many EHRs, data may be captured using various workflows over time, which can also affect the overall data completeness.

• For example, consider food security. 22 sites within the network were able to load some record of food security. There was a wide spread of information that was available. Insurance status is captured in an encounter level with the payor name. It is often that you have to take the raw values and harmonize to a particular insurance type (source of payment typology). Partners within the network have to take raw names and try to harmonize those to a specific type of insurance. It can be complicated to tease out by the insurance name.

• PCORnet has demonstrated that patient-level SDOH data can be incorporated to the CDM. Data availability is dependent on adoption and utilization by health systems. It may be suitable for studies on targeted populations but will depend on collection practices at a given health system.

• Area-level measures can provide population-level SDOH insights. 5-digit zip and county can be included in Limited Data Sets and are more easily used in distributed analytics. Capabilities for geocoding exist at many institutions but will require involvement of local personnel to generate values based on census tract or latitude/longitude. May be be best suited for specific studies.

 

Discussion Themes

-Have the data been used by PCORnet studies? It is new and has not been used widely across studies.

–What are you measuring with insurance status and can you capture in a model individual effect and social effect? We are working to get the best information we can. Having no insurance information about the patient can be problematic. We are trying to work with sites to find the right level of granularity when describing insurance. Insurance status does not highlight whether a patient lives in a food desert or in unsafe housing. It is important to look at what is available to people, what interventions are able to be done by the cluster data – by housing authorities, federal groups, and the health system. First steps are getting the data in such a state that would allow us to learn about some of those social determinants of health.

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Speakers

Speaker: David M. Murray, PhD
NIH Associate Director for Prevention and Director, NIH Office of Disease Prevention

Moderator: Jonathan C. Moyer, PhD
Statistician, NIH Office of Disease Prevention

Slides

Keywords

Implementation; Study design; Hybrid; Clustered; DECIPHeR

Key Points

• People often contest that hybrid designs are not as rigorous as they should be. The use of the term “hybrid design” is unfortunate, as it suggests that implementation research has different methods than other research and might not be held to the same standards. Instead, we should use the same rigorous methods for implementation research that we use for other research and simply change the focus.

• The Disparities Elimination through Coordinated Interventions to Prevent and Control Heart and Lung Disease Risk (DECIPHeR) initiative at the National Heart, Lung, and Blood Institute (NHLBI) is their first major effort to conduct implementation research. Through this initiative, 7 Clinical Centers are expected to test an evidence-based, multi-level intervention designed to reduce or eliminate cardiovascular and/or pulmonary health disparities. One of the key features was that implementation measures were to be used as primary outcomes.

• The NHLBI created the Technical Assistance Workgroup, building on the model established by the NIH Pragmatic Trials Collaboratory, with the goal of helping the Clinical Centers create the strongest possible application for the UH3 phase. The group considered the project aims, study design, statistical analysis plan, and power analysis for each center until all aspects were aligned. They also helped review each Clinical Center’s protocol before it went to the DSMB and NHLBI for transition to the UH3 phase.

• While working across the 7 projects, the Technical Assistance Workgroup encountered several design and analytic issues, including: ensuring emphasis on implementation outcomes, research designs for Type I, II, and III Hybrid Studies, intervention versus implementation strategies, the need to address clustering, cross-classification and multiple membership, time-varying intervention effects, data based parameter estimates, blinding, and adaptations of intervention and implementation strategies.

• Throughout the time the Technical Assistance Workgroup worked with the 7 Clinical Centers, they learned that implementation research has its own practices in many design and analysis areas. They also learned that consensus is lacking in many areas, such as blinding and adaptation, even among the implementation research community. They learned that researchers outside of the implementation research community often do not understand the features common to implementation research, and there’s a benefit to bringing the two communities together for review of proposed studies. They found that involving methodologists familiar with clustered designs and their analytic and power issues was a key factor in their success.

• The results of the Technical Assistance Workgroup’s involvement in the Clinical Centers’ development and proposal process was a much stronger set of proposals for the UH3 phase of DECIPHeR.

Learn more

Visit the DECIPHeR website.

Discussion Themes

-You previously mentioned that the practice of blinding is extremely common in clinical trials and less so in implementation studies. In implementation studies, how can you blind outcome assessor? Is independent adjudication a possible solution? Yes, that’s one solution. It’s relatively easy to blind outcome assessors. It’s not bulletproof, but first, you have intervention and implementation staff that are completely independent from measurement staff. Second, you shouldn’t tell the staff collecting the outcome data which arm the various sites are in. To the extent that you can use data from electronic health records, that will help with keeping the staff blinded. It’s important to note that it’s more difficult to keep the centers, or actual clusters, blinded. It’s important for them to know that they’re getting an intervention, even if they’re not aware which one.

-Could you expand more on what implementation outcomes are? Examples of implementation measures include acceptability, adoption, appropriateness, affordability, cost, feasibility, fidelity, reach, etc. In most clinical trials, we might measure these as process outcomes. However, in an implementation trial, these are the most interesting measures. An implementation study generally is used when you have an intervention that has already been shown to be effective on health outcomes, so it’s less important to be concerned about that. The real interest is in finding out about how to improve acceptability, adoption, fidelity, etc. so that people will use the research we’ve done.

-This was an impactful collaboration between NIH investigators and study teams and methodologists. How can this kind of collaboration happen more widely? I wish that those of us involved had enough bandwidth to get involved with every major initiative that NHLBI launches, but that’s unfortunately not possible. This collaboration was a special case with a very good biostatistics and design working group. DECIPHeR benefitted greatly from having the Techinical Assistance Workgroup, and anytime you have a study with a coordinating center, a similar group could be an important function of the coordinating center. However, I don’t have a great general solution for that issue at this point.  

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Speakers

Claire Snyder, PhD
Professor
Johns Hopkins Schools of Medicine and Public Health

Norah Crossnohere, PhD
Assistant Professor
Ohio State University College of Medicine

Anne Schuster, PhD
Research Scientist
Ohio State University College of Medicine

Slides

Keywords

Patient-Reported Outcomes, PROs, PROTEUS Consortium

Key Points

• The Patient-Reported Outcomes Tools: Engaging Users & Stakeholders (PROTEUS) Consortium initially focused on PROs in clinical trials and then expanded to use in clinical practice. The PROTEUS Consortium’s objective is to ensure that patients, clinicians, and other decision-makers have high-quality PRO data from clinical trials and clinical practice to make the best decisions they can about treatment options.

• The PROTEUS Consortium partners with key stakeholder groups to disseminate and implement tools that have been developed to optimize the use of PROs in clinical trials and practice.

• There are more than 50 organizations with participating in PROTEUS, including clinicians and patient advocates, research and methods organizations, clinical trials groups, funding and government agencies, and universities and health systems.

• Over the last decade, collection of guidance documents and resources have been developed to develop each of the steps in the clinical trial directory. The PROTEUS website has web tutorials, checklists, and a handbook on topics such as displaying data for patients and clinicians and researchers.

• The PROTEUS-Practice Guide offers support for designing, implementing, and managing PRO systems in clinical care. It collates and synthesizes foundational resources to create a unified, comprehensive resource. For each consideration, the Guide provides a range of options rather than one “right” way. In almost all cases, the options are not mutually exclusive, and it is advisable to adopt multiple approaches. The Guide is applicable to a broad range of health systems.

• The PROTEUS-Practice Learning Health Network includes 10 funded projects who come together with members across the PROTEUS Consortium for monthly meetings hosted by PROTEUS that provide a forum to share experiences and lessons learned.

• Building off the request for proposals process for the Learning Health Network, PROTEUS recognized that institutions caring for vulnerable and underserved populations may face unique challenges when aiming to implement PROs in routine care.

• PROTEUS and Pfizer partnered to form an Advisory Group that aimed to improve understanding of the facilitators and barriers of implementing routine PRO assessments in vulnerable and underserved populations and build capacity for PRO implementation to improve care for cancer patients who are vulnerable or underserved.

• The Advisory Group identified 47 different potential solutions to address the top barriers. PROTEUS leaders reviewed and categorized the solutions into 4 categories: education and engagement, information technology or technological resources, incentives, mandates, and marketing, and research.

 

Learn more

Visit the PROTEUS Consortium

Discussion Themes

-Can you explain why the error bars and P values are not on some of the graphs in the presentation? This question gets at the importance of tailoring presentations for the intended audience. In our research, we learned that patients actively do not want to see error bars and P values on graphs. They didn’t know what they meant and found them confusing, and it strongly interfered with their ability to engage with the information. For clinicians and researchers, there was value in showing them.

-Can you elaborate on the incentives and marketing recommendation from the Underserved Advisory Group? Some of the discussion included insurance coverage where PRO monitoring is included with a prescribed treatment plan. There was discussion about paying patients and patient advocates for the time they spend advocating for PROs. In terms of marketing, including patient preferences in marketing throughout.

–How is PROTEUS following up on the recommendations regarding underserved populations? With current funding, PROTEUS can address recommendations specifically regarding education materials. We are pursuing funding. We are an implementation and dissemination project so not all traditional funding sources are available to us

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Speaker

Tumaini Rucker Coker, MD, MBA
Professor of Pediatrics
Division Head for General Pediatrics
University of Washington Department of Pediatrics
Seattle Children’s Hospital

Slides

Keywords

Pediatrics, Preventive Medicine, Community Health, Well Child Care

Key Points

• There are 10 preventive care visits from ages 0-3, usually scheduled as 15-20 minute visits with a pediatrician. This may be the only interaction the family has with a healthcare professional, so if there are needs around behavior, health, social needs they will be addressed during these visits. This time is not used appropriately now because there is so much that needs to fit into that visit.

• The American Academy of Pediatrics (AAP) Bright Futures guidelines for preventive visits include history, measurements, physical exam, developmental and behavioral screening, anticipatory guidance, and psychosocial and social needs screening and guidance.

• How might we better structure preventive visits? Donabedian’s Quality Framework includes a structure that supports care, a process for the provision and receipt of care, and health outcomes, providing a process without a structure to support it. Adapted for early childhood preventive care, the structure should be team- and community-based.

• The Parent-Focused Redesign for Encounters, Newborns to Toddlers (PARENT) study was a randomized controlled trial of PARENT verses usual care for parents with infants 12 months and younger over a 12-month study period. The PARENT group added interventions such as a community health worker “parent coach,” a pre-visit tool to help identify parent priorities, a text message service to keep in touch between visits, and a brief, focused clinician visit.

• In the initial PARENT trial, intervention families had better performance on receipt of well child care, and patients reported better patient experience and fewer emergency department visits. Parents loved the text messaging service.

• Based on these findings, the research team initiated a larger PARENT trial that had 3 parent coaches at federally qualified health centers. The trial was cluster randomized by clinic for usual care or intervention arms; participants were parents with infants 12 months and younger. The first year was spent allowing the clinics to make the project their own, with input from parents and clinicians.

• Ten clinics were randomized, and 937 parents participated with 785 completing 12-month follow up. The average child age was 4 months. 93% of participants were Medicaid insured, 95% were mothers, 8% had a college degree, and 63% had a household annual income of less than $30,000. The intervention families had better performance on receipt of well child care services, better parent experiences of care, no change in Emergency Department visits, and were more likely to be up-to-date on well child care visits.

• Integrating a community health care workers into the well child care team improves well child care for Medicaid-insured children. Evidence for PARENT, and other clinic-based interventions that utilize community health care workers in a team-based approach to early childhood well child care. Clinics and practices will need Medicaid state plan amendments that provide adequate funding for community health care workers in well child care and support for implementation.

• The research team is in the next phase of local adaptation, having received funding from PCORI for PARENT Adaptation for Black Families: NCH-PCN Partnership. The intervention was quite strong with Latino families but did not have the power to understand what it looks like with Black families. It will be a stepped-wedge randomized trial at 12 Nationwide Primary Care Clinics with adaptations and implementation and parent, staff, and provider engagement.

 

Learn more

Read more about PARENT in JAMA.

Discussion Themes

-What did stakeholder engagement change in the process? From the beginning we did not know what the intervention was going to be. As we went to each new clinical space there were things we could not have anticipated that they wanted to change for their clinic. For example, one local clinic did not have the room space, so the coaches called the family the day before then they could pop in quickly for a brief discussion. Doing the formative work builds ownership of the intervention.

-In areas of quality improvement people often do not consider cluster randomized design or people may be disappointed if they are randomized to usual care. In the PCORI funded trial we are doing stepped wedge so everyone gets the intervention at some point, which was important to our partners. For the first small trial, we randomized at an individual level, which takes a lot of manpower to maintain. Our partners wanted to know at the end of the trial, will this make care better, and when the answer is yes, they are willing to participate. Our partners want to know the data and what it takes to collect it in a rigorous way.

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Speaker

Pasquale Rummo, PhD, MPH
Associate Professor
NYU Grossman School of Medicine

Slides

Keywords

Randomized clinical trial; Food insecurity; Nutrition; Online retailer; Equity

Key Points

• Supplemental Nutrition Assistance Program (SNAP) provides nutrition assistance benefits to supplement food budgets with the goal of mitigating food insecurity. SNAP does not focus on mitigating nutrition insecurity, which is where nutrition incentive programs come into play. Nutrition incentive programs match SNAP dollars for people to use on fresh produce at supermarkets, which increases sales and consumption of produce in brick-and-mortar settings. However, the results of nutrition incentive programs had not been evaluated previously in an online setting.

• Behavioral nudges may increase healthy food purchasing by “nudging” consumers to purchase healthier options by providing discounts or lower-cost options. Default options have been shown to increase healthy food orders in restaurants, but no studies had not been previously conducted to represent the effects of online shopping carts pre-filled with tailored fruit and vegetable items.

• SNAP Online Purchasing Pilot rapidly expanded during the pandemic with the goal of creating equitable access to online grocery shopping, leading to the growth of online grocery shopping sales among those with low incomes.

• The goals of this project were to characterize online food shopping behaviors in an online survey and conduct a randomized clinical trial of behavioral economic strategies on fruit and vegetable purchases in an online grocery store.

• The sample included 2,744 adults at least 18 years of age who currently or had previously received SNAP benefits. Participants were randomized into four different groups: 1) Control – participants received no discount, and shopping cart was not prefilled; 2) Discount – participants who received a 50% discount on eligible fruits and vegetables; 3) Default – participants who had shopping carts were prefilled with tailored fruit and vegetables; 4) Combination (Discount and Default) – participants who received a prefilled shopping cart plus a 50% discount on eligible fruits and vegetables.

• Participants were asked to shop for a typical week’s worth of groceries for their household on a budget adjusted for household size. The team created an online grocery store that simulates a real online retailer to conduct this experiment.

• The team analyzed non-discounted dollars spent on eligible fruits and vegetables per food basket and out-of-pocket dollars spent on fruits and vegetables, as well as non-discounted dollars spent on fresh, frozen, and canned fruits and vegetables as well as the macronutrients from these fruits and vegetables.

• The results showed that about 20% of the cart total was spent on fruits and vegetables across all cases. Over 90% of people in the default and combination groups purchased at least one of the default fruits and/or vegetables in their cart. About half of those in the control and discount groups purchased at least one of the fruits and vegetables offered to the default group on their own accord. About two thirds of the participants stated that they would support the default strategy with or without the discounts offered to them. Compared with the control group, participants in the discount, default, and combination groups spent greater percentages of non-discounted dollars on fruits and vegetables.

• These findings suggest that the expansion of nutrition incentives to online settings is a potentially promising strategy to promote equitable access to food. Providing default options is also an effective strategy in motivating individuals to purchase fruits and vegetables. Additionally, combining discounts and default options creates a synergistic effect of increasing the amount of fresh produce people purchase. This may also be a more equitable strategy than defaults alone, as it may increase purchasing power for individuals with food insecurity.

 

Discussion Themes

– Compared to some of the other work that you and your colleague shave done, would you describe this as easier to do since you’re taking advantage of online approaches, or was it harder? In terms of balance, it was easier. I think there’s probably a lot of people here who know how difficult it is to recruit people in person. It was also easier because we used a survey research firm, although I will say we are progressively considering and have started recruiting people through alternate methods. The part that’s more difficult is operationalizing all of this, as none of our team has expertise in computer science, for example, which has been really challenging. One thing we want to do in the future is pair with people who can help us do things that are more sophisticated, rather than just asking a question in a survey that says, “Here’s five options. Which do you purchase the most?”

– Did you have any intention to evaluate if the behavior would continue without the incentives? I want to acknowledge that there’s definitely questions about the sustainability of types of behavior changes like the default options or even the economic incentives. My thoughts differ for each strategy. I would love for the financial incentive for fruits and vegetables for participants to remain a permanent strategy. I’m a little less interested in the sustainability of those effects. I’d be more interested in this with respect to the default strategy. But, to answer your question more directly, we didn’t look at the sustainability of these behaviors, as this trial was cross-sectional. I’m proposing and writing a grant now to run a longitudinal study focused on at different strategies to address this issue over time.

– Could you comment on the magnitude of the changes in purchasing and whether you think those are of sufficient size to have an impact on clinical outcomes? I’m also wondering if we know people who receive these discounts and save the money on the healthy food are substituting it and using that money to purchase unhealthy food? To me, the goal of these incentives is to make healthy eating more affordable, so I think it accomplishes that goal. With respect to whether this would change clinical outcomes, I’m skeptical, especially given the size of the effect that we saw. I think we would need several different strategies all in combination to effect clinical outcomes. As far as the savings go, I looked at substitution spending in a prior study, and at least for other grocery orders, people didn’t substitute to some of the unhealthy categories. I’m proposing to evaluate another incentive program with that in mind in order to more closely track those purchase. For this study, people are ultimately buying more fruits and vegetables regardless of what they choose to spend their savings on.

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Speaker

Gabriela Schmajuk, MD, MS
Professor of Medicine
UCSF and the San Francisco VA

Slides

Keywords

Patient-Reported Outcomes, Rheumatoid Arthritis, EHR

Key Points

• Clinicians rely on patient-related outcomes (PROs) to track disease and function over time in patients with rheumatoid arthritis (RA). These outcomes include disease activity, function status, and pain score.

• These PROs have been integrated into RA guidelines and they are recommended by professional societies. The treatment philosophy for RA is to treat-to-target based on PRO disease activity scores. There are 4 components to the treat-to-target approach: record disease activity using a composite measure every 3 months; specify disease activity target; adjust medications to target; and document shared decision making. However, there is a gap between collecting the PROs and communicating about the PROs with patients.

• To try to bridge the gap between PRO collection and meaningful use during clinical encounters, the RA PRO dashboard was developed. The team collected focus group feedback from patients and clinicians and worked with IT professionals to pull data directly from the electronic health record and design an interface that could be used by all patients.

• The RA PRO dashboard was tested as part of a stepped wedge, cluster randomized trial at the clinician level. The hypothesis was that the RA PRO dashboard used during clinical encounters will increase patient engagement, foster shared decision making, and improve health outcomes for patients with RA.

• There were 4 clusters with 4-6 physicians each (a mix of attendings and fellows). There were 1:1 training sessions, regular reminders on how to use the dashboard, clinical research coordinators who collected patient information, conferences for sharing successes and challenges, training for medical assistants on collecting PROs, and information sheet for patients. 552 unique patient participated in the trial.

• In the 18 months of the trial, there were not see changes in the quantitative outcomes that were measured. Patients completed a dashboard survey, which gave the study a lot of qualitative measures. Despite the negative quantitative outcomes, 80% of patients said they would like to continue to use the dashboard, the dashboard helped them talk to their clinician about their symptoms and medicines, and it helped them understand more about their arthritis.

• The trial team also did a qualitative analysis with the clinicians and analyzed according to the technology acceptance model. In general, the clinicians perceived a lot of usefulness of the dashboard though they were anxious about discussing the PROs when the disease score came up high and it might not have to do with RA. They found the dashboard easy to use and discussed the lack of patient data that made it less useful.

• There were technical and non-technical challenges of maintaining the intervention. There was a big gap between when the dashboard was built and launched. There are new medications now that were not showing up that required regular updates to the dashboard. There were also challenges with many software and security updates that impacted to the dashboard. There were also major changes to the clinic workflows during the COVID-19 pandemic and issues with data completeness with clinic turnover.

 

Discussion Themes

-Do you think you would have gotten increased adoption, less technical difficulties if you had built the dashboard within EPIC instead of as a Sidecar app. Building in EPIC as we did several years ago, we would not have been able to get the customization that we wanted for the dashboard.

-Was patient recall about past visits accurate several months later? Could this be a reason for a no effects trial? We struggled with when to collect the surveys. We tried collecting the surveys through a phone call after the visit and found we were not able to reach many of the patients. We tried collecting some of the information at the end of the visit and found people were in a hurry. We had to balance the proportion of people who were going to respond to the survey against the timing of when the survey was delivered.

–For an intervention that did not have demonstrable effect but did have qualitative patient value, how do you think about continuing the dashboard verses de-implementing it or do you think over time it will become more useful in more ways? I am still optimistic that we will see some improvements in the outcomes we specified. I am interested in doing a future survey on some of the outcomes.

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