Are you still missing the code? - Blog

Accurate and Compliant Coding – The Need of the Hour for Hospitals

Growing consumerism and technological developments have caused healthcare organizations to move away from fee-for-service business models to value-based care. However, cost burdens like legacy infrastructure and technologies, compounded by factors such as risk-based contracting and falling inpatient revenues, are leading to declining profit margins and strained operational budgets.

A recent survey revealed that 93% of healthcare enterprises are concerned about process inefficiencies, while 92% of them are worried about thinning margins.

Moreover, the 2016 HIM (Health Information Management) benchmark report showed that challenges like discharged not final billed (DNFB), productivity, and patient information monitoring continue to be a liability for the healthcare sector. As such, decision makers are being forced to re-evaluate and optimize their mid-revenue cycles. Besides, industry leaders are expected to emphasize more on risk adjustment, which in turn will help enhance the accuracy of hierarchical condition codes (HCCs). According to a report, 58% of CMS Condition Categories belong to HCC, which shows that better outpatient coding can be achieved by accurate inpatient coding. An accurate HCC can help improving decision support, tracking performance, identifying opportunities, and most importantly, help in having a specific problem list.

Market value of mid-revenue cycle management (mid-RCM) and CDI solutions (in USD)

The processes that lie between patient access and billing, specifically clinical documentation improvement (CDI), have become critical in bettering outcomes, case mix indices, physician incentives, and claims settlement. According to ResearchAndMarkets, the market value of mid-revenue cycle management (mid-RCM) and CDI solutions are set to increase from USD 3.1 billion in 2018 to USD 4.5 billion by 2023. Critical growth catalysts include revenue loss due to coding errors, drop in reimbursement rates, and the growing volume of unstructured data.

With outcomes gaining precedence over all other metrics, even minor coding errors can directly impact the quality of patient care delivered. Accurately capturing the patient story through coding can greatly help hospitals in profiling and quality reporting. Unfortunately, coding errors due to inadequate medical documentation are quite common and have been known to affect a patient’s course of treatment adversely. For instance, miscoding a 2-digit modifier for a leg injury can result in an MRI study conducted on the wrong leg. An even more severe case could involve an obstetrician administering the incorrect dosage of pain medicine to a pregnant woman in the delivery room. Common errors in the code include assigning non-billable or non-existent codes and missing Healthcare Common Procedure Codes (HCPC) for separately paid drugs.

Challenges with Medical Coding

Changing regulatory guidelines have proved to be an on-going hurdle for healthcare organizations since it has resulted in a shortage of skilled coders. In 2015, the changeover from ICD-9 to ICD-10 caused the drop in the productivity of medical coders. The reason for this being the jump from 3,824 procedure codes in ICD-9 to 71,924 in ICD-10. Moreover, code descriptions in ICD-10 are longer and tolerance for unspecified codes, lower.

Healthcare organizations also need to ensure that their existing IT infrastructure strategy takes into account the adoption of computer-assisted coding solutions (CACS) to meet up and coming requirements. Other challenges that can adversely affect healthcare organizations include overcoding and undercoding. Considering that overcoding Current Procedural Terminology (CPT) and HCPC can lead to inaccurately high reimbursements from insurance firms, it could, potentially, result in charges of fraudulence. On the other hand, undercoding causes care providers to lose revenue. Undercoding does not accurately record the actual scope of diagnosis and care. In these instances, properly documenting symptoms, diagnoses, treatments, medications, patient history, and health risks will result in the abatement of insurance denials.

Why Medical Coders Need Artificial Intelligence

Hospitals bills average errors.According to Derek Fitteron, the CEO of Medical Cost Advocate, 80% of medical bills in the U.S., comprise errors. Furthermore, an audit by Equifax, a credit rating agency, revealed that hospital bills totaling $10,000 or more tend to contain an average error margin of $1,300.

The attempt to contain the fallout of such financial damage has led payers and healthcare facilities to adopt new-gen technologies, the chief among them being artificial intelligence (AI) and cloud computing. And CAC is has been the result. With the help of an NLP engine, the right CAC software can automatically identify and extract data from documents and insert them into the system. It can then suggest codes for the concerned treatment, along with a round of manual review. CAC also helps companies create customized dictionaries and ontologies for precise users and recognizes the various intricacies of patient records and their bills. The result: higher medical coding accuracy, faster billing, and greater coder productivity for care systems and facilities. According to a Frost & Sullivan report, it is estimated that the market for NLP and other AI tools in healthcare will be worth $6 billion by 2021.

NLP in Medical Coding

The capacity to analyze and decode annotations from unstructured data makes NLP the preferred technology of medical coding. It allows users to dictate clinical notes or other information that can be converted to text through speech recognition algorithms. Furthermore, when applied to clinical narratives, NLP helps to overcome the challenges of billing code algorithms for CLI recognition of text that describes symptoms used to establish a diagnosis. It is also being utilized to create better imaging workflows and help enhance value-based reimbursement programs.

Getting Past the Obstacles

Reconciling data from multiple systems and manual data entry are two of the biggest impediments to medical coding productivity. This problem, too, can be tackled by CAC by automating manual tasks and automatically suggesting potential billable codes. This would help coders spend less time keying in the information and more time validating and generating accurate and compliant reimbursement.

That said, choosing the right CAC system can be a daunting task for hospitals. Despite being bombarded with information on how CAC can ease their processes, healthcare institutions often face challenges that hurt their facility. For instance, with the improper integration of CAC solution into the system, one has to refer to multiple screens. This means one has to refer to the CAC solution while keeping an EHR window open, which makes the entire task even more cumbersome and confusing than before. Also, the presence of multiple vendors can result in coding duplication.

Hence, it is essential to adopt a solution that has specifically been developed, keeping the existing challenges in mind. ezDI’s computer-assisted coding solution, ezCAC, can efficiently predict accurate billable codes, making it easier for coders who then need to just audit or review the code. Furthermore, the capabilities of ezCAC can help reduce inpatient time-to-code by at least 40% and outpatient time-to-code by 50% or more.

Reduce in time-to-code the case.

NLP Used in the Right Way – The Drexel Clinic Use Case

Instead of manually reviewing patient charts, Drexel clinical researchers adopted NLP to examine 5700 patient records for HIV and hepatitis comorbidity and came up with 1150 relevant hits. With use cases like this becoming public, the adoption of NLP is gradually gaining momentum. Some of the benefits of using NLP in the domain are:

  • Transparent Documentation: NLP can provide clinical analytics to find potential gaps in clinical documentation.
  • Improved Efficiency: NLP reduces human effort and time as it can automate the coding process, and allow staff to focus on maintaining quality and accuracy.
  • Better Accuracy: With better accuracy levels in coding, hospitals have the scope to reduce compliance risk.
  • Enhanced Decision-making: Getting access to unstructured data from several sources such as office visits, lab results, diagnostic notes, and more, physicians can make accurate clinical decisions.
  • Reduced Costs: With more accurate diagnosis and better documentation, NLP can help healthcare organizations potentially diminish the overall spend.
  • Operational Visibility: With smart analytics and dashboards and reports, real-time operational visibility can be attained.

NLP can, thus, help healthcare enterprises maintain the level of accuracy needed in medical transcription, clinical documentation, and medical coding to pave the way for better clinical outcomes, enhanced patient care, and most importantly, boost revenue. Be it a lab or a diagnosis test report, applying NLP into the system can facilitate better mid-RCM to a much higher degree.

Using CAC in the right way is critical to ensure success today and in the future and healthcare organizations should be ready with solutions for common roadblocks such as:Using CAC in the right way is critical to ensure success of


Where Are We Heading?

With the industry transitioning towards a value-based reimbursement model, healthcare organizations are embracing new paradigms of care. Adopting AI-based technologies will ensure early benefits for stakeholders by helping them counter existing and counter emerging challenges.

By the end of 2020, the Health Care Transformation Task Force — a group of frontrunners across various healthcare segments established in 2014 — aims to bring 75% of their businesses under the value-based reimbursement model.

Also, patients who are part of this value-based reimbursement agreement were admitted to the hospital inpatient department was 23.4% less than patients in the traditional Medicare in 2017. This indicates that the healthcare industry is on the verge of a breakthrough, where patient care sees a whole new level of quality healthcare services.

Not Just a Solution Provider but also an Insight Giver

ezDI, a provider of an AI-based and integrated Computer-Assisted Coding, CDI, Coding Compliance, Quality Measures, and Enterprise Analytics to US Hospitals and Health Systems has already been in the news for providing best-in the industry AI-based mid-revenue cycle solutions to all premier member hospitals and health systems. Build on the principle of ensuring the utmost patient care, ezDI solutions assist caregivers in getting a better insight into the overall care patterns for all patients.

Furthermore, ezDI is capable of disrupting the mid-revenue cycle market with innovative technologies. It is also the home for medical coding that has developed the ezCAC solution keeping in mind the different challenges faced by hospitals. It aims to mitigate risks and overcome challenges for caregivers to achieve absolute coding productivity with reliable NLP solutions.

Are you still missing the code?

For more information on how ezDI software and services can help your organization, reach us at sales@ezdi.com or (866) 473-5655.

Devanshu Yadav

Devanshu Yadav

Devanshu Yadav is a corporate strategist at ezDI Inc. He works for continuous advancement in bringing ezDI’s award-winning natural language processing (NLP) technology and comprehensive medical knowledge base to the market. He is considered as a subject matter expert in an emerging Healthcare AI driven technologies for mid revenue cycle management. He has contributed to the literature of healthcare professionals by engaging in numerous publications with HFMA. He is trusted & accomplished expert in intersection of technology, HIM, CDI and Coding, with a mission to set others up for success.

Devanshu has earned his post graduate degree in Business Administration and graduate degree in Electronics engineering. He is a firm believer in the abilities of technology to change the world, making the world a better place for all of us.

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