7.5
Data Validation
Data validation is the process of checking and verifying collected information.
In data validation, the cue is the information acquired through the senses, and the cue's interpretation is called an inference.
The steps in data validation include identifying the cues, making inferences, and finally validating cues and inferences.
For example, a physical examination of a bedridden patient reveals swelling and pain in the calf muscle. This is the cue. A literature search points to symptoms of deep vein thrombosis—the inference. Further evaluation confirms the condition—and so the inference is validated.
In another scenario, a gestational mother's urine sugar dipstick shows a positive result. However, the literature suggests the probability of a false positive strip test due to incorrect strip storage or usage. A 3-hour glucose tolerance test shows a positive, and the inference was rejected.
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical models. For example, Gordon's framework (1994) identifies 11 functional health patterns and organizes patient data into these patterns. Maslow (1943) uses a hierarchy of five sets of human needs. Contrarily, the body systems model is a medical model used to organize collected data according to organ and tissue function in various body systems. Although it helps formulate diagnoses related to physiologic problems, the body systems mostly neglect to identify the patient's problems and strengths in psychosocial and spiritual dimensions of health and well-being.
Information acquired through the five senses is cueing, whereas judgment or interpretation of informational cues is called inference. The steps in data validation include identifying the clues, making inferences about clues, and validating cues and inferences.
Inferences can be validated in several ways:
- Physical examination using the proper equipment, technique, and procedure. The findings must be confirmed by an expert.
- Clarifying statements
- Sharing the inferences with other respected members of the team and seeking consensus
- Checking the findings with research reports, textbooks, or journals
- Comparing cues to the knowledge base of normal function
- Checking the consistency of cues
The nurse may validate data as it is collected or at the end of the data-gathering process. When the data is clear, the nurse analyzes the data and formulates nursing diagnoses—the next step of the nursing process.
Data validation is the process of checking and verifying collected information.
In data validation, the cue is the information acquired through the senses, and the cue's interpretation is called an inference.
The steps in data validation include identifying the cues, making inferences, and finally validating cues and inferences.
For example, a physical examination of a bedridden patient reveals swelling and pain in the calf muscle. This is the cue. A literature search points to symptoms of deep vein thrombosis—the inference. Further evaluation confirms the condition—and so the inference is validated.
In another scenario, a gestational mother's urine sugar dipstick shows a positive result. However, the literature suggests the probability of a false positive strip test due to incorrect strip storage or usage. A 3-hour glucose tolerance test shows a positive, and the inference was rejected.
From Chapter 7:
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7.5 : Data Validation
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7.8 : Formulating and Validating Nursing Diagnosis I
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