Big Data Panel
Good news: We dug to the bottom
of the pile and found a pony!!!
Kathryn H. Bowles, PhD, FAAN, FACMI
vanAmeringen Professor in Nursing Excellence; Director
of the Center for Integrative Science in Aging,
University of Pennsylvania School of Nursing
Vice President of Research and Director of the Visiting
Nurse Service of New York Research Center.
Acknowledgments
Co-Investigators:
John Holmes, Sarah Ratcliffe, Mary Naylor
Funder:
The project was supported by the National
Institute of Nursing Research (award 2R01NR007674).
The content is solely the responsibility of the authors
and does not necessarily represent the official views of
the National Institute of Nursing Research or the
National Institutes of Health.
The authors declare no conflicts of interest
Background and study aims
Six hospitals all with an EHR from the same
vendor
Chose this particular EHR because of
standardized assessments and evidence based
tools
Obtained data from the nursing admission
assessment and documentation near discharge
Data was used to build case studies of
hospitalized patients, used to elicit discharge
referral decisions
Outline
Challenges
and
Solutions
Standardized assessments
Different versions had different data elements
Ignorance about the validity of EB tools
Variation in what was mandatory to document
Getting the data out
Each site had varying skill in their ability to
extract data from the EHR
Sites had changed table and field names so
queries written at one site could not be used at
the others
Data elements with one to many relationships
were especially challenging (wounds)
Customization
Adding detailed data elements (home care
versus “St. Mary’s home care”)
Removing data elements
Allowing free text (wheelchair, Wheellchair,
Wheelchair)
Burying important elements (ADL assessment)
Avoiding upgrades to avoid overwriting
Documentation Policies
Charting by exception
•
Reversing the meaning of the question!
Oriented to? Disoriented to?
What is required and what is optional?
Timing of assessments (daily, adm/dc,
once/shift?)
Interface Design
Clarity of the documentation (understanding the
questions)
Fit within the workflow
Notification about incomplete data
Being able to navigate easily
How to answer when the patient can’t
Advice and solutions
Create a spreadsheet of all data elements
of interest to understand:
• what is collected
• when is it collected
• by whom
• for what purpose
• where is it stored
• how to extract it
Advice and solutions
Educate clinicians and students about Big
Data
• Data now used for broader purposes
• The consequences of missing data
• The consequences of customization
• The pitfalls of using EHR data for research
• Skills in merging, cleaning, and assessing
the quality of data
Advice and solutions
Avoid customization
Participate and set policies in a wider user’s
group
Critically review your systems for workflow
issues that may impact data collection
Keep your system versions up to date
Advice and solutions
Assure that nurse collected data is included in
data warehousing efforts
Seek standardized nursing languages and
mapping to SNOMED for documentation systems
Appoint nurses to IT committees to assure
representation
Suggested reading
Conducting Research Using the Electronic Health
Record Across Multi-Hospital Systems
Semantic Harmonization Implications for
Administrators
Bowles, KH, Potashnik, S, Ratcliffe, SJ,
Rosenberg, M, Shih, N-W, Topaz, M, Holmes, J,
Naylor, M
Journal of Nursing Administration (2013)
43(6), 355-360.