Healthcare in the US continues to suffer from the poor data quality practices processes that would ensure accuracy of patient health care records and information. A lack of current scholarly research on best practices in data quality and records management has failed to identify potential flaws within the relatively new electronic health records environment that affect not only patient safety but also cost, reimbursements, services, and most importantly, patient safety. The focus of this study was to current best practices using a panel of 25 health care industry data quality experts. The conceptual lens was developed from the International Monetary Fund's Data Quality Management model. The key research question asked how practices contribute to identifying improvements healthcare data, data quality, and integrity. The study consisted of 3 Delphi rounds. Each round was analyzed to identify consensus on proposed data quality strategies from previous rounds that met or exceeded the acceptance threshold to construct subsequent round questions. The 2 best practices identified to improve data collection were user training and clear processes. One significant and unanticipated finding was that the previous gold standard practices have become outdated with technological advances, leading to a higher potential for flawed or inaccurate patient healthcare data. There is an urgent need for health care leaders to maintain heightened awareness of the need to continually evaluate data collection and management policies, particularly as technology advances such as artificial intelligence matures. Developing national standards to address accurate and timely management of patient care data is critical for appropriate health care delivery decisions by health care providers.