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What Will the Shifting Reimbursement Model Require?

Posted on July 29, 2014 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I was really intrigued by this post on the First Databank blog titled “Making the Most of the Meaningful Use Extension.” Here’s an excerpt of the post that really struck me.

We are looking at an industry undergoing a change in the reimbursement model, from fee for service to a risk-based model. Therefore, our systems must support quality measure reporting, tracking of specific treatment responsibilities for improving outcomes, and they must provide health information to the patient that allows them to be an active participant in their care.

We have to incorporate access to the care guidelines and research that has been proven to provide the best outcomes. There are a number of areas where there is overwhelming evidence as to what is the best course of care for the patient. For reasons that sometimes escape me, providers often do not follow these best practices.

We’re absolutely going through a shifting reimbursement model. In this post, Tom Bizzaro outlines what he thinks is needed to be able to handle this changing reimbursement model. Do you agree with Tom’s ideas? Is there anything he missed?

The last part of the above quote really hit me since I’ve seen the same thing. I don’t think we’re going to do much to change people who choose to go against evidence based medicine. However, I do think there’s a great opportunity for technology to more quickly diffuse the evidence based practices throughout the medical profession. While some people ignore best practices, I think the bigger problem is that there is just so much information out there that it’s hard for healthcare professionals to keep up to date.

This is just one example of how technology is going to improve patient care. Plus, I believe access to the best information at the point of care is going to be an essential part of the changing reimbursement model. This is just one reason why I don’t think you’ll be able to practice medicine without technology in the future.

Healthcare Standards – Opportunities and Challenges Remain for SNOMED CT, RxNorm and LOINC

Posted on October 22, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

The following is a guest blog post by Brian Levy, MD, Senior Vice President and Chief Medical Officer for Health Language.
Levy Low Res

Health IT standards and interoperability go hand-in-hand. Going forward, the success of the industry’s movement towards greater health information exchange (HIE) will hinge on the successful uptake and adoption of standards that will ensure reliable communication between disparate systems.

Progress is being made in this area through both messaging and coding standards introduced as part of Meaningful Use (MU). Specifically, MU coding standards that draw on such industry-respected clinical vocabularies as RxNorm, SNOMED CT® and LOINC® have the potential to drive more accurate, detailed sharing of patient information to promote better decision-making and patient outcomes.

Effectively deploying and adopting these standards is a huge undertaking with responsibilities falling to both vendors and providers going forward. To survive in future of healthcare, EMR vendors will have to evolve to support current and future industry standards. Providers will also have to grow their knowledge base and become more aware of how standards impact care delivery—instead of simply relying on vendors to pick up the slack.

The ability to “normalize” data to support all of these standards will be critical to advancing interoperability and communication between healthcare providers. With so many federal health IT initiatives competing for resources, the integration and use of terminology management solutions will become an important element to any data normalization strategy.

As providers assess their current needs and vendors move towards more enhanced offerings to align with new standards, the combined effort should produce significant progress towards improved information sharing. In the meantime, many challenges and opportunities exist along the roadmap to full implementation and adoption.

Vendor Readiness

While the EMR vendor market hit $20 billion in 2012, recent surveys suggest that many will not have staying power for Stage 3 MU. And one of the primary reasons, according to a 2013 Black Book Market Research report, is lack of focus on usability. An earlier report also pointed to 2013 as the “year of the great EHR switch,” pointing to provider frustrations that their current EMRs do not address the complex connectivity and sophisticated interface requirements of the evolving regulatory landscape.

Stage 1 MU created an artificial opportunity for many vendors to enter the market through government incentive grants. Because most initial EMR systems were not designed with Stage 2 requirements for HIE standards in mind, many vendors may find that they are not in a position to fund the infrastructure advancements needed to support future interoperability.

For instance, many EMRs support ICD-9 or free text for the development of problem lists. Under Stage 2 MU, problem lists must now be built electronically using SNOMED CT, requiring EMR vendors to develop and put out new releases to support the conversion. In tandem with this requirement, EMRs will also have to be designed to support RxNorm and LOINC.

It’s a time of upheaval and financial investment in the EHR industry, and when the dust settles, healthcare providers will have designated the winners. The end-result will ultimately include those players that can support the long-term goals of industry interoperability movements.

Minimizing Workflow Impacts

In existence since 1965, the SNOMED CT code set has a long track record of success and international respect. A comprehensive hierarchical system that includes mappings to other industry terminology standards, the code set enables computers to understand medical language and act on it by organizing concepts into multiple levels of granularity.

Few would dispute the potential of SNOMED CT to enhance accuracy and address the detail needed to promote enhanced documentation practices, but the expansive nature of the code set is still not exhaustive. Searching and finding the SNOMED concepts to include in Problem lists often requires further expansion of synonyms and colloquial expressions commonly used in clinical practice.  In addition, an accurate SNOMED code may not equate to a billable ICD-10 code, potentially requiring clinicians to conduct multiple searches if EMR workflow is not carefully planned.

The challenge for healthcare organizations is two-fold when it comes to the complicated SNOMED CT conversion process. First, the conversion represents one more complex IT project that healthcare organizations must undertake  amid so many other competing initiatives. Second, the success of implementations will be diminished if clinician workflows are negatively impacted. With EMR documentation practices already requiring more time from a clinician’s day, the situation will only be exacerbated if multiple code searches are required to ensure regulatory compliance for MU and ICD-10.

Terminology conversion tools that leverage provider-friendly language can be a great asset to easing the burden by providing maps between ICD-9 or ICD-10 and SNOMED CT problems. Physicians search for the terms they are accustomed to using in the paper record, and terminology tools convert the terms to the best SNOMED CT and ICD-10 codes behind the scenes.

For example, a clinician may add fracture of femur to a problem list, but ICD-10 requires documentation of whether the fracture was open or closed, the laterality of the fracture and whether the fracture was healing. Provider-friendly terminology tools provide prompts for the additional elements needed and guide clinicians to the most appropriate choices without the need for multiple searches.

Improving Mapping Strategies Internally and Externally

Industry crosswalks and maps exist to help ease the transition to new standards like SNOMED CT, RxNorm and LOINC. While these tools provide a good starting point in most cases, there is simply not a gold standard map that will work for every case.

Consider RxNorm, a naming system that supports semantic interoperability between drug terminologies and pharmacy knowledge base systems. Working in tandem with SNOMED CT to improve accurate capture of patient information from external systems, RxNorm codes are now required as part of the CCD (Continuity of Care Document) and HL7 messages for capture of medication information.

While designing EHRs with the capability to send and receive RxNorm codes is the first step, healthcare providers will still require a method of converting codes from RxNorm to internal medicine systems and drug information and interactions databases like Medi-Span, First Databank, Micromedex and Multum. Another challenge to standardizing medication information is the use of free text. Many healthcare providers receive drug information that is not coded at all, requiring a specific, customized mapping.

LOINC, a universal standard for identifying medical laboratory observations, is particularly challenging in this arena. Because the industry is home to hundreds of local lab systems and thousands of local lab codes, creating a single industry mapping solution is nearly impossible. The process often requires that sophisticated algorithms be built by performing an analysis of individual lab tests that are conducted in a particular hospital.

By leveraging the expertise and sophistication of a terminology management solution, healthcare providers can more easily automate and customize mapping of patient data to standardized terminologies. Otherwise, IT departments must expend valuable staff time to build complex mapping systems to address the myriad of needs associated with an influx of new standards.

Conclusion

The healthcare industry has identified use of a common medical language as a key foundational component to advancing information sharing capabilities. By designating such standards as SNOMED CT, RxNorm and LOINC as MU requirements going forward, the industry is taking a progressive step forward to ensuring clinicians have more efficient access to better patient information.

It’s a critical step in the right direction, but the road to success is complex. Healthcare organizations that draw on the expertise of terminology management solutions will be able to achieve the end-goals of this movement much quicker and with fewer headaches than those trying to implement these complex standards on their own.

Laying the Best Foundation for Medication Reconciliation

Posted on September 6, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

The following is a guest blog post by Brian Levy, MD, Senior Vice President and Chief Medical Officer for Health Language.
Levy Low Res
Effective medication reconciliation across the continuum of care is a critical element to eliminating medication errors and adverse drug events (ADEs). It is a focal point of such national initiatives as Meaningful Use (MU) and the Joint Commission’s National Patient Safety Goals and will also be crucial to ensuring performance metrics are met under Value-Based Purchasing and the Hospital Readmissions Reduction Program.

Simply put, one of the primary end-goals of current industry movements is to eliminate the revolving door effect in healthcare where patients are readmitted soon after discharge due to ADEs or lack of good information across the continuum. A growing body of research points to enhanced medication reconciliation as an effective way for hospitals to reduce readmission rates to meet this objective.

A 2012 study published in the Joint Commission Journal on Quality and Patient Safety found that accurate preadmission medication lists—acquired as part of medication reconciliation strategies— reduced ADEs both in the hospital and following discharge. Another paper published in the November 2012 edition of Pharmacotherapy also points to the critical role ADEs play in readmission rates and how ineffective care transitions, especially as they relate to medication management, exacerbate the situation.

The logistics of medication reconciliation has historically been an uphill battle for many clinicians. Without an electronic method for capturing information, the scene usually comes down to a Q&A session where physicians, nurses or other clinicians rely on patients to give them an accurate medication list. When a patient is unaware of the name of a medication, it usually results in a protracted delay in patient care while phone calls are made and consults conducted to accurately identify medications and avoid the potential for error.

EHRs provide the first step to correcting this inefficient way of gathering information. And while these systems are great repositories of patient information, the difficulty for medication reconciliation has been a lack of standards—specifically the lack of a standardized medical vocabulary. A number of proprietary medical terminologies exist within the industry, and without a standard for information exchange, the risk is that one drug could be identified by a number of different terminology codes depending on the proprietary system used.

Clinicians need an effective method for exchanging patient medication information between disparate systems in a standardized format that can be translated accurately by various healthcare organizations, providers and departments involved in patient care. MU is addressing this issue on one level through the introduction of RxNorm, a normalized naming system produced by the National Library of Medicine for generic and branded drugs and a tool that supports semantic interoperability between drug terminologies and pharmacy knowledge base systems.

RxNorm is a critical first step to ensuring the feasibility of building and accessing an accurate medication summary, thus minimizing the possibility of duplicate therapies, drug allergies and drug interactions. By adopting this standard, healthcare organizations and providers will begin receiving RxNorm codes in important CCD summary of care documents and HL7 messages. This standard will complement the use of the Systematized Nomenclature Of Medicine Clinical Terms (SNOMED CT®), a widely-used clinical terminology set also required under MU for the creation of problem lists.

While RxNorm provides efficient and accurate capture of medication information from external systems, healthcare organizations and providers will still require a method of converting codes from RxNorm to internal systems and visa-versa. This step ensures that internal medicine systems and drug information and interactions databases like Medi-Span, First Databank, Micromedex and Multum can also reconcile important patient medication information.

To address the full picture of data normalization, healthcare providers can leverage a healthcare terminology management solution to ensure automated mapping of patient medication data received from disparate sources to standardized terminologies. This process de-duplicates data, creating a normalized code across all clinical systems used internally, minimizing the potential for error.

This approach also provides an effective way for leveraging a comprehensive, longitudinal patient record, which is a primary goal of the health IT movement to enhance patient care. A foundation of standardized codes enables healthcare organizations to more fully develop advanced clinical decision support functions, where alerts can be received immediately for clinical activity impacting individual patients or within populations of patients.

As the healthcare’s industry move toward higher-quality care and more efficient care delivery continues to mature, the use of standardized medical terminologies will be paramount to effective clinical information exchange. While some initiatives like RxNorm and SNOMED CT are addressing this need for standardization, healthcare organizations can further advance data normalization strategies by leveraging the efficiencies and advantages of healthcare terminology management solutions.