Health Language Blog

Three Reasons DSM-5 Mapping Strategies Matter

Posted on 09/07/17 | Comments

The introduction of industry standards such as SNOMED CT, ICD-10, LOINC, and RxNorm is an important step toward achieving the goals of interoperability and information sharing. Yet healthcare organizations still face notable challenges to laying the best frameworks for normalizing data to these standards. Since there is no one standard that addresses all healthcare information, clinical and financial data must be “cleaned” and appropriately mapped to a single source of truth to remove semantic ambiguity.


Topics: Standard terminologies, Analytics, mapping, DSM-5

Are Your Reports Showing Pregnant Men and Smoking Babies?

Posted on 08/16/17 | Comments

Four Reasons Dirty Data Destroys Healthcare Analytics


Topics: Analytics, data quality

Meaningful Analytics: Improving Data Integration and Quality by Leveraging the HIE Webinar Recap

Posted on 11/29/16 | Comments

Healthcare organizations must achieve mastery of high-quality data and analytics to thrive within value-based care models. Today’s IT professionals are challenged to design systems that improve data exchange with industry stakeholders as well as acquire more complete and accurate patient information for quality measures reporting. Without a strategy in place that addresses each of these key areas, hospitals and health systems face significant barriers to achieving their overall population health or financial goals.


Topics: Analytics, HIE, cohort rules management, mapping, CMS, data integration, data quality, APM, VITL

The Role of Healthcare Data Governance in Big Data Analytics

Posted on 08/01/16 | Comments

On Friday last week, the online news publication published a feature story about the importance of data governance, “The Role of Healthcare Data Governance in Big Data Analytics.” The author, Jennifer Bresnick, does an excellent job of explaining what data governance is, how it differs from information governance, and why healthcare IT professionals need to take a closer look at how data is managed at their organizations. Without strong data governance in place, she writes, “organizations will not be able to move beyond the basics of record keeping and develop the analytics competencies that will become vital survival skills in the emerging world of value-based care.”


Topics: Analytics, data governance, big data, information governance

Are you Dealing With Lackluster Data Analytics?

Posted on 04/18/16 | Comments

Missed_Target.jpeg A recent article, “Quality Metrics, Data Analytics are Top Value-Based Care Fears,” highlighted some of the problems that health systems and ACOs are having with data management and analytics. Author Jennifer Bresnick wrote, “...providers confess that the big data analytics competencies required to make the most of value-based reimbursements may be too much for them to handle.”

In fact, the article references a recent Xerox Healthcare Attitudes 2016 survey that revealed that 80% of providers “expressed some level of uncertainty about not being able to leverage their patient data for improved outcomes.” Additionally, the same number of providers said “a lack of claims data transparency may inhibit their ability to perform comprehensive data analytics and population health management.”


Topics: Analytics

Terminology & Analytic Challenges for Accountable Care Organizations

Posted on 05/15/13 | Comments


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.


Topics: Analytics, Coding Challenges, terminology services, health information, medical data

How a Terminology Platform Can Support an EHR

Posted on 02/05/13 | Comments

Vocabulary standards are playing a key force in enabling interoperability of patient data. Meaningful Use may be accomplishing what hasn’t happened before – a set of rules everyone will follow to allow patient data to flow between EHR systems, delivery networks and regional organizations.  Whether the vocabulary standard is a classification system, terminology, controlled vocabulary, or nomenclature, a terminology server can provide a range of services to use and manage these complex entities. 

Vocabularies named to achieve Meaningful Use have been thought of as fairly fixed and simple entities needing little care and feeding. As organizations begin to implement these vocabularies, the complexities of the concepts and relationships become apparent. 


Topics: Meaningful use, clinical documentation, Analytics

Impact of Analytics on Medical Plans & Policies

Posted on 01/23/13 | Comments

One of the most common questions that we hear from current and prospective clients is, “What are payers doing to prepare for the transition to ICD-10 CM/PCS?”  My response to that question has changed over time as payers who have already dived into the new code set begin to identify the immense opportunities available with the more precise codes available in ICD-10 CM/PCS.  Initially, most LEAP I-10 users were focused on strictly translating codes from I9 to I10, and some early adopters even anticipated a best one-to-one match between the code sets. 

Now we see more experienced ICD-10 translators and analysts thoroughly evaluate the nuances of the new code descriptions.  Payers are recognizing that some of the ICD translations will require greater precision in documentation and many that significantly impact reimbursement.  For example, in ICD-10, there is further specificity in factors such as acuity and site of disease; causative agents, drugs, diseases, and genetics; and expanded surgical approaches that impact the intensity of service provided.  A provider can now distinguish between and reimburse accordingly for a condition of the peritoneum versus the retroperitoneum with the latter condition resulting in a higher-weighted MS-DRG. Another option many payers are considering is the exclusion from coverage newly available information related to patient ownership of a disease condition such as alcohol-induced chronic pancreatitis, or intentional poisoning codes. 


Topics: Medical Plans, Payers, Analytics