How OASIS data quality affects you!

Abstract

In general, each of these methods require time and energy from staff members and therefore affect both clinicians and the home health agency. Because the assessor is usually a field nurse or therapist, field staff are very vulnerable to a top down audit approach unless staff are included in the development cycle to achieve a non-threatening quality improvement process. Furthermore, an understanding of the research concepts provides agencies with a creative opportunity to develop their own audit processes. In addition, these concepts offer an awareness as to why HCFA may have chosen the specific areas and methods they have recommended to use when identifying errors. HCFA encourages agencies to consider different methodologies and approaches and to use what works best in order to ensure data accuracy. This is one of the first times in which HCFA has given so much latitude to agencies and their staff. It is hoped that this article fosters interest and insight as to why field staff should actively become involved in audit processes that check their skills and accuracy, which directly correlates to such a huge impact on all involved. In order to improve reliability, an agency should try to minimize external sources of variation and standardize the conditions under which measurement occurs. Initial and ongoing training sessions for staff on each of the OASIS data elements can establish higher reliability and can be streamlined and targeted on areas that staff have the most questions and areas that audits indicate high error rates with data inaccuracies. It is important to remember that each OASIS question has been proven to be valid and reliable; therefore, the only variable left is the source, i.e., those who use the instrument to determine where data inaccuracies could be generated (the field assessor, data entry staff, vendor transmitters, etc.). Agencies should develop the audit functions that will best meet their needs, and minimize the workload for all involved in finding these data inaccuracies.