This book includes a plain text version that is designed for high accessibility. To use this version please follow this link.
monthly to the USPS address data fi le such as additions, deletions and modifi cations.

Because a patient’s well-being relies in part on data integrity, health- care organizations are moving to de- velop policies of routine updating and verifi cation of information. Ongoing data hygiene is refl ected in accurate patient records and is achieved with incremental as well as batch preven- tion of new duplicate records. Simple mistakes such as typos or improper formatting (Are we last name fi rst? Do we ever combine data fi elds?) can be eliminated, preventing duplicate records entirely. When providers implement the correct data tool, they streamline and fine-tune data op- erations by reducing excessive rules- based matching and replacing it with state-of-the-art matching algorithms. This sophisticated level of data hygiene has business value far beyond pa- tient care, consolidating master data into individual and unique customer records, reducing printing and mailing costs for providers.

When providers implement the correct data tool, they streamline and fi ne-tune data operations by reducing excessive rules-based matching and replacing it with state-of-the-art matching algorithms. This sophisticated level of data hygiene has business value far beyond patient care.

Taking an enterprise approach to healthcare data management

Healthcare providers are meeting these data goals by integrating dedicated servers to house contact-data verifi cation and enrichment programs. Standard data fi elds such as name and address can be associated with customized information determined to be of specifi c value to the provider. Verifying this information (name, old and new addresses, phone, e-mail, name parsing, geocoding and more) can result in managing millions of records hourly. Such high-level data demands are ideally handled with dedicated computer power that enables enterprise-level speed and processing.

These same systems that handle data processing so

Bad data can result in duplicate records, returned mail and costly errors in patient communications. Melissa Data’s Data Quality Suite is a toolkit of APIs that works to standardize, verify and correct addresses, telephone numbers, e-mail addresses and names so practitioners have clean, usable patient contact data on an ongoing basis.

effi ciently also allow providers to cluster operations with other systems and devices, increasing scalability, throughput and redundancy. Processes can be automated by implementing “smart scripts,” automatically collect- ing and installing the most current contact datasets on a predetermined weekly, monthly or quarterly basis. Further, privacy and compliance needs can be met and any real-time failover can be addressed quickly by hosting a data-quality server on-site. “While manag- ing patient data for the most effective treatment and care, healthcare providers must also meet security and compliance guidelines established by HIPAA, Sarbanes- Oxley and other regulations,” says Hayler. “These are signifi cant drivers that have providers embracing data quality as a business necessity, made easier by the ability to enrich, scrub and validate data entirely within their own operational network.”

Keeping data fi t for the long term

Healthcare systems are rising to place greater value on the power of data by identifying patients properly, channeling communications effectively and integrating patient information from a range of varied sources. These functions may seem like the basics for major healthcare organizations, but they’ve never been more important as providers are facing ongoing shifts in technology, a focus on accommodating an aging popu- lation and healthcare costs that are increasing steadily. On-site data-quality operations are helping medical professionals manage data quality automatically and securely – complementing privacy and compliance re- quirements and keeping clinicians focused on delivering excellent patient care.


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68