Why The Time Is Right For Reference Data Management

Summary: The management and use of the growing volume of clinical and claims data to navigate evolving regulatory initiatives such as the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) and the 21st Century Cures Act is leading many healthcare organizations to turn to reference data management (RDM). This technology provides the infrastructure needed to establish a single source of truth, enable interoperability, and optimize analytics for regulatory and value-based programs. RDM plays a vital role in normalizing your data, achieving semantic interoperability, and accurately representing a patient population for accurate quality measures reporting and analytics.

Date: 3/01/2017

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Have we got a solution for you

Summary: Looking for an answer to your interoperability woes? You’ve come to the right place. Our HMT middleware roundtable executives describe their latest offerings for applications that make the enterprise more streamlined and information more usable.

Date: 01/06/2017

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Reference Data Management: The Cornerstone of Reliable Analytics

Summary: Simply put, data remains one of healthcare’s greatest opportunities and challenges. As such, healthcare organizations are increasingly turning to reference data management (RDM) as a best-practice strategy for managing the growing volumes of clinical and claims data needed to successfully position within an evolving regulatory environment. Increased momentum with value-based care through such initiatives as the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) and 21st Century Cures Act notably increase the urgency to act.

Date: 2/28/2017

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New Metrics Spotlight Interoperability Progress

Summary: National healthcare movements demand an interoperable framework for accurate data exchange across healthcare continuums. As value-based care continues to unfold, the industry at large remains focused on efforts to mature interoperability to support high-level quality initiatives aimed at improving population health and cutting costs.

Date: 12/15/2016

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A Better Data Governance Model

Summary: Access to timely, accurate information is foundational to smart decision-making. While most industries have long recognized the value of data-driven operations, healthcare is later to the game when it comes to the concepts of Big Data and analytics. Current industry movements around the Triple Aim of improved population health, patient experience, and costs are driving uptake of analytics strategies to leverage the power of data. Yet, even as the healthcare industry at large rallies behind the promise of population health management and health information exchange, one critical part of an effective strategy often falls short in today’s health systems — data governance.

Date: 10/26/2016

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Steps to Successful Analytics

Summary: In October, hospitals’ fledgling ICD-10 systems and processes will be put to their first major test with the addition of more than 6,130 new codes. Without sufficient bandwidth to manage and verify appropriate utilization of the expanded code set, risk adjustment and quality measures reporting and reimbursement levels may all be impacted, as well as revenue cycles that are likely still bouncing back from the initial post-ICD-10 hit – not to mention the impact on data integrity and, subsequently, the effectiveness of the analytics needed for population health reporting. But the code set expansion isn’t the only data-driven challenge on the horizon. MACRA and MIPS will both require accurate analytics for successful participation. This Q&A will examine the role of analytics in effective ICD-10 management, MIPS and MACRA participation and quality reporting, and how hospitals can ensure that they are ready for the enhanced analytics these changes require

Date: 9/9/2016

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Healthcare’s Growing Analytics Conundrum

Summary: Data analytics plays a critical role in today’s quality- and value-based healthcare environment. Yet hospitals and health systems struggle with designing systems to capture information in a way that is complete and accurate. As a result, many are calling into question the integrity of their data and its usefulness as a tool for achieving desired outcomes.

Date: 8/23/2016

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ICD-10 Unspecified codes

A Weightier ICD-10 — Is the Industry Ready for the First Round of Updates?

Summary: Beginning October 1, 2016, the annual update cycle for the ICD-10 code set will resume. A March review of proposed changes to both ICD-10-CM and ICD-10-PCS by the ICD-10 Coordination and Maintenance Committee yielded recommendations for new code descriptions, but no final decisions.

Current proposals point to the potential introduction of a whopping 5,550 revisions to ICD-10-CM and ICD-10-PCS in the October update. As such, the fiscal year 2017 classification systems would grow to a total of 75,625 PCS codes, including approximately 3,650 new codes. Approximately 1,900 new ICD-10-CM codes would also be introduced along with nearly 500 revised ICD-10-PCS codes and 351 revised ICD-10-CM codes.

Date: 6/20/2016

 

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Tina Moen

Partnership Enhances Population Health Analytics

Summary: Health Language partners with Caradigm to improve population health and analytics initiatives. Tina Moen, VP of Client Strategy at Health Language, discusses the best practices when you may be just getting started on your analytics journey. Mark Pilarski, VP and General Manager, and Corinne Stroum, Senior Program Manager from Caradigm, walk through their challenges they have overcome during their journey with Health Language at their side.

Date: 2/29/2016

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Avoiding One of the Most Common Analytics Pitfalls

Summary: Today’s hospital and health system executives cannot help but hear the clamor to advance data-driven care delivery models focused on proactive management of populations. Pressed by a sense of urgency to position for the future of risk-based reimbursement arrangements, executives can often overlook a cornerstone element to success with analytics: data normalization. In fact, it is not uncommon for a health system to invest millions of dollars in an analytics solution, only to realize later that the reports being produced are inaccurate or meaningless due to missing or poor quality data.

Date: 3/22/2016

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National Efforts Take Aim at Reaching Semantic Interoperability By Greg Slabodkin Health Data Management

Summary: As the healthcare industry grapples with the daunting challenges of achieving interoperability, stakeholders are focused on solving the fundamental problem of ensuring that all parties “speak the same language,” through the use of common data models and vocabularies. Semantic interoperability, the ability of two or more healthcare systems to share clinical information and use it meaningfully, is a critical requirement for enabling population health management and the rapidly approaching transition from fee-for-service to value-based care models.

However, the lack of a universal terminology standard is a major barrier to communication between different electronic health record systems and the ability to derive clinical meaning from EHR data imported or queried from elsewhere.

Date: 3/3/2016

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Beyond Words: Terminology’s Role in Meaningful Use By Jason Wolfson For The Record

Summary: As a focal point of national quality initiatives, meaningful  use (MU) is unfolding across the industry as a strategic effort to increase the  momentum of health information exchange (HIE). The initiative accomplishes this  goal by progressively laying a foundation of attestation requirements for the  collection, exchange, and reporting of data using certified EHR technology  (CEHRT).

As the MU timeline matures, the stakes for meeting  attestation requirements become increasingly high. More rigorous requirements  related to HIT terminology standards are introduced with each stage as critical  steps to positioning the industry for accurate and consistent exchange of  patient information. The adoption of such industry-respected clinical  vocabularies as RxNorm, SNOMED CT, and LOINC provide the needed infrastructures  for improving communication between disparate IT systems, enabling better  sharing of critical patient data and ultimately leading to better  decision-making and outcomes.

While the burden for designing infrastructures that meet  CEHRT requirements rests with the vendor community, it’s important to note that  solely adopting a certified EHR does not qualify a provider to receive  incentive payments. Providers must successfully attest to MU of the certified  EHR. The interoperability challenges for attestation go much deeper than what  is provided by the CEHRT. For example, the burden of integrating structured  data from systems outside of the CEHRT rests with the provider.

Going forward, providers will need to implement enterprise  terminology management strategies to comply with MU terminology standards and  to advance interoperability. It’s a complex undertaking for the average  resource-strapped IT department. For this reason, many facilities are turning  to advanced technology and automation to lay a foundation that supports  semantic interoperability and MU compliance.

Date: 11/2015

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Health IT News: Data Normalization: A foundational step to achieving Triple Aim goals

Summary:  Actionable data is critical to advancing healthcare’s Triple Aim: improving patient experience, population health and costs. Data accuracy for analytics and information sharing must exist to accomplish these goals.

Interoperability is a focal point of national efforts to advance information sharing and data analytics, but lack of a common clinical vocabulary is currently a primary roadblock to forward momentum. Until a foundation for semantic interoperability is laid that standardizes clinical terminology across disparate systems, success with such care delivery models as population health management, health information exchange and accountable care organizations will be limited.

Industry standards such as SNOMED CT, LOINC and RxNORM are important steps towards achieving this goal, but there is currently no existing standard that addresses all clinical terminology. Patient data shared between health information systems must be “cleaned” before data warehousing and analytics strategies can be realized.

Simply put, data must be normalized to remove semantic ambiguity.

Date:  October 8, 2015

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Health Payer News: The Quest for a Single Source of Terminology Truth

Summary: As federal regulatory initiatives up the ante on information sharing capabilities inside and outside a healthcare organization, the C-Suite is increasingly finding that a comprehensive terminology management strategy is critical.

Lack of a common clinical vocabulary across disparate systems is a primary roadblock to greater collaboration between payers and providers and the greater goals of health information exchange and accountable care.

Like many health systems, Wisconsin-based Dean Health Plan faced the challenge of managing code sets across numerous departments and disparate IT systems, each with their own inherent language and clinical terminology infrastructure. Largely governed by error-prone manual processes and workflows, the organization faced inevitable conflicts regarding terminology intake, management and distribution and lacked a formal process for governance and accountability.

Date: April 17th, 2015

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Government Health IT: Intersection of ICD-10 and Meaningful Use – Clinical Documentation Improvement

Summary: As hospitals, health systems and payers navigate the new risk-bearing landscape, synergies exist when clinical documentation improvement strategies are expanded to address both meaningful use (MU) SNOMED CT requirements and ICD-10. While the magnitude of the ICD-10 transition itself and the ongoing rumors of additional delays may tempt some organizations to pause in their pursuit of readiness, the bottom line is that advantages to clinical documentation can be realized even before the transition by using SNOMED CT within electronic health records.

Date: January 2nd, 2015

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Becker’s Hospital Review: Data Normalization for Semantic Translation

Summary: Why normalizing your clinical and claims data into standard terminologies is critical to supporting forward-thinking initiatives such as big data analytics, population health management and semantic interoperability.
Date: October 27th, 2014

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ICD-10 Coding Success: The Devil is in the Details

Summary: ICD-10 is a brave new world.  The differences in terminology offer opportunities to broaden the outlook of patient care as well as notable challenges to HIM professionals.  These realities are helping health care organizations develop a deeper awareness of what the broader scope of readiness really entails.
Date: September 25, 2014

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Advance Healthcare: Enterprise Terminology Management

Summary: Terminology is core to everything in healthcare – from diagnoses to procedures to outcomes, healthcare IT systems represent clinical concepts in coded terminologies or free text. The lack of a common clinical vocabulary across disparate systems is currently a primary roadblock to the national efforts to increase interoperability, transparency and collaboration within our healthcare system.
Date: July 14, 2014

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Healthcare IT News: Clinical Informatics

Summary: If there is one emerging pattern within the clinical informatics field, it is the quest to make data “actionable” for users. With all the technology infrastructure development over the past decade to facilitate electronic health record installation in healthcare facilities, providers are finding that the data generated often can’t be used in a timely and constructive manner.
Date: July 11, 2014

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For the Record: Healthcare’s Semantics Challenge

Summary: A bird’s-eye view of the current federal health care initiatives reveals a synergistic effort to move the industry toward the triple goal of enhanced patient care, improved population health, and lower costs.  But the health information exchange, Big Data, and improved analytics that facilitate these goals all hinge on health care organizations’ ability to accurately aggregate patient data and share them with stakeholders along the continuum, interoperability is the largest obstacle to achieving this longitudinal view of patient data.
Date: May 3rd, 2014

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