Secure and Accurate Patient Information Requires Routine Check-Ups
By Bud Walker
The healthcare industry consumes data in large amounts and faces the challenge of being both responsible and effective in confronting the maintenance of patient information. It’s a complex effort from step one, complicated further by a steady stream of patient records and varying contact points, deep legacy data found throughout organizations, social trends such as mass migrations to new healthcare plans, and federal guidelines for compliance with data security and privacy requirements.
Improved data management (such as better flow, secure access, and valuable analytics) starts with the most basic look at the quality of the data themselves. While this tenet has become universal across any number of industries, perhaps none are more complex than healthcare. Filing claims, coding procedures, and updating medical records are prime examples of day-to-day data entry points that can quickly degrade the quality and effectiveness of an organization’s database.
So how are healthcare providers working to take on the data-quality challenge? To keep data healthy for the long-term, organizations should implement a data-quality firewall of sorts—instantaneous, point-of-entry data cleaning tools to prevent bad data from entering the database in the first place. Beyond this fresh start, it’s necessary to maintain a regimental process for data quality as even good data degrade over time. This seamless and continuous approach to data quality acknowledges that data aren’t stagnant and require the right on-site tools as essential elements of healthcare systems that manage both clinical and business processes.
Healthcare and Big Data
Medical providers are navigating a landscape that is shifting quickly and dramatically, characterized by unmatched technology consumption and continual innovation to accommodate an aging population and healthcare costs that are increasing globally. This complex environment points back to the power and value of data—the ability to identify patients, communicate with them seamlessly, and integrate all their related information from any number of disparate medical sources.
Yet clinicians are caregivers and are ultimately most concerned with keeping their time and their brainpower free for excellent patient care. Coupled with an administrative staff tasked with managing unique government reporting requirements, there are historically consistent challenges to keeping data quality a top priority. Disparate departments, unique medical specialties, duplicate records, and the overall belief that “the data are fine” are common battles among clinicians, administrators, and the broad range of healthcare executive staff. That is changing, however, with the rapid growth of telemedicine and healthcare professionals’ increasing reliance on electronic communications—sharing information quickly and effectively to improve treatment, diagnosis, and overall patient health.
“Avoiding data-quality commitments is not really an option anymore, and healthcare communities now have a responsibility to improve and standardize data quality for health professionals and the patients who rely on them,” says Andy Hayler, president and CEO of The Information Difference, a research firm focused on master data management. “A cultural change is afoot, largely because there is greater awareness of the importance of accurate data and the potential consequences of not addressing this issue properly. Data-quality initiatives can now be integrated as a foundational element of healthcare administration. This ability to streamline complex tasks has established providers learning more about the value of their data and newer providers hitting the ground running with the cleanest data in town.”
Ensuring Better Care With Better Data
Clean, standardized data enable providers to match, link, merge, and purge records to achieve a single view of their customer, the patient. Simple problems, such as “householding,” in which people in the same house may share the same surnames, be a party to divorce, or have changed names, can be readily solved at the point of data entry.
Consider that 43 million Americans (one in six) move annually. Of those, as many as 33% don’t file a change of address. For healthcare facilities attempting to provide patient care to this population, the degradation point of their data can be extremely basic. Has this patient changed her name after a marriage or divorce? Are first and last names included as separate data fields? Is the correct address 12th Avenue or Twelfth Avenue? The Data Warehousing Institute reports that data-entry errors such as these account for 76% of data-quality errors. Add to this the fact that there are U.S. carrier route changes every day, and more than 100,000 changes to the U.S. Postal Service address data file (additions, deletions, or modifications) each month. These are issues of data integrity and specifics and require routine updating and verification when accurate data are imperative to patient health and well-being.
In fact, accuracy of patient identification and record saving (Smythe or Smith; 12th Avenue or Twelfth Avenue) are key benefits to the ongoing upkeep of hygienic data. Incremental as well as batch prevention of new duplicate records ensures a high level of data precision in healthcare settings. Minor issues such as typos or improper formatting—Is it last name first? Two fields or one?—can be matched to avoid the creation of a duplicate record entirely. In fact, when the correct data tool is implemented, providers can solve the challenges of excessive rules-based matching and instead use state-of-the-art matching algorithms for more fine-tuned functionality. Beyond patient care, this level of data hygiene enables the consolidation of master data into one unique customer record, reducing printing and mailing costs for providers.
Standardized, Verified, and Powerful
To achieve this level of data integrity, providers must integrate a dedicated server that houses a contact data verification and enrichment program. Customized data fields can be aligned with the standards, allowing for the verification of address, phone, and e-mail; name parsing; geocoding; and change-of-address processing. This is no small feat, and it takes dedicated computer power to enable the enterprise-level speed necessary to verify millions of records per hour. Ideal systems also offer the ability to be clustered with other devices for increased scalability, throughput, and redundancy. Automation is a key part of a successful system as well, and by incorporating “smart scripts,” the latest contact datasets (weekly, monthly, or quarterly) can be automatically gathered and installed for simple maintenance.
By hosting a data-quality server on site, privacy and compliance needs can be met and any real-time failover can be addressed quickly. “Enriching, scrubbing, and validating patient data completely in-house can be a real plus for healthcare providers,” adds Hayler. “A data-quality firewall allows healthcare systems to safely and securely meet HIPAA, Sarbanes-Oxley, and other privacy and compliance guidelines—another element fueling the broader acceptance of data-quality initiatives as operational imperatives.”
Keeping Data Healthy for the Long-Term
Capturing clean data or cleansing them at the source keeps healthcare data more fit than ever. Data-quality operations hosted on site are rising to the challenge, complementing privacy and compliance requirements and offering a cost-effective alternative to attempting to correct data later—or never correcting it all.
Good data are good medicine—and unfortunately, bad data can be harmful to healthcare settings and their effectiveness. This is a critical issue given the immense quantity of data being captured and reused in any healthcare system. The blending of new and legacy data from disparate sources is vital to effectively meeting regulations, identifying patients, and streamlining communications with those patients.
Today’s clinicians and administrators are being pushed and pulled in myriad directions, but their main focus remains patient care. With data quality managed automatically and unobtrusively on site, providers can concentrate on what they do best while leaving integrated data tools to keep information secure and healthy.
— Bud Walker is director of data-quality solutions at Melissa Data, where he manages new and current product requirements, development, and market analysis.