December 13, 2019: Reissuance of Funding Opportunity Announcement for HEAL Initiative/PRISM Coming January 2020

The National Center for Complementary and Integrative Health (NCCIH), with other NIH Institutes, Centers, and Offices, intends to reissue Funding Opportunity Announcement (FOA) HEAL Initiative: Pragmatic and Implementation Studies for the Management of Pain To Reduce Opioid Prescribing (PRISM) (UG3/UH3 Clinical Trial Optional).

This RFA solicits applications for phased cooperative research applications to conduct efficient, large-scale pragmatic or implementation trials to improve pain management and reduce the unnecessary use of opioid medications in the health care delivery setting. The re-issuance of the FOA will prioritize the following areas for pragmatic trials to integrate multimodal or multiple interventions that have demonstrated efficacy into health care systems or implement health care system changes to improve adherence to evidence-based guidelines:

  • Pain management in emergency departments, dental clinics, primary care, and hospitals
  • Chronic overlapping pain conditions
  • Pain management in individuals at risk of or with opioid use disorder
  • Pain management in those with co-occurring mental health disorders
  • Noncancer pain management in persons with medical comorbid conditions

The FOA is expected to be published in January 2020 with an expected application due date in March 2020.

The announcement is part of the NIH Heal (Helping to End Addiction Long-term) Initiative, which was created in April 2018 in an effort to speed scientific solutions for addressing the national opioid public health crisis.

 

November 15, 2019: PCORnet: Health Plan Research Network Data Linkage and Patient Engagement with Patient-Powered Research Networks (Kevin Haynes, PharmD, MSCE)

Speaker

Kevin Haynes, PharmD, MSCE
Principal Scientist
HealthCore

Topic

PCORnet: Health Plan Research Network Data Linkage and Patient Engagement with Patient-Powered Research Networks

Keywords

Data linkages; PCORnet; Patient-powered research networks; Health plan research networks; Computable phenotypes

Key Points

  • One of the biggest challenges facing healthcare today is reducing gaps in evidence necessary to improve health outcomes. Research collaborations between health plans and patient-powered research networks (PPRNs) can help close this gap.
  • PCORnet enables linkages with patient groups through PPRNs, which include participating organizations and leadership teams of patients, advocacy groups, clinicians, academic centers, and practice-based research networks.
  • From the health plan perspective, postal mail outreach to members was more effective than email outreach around engaging patients in research opportunities.

Discussion Themes

When engaging with different patient-powered research networks, are there differences around common conditions compared with rare or stigmatized conditions?

What are participants told about the commercialization of findings, whether in terms of new treatments that might be identified, or the ways in which findings might affect health plans’ willingness to continue to cover certain treatments?

An essential aspect of collaboration is building and maintaining the trust of members in the research networks.

Read more about collaborations between PPRNs and health plans in a recent JAMIA publication and the PCORnet website.

Tags
#pctGR, @Collaboratory1, @KHaynes001

October 4, 2019: Ascertaining Death and Hospitalization Endpoints: The TRANSFORM-HF Experience (Eric Eisenstein, DBA, Kevin Anstrom, PhD)

Speakers

Eric L. Eisenstein, DBA
Associate Professor in Medicine
Duke University School of Medicine

Kevin J. Anstrom, PhD
Professor of Biostatistics and Bioinformatics
Director of Biostatistics, Duke Clinical Research Institute
Duke University School of Medicine

Topic

Ascertaining Death and Hospitalization Endpoints: The TRANSFORM-HF Experience

Keywords

Clinical endpoints: Ascertaining death; Hospitalization; TRANSFORM-HF; National Death Index

Key Points

  • When patient deaths occur outside the care setting, the cause of death may not be reliably documented. For researchers, the challenges of measuring deaths include the lack of a national death data source and incomplete or hard-to-access sources.
  • The death identification and adjudication process differs for explanatory versus pragmatic trials, and has implications for how death endpoints are acquired and measured.
  • The TRANSFORM-HF pragmatic trial is comparing the effects of treatment strategies on long-term outcomes for hospitalized patients with heart failure. The primary study endpoint is all-cause mortality, which is ascertained and verified using a hybrid approach at the clinical site and call center, and includes searching the National Death Index data.

Discussion Themes

What are the tradeoffs in making endpoint ascertainment more simple?

If using a hybrid death data collection strategy, how are discrepancies adjudicated?

Use of call centers that coordinate follow-up patient contact and data collection is a valid approach that ensures a single point of contact for patients or proxies and care providers. This approach should also be supplemented with redundant data sources.

Read more in the Living Textbook about Using Death as an Endpoint and Inpatient Endpoints in Pragmatic Clinical Trials.

Tags
#pctGR, @Collaboratory1, @DCRINews

August 2, 2019: AI and the Future of Psychiatry (Murali Doraiswamy, MBBS)

Speaker

Murali Doraiswamy, MBBS
Professor of Psychiatry and Behavioral Sciences
Duke School of Medicine

Topic

AI and the Future of Psychiatry

Keywords

Artificial intelligence; Machine learning; Psychiatry; Ethical adoption of technologies; Mental health; Wearables; Mobile health

Key Points

  • There is growing evidence from randomized controlled trials of the efficacy of using digital tools in mental health diagnosis and treatment.
  • Could artificial intelligence (AI) and machine learning technologies be used to:
    • Reduce the stigma associated with mental health treatment?
    • Predict the risk for future suicide?
    • Detect Alzheimer’s years before diagnosis?
  • Categories of AI applications include low-risk apps that measure but do not diagnose, and apps used in diagnosis or treatment that must meet the same high standards of evidence as medications.
  • Clinicians still struggle with how to integrate patient data from wearable devices. AI technology might help if it could be used to synthesize the data into a risk profile for an individual.

Discussion Themes

What are the roles of stress, exercise, and sleep in mental health, and can autonomic data from wearables help explain the variance in mental health symptoms?

To develop evidence thresholds for AI, we need larger scale public-private partnerships as well as pragmatic trials addressing key clinical questions.

Read more from Dr. Doraiswamy in How to Use Technology Ethically to Increase Access to Mental Healthcare.
Tags

#AI, #pctGR, @Collaboratory1

June 28, 2019: Moving Beyond Return of Research Results to Return of Value (Consuelo Wilkins, MD, MSCI)

Speaker

Consuelo H. Wilkins, MD, MSCI
Vice President for Health Equity, Vanderbilt University Medical Center
Executive Director, Meharry-Vanderbilt Alliance

Topic

Moving Beyond Return of Research Results to Return of Value

Keywords

Health outcomes; Research results; Patient preferences; Value of information

Key Points

  • In returning value to research participants, results are shared with added context, are prioritized by each participant, include specific suggestions for relevant actions, and incorporate participant recommendations and preferences.
  • Data captured for research purposes, including EHR data, vital signs, and genetic data, can be repurposed and reoriented for study participants.
  • Participants are more likely to trust research if results are returned—and they are more likely to participate again.

Discussion Themes

We need to return study results that are informed by participants, and we need to design approaches for accessing and understanding results that participants will want to use.

We should think carefully about risk mitigation when returning research results for which there is a clear next step or action for the participant.

Read more about understanding what information is valued by research participants in a recent article by Dr. Wilkins and colleagues in Health Affairs.

Tags

#pctGR, @Collaboratory1, @drchwilkins, @vumchealth

June 7, 2019: In Dreams Begin Responsibilities: Data Science as a Service—Using AI to Risk Stratify a Medicare Population and Build a Culture (Erich Huang, MD, PhD)

Speaker

Erich S. Huang, MD, PhD
Co-Director, Duke Forge
Departments of Biostatistics & Bioinformatics and Surgery
Duke University School of Medicine

Topic

In Dreams Begin Responsibilities: Data Science as a Service—Using AI to Risk Stratify a Medicare Population and Build a Culture

Keywords

Data science; Data liquidity; Data standards; Machine learning; Duke Forge; Application programming interface; Artificial intelligence

Key Points

  • Duke Forge focuses on bringing the best methodological approaches to actionable data problems in health. It is motivated by a framework of value-based healthcare to address societal inequities in health.
  • Essential components to building a data science culture include clinical subject matter expertise, quantitative and methodological expertise, and software architecture and engineering expertise, along with interoperable tools and applications.
  • Like freight shipping containers, health-relevant data needs standardized containers that make any type of data easy to pack, grab, combine, and move around. The aim should be to build a “data liquidity ecosystem” equivalent to freighters, cranes, trains, and trucks that facilitate the logistics of health data transport.

Discussion Themes

If we’re trying to build an ecosystem, then the electronic health record (EHR) platform needs to be evaluated by whether it is truly participatory in this ecosystem. If not, then its deficiencies must be remediated.

The faster we can move to the cloud and use building blocks that “snap” together, the faster we can get answers. We want to be building applications instead of infrastructure.

Algorithms don’t have ethics; some have hidden biases. Algorithms need to be scrutinized and tested for such biases. They also must be secured so they cannot be manipulated.

Read more about Duke Forge and check out articles on the blog.

Tags

#pctGR, @Collaboratory1, @DukeForge

May 31, 2019: Adapting Clinical Trial Design to Meet the Needs of Learning Health Systems (Harriette Van Spall, MD, MPH)

Speaker

Harriette G.C. Van Spall, MD, MPH, FRCPC
Associate Professor of Medicine
Department of Medicine, Division of Cardiology
Department of Health Research Methods, Evidence, and Impact
McMaster University
Population Health Research Institute

Topic

Adapting Clinical Trial Design to Meet the Needs of Learning Health Systems

Keywords

Learning health system; Pragmatic clinical trial; Patient-Centered Care Transitions in Heart Failure (PACT-HF); Heart failure; Stepped-wedge cluster trial

Key Points

  • Characteristics of a learning health system include:
    • Possessing a culture of knowledge and quality improvement
    • Encouraging research innovation by embedding research into clinical practice and generating knowledge at the point of care
    • Harnessing data from electronic health records and claims/administrative databases
    • Fostering trust between research and clinical teams
    • Engaging patients, clinicians, and key stakeholders
  • The Patient-Centered Care Transitions in Heart Failure (PACT-HF) trial evaluated the effectiveness of a group of transitional care services in patients hospitalized for HF within a publicly funded healthcare system.
  • Challenges of a learning health system include integrating care, intervention, and communications across silos; streamlining workflow; preventing “contamination” of usual care; and the limited interoperability of EHRs and slow updates to claims/administrative datasets.

Discussion Themes

Efficacy in explanatory randomized clinical trials (RCTs) does not equate to effectiveness in real-world settings.

Decisions about implementation of an intervention are not made “live”; you must wait until the study has ended, all the data are available for analysis, and analysis is complete before you can inform decision-maker partners about the risks and benefits of the intervention.

Read more about the PACT-HF study and results in JAMA Network (Van Spall et al. 2019)

Tags

#pctGR, @Collaboratory1

May 10, 2019: Treating Data as an Asset: Data Entrepreneurship in the Service of Patients (Eric Perakslis, PhD)

Speaker

Eric D. Perakslis MS, PhD
Rubenstein Fellow, Duke University
Lecturer, Department of Biomedical Informatics
Harvard Medical School

Topic

Treating Data as an Asset: Data Entrepreneurship in the Service of Patients

Keywords

Digital health; Health data; General Data Protection Regulation (GDPR); Data sharing

Key Points

  • The only 100% common element of digital transformation across all industries is data.
  • With data and digital transformation, patients are changing: They are active, connected, informed, and savvy.
  • Security, compliance, and privacy are different things.

Discussion Themes

Is there any hope of data sharing policies helping to bridge the micro and macro silos of healthcare data?

As data starts to flows through institutions, it ends up in multiple places. Part of sharing data is protecting a single source of truth.

If something is relevant to the bedside, it’s worth doing.

Read Dr. Perakslis’s commentary in The Lancet (May 2019).

Tags

#healthdata, #pctGR, @Collaboratory1

May 8, 2019: Dr. Greg Simon Receives National Suicide Prevention Award

At the Lifesavers Gala in New York last night, Dr. Greg Simon received the American Foundation for Suicide Prevention (AFSP’s) Research Award for his contributions to suicide prevention. Dr. Simon leads the Suicide Prevention Outreach Trial (SPOT), an NIH Collaboratory Demonstration Project that builds on previous work demonstrating that patients who answer “yes” to thoughts of self-harm on routinely administered PHQ-9 questionnaires at primary care visits are more likely to attempt suicide. For these high-risk patients, SPOT explores different modes of outreach (care management or online skills training versus usual care) to prevent suicide.

“There’s a conspiracy of silence around suicidal thoughts, because it’s awkward to discuss. So we’ve found that we have to incorporate talking about it into our standard care. Our suicide prevention work is a great example of how research and care keep influencing each other to improve our patients’ health. When research springs from clinicians’ and patients’ questions, ‘learning health systems’ can put results into practice much faster than the oft-cited 17-year lag.” — Dr. Greg Simon, from the Kaiser Permanente Washington Health Research Institute Press Release

Dr. Simon and his colleagues are also studying how machine-learning models can be used to predict risk of suicide. The models combine the PHQ-9 mental health questionnaire responses with information from electronic health records, including prior suicide attempts and mental health and substance use diagnoses. In a blog post regarding his research (and recent publication) on machine learning, Dr. Simon compares machine learning to warning lights on cars:

Our paper prompted many questions from clinicians and health system leaders about the practical utility of risk predictions:

“Are machine learning algorithms accurate enough to replace clinicians’ judgment?” our clinical partners asked.

“No,” I answered, “but they are accurate enough to direct clinicians’ attention.”

The AFSP also honored four others, including Anderson Cooper, a CNN and 60-minutes correspondent, and Kate Snow, an NBC news correspondent, for their work raising public awareness of suicide prevention.

Read more about what inspired Dr. Simon to study mental health.

April 12, 2019: Development of Harmonized Outcome Measures for Use in Research and Clinical Practice (Richard Gliklich, MD, Michelle Leavy, MPH, Elise Berliner, PhD)

Speakers

Richard Gliklich, MD
CEO, OM1, Inc.

Michelle B. Leavy, MPH
Head, Healthcare Research and Policy
OM1, Inc.

Elise Berliner, PhD
Director, Technology Assessment Program
Center for Evidence and Practice Improvement (CEPI)
Agency for Healthcare Research and Quality (AHRQ)

Topic

Development of Harmonized Outcome Measures for Use in Research and Clinical Practice

Keywords

Health outcomes; Patient-centered outcomes; Agency for Healthcare Research and Quality; Patient registries; Clinical data; Patient-reported outcomes; Value-based care; Electronic health records; Learning health system; Conceptual framework

Key Points

  • The goal of the Outcome Measures Framework is to create a common conceptual model for classifying the range of outcomes that are relevant to patients and providers across most conditions.
  • Harmonization of outcome measures is essential to comparing and aggregating results between and among registries, clinical research, and quality reporting, and to facilitating performance and value-based measurement.
  • A minimum measure set is the minimum set of harmonized measures that can be captured consistently in research and clinical practice.
  • Developing the framework used a stakeholder-driven process that categorized outcomes as clinical responses, patient-reported, survival, resource utilization, and events of interest for a sample set of 5 clinical areas.

Discussion Themes

The benefits of developing a core set of measures include reduced clinician burden and improved patient care.

How is this work informing the HL7 work group that is defining standards for registries?

Next steps include implementation of the minimum measure sets in EHRs, registries, and other research efforts; demonstrating the value of a minimum measure set; and encouraging adoption of the measures.

Learn more about AHRQ’s Outcome Measures Framework.

Tags

#pctGR, @Collaboratory1, @AHRQNews