Implications of poor data quality in healthcare

Organizations are constantly challenged to maintain the right level of data quality. This is especially true in a risk-averse industry such as healthcare, where decisions could literally mean the difference between life and death. In addition, ensuring the privacy of patient data and compliance with various regulations from HIPAA, HITECH, PSQIA and others is not only mandatory and complex, but also could be costly in fines and fees. Noncompliance is not a viable alternative when someone’s life could be at risk.

Whether it is a physician accessing patient records via a tablet at a bedside through a cloud service or administrators accessing data during normal hospital facility operations in a Data Center, the regulators require that the data be accurate and maintained with the proper level of governance as dictated by the law.

The metrics for complying with relevant regulations are specified in great detail. The collection and maintenance procedures for the data must ensure that healthcare professionals and organizations can deliver these metrics without additional scrutiny. This need is putting pressure on organizations to leverage supplemental tools to process data and improve their quality before decisions are made or regulations compromised. To achieve these regulatory goals and ensure high data quality for the healthcare industry, organizations must employ strong data cleansing routines.

Data cleansing is the identification and correction of corrupted, duplicate, missing or inaccurate data. This data, especially when related to healthcare, cannot be wrong, inaccurate, incomplete or unrecognizable to the operations and processes that consume them. The ramifications of inaccurate data could impact patient safety, accurate reimbursement for services, and many other aspects of healthcare delivery. Data cleansing also identifies duplicate data, which directly affect the organization’s efficiency and effectiveness.  The capability of the organization to operate efficiently and to make accurate decisions that lead to positive outcomes requires these activities and processes be engrained in daily operations.

Regulators are well aware of the challenges that organizations face with poor data quality, but their focus is on preventing what could happen when poor data quality exists and is used without knowledge of its accuracy. As demanding as it might be to abide by the HIPAA, HITECH and PSQIA requirements, these regulations are needed and organizational leaders must ensure they are fully compliant. One of the primary ways to do this is by defining and implementing a robust data cleansing strategy that not only addresses regulations, but also ensures data accuracy and privacy. Only then will healthcare organizations minimize the implications of poor data quality in their industry.