January 12, 2018: Behavioral Economic Principles to Understand and Change Clinician Behavior

Speakers

Jeffrey A. Linder, MD, MPH, FACP
Chief, Division of General Internal Medicine and Geriatrics
Michael A. Gertz Professor of Medicine
Northwestern University Feinberg School of Medicine

Topic

Behavioral Economic Principles to Understand and Change Clinician Behavior

Keywords

Pragmatic clinical trial; Behavioral economics; Antibiotic resistance; BEARI; Antibiotic stewardship

Key Points

  • As many as 30% of antibiotic prescriptions are unnecessary. Can behavioral economics explain and help guide how to change this clinician prescribing behavior?
  • Habit, pressure from patient, and a “just to be safe” mentality are the most common factors driving inappropriate antibiotic prescribing, but antibiotic stewardship is critical to improving patient outcomes.
  • The Behavioral Economics/Acute Respiratory Infection (BEARI) trial looked at three behavioral interventions to reduce inappropriate antibiotic prescribing for acute respiratory infections: suggested alternatives to antibiotics, accountable justification, and peer comparison.
  • Because doctors are people who are affected by emotion and social interaction, peer comparison had greatest effect on prescribing behavior, even after the intervention period ended.

Discussion Themes

Overall, clinicians in the BEARI trial expressed desire to follow guidelines for good antibiotic stewardship, but some of the responses in the intervention group indicated a misunderstanding of the guidelines.

Is some of the impact of interventions due to the Hawthorne effect, in that these clinicians knew they were being  enrolled in a trial, and thereby aware they are being watched and having their work reviewed?

Older doctors have been shown to inappropriately prescribe at a higher rate than younger doctors, but the question remains whether this is based on generational learning or based on decision fatigue over time.

There have been significant efforts by the Centers for Disease Control and Prevention and other public health groups to spread awareness in recent years about the antibiotic resistance and the importance of good antibiotic stewardship, so it is possible outside factors also impacted clinician behavior modification.

Tags

@PCTGrandRounds, @Collaboratory1, @NUFeinbergMed, #BehavioralEconomics, #AntibioticResistance, #AntibioticStewardship, #pctGR

January 10, 2018: New Podcast: Drs. Richard Platt and Christopher Granger discuss IMPACT-AFib and the Role of Sentinel

The NIH Collaboratory is pleased to announce that the new episode of the Grand Rounds podcast is now available, featuring Dr. Richard Platt of Harvard Pilgrim Health Care Institute and Dr. Christopher Granger of Duke University. In this episode, Drs. Platt and Granger speak with moderator Dr. Adrian Hernandez about the IMPACT-AFib atrial fibrillation trial and the role of the FDA’s Sentinel Inititative in leveraging pharmacy data to find eligible participants.

Listen to the episode here:

At least once a month, we will release interviews with Grand Rounds speakers that delve into their topic of interest and give listeners bonus time with these featured experts.

Please let us know what you think by providing your feedback through the podcast page. We also encourage you to listen and share the recordings with your colleagues!

Podcast January 5, 2018: IMPACT-AFib: An 80,000 Person Randomized Trial Using the Sentinel Initiative Platform (Richard Platt, MD, MS; Christopher Granger, MD, FACC, FAHA)

In this episode of the NIH Collaboratory Grand Rounds podcast, Drs. Richard Platt and Christopher Granger speak with moderator Dr. Adrian Hernandez about the IMPACT-AFib atrial fibrillation trial and the role of the FDA’s Sentinel Inititative.  In this trial, researchers used Sentinel pharmacy data to find eligible patients with at least one oral anticoagulation prescription fill, and the speakers describe how the platform provided them with an efficient and cost-effective way to access the data of over 80,000 potential participants.

Click on the recording below to listen to the podcast.

Want to hear more from Drs. Platt and Granger? View the full Grand Rounds presentation.

We encourage you to share this podcast with your colleagues and tune in for our next episode with Grand Rounds speaker Dr. Andy Faucett and his presentation “Considerations for the Return of Genomic Results,” which will be posted the week of February 19th.

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January 5, 2018: IMPACT-AFib: An 80,000 Person Randomized Trial Using the Sentinel Initiative Platform

Speakers

Noelle M. Cocoros, DSc, MPH
Epidemiologist, Department of Population Medicine
Harvard Medical School and Harvard Pilgrim Health Care Institute

Christopher B. Granger, MD, FACC, FAHA
Professor of Medicine, Duke University
Director, Cardiac Care Unit
Duke University Medical Center

Richard Platt, MD, MS
Professor and Chair, Department of Population Medicine
Harvard Medical School and Harvard Pilgrim Health Care Institute

Sean Pokorney, MD
Assistant Professor of Medicine, Duke University

Topic

IMPACT-AFib: An 80,000 Person Randomized Trial Using the Sentinel Initiative Platform

Keywords

Pragmatic clinical trial; Sentinel Initiative; IMPACT-AFib; Atrial fibrillation; Data sharing

Key Points

  • The Sentinel Initiative uses the Common Data Model to curate and distribute large amounts of electronic health record (EHR) data from a diverse group of data partners.
  • IMPACT-AFib (an atrial fibrillation trial) used Sentinel registry data to find eligible patients with at least one oral anticoagulation prescription fill.
  • The trial looked at usual care with delayed provider anticoagulation intervention versus early patient and provider anticoagulation intervention, using access to pharmacy records.
  • Is there an ethical question raised by delaying intervention in the usual care group?

Discussion Themes

Oral anticoagulant (OAC) underuse is a public health priority and also a priority of health plans, which made health plan stakeholders very engaged in the IMPACT A-Fib trial.

IMPACT-AFib found efficiencies with a single IRB that facilitated streamlined processes across multiple institutions.

Research weighed practical considerations against ethical concerns, in that by consenting individuals for the trial, researchers would be performing a type of intervention and thereby negating the comparison between true control and intervention groups.

FDA sponsored the IMPACT-AFib trial to demonstrate feasibility but researchers hope that other sponsors will be open to trials leveraging Sentinel in the next year or so.

For More Information

For information on data sharing solutions, visit the Living Textbook http://bit.ly/2m24zMc
Tags

@PCTGrandRounds, @Collaboratory1, #SentinelInitiative, #EHR, #PatientBrochure, #PatientBrochure, #pctGR

December 15, 2017: Does Machine Learning Have a Place in a Learning Health System?

Speakers

Michael Pencina, PhD
Professor of Biostatistics and Bioinformatics, Duke University
Director of Biostatistics
Duke Clinical Research Institute

Topic

Does Machine Learning Have a Place in a Learning Health System?

Keywords

Machine Learning; Artificial Intelligence; AI; Learning Health Systems

Key Points

  • Machine learning has many different applications for generating evidence in meaningful ways in a learning health system (LHS).
  • Although other industries are using machine learning, the health care industry has been slow to adopt artificial intelligence (AI) methodologies.
  • The Forge Center was formed under the leadership of Dr. Robert Califf and uses team science—biostatisticians, engineers, computer scientists, informaticists, clinicians, and patients collaborate to develop machine learning solutions and prototypes to improve health.
  • In a learning health system, the process is to identify the problem, formulate steps to solve it, find the right data and perform analysis, test the proposed solution (by embedding randomized experiments in a LHS), and implement or modify the solution.
  • Machine learning is a small piece of a LHS, but an important one, and methods are characterized by the use of complex mathematical algorithms trained and optimized on large amounts of data.

Discussion Themes

Demonstrating enhanced value of machine learning over existing algorithms will be an important next step. An ongoing question is how do models get translated into clinical decision making? Machine learning is a tool to develop a model, but implementation of the findings will require team science.

Prediction models can be calibrated to work across health systems to an extent, but there are many unique features of individual health systems, so large health systems should use their own data to optimize the information and learning in a specific setting.  

There are key issues related to accurate ascertainment of data, especially with relation to completeness. For example, inpatient data collected during a hospital stay are likely to yield models that have value. If data rely on events that happen outside the system, it can be harder to get the complete picture.

For More Information

For information on #MachineLearning in a #LearningHealthSystem visit http://bit.ly/2D5ATEG

Tags

@PCTGrandRounds, @Collaboratory1, @DukeForge, @Califf001, #MachineLearning, #LearingHealthSystem, #pctGR

December 18, 2017: New Podcast: Drs. Greg Simon and Susan Ellenberg Discuss Data and Safety Monitoring in Pragmatic Trials

The NIH Collaboratory is pleased to announce that the new episode of the Grand Rounds podcast is now available, featuring Dr. Greg Simon, principal investigator on the Suicide Prevention Outreach Trial (SPOT), and Dr. Susan Ellenberg of the Regulatory/Ethics Core. In this episode, Drs. Simon and Ellenberg discuss the need for a data and safety monitoring plan in any clinical trial, and specifically when a Data and Safety Monitoring Board (DSMB) might be needed in a pragmatic trial.

Listen to the episode here:

At least once a month, we will release interviews with Grand Rounds speakers that delve into their topic of interest and give listeners bonus time with these featured experts.

Please let us know what you think by providing your feedback through the podcast page. We also encourage you to listen and share the recordings with your colleagues!

December 8, 2017: Data and Safety Monitoring in Pragmatic Clinical Trials

Speakers

Greg Simon, MD, MPH
Senior Investigator
Kaiser Permanente Washington Health Research Institute

Jeremy Sugarman, MD, MPH, MA
Harvey M. Meyerhoff Professor of Bioethics and Medicine
Johns Hopkins Berman Institute of Bioethics

Susan S. Ellenberg, PhD
Professor of Biostatistics
Professor of Medical Ethics and Health Policy
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania

Topic

Data and Safety Monitoring in Pragmatic Clinical Trials

Keywords

Pragmatic clinical trial; Safety monitoring; Data monitoring committee; Data and safety monitoring board; DSMB; Patient privacy; Research ethics

Key Points

  • An external approach to monitoring can yield usable results, guard trial integrity, and also ensure patients aren’t exposed to undue risk.
  • A Data Safety and Monitoring Board (DSMB) panel of expertise is not set in stone, so an ethicist, a patient advocate, or a sponsor representative could make valuable additions.
  • There are special issues needing monitored in pragmatic trials including protocol adherence, eligibility, and subjective study outcomes.
  • Privacy concerns may prevent merging data from multiple electronic health record systems at one central site; therefore, quality control across sites is crucial to assure analyses are conducted identically.

Discussion Themes

Emerging experiences and data can pose ethical quandaries for investigators in meeting their obligations to minimize risk to participants, which is why monitoring is so crucial.

Since pragmatic trials will typically be addressing questions intended to impact health practices, an expert oversight group will be important for most PCTs.

A DSMB typically considers monitoring study quality as one of its mandates, and may be uncomfortable making recommendations based on observed treatment effects without a sense of how effectively interventions are being administered.

For More Information

For information on Data & Safety Monitoring for #pragmatictrials, visit the Living Textbook http://bit.ly/2Bv2RZR #pctGR

Tags

@PCTGrandRounds, @Collaboratory1, @GregSimonKPWHRI, @KPWaResearch, @JohnsHopkinsSPH, @UPenn_MedEthics, #DataSafety, #PatientAdvocate, #Pragmatictrials, #EHR, #Qualitycontrol, #AdverseEvents #pctGR

December 1, 2017: Providing a Shared Repository of Detailed Clinical Models for All of Health and Healthcare

Speakers

Stanley M. Huff, MD
Chief Medical Informatics Officer
Intermountain Healthcare and Professor (Clinical) of Biomedical Informatics
University of Utah

W. Ed Hammond, PhD
Duke Center for Health Informatics
Clinical & Translational Science Institute
Duke University

Topic

Providing a Shared Repository of Detailed Clinical Models for All of Health and Healthcare

Keywords

Pragmatic clinical trial; Clinical research; Clinical Information Interoperability Council; Repository; Learning Health System; Patient care; Data collection; Data dissemination

Key Points

  • The Clinical Information Interoperability Council (CIIC) was created to address the lack of standardized data definitions and to increase the ability to share data for improved patient care and research.
  • Accurate computable data should be the foundation of a Learning Health System (LHS), which will lead to better patient care through executable clinical decision-support modules.
  • The ultimate goal of the CIIC is to create ubiquitous sharing of data across medicine including patient care, clinical research, device data, and billing and administration.
  • The three most important questions for the CIIC are what data to collect, how the data should be modeled, and what are computable definitions of the data?

Discussion Themes

All stakeholders need to agree to work together and to allow practicing front-line clinicians to direct the work.

Stakeholders should use and share common tools to create models, and share the models through an open internet accessible repository.

The goal of the repository is to have a common representation digitally for what happened in the real world, by creating agreed-upon names and definitions for a common data set.

What level of vetting is appropriate for data definitions? This should not be a popularity contest for data, but rather a decision made by expert judges.

For More Information

For information on dissemination approaches for different healthcare stakeholders, visit the Living Textbook http://bit.ly/2kcSqGb

Tags

@PCTGrandRounds, @Collaboratory1, @UUtah, @DukeHealth, #Healthcare, #ClinicalDecision Support, #LearningHealthSystem, #ClinicalResearch, #PatientCare, #pctGR

Podcast Blog: Reflections on the NIH Collaboratory: Highlights from the Past and Hopes for the Future

A podcast discussion with Drs. Adrian Hernandez and Kevin Weinfurt

NIH Collaboratory investigators and podcast moderators Drs. Adrian Hernandez and Kevin Weinfurt took time on the latest podcast to discuss the important work done by the NIH Collaboratory this year and what’s next.

Dr. Weinfurt spoke about his excitement to begin seeing results from Collaboratory Demonstration Projects in the coming years, and how those results will be received by various stakeholders. Specifically, he referenced groundbreaking results presented at a recent Grand Rounds by Dr. Greg Simon on the Suicide Prevention Outcomes Trial (SPOT), a project looking at the use of machine learning to predict patient behaviors and outcomes. Dr. Simon recently spoke on the podcast about the implications of using computer algorithms not only to prevent suicides, but also in broader healthcare settings to predict a variety of patient outcomes.

Dr. Hernandez noted that the Demonstration Projects to date have been truly pragmatic trials, and could offer valuable insights to inform the next wave of projects in the coming year. Dr. Weinfurt agreed that investigators could learn from these original Demonstration Projects, as well as reference The Living Textbook, to develop more mature research questions in the future. In particular, he said he looks forward to moving in the direction of “A versus B testing” in future trials.

Both Drs. Hernandez and Weinfurt said they expect that the Revised Common Rule will present a challenge for the upcoming year, as the research community explores how to run trials within the new regulations. Dr. Hernandez noted that there has often been an emphasis on the front half of the trials, and less clarity around the back half, when the data is collected and analyzed.  Refocusing on this later portion of trials, he said, will be crucial in order to ensure data is disseminated properly and ultimately used to create real change for patients.

In terms of expectations for the future, Dr. Hernandez said he hopes that there will be more data streams available, as companies like Amazon begin to move into the health data space. Both he and Dr. Weinfurt shared a hope that health systems will ultimately be able to pull data from a number of sources, from electronic health records (EHRs) to wearable devices, creating richer data sets to examine outcomes. In addition, Dr. Weinfurt proposed that moving forward, the NIH Collaboratory should include more educational opportunities for the research community on pragmatic trials, in order to teach investigators not only how to run these trials but also what to do with the results.

The NIH Collaboratory looks forward to presenting regular educational opportunities, in the form of weekly Grand Rounds presentations and monthly podcasts.

Read the full podcast transcript here.

 

November 27, 2017: New “Moderators’ Edition” Podcast Episode is Live

The NIH Collaboratory is pleased to announce that the next episode of the Grand Rounds podcast is posted, featuring podcast moderators Dr. Adrian Hernandez and Dr. Kevin Weinfurt. In this episode, Drs. Hernandez and Weinfurt discuss their visions for the future of the NIH Collaboratory, and specifically how past Demonstration Projects might inform future pragmatic trials.

Listen to the episode here:

There is also a blog to accompany this podcast.

At least once a month, we will release interviews with Grand Rounds speakers that delve into their topic of interest and give listeners bonus time with these featured experts.

Please let us know what you think by providing your feedback through the podcast page. We also encourage you to listen and share the recordings with your colleagues!