Big Data Panel

Good news: We dug to the bottom of the pile and found a pony!!!Kathryn H. Bowles, PhD, FAAN, FACMIvanAmeringen 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.