Keeping the organization’s master patient index clean leads some HIM departments all the way back to patient registration, where they collaborate to prevent errors before care starts. Accurate registration helps keep patient data complete and clean as it moves throughout the organization.

Long-term trouble can start during a brief check-in. A rushed or incomplete search of the organization’s MPI can cause clinical registrars to create duplicate patient records or even select the wrong record.

Faulty information entered at check-in streams straight through the system, risking treatment errors and leading to eventual billing problems. Entities that participate in health information exchange will export bad information into their networks.

Error-ridden MPIs also hamper an organization’s ability to understand its patient population and its own performance, both for internal and external reporting. Patient information spread across multiple records can distort measures of patient severity and overall risk of mortality. And correcting errors consumes time.

HIM departments often are the hub of identifying and mitigating registration errors. HIM staff sift through the MPI, merging duplicate records and  separating out information that has been overlaid into the wrong patient account. (more…)

New Health and Human Services secretary Kathleen Sebelius was on Capitol Hill yesterday with two new reports from the Agency for Healthcare Research and Quality in hand. Both had discouraging news about the quality of healthcare Americans received in 2008.

In particular, Sebelius singled out unequal care. AHRQ’s “2008 National Healthcare Disparities Report,” she told the House Ways and Means Committee, “highlights that severe and pervasive disparities continue to persist in this county. Minority patients still receive disproportionately poor care compared to their Caucasian neighbor.”

Solving healthcare disparities is complicated by a lack of comprehensive data about its prevalence.

Last month in the Journal, Jennifer Hornung Garvin and coauthors wrote, “At the heart of … efforts to develop effective strategies to address healthcare disparities is the need for accurate and complete data. However, data describing racial, ethnic, language, cultural, and socioeconomic characteristics are frequently inaccurate, incomplete, and lacking in detail in the healthcare setting. Sometimes they are not collected at all.”

Addressing healthcare disparities, the authors stress, “requires that providers capture better data about race, ethnicity, and socioeconomic status, an effort complicated by the sensitive nature of the data and the challenges of categorizing them appropriately.” They point to several data sets that providers can adopt to improve their collection of this so-called equity data in support of efforts to create equal care for all.

See “Data Collection and Reporting for Healthcare Disparities” in the April 2008 issue.

The National Quality Forum is in the process of establishing consensus standards for the use of clinically enriched administrative data for reporting performance measures in ambulatory care. In January an NQF work group began identifying and endorsing a set of measures suitable for both public accountability and quality improvement.

Given the difficulty of reporting performance measures from paper records, healthcare has been making do by using administrative data, the only data widely available in electronic form. The trade-off is the quality of the quality measures: administrative data produce a narrow and less reliable look at the care delivered.

(In the worst case, as a Boston Globe story on Google Health related earlier this month, poorly managed use of claims data can result in outright misrepresentation of care delivered. The Journal wrote about this danger in personal health records back in April 2007.) (more…)

Several stories in the current print issue describe efforts to streamline data collection and reporting for quality measures. Two touch on the Quality Reporting Document Architecture (QRDA), a Health Level Seven draft standard based on HL7’s approved clinical document architecture (CDA).

The QRDA initiative is developing CDA standards for reporting quality measure data across health IT systems that are EHR-compatible. Currently, the work is published in part as an HL7 draft standard for trial use and is being tested in pilot implementation.

In “Advancing Quality Measures Reporting in HIEs,” Randolph C. Barrows Jr. describes the use of the QRDA in the quality measure use case featured in the NHIN demonstrations earlier this year. The QRDA was used in drafting functional requirements to support the exchange of patient-level quality data from provider systems to quality data measurement and reporting facilities. It also factored in writing functional requirements for the exchange of population-level quality measures results from a measurement and reporting facility to quality data recipients. (more…)

For organizations that allow clinicians to carry forward clinical documentation in electronic records, auditing its proper use is key to ensuring document integrity. Copying clinical documentation poses both clinical and compliance risk. The feature “Auditing Copy and Paste” offers guidance in creating a solid audit plan.

The story is adapted from the broader AHIMA resource “Copy Functionality Tool Kit.” It offers sample policies, testing activities, case scenarios, and questions organizations can ask when considering the use of copy and paste.

You’ll find a lot of other good practice resources on that page.

If you feel there are a thousand healthcare quality measures out there, you’re about right. The Department of Health and Human Services has compiled an inventory of that many measures and more used by its agencies and operating divisions for reporting, payment, or quality improvement.

HHS says that this is the first time it has compiled a comprehensive list of the quality measures in a single location. It intends the inventory as a step in the effort to advance collaboration and synchronization within the quality measurement community. The measures and specifications in the inventory were self-reported by HHS divisions.

The list is available as a spreadsheet, sortable through dropdown menus. HHS says it will be adding more sorting options in the coming months.

An overview of industry activity around data quality, quality management, and data content standards is available on AHIMA’s Web site.

Clinical documentation improvement programs can enhance the clinical record and capture lost reimbursement. But they can be a challenge. Getting physician buy-in, avoiding turf wars between documentation specialists, coders, and nurses, and measuring program success are just a few of the challenges many facilities face with their programs. Below are a few tricks of the trade on running a successful CDIP, lent by experienced clinical documentation specialists. (more…)