Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions

Better Healthcare with Data Mining

Decisions through data: analytics in healthcare.

The amount of data in healthcare is increasing at an astonishing rate. However, in general, the industry has not deployed the level of data management and analysis necessary to make use of those data. As a result, healthcare executives face the risk of being overwhelmed by a flood of unusable data. In this essay I argue that, in order to extract actionable information, leaders must take advanta...

From Data to Predictions and Decisions: Enabling Evidence-Based Healthcare

Nearly 2500 years ago, Hippocrates kicked off a revolution in healthcare by calling for the careful collection and recording of evidence about patients and their illnesses. This call—which first introduced the goal of sharing data among physicians to provide the best care possible for patients—established a foundation for the evolution of modern healthcare. Although 25 centuries have passed sin...

Understanding the Outliers in Healthcare Expenditure Data

When data are distributed normally, distanceand ranking-based outlier detection methods based on interquartile ranges, standard deviations, etc. can be simple yet powerful tools to understand variation. Data related to healthcare expenditures generally have skewed distributions, however, and may include many extreme values. Often these data are used in case-control or other studies to estimate ...

Healthcare Data Warehousing and Quality Assurance

Healthcare Data Warehousing and Quality Assurance T he US allocates nearly a trillion dollars annually to healthcare expenditures—more than any other nation. However, this tremendous expenditure has not secured the US a rank among the healthiest nations. Instead, prolonged public debates on healthcare policy in the US have focused on insurance coverage and medical care financing programs, avoid...