Toward a Central Repository for Sharing Nursing Informatics’ Best Practices

Effken, Judith A.; Weaver, Charlotte A.; Cochran, Kelly A.; Androwich, Ida M.; OʼBrien, Ann

doi:10.1097/cin.0000000000000258

Cited by 2 publications

(1 citation statement)

References 1 publication

(1 reference statement)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In hospitals, nurses typically generate the highest volume of discreet data and information in structured formats such as flowsheet-style tools, to the extent of millions or billions of data points per year. Nurses struggle under the burden of ever-increasing documentation requirements, and many organizations have begun to tackle the underlying mandate assumptions, such as intra-organization interpretation of regulatory and accreditation standards, and problematic workflows including automation of paper-based workflows intended to increase adoption that contribute to documentation burden (Effken, Weaver, Cochran, Androwich, & O'Brien, 2016). Nurses may see little value to the data entered in the EHR for a number of reasons, including lack of input into workflow design and decision support mechanisms.…”

Section: Ehr Flowsheetsmentioning

confidence: 99%

Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar

Lytle

Westra

Whittenburg³

et al. 2021

J of Nursing Scholarship

View full text Add to dashboard Cite

Purpose The rapid implementation of electronic health records (EHRs) resulted in a lack of data standardization and created considerable difficulty for secondary use of EHR documentation data within and between organizations. While EHRs contain documentation data (input), nurses and healthcare organizations rarely have useable documentation data (output). The purpose of this article is to describe a method of standardizing EHR flowsheet documentation data using information models (IMs) to support exchange, quality improvement, and big data research. As an exemplar, EHR flowsheet metadata (input) from multiple organizations was used to validate a fall prevention IM. Design A consensus‐based, qualitative, descriptive approach was used to identify a minimum set of essential fall prevention data concepts documented by staff nurses in acute care. The goal was to increase generalizable and comparable nurse‐sensitive data on the prevention of falls across organizations for big data research. Methods The research team conducted a retrospective, observational study using an iterative, consensus‐based approach to map, analyze, and evaluate nursing flowsheet metadata contributed by eight health systems. The team used FloMap software to aggregate flowsheet data across organizations for mapping and comparison of data to a reference IM. The FloMap analysis was refined with input from staff nurse subject matter experts, review of published evidence, current documentation standards, Magnet Recognition nursing standards, and informal fall prevention nursing use cases. Findings Flowsheet metadata analyzed from the EHR systems represented 6.6 million patients, 27 million encounters, and 683 million observations. Compared to the original reference IM, five new IM classes were added, concepts were reduced by 14 (from 57 to 43), and 157 value set items were added. The final fall prevention IM incorporated 11 condition or age‐specific fall risk screening tools and a fall event details class with 14 concepts. Conclusion The iterative, consensus‐based refinement and validation of the fall prevention IM from actual EHR fall prevention flowsheet documentation contributes to the ability to semantically exchange and compare fall prevention data across multiple health systems and organizations. This method and approach provides a process for standardizing flowsheet data as coded data for information exchange and use in big data research. Clinical Relevance Opportunities exist to work with EHR vendors and the Office of the National Coordinator for Health Information Technology to implement standardized IMs within EHRs to expand interoperability of nurse‐sensitive data.

show abstract

Section: Ehr Flowsheetsmentioning

confidence: 99%