The ability of public health organizations to efficiently track and monitor cancer cases via registries is critical to the national effort to reduce morbidity and mortality rates. That’s why, in 1992, Congress mandated that individual states establish central cancer registries (CCRs) to capture diagnostic, treatment and outcomes data for every cancer patient living in the U.S.
Hospitals have long submitted data to these registries, but today a growing number of cancer patients receive treatment outside of hospitals in ambulatory healthcare settings. As a result, Meaningful Use (MU) Stage 2 includes an objective that requires providers in ambulatory settings to identify and report cancer cases to state registries. The MU Stage 2 measure requires healthcare providers to consistently and successfully submit cancer data through a certified EHR to their state’s cancer registries for the entire EHR reporting period.
To attest to MU Stage 2 criteria for cancer registry reporting, the CDC’s Implementation Guide for Healthcare Provider Reporting to Central Cancer Registries recommends that a provider’s EHR be capable of:
- Identifying reportable cancer cases;
- Identifying the specific data elements to be retrieved and included in the cancer event report;
- Creating a valid HL7 CDA R2 cancer event report; and
- Transmitting the cancer event report securely to a CCR electronically
Meaningful Use (MU) Stage 2,
MU Stage 2,
LOINC & SNOMED,
Health information exchanges (HIEs) are an appealing way of harnessing technology to improve care. Too often, doctors must make medical decisions based on incomplete clinical information. Electronic health records are part of the solution, but their full value won’t be achieved until they’re connected to provide a cross-institutional, comprehensive, timely and accessible view of a patient’s medical history.
Public and private HIEs are creating the connections and clearinghouses to make this possible. However, while everyone recognizes that better information can lead to better care, financing HIEs has proved to be very challenging. One hope has been that HIEs could pay off quickly in early cost savings. It stands to reason that doctors wouldn’t need to order as many tests if they knew the results of previous ones, and early studies led to very promising projections of costly tests averted.
Dr. Steve Ross,
Health Information Exchange,
Health Information Technology,
We are excited to announce the launch of a new blog series called “Did You Know?” that will include one post each month on an interesting fact that you might not know about clinical terminology management, standardized vocabularies, industry regulations and more!
ICD-9 to ICD-10,
clinical terminology management,
Dr. Brian Levy
Originally written by Susan D. Hall
A Colorado study of ambulatory practices found no significant reduction in the number of tests ordered or in the cost of tests among the participants of a Health Information Exchange (HIE). The Study analyzed claims data from Rocky Mountain Health Plans, a leading health plan in the market, for 306 providers in 69 practices in Mesa County, CO. The study compared the number of tests ordered and their costs before and after the providers joined the HIE, which was launched in 2005 by Quality Health Network (QHN). By 2010, 85 percent of the providers in the area were participating in the HIE. The study was published in the Journal of the American Medical Informatics Association.
Dr. Steve Ross,
Health Information Exchange,
Wolters Kluwer Health,
Rocky Mountain Health Plans,
American Medical Informatics Association,
At the beginning of the year, we presented three terminology management trends to watch for in 2013. You can read the full post here, but in summary, we predicted that the trends providers would be most focused on this year would involve: Meaningful Use; ICD-10; and a lesser discussed concept, Hierarchical Condition Categories (HCCs).
Hierarchical Condition Categories,
Logical Observation Identifiers Names and Codes (LOINC®) has been named as a standard in both Stage One and Stage Two Meaningful Use. Earlier posts have discussed the utility and importance of LOINC as a standard. LOINC has been used sporadically by some organizations, but full use of LOINC is coming into its own.
As with the journey of a thousand miles, taking that first step on the catalog mapping project is truly the hardest part. To make your journey a bit easier, here are three first steps to consider:
1. Understanding LOINC
- Do you understand LOINC?
- Do you understand data?
- What are the right resources and tools to use?
Meaningful use stage 2,
Meaningful use requirements,
As the United States strives to get more value out of its health care expenditures, Accountable Care Organizations (ACOs) make sense. As both a clinician and a consumer of health services, I’m enthusiastic about providers, hospitals, and health plans jointly taking responsibility for providing holistic, coordinated, effective and efficient care. But given the at-risk nature of payments to ACOs, only organizations with the right scale of providers and patients – and the right information technology resources – are positioned to be successful ACOs.
ACOs need to analyze diverse streams of health information. Many organizations already make excellent use of administrative and claims data. As the quality of care is further emphasized in the ACO model, organizations increasingly need to analyze clinical data as well. These data are likely to come from a variety of EHRs using different data representations. For example:
- Lab data can be crucial indicators of quality of care (for instance, hemoglobin A1c in diabetes, microbiology data for surveillance of postoperative infections). In today’s world, these data are largely represented as local laboratory codes. Although Meaningful Use 2 (MU-2) should increase the availability of LOINC-encoded laboratory results, organizations can’t wait for MU-2 to mine the wealth of laboratory data in existence.
- For patients with chronic diseases, providing personalized support on medication use will be key to improving quality and outcomes. ACOs don’t just need to know whether doctors prescribed the right drugs – they need to know whether patients with chronic diseases are filling those drugs, and if not, why not? With electronic prescribing, organizations can access burgeoning data on drugs prescribed and drugs actually dispensed. But these data often come in incompatible proprietary formats (Medi-Span, FDB, Multum) that need to be reconciled.
A tsunami of big data, and the evolving methods of making all that data meaningful and useful, has huge potential for changing the way we deliver and consume healthcare services.
medical terminology management,
With the internal transition to ICD-10 well underway for most payers, now is the time to shift your focus to other industry initiatives, such as Stage 2 Meaningful Use. The move to ICD-10 has provided your organization with a breadth of granular codes, and it’s critical that you determine how best to incorporate these codes in your data strategy in order to maximize the benefits of this detailed patient and billing information.
Along with federal legislation, the rise in health information exchanges, ACOs and health insurance exchanges has shed light on the importance of data normalization beyond ICD-10. With interoperability at the heart of each of these initiatives, payers and providers alike will be required to use standardized terminologies for documenting and sharing patient information. Some of the most significant benefits of clinical terminology standardization and management for your organization in a value-based healthcare environment include:
- More timely and accurate provider reimbursement due to consistent code usage;
- Decreased administrative costs;
- Streamlined communication with customers; and
- Enhanced analytics for identifying cost-effective treatments offered by providers and supporting population health management.
medical terminology management
Recording a structured family health history is a menu requirement in Stage 2 of Meaningful Use, both for eligible providers* and eligible hospitals**. Both specifications require that the health history of first-degree relatives be recorded, and provide the choice of recording the history either in SNOMED CT or using the HL7 Pedigree standard. One of the key issues we’ve been working with clients on is how to capture both the context (which relative is being discussed) and the condition (medical issues associated with the relative).
The HL7 Pedigree standard provides very explicit directions on expressing context (using the Family Member Value Set). To express the condition, the Pedigree standard is flexible, allowing the use of a variety of terminologies (e.g. SNOMED CT, ICD-9-CM, ICD-10-CM) to capture the conditions that each relative has. Examples of the XML format for the HL7 Pedigree standard can be generated using the US Surgeon General website My Family Health Portrait.
Family health history,