Related Experiment Video
Updated: Feb 5, 2026
08:17
Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
Published on: June 7, 2015
16.2K
Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology
Martha M Matuszak1, Clifton D Fuller2, Torunn I Yock3
1University of Michigan, Ann Arbor, MI, USA.
Medical Physics
|September 20, 2018
Summary
Big data in radiation oncology is advancing research in patient outcomes and decision-making. However, significant challenges remain in collecting and using this valuable performance data.
Area of Science:
- Radiation Oncology
- Medical Informatics
- Health Outcomes Research
Background:
- The integration of big data analytics is transforming healthcare research.
- Radiation oncology is increasingly leveraging large datasets for clinical insights.
Purpose of the Study:
- To summarize the current landscape of big data utilization in radiation oncology for outcomes research.
- To identify key efforts and challenges associated with big data in this field.
Main Methods:
- Review of current big data initiatives in radiation oncology.
- Analysis of challenges in data acquisition and application for outcomes and decision-making.
Main Results:
- Big data is becoming a reality for aiding outcomes and decision-making research in radiation oncology.
- Significant hurdles exist in gathering and effectively utilizing performance and outcomes data.
Conclusions:
- Despite challenges, big data holds immense potential to advance radiation oncology.
- Addressing data-related challenges is crucial for realizing the full benefits of big data in radiation oncology research.
Related Concept Videos
01:17Performing a Simple Data Analysis using MS-Excel Function
1.1K
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
1.1K
01:11How Data are Classified: Categorical Data
44.7K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
44.7K
00:59How Data are Classified: Numerical Data
38.1K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.1K
01:24Data Reporting and Recording
5.5K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.5K
01:15Data Validation
1.9K
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
1.9K
01:03Data Validation
6.8K
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...
Nursing assessment guides are generally based on holistic models rather than medical...
6.8K