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.

Data in the Scholarly Communications Solar System

Guest post by Kathryn Funk, program manager for NLM’s PubMed Central.

The Library of the Future. What will it look like?  The NLM Strategic Plan envisions it partly as “one of connections between and among literature, data, models, and analytical tools.” In this future, journal articles are no longer lone objects drifting in space, but, rather, each a solar system waiting to be explored. Indeed, we’re already seeing the published literature associated with datasets, clinical trials, protocols, software, earlier versions (including preprints), peer review documents, and so on through consistent identifiers and standardized publishing and archival practices.

To help researchers and the public navigate this new solar system, PubMed Central (PMC), NLM’s full-text archive of journal literature, has been collaborating with publishers and funders for the last year to support efficient ways of linking journal articles with associated data. We’re encouraging authors to cite their open datasets and publishers to archive and make available those data citations in a machine-readable format. Though data citations represent only a small percentage of how PMC articles are linked to data (supplementary material continues to be the predominant method for associating data with articles in the archival record), the growth in data citations in the last year has been promising, nearly doubling the previous year’s total (i.e., 850 articles with data citations in 2017 vs.  approximately 440 in 2016). NLM is also supporting the public access policy requirements of our research funder partners by encouraging authors to deposit datasets as supporting documents via the NIH Manuscript Submission (NIHMS) system.

But solar systems, even the metaphorical kind, are meant to be explored, so we’re also working to expose each journal article solar system in a way that promotes discoverability. We want to make it easier to discover articles in PMC with associated data citations, data availability statements, and supplementary data, through improved record displays and new search facets, leveraging the data-related search filters announced earlier this year.

NLM is also looking beyond datasets to archive and expose articles’ key satellites, including, for example, comments generated during the peer review process. As the effort to expand the openness of peer review gains traction, PMC staff have been collaborating with publishers and Crossref on standardized ways to make readily available those peer review materials.

As with any exploration of new solar systems, it’s our hope that taking these steps will help generate new knowledge, and in so doing drive research that is reproducible, robust, transparent, and reusable. And as we move toward becoming the Library of the Future, how we can best support your research needs in connecting the literature with the rest of the research universe? Please let us know.

With thanks to Jeff Beck for the solar system analogy. 

casual headshot of Kathryn FunkKathryn Funk is the program manager for PubMed Central. She is responsible for PMC policy as well as PMC’s role in supporting the public access policies of numerous funding agencies, including NIH. Katie received her master’s degree in library and information science from The Catholic University of America.

Clarity Across Languages

The art and science of translating health information

Guest post by Fedora Braverman, team lead for the MedlinePlus en español website.

Communication can be tricky, regardless of what language you speak. Take, for example, this conversation I had some time ago with a Hispanic acquaintance:

Him (in a panic): “My friend has a tumor. The tumor is benign!”

Me (not understanding why he is panicking): “That’s great. That’s (kind of) good news.”

Him (looking at me like I had two heads and with his eyes wide open): “It was BENIGN!”

He thought “benign” (benigno in Spanish) meant cancer. He thought his friend had cancer.

I work for MedlinePlus en español, and moments like these make my job so rewarding because I can ease that man’s worries. By pointing him to our website with reputable and reliable health information, I can help him understand his friend’s condition.

But the conversation also made me think: if he thought “benign” meant cancer, then others might, too.

We work in the largest biomedical library. We are used to these words. Our audience is not.

Because our goal is to reach out to the Hispanic population as a whole, regardless of health literacy levels, our site needs to address disparities in health literacy by striving for clarity. So, the next day, after relaying this conversation to my team, we updated all instances of the word benigno on the MedlinePlus en español site, clarifying that it meant “non-cancerous.”

We are constantly learning and improving, refining our translation of the MedlinePlus en español site to enhance its cultural sensitivity and accessibility. It’s an art and a science. We use tools—from print dictionaries to Google searches and everything in between—to determine which word is used across Latin America  for a particular ailment, condition, or medical term. But translating text calls for far more than just swapping an English word for a Spanish equivalent. Word choice matters, as we try to accommodate regional linguistic and cultural differences along with the subtlety and nuance inherent in any language. That’s the art, as we not only translate the words but also adapt the text to our audience’s culture. Only then can we expect readers to connect with and understand the information.

Understanding the culture of our audience is imperative to building a site like MedlinePlus en español. Being knowledgeable about how Hispanics talk about their health issues (e.g., referring to diabetes as “this condition that affects the pancreas”), how they deal with certain hot topics or sensitive issues like sexually transmitted diseases, and what their health challenges are is crucial. That’s why my team and I work together, going back and forth until the text is as understandable and as culturally relevant as possible—even if that means re-working text we had previously translated.

So, whether you call it gripe, trancazo, influenza o gripa, benigno or no canceroso, NLM’s MedlinePlus en español is the trusted website for you.

headshot of Fedora BravermanFedora Braverman leads the MedlinePlus en español website’s operations, outreach activities, and social media platforms. She previously worked as a consultant for the US State Department and for the Library of Congress Hispanic Division. She has also served as an information specialist at the US Embassy in Buenos Aires, Argentina.

Asking the right questions and receiving the most useful answers

Guest post by Lawrence M. Fagan, Associate Director (retired), Biomedical Informatics Training Program, Stanford University.

As online resources proliferate, it becomes harder to figure out which resources—and which parts of those resources—will best answer patients’ questions about their medical care. Patients have access to multiple websites that summarize information about a particular disease, myriad patient communities, and many online research databases.

This resource overload problem isn’t new, however. As more and more data became available in the Intensive Care Units of the 1980s, it grew increasingly difficult to determine the most important measurement to track for optimal care.

In short, more information isn’t necessarily the best solution when it comes to answering patient questions.

After a career in informatics, I now moderate an online community for patients with a particular subtype of lymphoma. Many questions that arise in the group can easily be answered by reviewing existing online content. Health librarians are excellent resources to help guide patients to the correct resources and articles.  However, some queries are less straightforward than others, such as: “What is the one thing you wished you knew before the procedure?” Rather than asking for a recitation of the steps in the procedure, this question is asking what was unexpected—or what step would have benefited from more patient or caregiver preparation. Correspondingly, these types of questions are hard to organize, store, and retrieve from patient-oriented databases.

Sometimes, the community of patients can recognize patterns that escape the notice of medical providers. For example, a lymphoma patient may complain of repeated sinus infections. It’s worth noting that patients often turn to their primary care provider to treat their sinus infections, and those visits may lead to antibiotic prescriptions. In this scenario, group members have pointed out the potential link between treatment with the drug Rituximab and a decrease in the body’s immunoglobulin levels. This connection leads to suggestions to explore an alternative treatment for chronic sinus infections (in this special context) using immunoglobulin replacement therapy rather than antibiotics.

Specialized online communities can also provide help with detailed care issues, including the treatment of side effects for uncommonly used drugs with which local healthcare providers might not be familiar.

Online communities can also suggest researching databases to answer patient questions. ClinicalTrials.gov helps locate experimental treatments for specific medical conditions. Some community discussions about trials go beyond what’s included in the ClinicalTrials.gov database. For instance, group members may discuss the optimal order of clinical trials in a specific medical area, based on an analysis of the inclusion criteria for the various trials. In addition, there are ancillary questions about trial logistics that aren’t found in the database, such as, “I live in the San Francisco area—is it feasible to participate in Trial X at City of Hope in Southern California?” Setting up comprehensive links between the clinical databases and discussions in patient communities would help patients access the answers to their questions more efficiently.

The answers to these specialized questions are often found in the archives of online communities or in the memories of group participants. Yet, it is not easy to find the right community for a particular medical problem, and in my understanding there is no central repository of links to online communities. Moreover, while many community links can be found in MedlinePlus, static links to community websites often become stale, as sites may migrate locations over time. Some of the ACOR cancer communities, for example, have migrated to SmartPatients.com.  As patients find a community of interest, it is important that they determine whether the conversations are ongoing and whether the participants are knowledgeable and supportive. The Mayo Clinic offers a short discussion detailing the pros and cons of support groups.

Researchers have examined patient and clinician information needs for more than a quarter century. These models, however, have only rarely been incorporated into information retrieval systems. One successful example (aimed at providers) is the use of “clinical queries” in PubMed, designed for searching the scientific literature. This brings us to a critical question: What would it take to reengineer the patient-oriented retrieval systems so that these focused queries drive most patient sites?

For now, we have communities of patients and dedicated professionals who are ready and willing to help point to the most useful answers.

Please note: The mention of any commercial or trade name is for information and does not imply endorsement on the part of the author or the National Library of Medicine.

Many thanks to Dave deBronkart, Janet Freeman-Daily, Robin Martinez, Tracie Tavel, and Roni Zeiger who reviewed earlier versions of this blog post.

Outdoor portrait of Lawrence M. Fagan.Lawrence Fagan, MD, PhD, retired in 2012 from his role as Associate Director of the Stanford University Biomedical Informatics Training Program. He is a Fellow of the American College of Medical Informatics. His current interests are in patient engagement, precision health, and preventing medical errors.

How much does it cost to keep data?

Study to forecast long-term costs

Guest post by Elizabeth Kittrie, NLM’s Senior Planning and Evaluation Officer.

As scientific research becomes more data-intensive, scientists and their institutions are increasingly faced with complex questions about which data to retain, for how long, and at what cost.

The decision to preserve and archive research data should not be posed as a yes or no question. Instead, we should ask, “For how many years should this subset of data be preserved or archived?” (By the way, “forever” is not an acceptable response.)

Answering questions about research data preservation and archiving is neither straightforward nor uniform. Certain types of research data may derive value from their unique qualities or because of the costs associated with the original data collection. Other types of research data are relatively easy to collect at low cost; yet once collected, they are rarely re-used.

To create a sustainable data ecosystem, as outlined in both the NLM Strategic Plan and the NIH Strategic Plan for Data Science, we need strategies to address fundamental questions like:

  • What is the future value of research data?
  • For how long must a dataset be preserved before it should be reviewed for long-term archiving?
  • What are the resources necessary to support persistent data storage?

We believe that economic approaches—including forecasting long-term costs, balancing economic considerations with non-monetary factors, and determining the return on public investment from data availability—can help us make preservation and archiving decisions.

Economic approaches…can help us make preservation and archiving decisions.

To that end, NLM has contracted with the National Academies of Sciences, Engineering, and Medicine (NASEM) for a study on forecasting the long-term costs for preserving, archiving, and promoting access to biomedical data. For this study, NASEM will appoint an ad hoc committee that will develop and demonstrate a framework for forecasting these costs and estimating potential benefits to research. In so doing, the committee will examine and evaluate the following:

  • Economic factors to be considered when examining the life-cycle cost for data sets (e.g., data acquisition, preservation, and dissemination);
  • Cost consequences for various practices in accessioning and de-accessioning data sets;
  • Economic factors to be considered in designating data sets as high value;
  • Assumptions built in to the data collection and/or modeling processes;
  • Anticipated technological disruptors and future developments in data science in a 5- to 10-year horizon; and
  • Critical factors for successful adoption of data forecasting approaches by research and program management staff.

The committee will provide a consensus report and two case studies illustrating the framework’s application to different biomedical contexts relevant to NLM’s data resources. Relevant life-cycle costs will be delineated, as will any assumptions underlying the models. To the extent practicable, NASEM will identify strategies to communicate results and gain acceptance of the applicability of these models.

As part of its information gathering, NASEM will host a two-day public workshop in late June 2019 to generate ideas and approaches for the committee to consider.  We will provide further details on the workshop and how you can participate in the coming months.

As a next step in advancing this study, we are supporting NASEM’s efforts to solicit names of committee members, as well as topics for the committee to consider.  If you have suggestions, please contact Michelle Schwalbe, Director of the Board on Mathematical Sciences and Analytics at NASEM.

casual headshot of Elizabeth KittrieElizabeth Kittrie is NLM’s Senior Planning and Evaluation Officer. She previously served as a Senior Advisor to the Associate Director for Data Science at the National Institutes of Health and as Senior Advisor to the Chief Technology Officer of the US Department of Health and Human Services. Prior to joining HHS, she served as the first Associate Director for the Department of Biomedical Informatics at Arizona State University.