Pitfalls with data collection and data quality in Healthcare

Hospitals and clinics face new challenges to meet clinical and business goals. Many hospitals and clinics are turning to the use of data to support continuous improvement and the realization of these goals. The use of data in business to drive improvement is becoming more prevalent, especially with the increasing presence of technology and healthcare is no exception. However without a structured, systematic and scientific method of data collection, elaborate uses of data are counterproductive. Healthcare organizations must be aware of these pitfalls to enable intelligent use of data to improve the organization through data-based decision making. Typically data is collected to obtain information regarding key performance metrics associated with a clinic or hospital, such as patient wait times. Obtaining an accurate portrayal of patient wait times should be systematic, yet many organizations fail to adopt a structured, systematic and scientific methodology of data collection. The data collection process has vulnerabilities from collection through analysis, which can lead to inaccurate or irrelevant information ultimately resulting in poor-data driven decision making. The primary aspects of data collection include planning, sampling, organization, analysis, and presentation. Often, hospitals lack a robust data collection process with a sound scientific methodology. In addition to a faulty collection methodology, an unrepresentative sampling population may not account for process variation. Often the collected data is not organized and stored so it is easily accessible or easy to analyze. All or any of these data issues can lead to missed opportunities and unrealized goals in healthcare.