HealthDecember 11, 2015

Webinar recap: Code groups - The building blocks for your population health initiatives

Whether you’re providing patient care or managing coding and patient data, you have no doubt felt the impact of managing code groups on your day-to-day responsibilities, whether you know it or not.

The use of code groups, otherwise known as value sets, is becoming a necessity for healthcare providers – but the technology to manage them is only now just beginning to catch up with the need.  So what are code groups and why are they so important? What are the problems that providers face when they’re trying to manage code groups on their own? And how can a terminology management system provide solutions?

During a Health Language sponsored webinar, Dan Exley, Executive Director of Data Strategy and Reporting at MemorialCare, Dr. Barbara Antuna, Medical Informatics Specialist at Health Language, and Sarah Bryan, Director of Product Management at Health Language, gave healthcare professionals a look into the significance, value, and increase importance of code groups from numerous different vantage points in the healthcare world.

The following takeaways from each perspective shared in the webinar will help you understand how the healthcare industry is using code groups to handle the complexity of analytics and what’s at stake when it comes to CGM, and will show you how Health Language’s tools are already solving the familiar problems that plague enterprises trying to handle code groups the inefficient, manual way.

Dan Exley has seen the tools and technology of the healthcare analytics world move from the theoretical to reality. What informatics professionals and providers only dreamed of doing fifteen, ten, or even five years ago, has now become part of the day-to-day operation of any healthcare enterprise.

Likewise, he has seen the place of the hospital change in the healthcare landscape. When once the hospital was the center of the healthcare universe, now it is merely one of many different interconnected systems – from imaging centers to surgery centers to laboratories – all providing their own particular form of care and all recording data.

Exley explained that making use of a combination of live data and legacy data from numerous different technological platforms and EMRs with their own use cases poses its own set of complex challenges. That, he said, is where data integration and analytics work comes into play. He indicated that it is key to tackle the complexity of the healthcare landscape in order to provide unprecedent

ed value to patients. His role in tackling this complexity, he said, lies in getting people on the same page , no matter what EMR platform or they are using or set of codes they are working with.

So how are healthcare enterprises using data?

Exley said that MemorialCare has been using data and analytics to take on such challenges as:

  • Preparing for alternative payment models
  • Participating in CMS bundle payment initiatives
  • Advance the population health roadmap and defining what population health means
  • Bringing the use of advanced analytics into the research department to leverage and optimize research capabilities

Bringing the view together

Exley gave a concrete example that highlighted the need for, and the benefits of, an organized approach to managing cross-enterprise healthcare data.

He said that if he needed, for instance, a report that told him about pediatric diabetics cases that were discharged from the hospital in the last month, that had received certain medications and had certain lab results, it would require information from four or five different code groups (diagnosis codes, medication information, lab results and so on).

Whereas once, getting this information would rely on guesswork, now it is well within the realm of possibility to aggregate and make use of such information. By employing informatics professionals in conjunction with medical staff, MemorialCare is working towards:

  • Developing groups of codes and common definitions
  • Developing common criteria
  • Finding ways to push out definitions across all the different nodes in the healthcare ecosystem

Exley also discussed the challenges that some clinicians have faced in terms of Pay for Performance initiatives and the lack of clarity over how external quality measures are determined. He likewise explained the value of code group accuracy for meeting both external and internal quality measures. For instance, code groups can allow a provider to determine accurate criteria for determining what constitutes an “asthmatic” if there is a quality measure requiring a certain number of asthmatics to be signed up for a patient portal.

Finally, Exley explored the uses for data analytics in the research department.

His examples demonstrated that no matter where data is collected, a common view, common definitions, and information shared throughout a healthcare ecosystem can:

  • Cut down on duplicated work
  • Create previously unimaginable value
  • Facilitate innovative patient care

Coding from the clinical perspective

What is the importance of code groups from the perspective of a person who provides care? Dr. Barbara Antuna, a practicing physician, gave her insights. Antuna indicated that different, non-standard terminologies proliferate throughout an enterprise because the standardized vocabularies don’t meet the needs of those using the information. Thus data silos are created, in which each portion of an enterprise uses data its own way. Data silos, she indicated, stand in the way of getting meaningful use out of the data.

Accurate patient cohorts and the many definitions of diabetes

Dr. Antuna cited a study from the Journal of the American Medical Informatics Association out of Duke University, in which informatics researchers looked at 24,500 diabetics using seven different definitions of “diabetic.” They discovered that if only ICD-9 claims data were used, and data coded in other languages such as LOINC (encoded lab data) and RxNorm, or other proprietary drug database (drug encoded data), were not included in the cohort, a full 22 percent of that population would not be reflected in the analytics.

This drove home the importance of normalizing and aggregating as much data as possible while defining a disease cohort. It is easy to see from this example how incomplete code groups can negatively impact:

  • Downstream analytics
  • Quality of patient care
  • Safety
  • Reimbursement
  • Meeting clinical quality measures and avoiding related penalties

Helping decisions and triggering alerts

Antuna explored another important way in which code group management is advancing the field of healthcare – and with which she has had hands-on experience as a clinician. Many clinical decision alerts are constructed using code groups. Therefore, it is important that these code groups be accurate. For instance, in the case of diabetics, an EHR using accurate code groups as its underpinning can send auto-alerts to suggest that patients defined as diabetic across multiple code sets, seen in multiple environments, receive examinations for peripheral neuropathy.

Sarah Bryan discussed the numerous potential problems and inefficiencies that arise when healthcare enterprises manage their code groups through traditional methods, and the new tools emerging to address these inefficiencies. Oftentimes, enterprises handle code groups with spreadsheets. This causes problems such as:

  • The Proliferation of Multiple Code Group Spreadsheets – Different departments acquire and manage code groups according to their needs. That means across an enterprise, there can be duplicate code groups, old and inaccurate code groups, and all sorts of other potentially conflicting material floating around, causing inefficiency and confusion.
  • Lack of an Audit Trail – As code groups are edited and updated, questions may arise as to who made changes, why they made changes, and if those changes are accurate or need to be fixed. Managing code groups through spreadsheets can lead to both a lack of accuracy, and a lack of accountability for those inaccuracies. Plus, if you don’t know where something went wrong, it can be far harder to fix.
  • Managing Updates – Code groups such as the CQM value sets, like terminologies, are updated frequently. Attempting to manage all of the updates manually is a time-consuming task riddled with potential pitfalls and room for human error.

How Health Language can help

Bryan finished out the webinar by exploring the ways that Health Language solutions address these issues. Health Language’s solutions offer:

  • A single repository for both locally-created and acquired code groups, alleviating the problem of multiple code groups across an office or enterprise.
  • A single, collaborative online environment to work on code groups that automatically tracks changes.
  • Automatic updates for code groups alongside automatic updates for terminologies, so that the process is streamlined and offers no room for error.

Making use of the data

Perhaps one of the most important points to emerge from the webinar was that, with 80 percent of the audience polling that they were not able to make meaningful use of their data, there is still a long way to go in terms of getting enterprises, clinicians, and payers up to speed on how to effectively manage the mountain of data technology has made available to them.

With Health Language, enterprises can take the necessary steps towards unlocking the power of analytics in healthcare’s data-driven future. Health Language’s solutions offer healthcare providers the opportunity to move from a model of chaotic, ad hoc management to a structured, technologically-sophisticated, and user-friendly method of handling codes; one that sets up a healthcare provider for success. Speak to an expert to learn how Health Language solutions can help your organization improve the quality of your data. 

LOINC® is a registered trademark of Regenstrief Institute, Inc.

Sarah Bryan
Director of Product Management at Health Language, Wolters Kluwer, Health
Sarah supports the company’s Health Language Health Language solutions by understanding challenges managing healthcare terminologies to enable the semantic interoperability necessary for data accuracy
Health Language Reference Data Management
Centralize and manage your reference data for clinical, claim, and business data to enable interoperability, drive compliance, and improve operational efficiencies.