Tag Archives: Electronic health records

Use of Data from Electronic Health Records to Customize Medical Treatments

Data analysis, Data quality, Electronic health records, ,

In a recent segment on NPR’s Morning Edition, commentators discuss the potential of using electronic health records to customize medical treatments.

Dr. Harlan Krumholz, a professor of medicine at Yale University, says comparing data in electronic health records with genomic information holds great promise for customizing individual treatments, but he warns that the quality of data collected in the medical record is not research quality. While researchers are making a positive start with initiatives such as the Precision Medicine Initiative (re-branded as the All of Us research program), medicine still has a long way to go to fully realize the potential of these data.

Dr. Harlan Krumholz will be presenting at an upcoming NIH Collaboratory Grand Rounds on January 13 from 1:00 – 2:00 p.m. ET. “What’s Next: People-Powered Knowledge Generation from Digital Health Data.” Join the meeting here.

The full article and audio can be found on NPR Shots, an online channel for health stories from the NPR Science Desk.

New NIH Collaboratory resource for the transparent reporting of PCTs

Cluster Randomized Trials, Data quality, Electronic health records, Research reporting standards, Research tools, Writing and Publishing, , , ,

The NIH Collaboratory has developed a tool to assist authors in the complete and transparent reporting of their pragmatic clinical trials (PCTs). In the PCT Reporting Template, users will find descriptions of reporting elements based on CONSORT guidance as well as on expertise from the NIH Collaboratory Demonstration Projects and Core working groups.

Particularly relevant to PCTs are recommendations on how to report the use of data from electronic health records. Other elements of importance to PCTs include reporting wider stakeholder engagement, monitoring for unanticipated changes in study arms, and specific approaches to human subjects protection. The template contains numerous links to online material in the Living Textbook, CONSORT, and the Pragmatic–Explanatory Continuum Indicator Summary tool known as PRECIS-2.

This resource is intended to assist authors in developing primary journal publications. It will be updated over time as new best practices emerge for the transparent reporting of PCTs.

Download the PCT Reporting Template.

Please note: this document opens as an Adobe PDF. If you do not have software that can open a PDF, click here to download a free version of Adobe Acrobat Reader.

This work was supported by a cooperative agreement (U54 AT007748) from the NIH Common Fund for the NIH Health Care Systems Research Collaboratory. The views presented in this document are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Originally published on September 1, 2016.

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Collaboratory phenotypes paper published in eGEMs special issue

Clinical trials, Data standards, Electronic health records, Learning health systems, Research tools, , , ,

A recently published special issue of eGEMs explores strategic uses of evidence to transform healthcare delivery systems. In A Framework to Support the Sharing and Re-Use of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications, Rachel Richesson and Michelle Smerek of the NIH Collaboratory’s Phenotypes, Data Standards, and Data Quality Core, along with coauthor C. Blake Cameron, envision an infrastructure that facilitates re-use of computable phenotypes in a learning healthcare system.

The authors elaborate on four required components of the framework:

  • Searchable libraries of explicitly defined phenotype definitions
  • Knowledge bases with information and methods
  • Tools to identify, evaluate, and implement existing phenotype definitions
  • Motivated users and stakeholders

Read the entire eGEMs open access publication here. eGEMs (Generating Evidence & Methods to improve patient outcomes), a product of AcademyHealth’s Electronic Data Methods (EDM) Forum, is a peer-reviewed, open access journal that seeks to accelerate research and quality improvement using electronic health data.

Related resources:

You can find extensive information on computable phenotypes in the Living Textbook chapter and in Tools for Research.

New Lessons Learned Document Draws on Experiences of Demonstration Projects

Electronic health records, Institutional review boards, Pragmatic clinical trials, Regulatory issues, Research tools, , , , , , ,

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

Data quality, Data sharing, Data standards, Electronic health records, Pragmatic clinical trials, , , ,

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

Data privacy, Data sharing, Electronic health records, Genomics, Patient engagement, , , , ,

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

Data privacy, Electronic health records, Health outcomes, Patient-Centered Outcomes Research, Pragmatic clinical trials, , , , ,

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

Data privacy, Electronic health records, Health analytics, ,

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

Electronic health records, Patient-Reported Outcomes, Research tools, , , , , , , , , ,

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.

Collaboratory Phenotypes, Data Standards, and Data Quality Core Releases Data Quality Assessment White Paper

Data quality, Data standards, Guidance document, Pragmatic clinical trials, Research tools, , , , , ,

The NIH Collaboratory’s Phenotypes, Data Standards, and Data Quality Core has released a new white paper on data quality assessment in the setting of pragmatic research. The white paper, titled Assessing Data Quality for Healthcare Systems Data Used in Clinical Research (V1.0) provides guidance, based on the best available evidence and practice, for assessing data quality in pragmatic clinical trials (PCTs) conducted through the Collaboratory. Topics covered include an overview of data quality issues in clinical research settings, data quality assessment dimensions (completeness, accuracy, and consistency), and a series of recommendations for assessing data quality. Also included as appendices are a set of data quality definitions and review criteria, as well as a data quality assessment plan inventory.

The full text of the document can be accessed through the “Tools for Research” tab on the Living Textbook or can be downloaded directly here (PDF).