August 28, 2018: Spotlight on a New Demonstration Project: Nudge

More than half of patients with prescriptions for cardiovascular medications do not take their medications as prescribed. These patients are at greater risk of death and comorbid conditions and have higher healthcare costs. Strategies to improve medication adherence have had mixed results. Meanwhile, advances in mobile and digital technologies for health promotion and disease self-management offer new opportunities to influence patients’ health behaviors and improve health outcomes.

“One of the real benefits of using technology is that it can be widely disseminated. Studying that dissemination process is really where we are in the field. So a pragmatic trial makes a lot of sense.” — Sheana Bull, PhD, MPH

The NIH Collaboratory is pleased to welcome the Personalized Patient Data and Behavioral Nudges to Improve Adherence to Chronic Cardiovascular Medications (Nudge) study to its portfolio of Demonstration Projects. The Nudge study will use mobile phone text messages and an artificial intelligence chatbot to deliver behavioral “nudges” to patients to improve medication adherence. The study will access population-level pharmacy data in 3 integrated healthcare delivery systems to test the effectiveness of the nudges on adherence and outcomes among patients with chronic cardiovascular conditions who take medications to treat hypertension, atrial fibrillation, coronary artery disease, diabetes, or hyperlipidemia.

The Nudge study is led by co–principal investigators Drs. Sheana Bull and Michael Ho of the University of Colorado with support from the National Heart, Lung, and Blood Institute. Watch a video interview with Drs. Bull and Ho, and read more about Nudge.

 

August 20, 2018: Spotlight on a New Demonstration Project: EMBED

The NIH Collaboratory is pleased to introduce the Pragmatic Trial of User-Centered Clinical Decision Support to Implement Emergency Department-Initiated Buprenorphine for Opioid Use Disorder (EMBED). Led by co–principal investigators Dr. Ted Melnick and Dr. Gail D’Onofrio of Yale University, and supported by the National Institute on Drug Abuse, EMBED is a pragmatic, multicenter, group-randomized trial that will implement and evaluate a user-centered clinical decision support tool to facilitate the initiation of buprenorphine/naloxone therapy for opioid use disorder in emergency departments.

The intervention consists of computerized treatment guidance for emergency department physicians and is embedded in the existing care delivery workflow. By conducting the study under real-world conditions and employing passive collection of structured data from the electronic health record, EMBED will use an innovative approach to address public health concerns about opioid use in the United States. Watch a video interview with Dr. Melnick, and read more about EMBED.

“This is an area where there is already good efficacy data for the practice of ED-initiated buprenorphine treatment for patients with opioid use disorder, but the practice is not part of routine care right now.” — Ted Melnick, MD, MHS

 

EMBED is 1 of 6 new large-scale clinical trials launched by the NIH Collaboratory in 2018. The Demonstration Projects are multicenter pragmatic trials that engage healthcare delivery systems in research partnerships to gather real-world evidence and answer clinical questions of major public health importance. Learn more about the Demonstration Projects.

August 13, 2018: JAMA Commentary Highlights the Value of Data Enclaves and Distributed Data Networks

In a JAMA Viewpoint published online last week, NIH Collaboratory investigator Dr. Richard Platt and colleague Dr. Tracy Lieu discuss the value of “data enclaves” to facilitate information sharing in support of research, quality improvement, and public health reporting.

Creating data enclaves allows health systems to share useful information from their clinical data without releasing the actual data. Data enclaves can be linked with each other in distributed data networks to create powerful resources for researchers and other analysts. The authors note that efforts to realize this vision must address concerns about protecting patients’ personal information, the costs and work required to make the data usable for analysis, and incentives for health systems to participate.

Dr. Platt is a cochair of the NIH Collaboratory’s Distributed Research Network, which uses a common data model that enables investigators to collaborate with each other in the use of electronic health data while safeguarding protected health information and proprietary data.

August 10, 2018: STOP CRC Trial Finds Higher Rates of Colorectal Cancer Screening in Community Clinics Using an EHR-Based Outreach Tool

The primary results of the Strategies and Opportunities to Stop Colorectal Cancer in Priority Populations (STOP CRC) trial, an NIH Collaboratory Demonstration Project, were published online this week in JAMA Internal Medicine. The analysis found that colorectal cancer screening rates were higher in community clinics that implemented a mailed fecal immunochemical test (FIT) outreach program than in clinics that practiced usual care. The improved screening rates occurred despite low and highly variable rates of implementation of the program among participating clinics.

Almost half of eligible adults in the United States are not up to date with recommended screening for colorectal cancer, the second leading cause of cancer-related deaths. Screening rates are especially low among racial/ethnic minority and low-income populations, including those served at federal qualified health center clinics.

The STOP CRC trial tested a program to improve colorectal cancer screening rates in 26 clinics within 8 federal qualified health centers. The intervention involved embedding a tool in the electronic health record to identify patients who were overdue for colorectal cancer screening, mailing a FIT kit and reminder letter to eligible patients, and implementing a practice improvement process at participating clinics. Of the 26 clinics in the study, 13 received the intervention and 13 practiced usual care.

Compared with clinics that practiced usual care, intervention clinics had a significantly higher proportion of participants who completed a FIT (3.4 percentage points) and any colorectal cancer screening (3.8 percentage points). The higher screening rates occurred despite another important finding of the study, that low rates of implementation of the intervention were common. Higher rates of implementation were correlated with higher rates of FIT completion.

The STOP CRC experience offers lessons on how to use electronic health records to improve guideline-based screening. In a recent NIH Collaboratory Grand Rounds, investigators Dr. Gloria Coronado and Dr. Beverly Green presented findings from the trial and lessons from the implementation of the intervention. Download a study snapshot about the STOP CRC trial.

Additional reading:

Read the press release from the Kaiser Permanente Center for Health Research: Community Health Centers Can Help Boost Rates of Colorectal Cancer Screening, Kaiser Permanente Study Shows

Read Dr. Beverly Green’s blog post on the Kaiser Permanente Washington Health Research Institute’s Healthy Findings blog: Community Health Centers Can Boost Colon Cancer Screening

July 30, 2018: Registration Open for 3rd Seattle Symposium on Health Care Data Analytics

Registration is open for the 3rd Seattle Symposium on Health Care Data Analytics. The symposium will bring together biostatisticians, health informaticists, epidemiologists, and other data scientists to discuss health research and methods that involve large health care databases.

Experts involved in national research initiatives that use large health care databases will discuss methodological challenges encountered in this setting and share ideas for addressing them. Speakers will share their research on:

  • statistical approaches to learning from electronic health care data;
  • methods for precision medicine; and
  • health policy.

Space is limited, and registration is required.

The event is sponsored by the Biostatistics Unit at Kaiser Permanente Washington Health Research Institute and the Department of Biostatistics at the University of Washington.

July 23, 2018: New Report Summarizes Patient-Reported Health Data and Metadata Standards in the ADAPTABLE Trial

A new report in the Living Textbook describes results of a literature review of data standards and metadata standards for variables of interest to the ADAPTABLE trial. Based on the review, the authors recommend standards for ADAPTABLE, also known as the Aspirin Study, which is the first major randomized comparative effectiveness trial to be conducted by the National Patient-Centered Clinical Research Network (PCORnet). The trial aims to identify the optimal dose of aspirin therapy for secondary prevention in atherosclerotic cardiovascular disease.

Because the ADAPTABLE trial relies on patients to report key information at baseline and throughout follow-up, it represents a unique opportunity to develop, pilot, and evaluate methods to validate and integrate patient-reported information with data obtained from electronic health records (EHRs). In 2016, the National Institutes of Health implemented a project with the goal of using the ADAPTABLE study to develop methods to (1) assess the quality of patient-reported data and (2) integrate the data with existing EHR data. It is hoped that this project will inform future efforts to synthesize potentially inconsistent data from patient-reported and EHR sources and identify opportunities to streamline data.

Download the report.

February 28, 2018: New Meeting Summary Examines How to Integrate Patient‐Reported Health Data for Pragmatic Research

A recently released summary from the ADAPTABLE Roundtable Meeting explores ways to better understand the sets of circumstances and considerations that could guide when and how to gather and integrate patient-reported health data with other data sources in pragmatic trials.

For outcomes that represent subjective experiences, such as pain, symptoms, and physical functioning, the patient is the unique and privileged source of information. Other patient-reported health data may not have a clear source of truth, such as co-morbidities and hospitalizations. In such cases, patient-reported health data may supplement, contradict, or agree with EHR and claims data. For example, medication data reported by patients might be a more accurate reflection of what patients are actually taking than medication data in the EHR, especially for over-the-counter medications.

Patient-reported health data come from various sources and can be feasibly collected in the conduct of a pragmatic clinical trial, but the optimal approaches for capturing and analyzing these data are unclear. Questions include how to integrate this information with other data collected as part of a study, including data from the EHR.

To better understand patient-reported health data and how to use them in pragmatic trials, 18 experts from 8 institutions convened at the roundtable meeting, coming from a wide variety of backgrounds including biostatistics, epidemiology, oncology, nursing, psychiatry, health policy, and regulation. Representatives from the NIH Collaboratory included Drs. Lesley Curtis and Rachel Richesson from the EHR Core and Dr. Kevin Weinfurt from the Patient-Reported Outcomes Core.

In addition to the meeting summary, two white papers are forthcoming. For more information about using patient-reported data in pragmatic trials, see the Living Textbook Chapter on Endpoints and Outcomes.

This effort was funded by Office of the Assistant Secretary for Planning and Evaluation at the U.S. Department of Health and Human Services through a supplement provided to the NIH Collaboratory Coordinating Center.

October 10, 2017: NIH Collaboratory Core Working Group Interviews: Reflections from the Phenotypes, Data Standards, and Data Quality Core

At the NIH Collaboratory Steering Committee meeting in May 2017, we asked Drs. Rachel Richesson and W. Ed Hammond, Co-chairs of the Phenotypes, Data Standards, and Data Quality Core, to reflect on the first 5 years of their Core’s work and the challenges ahead.

Both were pleased with how the Core was able to provide guidelines for assessing data quality and the reporting of pragmatic trials, especially around issues with phenotypes and the use of electronic health record data. Future work in this area needs to advance the development of regulations and standards for the collection of clinical data to support learning healthcare systems.

“We’ve built a community in our Core that represents a diverse group of scientists and clinicians showing the many ways to look at data challenges.”
– Dr. Rachel Richesson

In Fall 2017, the Phenotypes, Data Standards, and Data Quality Core merged with the Electronic Health Records Core. The combined Core will continue to work on data standards and quality, and approaches to define clinical phenotypes and endpoints, extract information, and discover errors in data from healthcare systems.

Download the interview (PDF).

A PDF of the May 2017 interview with leaders of the Phenotypes Core Working Group.

New Lessons Learned Document Draws on Experiences of Demonstration Projects

The NIH Collaboratory’s Health Care Systems Interactions Core has published a document entitled Lessons Learned from the NIH Health Care Systems Research Collaboratory Demonstration Projects. The Principal Investigators of each of the Demonstration Projects shared their trial-specific experience with the Core to develop the document, which presents problems and solutions for initiation and implementation of pragmatic clinical trials (PCTs). Lessons learned are divided into the following categories: build partnerships, define clinically important questions, assess feasibility, involve stakeholders in study design, consider institutional review board and regulatory issues, and assess potential issues with biostatistics and the analytic plan.

Other tools available from the Health Care Systems Interactions Core include a guidance document entitled Considerations for Training Front-Line Staff and Clinicians on Pragmatic Clinical Trial Procedures and an introduction to PCTs slide set.

New Living Textbook Chapter on Acquiring and Using Electronic Health Record Data for Research

Topic ChaptersMeredith Nahm Zozus and colleagues from the NIH Collaboratory’s Phenotypes, Data Standards, and Data Quality Core have published a new Living Textbook chapter about key considerations for secondary use of electronic health record (EHR) data for clinical research.

In contrast to traditional randomized controlled clinical trials where data are prospectively collected, many pragmatic clinical trials use data that were primarily collected for clinical purposes and are secondarily used for research. The chapter describes the steps a prospective researcher will take to acquire and use EHR data:

  • Gain permission to use the data. When a prospective researcher wishes to use data, a data use agreement (DUA) is usually required that describes the purpose of the research and the proposed use of the data. This section also describes use of de-identified data and limited data sets.
  • Understand fundamental differences in context. Data collected in routine care settings reflect standard procedures at an individual’s healthcare facility, and are not collected in a standard, structured manner.
  • Assess the availability of health record data. Few assumptions can be made about what is available from an organization’s healthcare records; up-front, detailed discussions about data element collection over time at each facility is required.
  • Understand the available data. A secondary data user must understand both the data meaning and the data quality; both can vary greatly across organizations and affect a study’s ability to support research conclusions.
  • Identify populations and outcomes of interest. Because healthcare facilities are obligated to provide only the minimum necessary data to answer a research question, investigators must identify the needed patients and data elements with specificity and sensitivity to answer the research question given the available data.
  • Consider record linkage. Studies using data from multiple records and sources will require matching data to ensure they refer to the correct patient.
  • Manage the data. The investigator is responsible for receiving, managing, and processing data and must demonstrate that the data are reproducible and support research conclusions.
  • Archive and share the data after the study. Data may be archived and shared to ensure reproducibility, enable auditing for quality assurance and regulatory compliance, or to answer other questions about the research.