25×5: Decreasing Documentation Burden on U.S. Clinicians

Guest post by Sarah Rossetti, RN, PhD, FAAN, FACMI, FAMIA, and S. Trent Rosenbloom, MD, MPH, FACMI, FAMIA, Co-Chairs of the 25 By 5 Symposium

Health professionals are consistently being recognized for their heroic efforts to manage illness during the COVID-19 pandemic in the face of unprecedented challenges. As doctors, nurses, and all health care professionals faced their greatest challenges in more than a century, they did so while also dealing with the ongoing and increasing challenge of clinical documentation burden, which can be exacerbated by the widespread use of electronic health records systems.

The burden of clinical documentation on professionals has had a negative impact on health care since long before the first diagnosis of COVID-19. This burden can lead to a variety of negative outcomes including clinician burnout and decreased job satisfaction, medical errors, and hospital-acquired conditions. The pandemic increased recognition of the role of clinical documentation on workload. This recognition provided an opportunity to consider the contributions of inpatient and outpatient documentation on clinician well-being.

To establish strategies and approaches to reduce documentation burden on U.S. clinicians, we developed the 25 By 5 Symposium Series with the goal to reduce documentation burden to 25% of its current level by 2025. This symposium was sponsored by the American Medical Informatics Association, NLM, Columbia University Department of Medical Bioethics, and Vanderbilt Medical University Center.

The symposium, held virtually over six weeks in early 2021, addressed efforts to reduce clinical documentation burden, the associated challenges, and future innovations. More than 300 people representing clinical settings, academia, industry – electronic health record (EHR) vendors and start-up companies, government, payers, professional organizations, and patients participated in sessions featuring more than 30 presentations from stakeholders across health systems, academia, industry, government, payers, and professional societies.

Convening such a diverse group of key stakeholders and thought leaders resulted in the development of a national action plan focused on short, medium, and long-term approaches to reduce documentation burden to 25% by the year 2025.

To aid the work in addressing the complex issue of documentation burden, an organizing framework from the American Nursing Informatics Association 2020 Position Paper was used to outline the Six Domains of Burden.

These domains were used to organize breakout sessions and generate action items for reducing burden. An Executive Summary and Appendix of 82 Action Items from the Symposium are posted on the 25 By 5: Symposium website.

These action items are further categorized across four themes: 1) Accountability, 2) Evidence, 3) Education and Training, and 4) Innovation of Technology.

Action items—synthesized and prioritized in Calls to Action for key stakeholder groups — are highlighted below:

Call to Action for Providers and Health Systems

  • Establish guiding principles for adding documentation to EHRs and generating evidence for reduced documentation.
  • Develop a national roadshow and educate clinicians and clinicians in training on balancing brevity and completeness in documentation.
  • Increase support for functions like real-time information retrieval, documentation, and ordering in the EHR.
  • Implement interdisciplinary notes to decrease redundant documentation.

Call to Action for Health IT Vendors

  • Promote an ecosystem of interoperable systems to allow for complementary technology.
  • Develop measurement tools to categorize documentation practices.
  • Package best training practices into toolkits to promote best practice EHR use and plan recognition programs to publicize exemplars.
  • Create simplistic EHR views to see that new clinical data has been reviewed, then bookmark for the user and document as reviewed by that user in the EHR.
  • Implement user-personalized Clinical Decision Support to drive specific workflows.

Call to Action for Policy and Advocacy Groups

  • Urge agencies to fund innovative research that captures all billing code information without taking up clinicians’ time.
  • Select the best of breed approaches to documentation and implement throughout the health care system.
  • Develop technology to reliably and accurately create reimbursement/payment data for all care settings.

Now the hard work begins to turn these action items into change to benefit clinicians’ well-being and patient care.

This work will require the creation of a network of allies, convening sessions, and the creation of working groups from national health professional organizations in order to execute a national strategy for implementing and institutionalizing these changes.

Our clinicians are depending on concerted and coordinated engagement with key stakeholders from organizations within our health care community to mobilize strategies nationally.

On behalf of the 25 By 5 Symposium Steering Committee, we hope you will join us in this effort.

Funding sources: 
National Library of Medicine (1R13LM013581-01)
National Institute of Nursing Research (NINR): 1R01NR016941-01

Dr. Rossetti is an Assistant Professor of Biomedical Informatics and Nursing at Columbia University. Her research is focused on identifying and intervening on patient risk for harm by applying computational tools to mine and extract value from EHR data and leveraging user-centered design for patient-centered technologies.

Dr. Rosenbloom is the Vice Chair for Faculty Affairs and a Professor of Biomedical Informatics at Vanderbilt University. His research has focused on studying how health care providers, patients, and caregivers interact with health information technologies when documenting medical and health-related activities, and when making clinical decisions.

Individual and Organizational Health Literacy: A Key to the Future of Health

As the Secretary’s Advisory Committee on National Health Promotion and Disease Prevention Objectives for 2030 prepares its new statement for Healthy People 2030, NLM has been asked to review and comment on the definition of health literacy. This request has provided a good opportunity for me to consider how NLM facilitates health literacy — but more about that in a minute.

As a concept, health literacy has generated much attention and debate over the past 15 years. In 2004, the Institute of Medicine (now the National Academies of Sciences, Engineering, and Medicine) released Health Literacy: A Prescription to End Confusion. This report laid the groundwork for the idea that health literacy is more than the capacity of an individual to obtain, process, and understand the basic health information needed to make appropriate health decisions. Health literacy also involves system-level factors such as education, health services, and social and cultural influences.

This idea — of organizational health literacy — encompasses the ways in which services, organizations, and systems make health information and resources available and accessible to people, according to their individual health literacy strengths and limitations.

The white paper Ten Attributes of Health Literate Health Care Organizations proposes that health literate organizations share the following characteristics:

  1. Has leadership that makes health literacy integral to its mission, structure, and operations
  2. Integrates health literacy into planning, evaluation measures, patient safety, and quality improvement
  3. Prepares the workforce to be health literate and monitors progress
  4. Includes populations served in the design, implementation, and evaluation of health information and services
  5. Meets the needs of populations with a range of health literacy skills while avoiding stigmatization
  6. Uses health literacy strategies in interpersonal communications and confirms understanding at all points of contact
  7. Provides easy access to health information and services and navigation assistance
  8. Designs and distributes print, audiovisual, and social media content that is easy to understand and act on
  9. Addresses health literacy in high-risk situations, including care transitions and communications about medicines
  10. Communicates clearly what health plans cover and what individuals will have to pay for services

NLM contributes widely to individual health literacy. We provide information in many forms, from scientific articles in PubMed Central to health and wellness information for patients and their families and friends through MedlinePlus.

But what do we do in support of organizational health literacy?

When it comes to the accessibility of health information and services, we leverage technology to provide a range of machine-accessible pathways to our offerings. For example, our ClinicialTrials.gov application programming interface allows organizations to extract the clinical trials located in an organization’s specific region and display them on the organization’s own portal.

NLM’s MedlinePlus Connect allows health organizations and health information technology providers to link patient portals and electronic health record systems to MedlinePlus, supporting the in-the-moment delivery of personalized health information. We also foster the goals of organizational health literacy through our National Information Center on Health Services Research and Health Care Technology, which provides information on health services research and quality improvement as well as resources for public health professionals.

Please think about how NLM can better support health literacy — either individual or organizational — and share your ideas with me. It’s a key to the future of health!


Envisioning a Future of Better Patient Self-Management

For years, I’ve talked about the “care between the care,” which occurs between patients’ visits to the hospital, clinic, or ER. It’s abundantly clear that the real action is happening in everyday life, yet so much of our clinician education, information technology development, and standards of practice address only that very short time when patients are in the presence of physicians, nurses, pharmacists, or other providers.

We need to devise more interventions that target the care that happens between provider visits.

Like many directors of National Institutes of Health (NIH) Institutes, I have an active research program on the NIH campus. My lab, the Advanced Visualization Branch (AVB), is in the Division of Intramural Research at the National Institute of Nursing Research (NINR). The lab is motivated by one simple question: How can we help people with chronic conditions who are living at home better self-manage?

To address this question, we’re using a whole new set of methodologies involving immersive virtual reality (VR). VR is emerging as a robust research tool that lets investigators create realistic-appearing environments to study human behavior. This approach is particularly important for research on the challenges of self-management at home because the systematic study of behavior requires that we visit the same space repeatedly — something that’s hard to do with people in their homes in real life!

The AVB develops and evaluates augmented reality (AR) and VR experiences that engage participants in multisensory activities and examine their impact on health behaviors. Specifically, we develop interactive VR simulations that present health information to people with a variety of complex health conditions. These simulations provide visual cues that immerse participants in everyday settings.

Research demonstrates that teaching self-management skills is more successful and has a greater impact if those skills are taught in the environment where they will be used. There’s something about visual cues and spatial layouts that seems to reinforce the teaching process and help people develop new patterns.

Simulations that enable people to rehearse problem-solving behaviors in familiar, realistic-appearing environments foster improved health outcomes at home. AR/VR simulations also allow us to experiment with new ways of presenting information that aren’t possible right now but could be in the future.

Photo: University of WisconsinMadison

We could investigate, for example, whether a display of nutrition facts that hovers over a food container leads people to make more appropriate food choices. In addition, we’re developing methods to measure the impact of using AR/VR technology as a tool to aid disease-management nursing strategies during the transition from acute care settings to outpatient environments. Our work will help generate recommendations for how to best use emerging technologies in all types of patient care.  

The cross-disciplinary AVB team comprises staff with expertise in nursing research, informatics, neuropsychology, and digital engineering. The team creates virtual environments to observe, quantify, and assess objective indicators of behavior (e.g., performance, physiological) and subjective perceptions (e.g., mood, user experience, self-rated symptoms) in controlled, complex environments. The results are then used to evaluate individual differences in symptom expression, the delivery of effective health care education, and the utility of VR in engaging patients and improving health-related behaviors.

Photo: Advanced Visualization Branch, NINR

Currently, the AVB’s research focuses on environments for studying the impact of fatigue on everyday activities, evaluating interventions targeting dietary choices by individuals with chronic medical conditions, and helping people better use pillboxes for safe medication management at home.  

While the main focus of my work life is leading NLM, conducting a program of research at NINR enriches my thinking and helps me envision new ways for the Library’s resources to reach people. My research as a nurse-investigator will engender new ideas and help us devise new strategies for delivering the important, consumer-oriented information provided by NLM through our outreach initiatives and MedlinePlus.

Maybe you could help us, too! Researchers interested in the use of technology for patient self-management might consider submitting an application to our Personal Health Libraries for Consumers and Patients grant opportunity.


The Significance of Network Biology

Guest post by Teresa Przytycka, PhD, Senior Investigator, Computational and Systems Biology section of the Computational Biology Branch at the National Library of Medicine’s National Center for Biotechnology Information, National Institutes of Health.

The functioning of any complex system involves interactions between elements of that system. This is true at the cellular level, the macro level, and every point in between.

For example, within a cell, diverse molecules coordinate their activities and work together to carry out specific cellular functions. Cells then interact with each other to shape an organism’s development, tissue-level organization, and immune response. In turn, the organisms themselves interact to form various types of connections. In the case of people, those connections form the backbone of social systems.

These many interactions, from the microscopic to the macroscopic, can be described as networks, which comprise nodes connected via links that designate the relationships between nodes.

But how can we discover which nodes are connected? And how can we learn about the nature of those connections?

My research group and I try to answer these questions using network analysis, that is, working at the network level to uncover insights about the underlying system.

We can trace the beginnings of network analysis (also known as graph theory in mathematical circles) to Gottfried Leibniz’s geometria situs (“geometry of position”), a mathematical discipline focused on the relationship between positions and objects. The first recorded application of this new way of thinking was the famous solution to the problem known as “The Bridges of Königsberg,” published by Leonhard Euler in 1736.

Königsberg (now Kaliningrad) straddles the Pregel River. In Euler’s time, seven bridges connected the various parts of the city, including two islands in the middle of the river. The question asked was whether one could chart a walk through the city that required crossing each bridge only once and return to the start.

Euler tackled this question using what we today call network theory. If the regions of Königsberg are nodes and the bridges are links, Euler showed that, for such a walk to be possible, each node must have an even number of links. Why an even number? Because if we cannot cross the same bridge twice, then for every way into each region of the city there must be a new way out. Because that property did not hold for Königsberg, Euler concluded that no such walk could be devised.

As Euler’s argument shows, representing a complex system as a network of nodes and links can help uncover properties of the system that might have otherwise been obscured.

In biology and medicine, such network-centric approaches coincided with the emergence of high-throughput experimental techniques and advanced methods for collecting and storing diverse biomedical data. The protein interaction network for yeast became one of the first large biological networks obtained from high-throughput experiments. Analyzing that network revealed that a small fraction of proteins interacted with a disproportionately large number of other proteins. Additional research showed that these “hub” proteins are essential to the cell’s survival.

This intriguing relationship between a network property and a biological property begged for an explanation.

Our 2008 paper demonstrated that the majority of protein hubs are essential because of their involvement in complex, densely connected modules that carry out functions essential for cell survival. These results illustrate that, in addition to reporting binary relationships between individual nodes, interaction networks encode hidden higher-level organization.

In many networks, including biological and social ones, groups of nodes that interact with each other more tightly than with the rest of the network can be identified. We call these groups “modules.” In the context of biological networks, modules are often associated with groups of genes that work together to perform a specific biological function. At the same time, we’re beginning to see that complex diseases, such as cancer, are more likely caused by the dysregulation of a specific functional module than a dysfunction of an individual gene. That’s why, in recent years, cancer research has turned its attention to identifying dysregulated modules.

This effort includes several methods developed by our research group—Module Cover, Mutual Exclusivity Module Cover, and BeWith. These methods combine information from the human-interaction network with disease-specific information, such as abnormally expressed or mutated genes, to identify disease-associated modules. These modules can shed light on the mechanism of the disease, suggesting areas for further study and possible means of intervention.

We also use networks to discover how information flows between individual nodes. For example, by applying the principles of current flow within an electric circuit, we have been able to identify the causal (altered) disease genes and the pathways they dysregulate, providing another way to discover groups of genes involved in a disease.

Unfortunately, most currently available interaction networks are static depictions of a dynamically changing system. They typically do not account for tissue types, developmental stages, disease status, and other factors. As a result, these networks cannot tell us the full story.

To consider these and other factors, we need a context-specific network. But to build one, we must use context-specific data. How can we do that, given the myriad conditions we must consider? Our new method, NetREX, moves us in that direction.

Network biology has facilitated progress in many areas of biomedical science. This simple, yet powerful, concept allows us to abstract the essence of relations between genes and proteins, predict interactions between drugs, study disease comorbidity, and discover important associations. Of course, discovering an association is just the first step in uncovering a mechanism, but it is often a crucial step.

headshot of Dr. Teresa Przytycka

Teresa M. Przytycka, PhD, leads the Algorithmic Methods in Computational and Systems Biology section at the National Center for Biotechnology Information. Dr. Przytycka is particularly interested in the dynamical properties of biological systems, including spatial, temporal and contextual variations, and exploring how such variations impact gene expression, the functioning of biological pathways, and the phenotype of the organism.

Technology and Data in Mental Health: Applications for Suicide Prevention

Guest post by Elizabeth Chen, PhD, Associate Director of the Center for Biomedical Informatics, Associate Professor of Medical Science, and Associate Professor of Health Services, Policy & Practice at Brown University.

Biomedical informatics as a discipline is broadly concerned with the effective use of data, information, and knowledge to improve human health. Since its origins in the 1950s, we have watched this discipline evolve with advances in health information and communications technology as well as the explosion of electronic health data. During this time, we have also seen the emergence of sub-disciplines reflecting areas of specialization. In fact, a 2015 study uncovered almost 300 different “types” of informatics! Among these was mental health informatics, which first appeared in the title of a 1995 article indexed in PubMed.

Using technology to understand and support mental health dates to the 1950s when specialized television broadcasts delivered mental health training. In the 1960s, computers analyzed data for psychological diagnoses and housed “artificial intelligence” systems that simulated communication with a psychotherapist. More recently, with the rapid adoption of electronic health record (EHR) systems that can collect longitudinal patient information such as diagnoses and medications, we are observing the increased use of EHR technology and data for improving health care, including mental health care.

Mental health remains a global crisis. In the United States alone, mental health conditions affect 1 in 5 adults and children. These conditions are among the factors that contribute to making suicide the 10th leading cause of death overall and 2nd leading cause among 10- to 34-year-olds nationally. With suicide rates having increased by nearly 30% since 1999,  the National Strategy for Suicide Prevention calls for a comprehensive and coordinated approach that includes data-driven strategic planning and evidence-based programs.

There are numerous and wide-ranging applications of mental health informatics and EHRs contributing to these efforts, including the following:

  • Two independent datasets, one including EHR and biobank data from the Vanderbilt University Medical Center, have characterized the role of common genetic variants among those who have attempted suicide. These large-scale genetic analyses support a heritable component to suicide attempts and an incomplete genetic relationship with psychiatric and sleep disorders.
  • At the Parkland Health & Hospital System in Texas, a Universal Suicide Screening Program, initiated in 2012, led to implementing the Columbia-Suicide Severity Rating Scale in the EHR system for adults. The integration of this clinical decision support tool into the clinical workflow demonstrates how technology may be used to improve suicide risk recognition.
  • Researchers across the country are developing models for predicting patients’ future risk of suicidal behavior using “machine learning” techniques, state death certificates, and longitudinal EHR data from a range of health systems, including Partners Healthcare in Massachusetts [PubMed], HealthPartners in Minnesota, Henry Ford Health System in Michigan, and five different Kaiser Permanente locations [PubMed]. Implementing these predictive models as clinical decision support tools in EHR systems has the potential to improve screening, detection, and treatment of suicide risk.
  • In Connecticut, EHR data from the statewide health information exchange and five clinical partners are being used to identify patients at risk of suicide. Claims data from the All-Payer Claims Database and mortality data from the State Department of Public Health will be used to assess the outcomes and impact of the quality improvement efforts.

And these are just a few examples.

Technology and data will continue to play important roles in advancing mental health care. We have already seen the contributions of mental health informatics over the years and those of related areas such as behavioral health informatics and computational psychiatry. There is much more to come in the development of effective and innovative solutions for improving diagnosis, treatment, and prevention of mental health conditions, including those related to suicidal thoughts and behaviors.

headshot of Dr. Elizabeth ChenElizabeth S. Chen, PhD is the founding Associate Director of the Center for Biomedical Informatics, Associate Professor of Medical Science, and Associate Professor of Health Services, Policy & Practice at Brown University. She leads the Clinical Informatics Innovation and Implementation (CI3) Laboratory that is focused on leveraging EHR technology and data to improve healthcare delivery and biomedical discovery. Dr. Chen is an elected fellow of the American College of Medical Informatics and is a member of NLM’s Biomedical Informatics, Library and Data Sciences Review Committee.

 


Dr. Chen will deliver the next NLM Biomedical Informatics & Data Science Lecture on Wednesday, November 14, 2018, at 2:00 pm in the Natcher Conference Center (Building 45), Balcony A. Her talk, “Knowledge Discovery in Clinical and Biomedical Data: Case Studies in Pediatrics and Mental Health,” is free and open to the public. It will also be broadcast live globally and archived via NIH Videocast.