Nursing and Data Stewardship
Symbolic Presentation of Data Stewardship
Data and information are the symbolic representation of the phenomena with which nursing is concerned. Data are defined as discrete entities that are objective; information is defined as data that are structured and organized and that have meaning or interpretation.
Information that has been synthesized so as to identify and formalize interrelationships is referred to as knowledge. When one term represents all three types of content, it is usually information. From this perspective, data are viewed as the raw material on which nursing knowledge and science are developed.
Data stewardship refers to the
responsibility to manage, administer, attend to, and take charge of the
universe of relevant nursing data.
Nursing Data
Nursing data issues revolve around several factors. The first relates to identification of the universe of relevant nursing data. Currently, there is no consensus regarding what data elements make up a minimum nursing data set nor what data elements are required to capture nursing diagnoses, interventions, and outcomes.
Systems to label or name these elements also are inconsistently defined. Next, the complex nature of nursing phenomena poses measurement difficulties. Measurement is the process of assigning numbers to objects to represent the kind or amount of a character possessed by those objects.
It
includes qualitative means (assigning objects to categories that are mutually
exclusive and exhaustive) and quantitative measures (assigning objects to
categories that represent the amount of a characteristic possessed).
Nursing Data As Compare to Other
Unlike other biological sciences, few nursing phenomena can be measured by using physical instruments with signal processing or monitoring. Measurement difficulties occur because nursing consists of a multiplicity of complex variables that occur in diverse settings.
If one is able to identify what significant variables should be measured, then one is challenged with the difficulty of isolating those variables to measure them. Ambiguities and abstract notions must be reduced to develop concrete behavioral indicators if measurement is to be meaningful.
Measuring nursing phenomena also requires the acknowledgment of the "fuzzy" and complex nature of nursing phenomena and the richness of the meaning contained in the context of the data.
Finally, the value and use of data that are not coded or numeric, such as whole text data, must be studied to understand their benefits and boundaries for representing nursing phenomena. Content analysis of nursing data and their usefulness have to be further explored.
Transfer of Raw Data Into Structured
Processing data implies the transfer of data in raw form to a structured, interpreted information form. Information has characteristics of accuracy, timeliness, utility, relevance, quality, and consistency.
Data stewardship. suggests that attention be paid to these characteristics. For example, accuracy is of concern at the level of judgment in collecting data as well as at the level of the data collected.
Quality of data and information is related to the ability and willingness of clients to disclose information as well as to the nurse's ability to observe, collect, and record it. Reliability refers to random measurement errors such as ambiguities in data interpretation.
These
measurement errors that affect clinically generated data can occur at the point
of care delivery, the time of documentation, and when data are retrieved or
abstracted for studies (Hays, Norris, Martin, & Androwich , 1994).
With the advent of automated data processing and computerized information systems, decisions about data content, control, and cost need careful consideration. The content and design decisions concern format, standardized languages, level of detail, data entry and retrieval messages, and interfaces with nonclinical data systems.
A primary concern of clinicians is the amount of time invested in harvesting data and recording it. Minimum time investment, with maximum clarity and comprehensiveness of data collected and recorded, is needed. Redundancy must be eliminated.
Decisions related to content of data demand stewardship to ensure privacy, confidentiality, and security, especially when data are in electronic form. Requirements for legitimate access to data must be managed to facilitate the flow of clinical data while simultaneously restricting inappropriate access.
There is a cost associated with the use and development of automated databases; however, accuracy, reliability, and comprehensiveness of information should not be sacrificed because of cost.
Data Processing Challenges
Data stewardship poses challenges and responsibilities for nurses in building knowledge bases. Standardization of terms of data is critical, and coordination and synthesis of current efforts are needed. If nurses are to be stewards of their data, then further study should focus on the following areas:
(a) the definition and description of the data and information required for patent care.
(b) the use of data and knowledge to deliver and manage patient care.
(c) how one acquires and delivers knowledge from and for patient care (National Center for Nursing Research, 1993).
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