The transition to value-based payment calls for healthcare systems to rethink and redesign care delivery across services lines. Service line management is well defined in the healthcare industry as a means to determine which of its diverse services are profitable and how the market share of a given service compares to competing providers. Service lines are typically limited to a handful of well defined, mutually exclusive categories or groupings of individual services or interventions such as oncology, cardiovascular and orthopedics. Since no such service line designation exists in standard transactional coding systems or taxonomies, service line is generally assigned based on primary diagnosis codes, procedure codes and other patient attributes such as age, gender or genetic characteristics.
Healthcare data analytics is thriving as integrated delivery systems seek an understanding into their data to help guide their transition to a value-based contracts. The questions being explored may include: What service lines are profitable to our system? What service lines are linked to the changing demographics of our geographic location? What data supports our overall effectiveness in developing a quality outcome for our patients? The answers are in the data; but are they the right answers?
More Read
In analyzing any payment model, a variety of factors may impact the final conclusions of whether the revenue minus the costs results in a profitable service line. Without safeguards in place to ensure the integrity of the cost and revenue cycle data there is a risk in reaching the right conclusions. The cost side of the equation is on somewhat solid footing due to the industry-wide use of generally accepted accounting principles. Hampering both the cost and revenue cycle data is the need to identify standards for what data is in and what data is out of the formula. In moving to a value-based payment based on specific service lines all costs and services delivered related to that episode of care must be captured. This includes inpatient, outpatient, nursing home, professional fees and ancillary costs and services.
In today’s world, much of the data is on various platforms that include different discrete data points. You have the Medicare Severity – Diagnosis Related Groups (MS-DRG) identifying a mishmash of services on the inpatient side and Current Procedural Terminology/Implantable Cardioverter-Defibrillator (CPT/ICD) codes for much outpatient care. To negotiate a meaningful value-based contract an organization needs to be able to evaluate and analyze all data related to that patient’s episode of care. When the data is combined the financial ramifications of variances in the delivery of care may be spotted and addressed.
Even when the health system has anticipated the need for a robust clinical and financial data reporting capability, there is no guarantee that the right data is being captured unless the organization has verified the integrity of the data. This involves all clinicians and administrative staff that interact with the patient during that episode capturing the relevant data correctly. The old adage of “garbage in and garbage out” applies here. If the physician is omitting critical patient-specific health care data, or the coder is assigning an incorrect CPT or MS-DRG code, the value of the data is questionable and wrong conclusions are possible.
Electronic health record (EHR) optimization projects are the new trend. The goal is to alleviate the dissatisfaction by clinicians with too many clicks and worthless information while ensuring that the records are being used to mitigate recurring issues in the revenue cycle process through prompts or omissions of key data. For example, a physician cannot bypass the reason for ordering a specific diagnostic test. The physician would work with a clinical documentation specialist and an EHR technician to redefine the critical data elements of the EHR. In short, now that the rush to implement the EHR has passed, the technology can be fine-tuned to aid physicians rather than handicap them during a patient encounter.
The next step in the integrity review of the data is to verify that the coder and chargemaster are assigning the correct codes based on the medical record documentation. The billing, accounts receivable, contract management and denial management teams’ workflow and policies and procedures are always reviewed for accuracy and optimization.
Early adopters of value-based payment systems indicate that several key factors must be aligned. They are:
• strong leadership, core operational strength and an appropriate legal structure to move forward;
• provider collaboration and a delivery system that supports clinical protocols through the entire patient’s episode of care; and
• robust clinical informatics and reporting based on the integrity of the data.
In January, the Department of Health and Human Services announced its intent to tie 30 percent of Medicare’s provider payments to value-based payment models by 2016, with the share rising to 50 percent by 2018. Healthcare organizations’ only choice is to move forward now or face the consequences. A good place to start is at the beginning—the physician/patient encounter; from the initial touch point to the episode’s conclusion the integrity of the data is critical since the life of the organization’s future is resting on it.
Published on MiraMed Global Service’s Blog
__________________
Phil C. Solomon is the publisher of Revenue Cycle News, a healthcare business information blog. He serves as the Vice President of Global Services for MiraMed, a global healthcare Business Processing Outsourcing services company. Phil has 25 years of experience in healthcare as an industry thought leader, strategist, solution provider, author and featured speaker. In this blog, you will read about important industry updates, strategies for
improving financial performance, and commentary that challenges the status quo.
The post Revenue Cycle Integrity Must Precede Data Analytics appeared first on REVENUE CYCLE NEWS.
