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Related Concept Videos

Guidelines for Writing Outcome01:11

Guidelines for Writing Outcome

When developing expected outcomes for a patient care plan, the nurse should adhere to the following recommendations:
Patient outcomes reflect the patient's response to the goal rather than what the nurse aims to achieve. Terminology should be observable and measurable to avoid the reader's interpretation. The desired outcome should be realistic and achievable in the designated care timeframe. Expected outcomes should align with adjunctive therapies. The outcome should enhance care evaluation by...
Nursing Evaluation01:15

Nursing Evaluation

The evaluation stage signals the end of the nursing process. The nurse gathers evaluative data to assess whether or not the patient has attained the expected results. Whereas the nurse collects data in the nursing assessment to identify the patient's health concerns, the evaluation stage data determines if the indicated health issues are resolved. Evaluative data collection includes two sections: the data acquired to evaluate patient outcomes and the time criteria for data collection.
Section...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Statistical Significance01:37

Statistical Significance

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
Role of Communication in the Nursing Process II: Planning and Implementation01:25

Role of Communication in the Nursing Process II: Planning and Implementation

Several factors are considered while creating a patient's care plan. Motivation is a factor in improving communication, and patients often require encouragement to try different approaches involving significant change. It is essential to involve the patient and family in decisions about the plan of care to determine whether the suggested methods are acceptable. Consider meeting critical comfort and safety needs before introducing new communication methods and techniques. Allow adequate time for...

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Updated: Jun 4, 2026

Using Learning Outcome Measures to assess Doctoral Nursing Education
10:07

Using Learning Outcome Measures to assess Doctoral Nursing Education

Published on: June 21, 2010

Understanding meaningful outcomes.

Daniel C Armijo1, Eric J Lammers, Dean G Smith

  • 1Altarum Institute, Ann Arbor, MI 48105-1566, USA. dan.armijo@altarum.org

The American Journal of Managed Care
|February 17, 2011
PubMed
Summary
This summary is machine-generated.

Advancing learning healthcare systems requires better secondary data use. Explicitly classifying patient outcomes supports identifying patterns and improving care efficiency.

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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

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Last Updated: Jun 4, 2026

Using Learning Outcome Measures to assess Doctoral Nursing Education
10:07

Using Learning Outcome Measures to assess Doctoral Nursing Education

Published on: June 21, 2010

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Area of Science:

  • Health Informatics
  • Health Services Research
  • Clinical Informatics

Background:

  • Federal initiatives promote health information technology (HIT) adoption to enhance healthcare system efficiency and effectiveness.
  • Learning healthcare systems aim to improve patient outcomes through continuous learning and data utilization.
  • Secondary data use is crucial for research and quality improvement but faces challenges in data standardization and accessibility.

Purpose of the Study:

  • To propose a framework for codifying clinical outcomes to facilitate secondary data use.
  • To highlight the importance of explicit patient outcome classification at the point of care for advancing research.
  • To discuss key considerations for developing robust outcome classification systems.

Main Methods:

  • This commentary synthesizes current trends in health informatics and healthcare policy.
  • It outlines a conceptual approach for developing a standardized framework for clinical outcome data.
  • It identifies critical factors for successful implementation and secondary data utilization.

Main Results:

  • A framework for codifying clinical outcomes can significantly improve the ability to uncover associative patterns in patient care data.
  • Explicit classification of patient outcomes is a prerequisite for rapid exploration of achievable outcomes and their determinants.
  • Standardized outcome data enhances the value of health information technology adoption.

Conclusions:

  • Developing a framework for codifying clinical outcomes is essential for advancing learning healthcare systems.
  • Effective secondary data use hinges on precise outcome classification, enabling deeper insights into patient care.
  • Future work should address attributional validity, treatment appropriateness, patient perspectives, and pay-for-performance linkages.