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GMR Data Science

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The blog of NNLM Greater Midwest Region
Updated: 5 hours 18 min ago

Big Data in Healthcare: Finding Your Niche

Mon, 2018-09-17 12:39

In the NNLM Big Data in Healthcare: Exploring Emerging Roles course, we asked participants, as they progressed through the course, to consider the following questions: Do you think health sciences librarians should get involved with big data in healthcare? Where should librarians get involved, if you think they should? If you think they should not, explain why. You may also combine a “should/should not” approach if you would like to argue both sides. NNLM will feature responses from different participants over the coming weeks.

Written by: Brenda Fay, Library Specialist, Aurora Libraries – Aurora St. Luke’s Medical Center

For librarians in health science libraries, big data in healthcare might be something of a stranger. Sure, we know that data is being collected about patients, but how do we librarians fit in? Depending on what type of library you work in, whether you’re a solo librarian, and perhaps even your comfort level learning new skills, knowledge and familiarity with data and data practices may or may not be something in your wheelhouse. I work in a large healthcare system within a team of fourteen librarians and library staff. Our institution has a research arm that is growing and growing, and yet none of us have really been involved in big data or data management practices at our institution. I don’t think that’s very unusual for a place that isn’t also an academic medical center. Can healthcare big data be overwhelming? Yes. Is big data in healthcare worth all the fuss? Yes.

Why should health science librarians get involved with big data in healthcare? With the ever-growing interest and use of data all around us, data isn’t going away anytime soon. Librarians are great at continually staying on top of trends and changes in our field, and I truly believe that health science librarians will become more and more involved, in one way or another, with data initiatives at their institutions. It’s better to be in front of the curve and helping guide the conversation, than trying to catch up when the ship has sailed. Learning about big data will keep librarians relevant. If we look at skills librarians already have, like organization and classification, taxonomies and metadata, those could immediately be leveraged into increasing the quality of research data management practices at our institutions by working with researchers on their data management plans, which many need to include on grant and funding applications. We should also get involved because there are so many free training opportunities available to us from MLA, NLM, and others. If MLA and NLM/NNLM think big data is worth supporting on such a large scale, I am onboard, too.

How might health science librarians get involved with big data in healthcare? This is much trickier and depends a lot on the situation you find yourself in. You might not be able to start any of these activities today or even next year, but knowing how other health science librarians work with big data in their institutions can inspire you to find a way where you are. Reference questions might lead you to big data. If you’ve ever been asked to find data, Kevin Read and his NYU librarian colleagues have created a data catalog (NYU Health Sciences Library, n.d.) for those looking for data sets to use, or for researchers to publish their own data. Assisting on systematic reviews or publications might lead you to big data. A 2018 study looked at Google Trends, an online source for accessing trends in Google’s search data, and laypeople’s searches for asthma (Mavragani, A, K, & KP., 2018). It had some methodological issues that a librarian would have likely pointed out right away. Building relationships with library users might lead you to big data. Librarians at NU Health Sciences Library had conversation with basic and clinical researchers at their institution to learn more about their data needs. These conversations allowed them to tailor library services to fill a gap in “community’s data issues including, but not limited to, the challenges they face when collecting, organizing, and sharing their research” (Read, Surkis, Larson, McCrillis, & Nicholson, 2015).

I firmly believe that working with big data in healthcare will raise the profile of health science librarians and the libraries they work in.

Bibliography

Mavragani, A., A, S., K, S., & KP., T. (2018). Integrating smart health in the US health care system: Infodemiology study of asthma monitoring in the Google era. JMIR Public Health and Surveillance, e24.

NYU Health Sciences Library. (n.d.). Data catalog. Retrieved August 29, 2018, from https://datacatalog.med.nyu.edu/

Read, K. B., Surkis, A., Larson, C., McCrillis, A. G., & Nicholson, J. X. (2015). Starting the data conversation: informing data services at an academic health sciences library. Journal of the Medical Library Association, 131-135.

Categories: Data Science

Public Health Department Funded to Make Data FAIR

Thu, 2018-08-02 09:01

The GMR office is excited to announce that Richland Public Health has been granted a Research Data Award to make county-level health data FAIR.

Background:

Health assessments at the county-level are resources health professionals and librarians rely on heavily to inform the development of community health programming, interventions, and grant applications to fund efforts that improve the health, well-being, and quality of life of their constituents. These health assessments include, but are not limited to, Community Health Assessments (CHA), Community Health Improvement Plans (CHIP), and Community Health Needs Assessments (CHNA). These health assessments are expected to be performed and utilized by hospitals, public health departments, and other social service agencies to identify key community health concerns every 3-5 years.

Within Richland County, the raw data collected for these assessments are often siloed.

Project Description

The Making County-Level Health Data Findable, Accessible, Interoperable, and Reusable (FAIR) Initiative will consist of three phases in order to establish and sustain an online interface for local health professionals and librarians to access and analyze county-level health assessment data, as well as educate these individuals on utilizing this resource and creating their own data management plans.

The first phase of this proposed project will be to develop and implement a database where raw data from Richland County health assessments can be accessed and analyzed by local health professionals and librarians.

The second phase will consist of the creation and hosting of a data access and management webinar to introduce Richland County health professionals and librarians to the online interface.

Finally, phase three will provide an additional webinar to 12 rural North Central Ohio Counties in an effort to educate their local health professionals and librarians about data management plans as well as how to access, analyze, and contribute to the Making County-Level Health Data FAIR Initiative database.

Outcomes

A formative and summative evaluation will be used to measure the success of this project. First, the project will use a formative evaluation using the FAIR Guiding Principles to make sure the project meets the prerequisites for proper data management and stewardship. A summative evaluation will be used to determine the success in educating health professionals and librarians about the database.

Categories: Data Science

GMR to Fund UC’s 4th Annual Data Day

Mon, 2018-07-30 12:19

The GMR office is excited to announce that the University of Cincinnati (UC) has been granted a Research Data Award to host its 2019 Data Day Event!

Background:

UC Data Day is the only event on the University of Cincinnati campus that connects the libraries with researchers and community partners in a collaborative and informative medium. Data Day provides an opportunity to openly discuss opportunities and challenges related to data, and educates the research community on methods for driving discovery through data, a key area of interest for the National Library of Medicine.

Project Description

UC Data Day 2019 will build on the momentum of the three previous Data Day events, and endeavor to promote interdisciplinary learning and collaboration among the University of Cincinnati’s research community and broader Greater Midwest Region. Data Day 2019 will offer a full schedule to engage audience members, reveal solutions to data challenges and foster a community of best practices around improved data management.

The event will offer combinations of engaging keynote addresses, workshops on data analyses and visualization, graduate student poster forums, and panels that provide attendees with knowledge of data practices, usage and services. The official date of Data Day 2019 is in the process of being determined.

Outcomes

The essential goal of Data Day 2019 is to equip researchers with the knowledge and ability to effectively perform data driven research, to better manage their research data across the research lifecycle, to improve their skills in data analytics and visualization, and arm them with pertinent contacts that can address data related concerns.

Categories: Data Science

GMR Funds Innovative Pilot Program to Teach Graduate Students Research Data Management

Tue, 2018-06-26 16:12

 

The GMR office is excited to announce that Tina Griffin at the University of Illinois at Chicago has been granted a Research Data Award to develop the Research Data Management Best Practice Implementation Program for Graduate Students in STEM and Health Sciences!

Background:

Today, data management practices by students are largely learned by conforming to the laboratory culture and adopting habits from the environment in which they work. There is no known national mandatory data management training for students. The recent NLM strategic plan (PDF) recognizes the importance of the role of libraries in advancing open science and data management, and many academic libraries are heeding the call by providing research data management education services.

Project Description

This project will pilot a flipped classroom model to present students with appropriate research data management practices in an eight-week intensive program. In this program, the students are expected to engage with the instructional content outside the classroom, while using the in-person classroom time to engage in activities that demonstrate competency and understanding of the content. The 8-week program will cover the following topics:

  1. Introduction to Data management principles;
  2. Deep Dive – discipline standards, DMP draft;
  3. Project map, project narrative starts;
  4. Folder structure develops;
  5. File naming, table of contents, indexing develop;
  6. Templates develop;
  7. DMP finalized, project narrative finalized; and
  8. Ongoing practice, personal policy developed

The classroom time will be used by the students to systematically develop and holistically integrate these practices in to their research projects. This pilot project is unique in that it addresses both education about data management practices and the integration of best practices into the research workflow in a personalized manner.

Outcomes

The outcome of this pilot may introduce a new method to serve more students in a more effective manner with better long-term adoption of data management best practices. It also begins a longitudinal study to determine how these practices may contribute to successful dissertation/thesis completion and/or how they may prepare students for the workforce.

Categories: Data Science