Analyzing Relevant Data - Key Takeaways

Categorical variables

The value is in the name or label. Types of cancers (breast, skin, lung, etc.) are categorical variables.

Confounding Variable

It obscures the effect of another variable. The researcher may initially believe the variable will influence the research, but findings show it does not. As a researcher, you may not be able to control its influence on the result of the research, but you should have an awareness that it is impacting your results and not allow it to skew your data.

Continuous variable

This is also known as an interval variable. There is a meaningful difference between values. An example is body temperature.

Databases

A well-designed healthcare database captures data to support the organization’s analysis and comparison of safety, quality, effectiveness, efficiency, timeliness, and efficacy of actual care and services delivered to the patient over time.

Data warehouses

These assimilate data from multiple transaction systems. Data warehouses can be used to distinguish larger trends in data from multiple sources.

Dichotomous variable

This is also called a binary variable. It occurs in one of two possible states, for example, male or female. The patient has cancer or does not.

Disease registries

These are a hybrid between transaction systems and data warehouses. They are designed for tracking explicitly defined data at a case-specific level. Some examples are trauma registries to track emergency department data, cancer registries, immunizations registries, and numerous others.

Errors

Incorrect application of a statistical test can result in a type I error, which occurs when a null hypothesis is rejected when it should have been accepted. A type II error is experienced when the alternative hypothesis is rejected when it should have been accepted.

Measurement and decision support

Measurement is used within an organization to monitor improvements in systems and processes through analysis of current performance trends, identify key opportunities, and consider leading practices informed by new research-based knowledge. Decision support provides an information platform to evaluate leading, lagging, and real-time performance measures.

Outcome evaluation

An outcome evaluation focuses on the end result of a specific program or initiative, generally clinically measured by improvements in morbidity, mortality, or vital measures of symptoms, signs, or physiologic indicators.

Quality improvement

Quality improvement is measured internally and externally using various benchmarks and indicators. These indicators are quantified by proportions, percentages, ratios, means, medians, and counts to measure processes, perspectives, and outcomes aligned with a certain initiative or decision.

Quality measures

In today’s healthcare environment, administrators receive numerous quality reports on a regular basis. These reports are generated for internal quality improvement projects, for mandated external reports to government agencies, and for compliance with accrediting body requirements. As a value-based purchasing model evolves in healthcare, quality measures become pivotal operational "pulse checks" to administrators.

Statistical testing and treatment

The basis of statistical testing is whether or not the study results have a proven relationship to a change in processes or care modalities. Results that are statistically significant do not automatically indicate clinical significance.

Transaction systems

Transaction systems divide data according to individual operations. The data stored by transaction systems is granular and based on specialized systems.