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 11, 2017: New grant awarded to Dr. Jeffrey Jarvik and his team of investigators to promote pragmatic musculoskeletal clinical research

Congratulations to Dr. Jeffrey Jarvik and his colleagues at the University of Washington for their recent grant award to establish the Core Center for Clinical Research (CCCR). The initiative will promote pragmatic, multi-institutional clinical research on musculoskeletal conditions, such as the diagnosis and treatment of back pain. The new center—the UW Center for Clinical Learning, Effectiveness And Research (CLEAR)—will investigate the effectiveness of interventions such as imagining tests, physical therapy, opioids, spine injections, and spine surgery, as well as approaches for implementation.  The National Institutes of Health (NIH)/National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) are funding the 5-year, $3.7 million initiative, which will include pragmatic and comparative effectiveness trials and Core groups, including:

  • The Methodology Core, led by Patrick J. Heagerty PhD, Chair of the Department of Biostatistics, and Sean Mooney PhD, Professor of Biomedical Informatics and Chief Research Information Officer
  • The Resource Core, led by Janna Friedly MD, Associate Professor of Rehabilitation Medicine, and Danielle Lavallee PharmD PhD, Research Associate Professor of Surgery

Dr. Jarvik is a Professor of Radiology at University of Washington and the Principal Investigator the Collaboratory Demonstration Project, the Lumbar Imaging with Reporting of Epidemiology (LIRE) trial.


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.

October 3, 2017: New Collaboratory Article Explores Data Sharing and Embedded Research

In an article published in Annals of Internal Medicine, authors from the NIH Collaboratory describe concerns and solutions regarding data sharing and embedded research. Pragmatic research embedded in health systems uses data from the electronic health record and comes from a fundamentally different context than explanatory trials, which collect research-specific data. Data from embedded research have the potential to do harm if taken out of context or used for comparisons. Therefore, while the authors enthusiastically support data sharing, they also recognize that mandating data sharing may discourage health systems from volunteering to participate in embedded research.

“In an ideal world of transparency regarding healthcare processes and outcomes, health systems would have no expectation of or need for privacy regarding quality of health care delivery.  But the current world is not perfect, and unintentional disclosures from participation in embedded research could be far greater than that required for public quality measures. Health systems volunteering to participate in research to improve public health may not be willing to bear the additional risk of misuse of sensitive information.” — Simon et al. Ann Intern Med

The authors use examples from the NIH Collaboratory Demonstration Projects to illustrate potential solutions, and emphasize that data sharing plans for embedded research should be developed in partnership with health system leaders in ways that maximize the amount of data that can be shared while protecting patient privacy and healthcare system interests.

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.

In Nature: The Precision Medicine Initiative & DNA Data Sharing

A recent article in Nature highlights the Precision Medicine Initiative, launched in January 2015 and spearheaded by the National Institutes of Health. Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. This initiative will involve collection of data on genomes, electronic health records, and physiological measurements from 1 million participants. A main objective is for participants to be active partners in research.

But a major decision faced by the initiative’s working group is how much information to share with participants about disease risk, particularly genetic data. Though there is much debate in the field, the article suggests that public opinion on data sharing may be shifting toward openness.

The Precision Medicine Initiative working group will be releasing a plan soon. For details on the goals of the Precision Medicine Initiative, read the perspective by NIH Director Dr. Francis Collins in the New England Journal of Medicine.


In the News: Increase in Use of Personal Health Data

An explosion in the collection of personal data is fostering concerns about the extent to which health information is accessed—and about the privacy and confidentiality of this information. Two recent National Public Radio stories highlight a few of the burgeoning uses of these abundant data.

In the first, an insurer uses personal data to predict who will get sick so it can identify patients at highest risk for hospital admission, or readmission, and then provide them with personal health coaches. The coordinated care given to patients by the coaches (for example, arranging a visiting nurse or streamlining appointments) has been shown to improve hospitalization rates. The insurer says it follows federal health privacy guidelines for anonymity and uses the information to better serve its members.

The second story explains that results of online health searches aren’t always confidential, and data brokers are tracking information and selling it to interested parties. The author notes that data gathered on the Web are, for the most part, unregulated. Both stories raise questions about privacy and confidentiality of health information and how to best protect it.

Pragmatic clinical trials also seek to use personal health data to answer important questions on the risks, benefits, and burdens of therapeutic interventions. In a blog post in Health Affairs, Joe Selby, executive director of the Patient-Centered Outcomes Research Institute (PCORI), underscores the need for trust, support, and active engagement of patients when involving them in health data research, even with privacy protections in place. PCORI has launched the National Patient-Centered Clinical Research Network (PCORnet) as a means of harnessing large clinical data sets to study the comparative effectiveness of treatments, and a central tenet of the network is that patients, clinicians, and healthcare systems should be actively involved in the governance of the use of health information.

Read the full articles

From NPR: Insurer Uses Personal Data To Predict Who Will Get Sick
From NPR: Online Health Searches Aren't Always Confidential
From Health Affairs: Advancing the Use of Health Data in Research With PCORnet


Latest Truven Health Analytics–NPR Health Poll on Medical Data Privacy

How concerned are people about the privacy of their medical information? Not very—according to the November 2014 Truven Health Analytics–NPR Health Poll (opens as PDF). The poll asked how respondents feel about sharing their electronic health information and other data with researchers, employers, health plans, and their doctors. The majority expressed a willingness to share their anonymized health information with researchers; less than a quarter expressed willingness to share non-healthcare data with their healthcare providers.

Each month, the Truven Health Analytics–NPR Health Poll surveys approximately 3,000 Americans to gauge attitudes and opinions on a wide range of healthcare issues. Poll results are reported by NPR on the health blog Shots. Among the results of this survey:

  • 74% of respondents indicated that their physician uses an electronic medical record system.
  • 68% of respondents would share their health information anonymously with researchers.
  • 44% of respondents have looked through their health information kept by their physician.

The survey analyses were stratified by age, education, generation, and income. Poll questions were posed by cell phone, land line, and online during the first half of August 2014. The margin of error was plus or minus 1.8 percentage points. An executive summary of the survey, including questions and survey data, is here.

Computer Adaptive Testing Approach to Patient-Reported Outcomes

Michael Bass and Maria Varela Diaz of the Department of Social Sciences, Feinberg School of Medicine, Northwestern University, have kindly given the Living Textbook permission to post their presentation (link opens as a PDF) about how to use an application programming interface (API) to create a computer adaptive testing (CAT) program that integrates patient-reported outcome (PRO) measures with an institution’s electronic health record (EHR) system.

With a CAT approach, PRO assessment can cover a wide range of question/response items with increased precision. In their CAT application, the authors describe a clinical use case for a mobile health solution, using measures from the NIH-sponsored PRO Measurement Information System (PROMIS®) domain framework, in which a health assessment is issued by a physician, administered to a patient via phone, and then sent back to the EHR.

You can read more about CAT in the Patient-Reported Outcomes chapter of the Living Textbook.