EHR's effect on the revenue cycle management Coding function (PDF ...

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The Electronic Health Record

EHR’s Effect on the Revenue Cycle Management Coding Function As EHR Applications Utilizing Terminologies Are Implemented, Providers Need to Consider the Effect on Coding Function and the Revenue Cycle By Kathy Giannangelo, MA, RHIA, CCS, CPHIMS, and Susan Fenton, PhD, RHIA

Abstract Without administrative terminologies there is no revenue to manage. The use of healthcare IT to capture the codes for administrative and financial support functions will impact the revenue cycle and the management of it. This is presumed to occur because clinical data coded at the point of care becomes the source for claims data. Thus, as electronic health record system applications utilizing terminologies are implemented, healthcare providers need to systematically consider the effect on the coding function and management of the revenue cycle. A key factor is the sequence of events changes, i.e., instead of a health information management professional selecting billing codes at the conclusion of an encounter based on the review of the record, clinical data generates the claims data via mapping. Efficiencies and management challenges result.

Keywords Electronic health record, terminology, revenue cycle management, computer-assisted coding, SNOMED CT, data standards, natural language processing, natural language understanding.

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he availability of healthcare IT for implementation by care delivery organizations is expanding. This technology encompasses information systems for clinical, administrative and financial data. While much of the recent focus has been on capturing clinical data, the data from administrative and financial applications continue to generate revenue. What feeds these applications, and what is needed to request payment, are the administrative terminologies. However, the documentation and coding processes by which this data is captured are undergoing changes. The primary external force influencing this change is the transition from paper to electronic health records (EHR). The introduction of technology not only expands data capture capabilities, it also affects workflow. In addition to the EHR, the development and implementation of information and enabling technologies for coded data has specific significant impact on the domain of coding practice and, as a result, the management of the revenue cycle. Examples are computer-assisted coding (CAC), automated mapping and natural language processing/understanding (NLP/NLU) technologies. Two secondary factors are shaping both the transition and technologies. One is the building of a new information infrastructure at the national and regional level which requires standardized clinical terminologies for data supply. Second is the adoption of www.himss.org

standards for data interchange and system interoperability which translates to transformational changes in clinical terminology and classification systems. A recent example is the transfer of ownership of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) from the College of American Pathologists to the newly formed International Health Terminology Standards Development Organization. The capture of the clinical data which fully describes a patient’s encounter with the healthcare system is expected to evolve, eventually occurring at the point of care. A process change such as this has an effect on the way in which coding is done and also modifies the use case to one where EHR systems rely on codes as a data resource for patient care. For example, when applications using CAC technology assign the code clinical data becomes the source for the claims data through mappings. This shifts the coding process from back- to front-end. In addition, rather than collecting only billing data, care delivery organizations have the clinical data needed for decision support. A health information management (HIM) professional no longer selects billing codes at the conclusion of an encounter based on a review of the record. Instead, the encoded clinical data is either made available to the financial system in the correct coding system for direct billing or provided for human review and approval prior to transfer to the financial data systems. With these changes come revenue cycle efficiencies and management challenges.

The National Center for Vital and Health Statistics (NCVHS) was tasked with making recommendations for uniform data standards for patient medical record information. In 2003, they identified a core set of terminologies including SNOMED CT, Logical Observation Identifiers Names and Codes (LOINC®), RxNorm and Universal Medical Device Nomenclature System (UMDNS®)2. Administrative terminologies such as ICD-9-CM and CPT were recognized by NCVHS as “important, related terminologies” and the need for maps between them and the core set of terminologies were noted in the recommendations. Uniform standards for the electronic exchange of clinical health information were also designated in 2003. There were the result of the Consolidate Health Informatics (CHI) Initiative and intended for adoption by the federal agencies. The HIPAA transactions and code sets for electronic exchange of health related information to perform billing or administrative functions, SNOMED CT

HITSP is focused on achieving standards that will enable widespread interoperability among healthcare information technology.

Clinical Reference and Administrative Terminology A principle behind any terminology is the standardization of data. Having standard data supports efficiencies for retrieving, linking and exchanging data. Terminology selection is predicated on the purpose for which the data it represents will be used (i.e., its use case). Just as there are many types of healthcare data there are numerous terminologies. For example, a terminology appropriate for clinical data used for direct patient care would not likely be used for billing. Identifying standard terminologies specific to a use case is essential to having the encoded data to access, combine and manipulate. When a terminology becomes a standard, reproducible transmission of patient data between internal and external information systems is achievable. The move toward standard terminologies began in 1996, with the implementation of HIPAA and the naming of several electronic standard transactions for use in most healthcare administrative functions. Under HIPAA, covered entities must report using certain terminologies—“medical code sets,” as they are called in HIPAA—or the healthcare claim or payment will not be made. The administrative terminologies adopted for claim submission include the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) Vol. 1-3; Current Procedural Terminology, Fourth Ed. (CPT); Code on Dental Procedures and Nomenclature (CDT); National Drug Codes (NDC); and the Centers for Medicare and Medicaid Services (CMS) Healthcare Common Procedure Coding System (HCPCS).1 These systems form the foundation for determining payment for healthcare products and services. www.himss.org

for laboratory result contents, non-laboratory interventions and procedures, anatomy, diagnosis and problems, and nursing, and LOINC to standardize the electronic exchange of laboratory test orders and drug label section headers were all named as terminology standards.3 In the past year the Healthcare Information Technology Standards Panel (HITSP) recommended, and US Department of Health and Human Services Secretary Mike Leavitt accepted, standards specific to certain use cases. The major terminology standards are for the electronic health records, bio-surveillance and consumer empowerment use cases and include ICD-9-CM, CPT, HCPCS, LOINC and SNOMED CT. HITSP is continuing its work and is focused on achieving widely accepted and readily-implemented consensus-based standards that will enable and support widespread interoperability among healthcare information technology.4 Use-case scenarios are being used to identify, prioritize and promote relevant standards.

Electronic Health Record System There are a number of types of terminologies, each important to the functions of an electronic health record system (EHR-S). Two—clinical reference and administrative terminologies—have an effect on the revenue cycle management coding function. An EHR-S has been described as a number of integrated component information systems and technologies where the electronic files making up these systems and technologies contain different data types.5 Health Level Seven (HL7) published the EHR-S Functional Model Normative Standard (ANSI-approved) consisting of three sections—direct care, supportive and information infrastructure—with more than 150 functions that may be present in an EHR-S.6 Functions that enable delivery of healthcare and offer clinical decision support are described in the direct-care section. A subset of this section is the care management (CM) functions. The CM volume 22 / number 1 WINTER 2008 jhim n

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functions are of the type used as patient care is delivered and an EHR is produced. For example, the direct-care function operations management and communication’s conformance criteria 14 and 157 state the following regarding terminologies: 14. “If the system processes data for which generally accepted standard terminologies have been established, then the system shall conform to function IN.4.1 (Standard Terminolo gies and Terminology Models) to support semantic interop erability.” 15. “If the system processes data for which generally accepted standard terminologies have been established, then the system shall conform to function IN.4.2 (Maintenance and Versioning of Standard Terminologies) to preserve the semantics of coded data over time.” These conformance criteria point out that when controlled clinical reference terminologies are used by healthcare providers they have the capability to ensure data interchange and system interoperability. As previously mentioned, when EHR systems rely on codes as a data resource for patient care, the use case is modified. Terminologies provide the capability for clinical decision support to occur (e.g., alerts and reminders and links to medical knowledge are possible). The administrative and financial requirements associated with the delivery of healthcare are addressed in the supportive function section. Terminologies are referenced in several places in subsection S.3, Administrative and Financial, linking their importance to this group of functions. Within this ection of the standard the EHR-S functions connected to the revenue cycle management coding function. Subsection S.3 also contains the function titled S.3.3.5, Claims and Encounter Reports for Reimbursement. A conformance criterion for this function states the EHR-S “shall provide the ability to view available, applicable information needed to enable the creation of claims and encounter reports for reimbursement.”8 This list raises the points of automatic generation of data, mapping, rulesdriven coding assistance, and the need for connections to occur between systems and applications in order to generate claims for reimbursement. The final section, information infrastructure, contains functions that support the other two sections of the model. They do not in themselves involve aspects involving the delivery of healthcare. However, the functions are a requirement for EHR-S operational efficiencies and minimum standards for interoperability.9 As in previous sections of the functional model, a subset of functions dedicated to standard terminologies and terminology services are named. If information infrastructure functionality is present in an EHR-S it is possible to exchange data accurately, effectively, and consistently. Terminology mapping is explicitly described in IN.4.3. The functions in the EHR-S Functional Model utilize terminologies in different ways expanding their use beyond billing and at the same time recognizing the potential for efficiencies in the ability to “code once, use many times.” All of these functions have a direct impact on revenue cycle management. Revenue cycle

management involves the administrative and clinical functions that allow capturing, managing, and collecting of revenue related to a service rendered to a patient.10 For example, mapping allows the capture of data with the terminology appropriate for one use case while sharing the data for a different use case using another terminology. Efficiencies result because a clinical concept can be captured once and secondary uses derived from it.

Automated Coding, Mapping and the EHR Assigning codes using terminologies to represent a patient’s healthcare encounter is a core function performed by HIM professionals. Coding is the process of encoding the details of the encounter using standard terminologies. Historically, medical diagnoses and procedures have been captured via a manual code assignment process where no electronic resources or tools are used. In instances where a computer application assigns a code and no human is involved “auto-coding” is said to have occurred. Computer-assisted coding (CAC) is a blend of the two. CAC is the “use of computer software that automatically generates a set of medical codes for review, validation, and use based upon clinical documentation provided by healthcare practitioners.”11 There are different methods by which automated coding may occur to support the functions of an EHR-S and affect the revenue cycle management coding function. They include structured input, natural language processing (NLP), and natural language

The EHR-S must use consistent, codified terminology to eliminate ambiguity and confusion, and ensure data correctness and interoperability.

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understanding (NLU). Any CAC technology that automates the coding process results in a modification of the coding workflow and, consequently, the management of the revenue cycle. “Workflow refers to the sequence of tasks that need to be performed within a process.”12 Establishing the coding workflow requires determining the order of tasks and the process by which the work is passed along to others and/or transmitted to a system. The coding process involves basically four tasks: analysis of the clinical documentation; encoding using standard terminologies; noting/storing the assigned codes; and passing/transmitting the codes. The coding workflow without the use of CAC tools begins with the analysis of a created (written or dictated) patient encounter document. The HIM professional evaluates the report and assigns codes. A coding software program such as an encoder or books along with guidance on code usage is used in the process. During the report analysis step, the HIM professional determines whether the documentation contained in the record is complete and whether there are any coding issues evidenced by the software, code book or guidelines warranting further explanation by the provider. If there is, the HIM professional does not proceed to the next task until clarification is received. Once it is determined the record is ready for coding, it is encoded using standard terminologies. The assigned codes are then noted and stored. The final www.himss.org

task is the passing or transmitting according to the codes’ purposes such as billing for services rendered. The main problems with the above process are the inefficiency, inconsistency and costs associated with the HIM professional selecting billing codes at the conclusion of an encounter based on the review of the record. Automated solutions are needed to transform the coding process into a more productive, efficient, accurate, and consistent one.13 The use of CAC tools affects tasks one and two in the coding workflow. Software applications analyze the clinical documentation and encode the documentation using standard terminologies. The technologies include structured input, natural language processing (NLP), and natural language understanding (NLU). Frontend applications using these technologies change the coding function dynamics. Documentation is still the source for encoding the record and terminologies are still used to translate the narrative into data the computer can process. However, the clinical data coded at the point of care becomes the source for the claims data via mapping. “Mappings are sets of relationships of varying complexity established between two vocabularies in order to allow automated translation or connection between them.”14 Having an EHR-S with the functionality of the automated generation of administrative and financial data from the clinical record means a clinical concept is captured once and secondary uses derived from it. Mapping creates links between concepts within one data set to the same or substantially similar concepts in another data set.15 However the prevalence of maps in EHR systems is limited. A recent survey of health information technology vendors on SNOMED CT implementation in EHR applications investigated the availability of maps between SNOMED CT and other administrative terminologies. This study showed the SNOMED ICD-9-CM map created internally by the vendor was the most prevalent.16

Efficiencies and Management Challenges As healthcare information requirements have changed, the need for more granular clinical data has increased. Coding must now meet an emerging need to capture healthcare data in a standard format that has universal meaning and can be applied both at the individual and aggregate levels.17 With manual coding, the costs to collect this additional data are high and inefficiencies abound. A different way of encoding the clinical and administrative data must be sought out and implemented. Automated coding shows promise as the answer to this challenge. When combined with the EHR it has the capability to simplify the way healthcare organizations gather data and submit claims for services.18 To gain synergy for combined clinical and financial process improvement and facilitate timely billing of healthcare services, integration of the financial systems with the clinical systems should also be a strategy19 and goal.20

interoperability.21 Standard terminologies identify the discrete or structured data that allow data capture and processing possible. The intended use and the structure of the clinical terminology must be considered if the terminology is to be successful within an EHR.22 Certain terminology systems are appropriate for specific applications. A difference exists between the types of terminologies needed for EHR-S direct care functions and those of supporting functions. Since the intent of the EHR is to provide access to complete and accurate clinical information central to healthcare delivery, it is the applications relevant to the HL7 EHR-S Functional Model Normative Standard’s direct care functions where controlled clinical reference terminologies work best. The applications for the supportive functions require less detailed data and therefore administrative terminologies such as ICD-9-CM and CPT are best used for billing and reporting. The convergence of the evolution and growth of clinical terminologies and enabling information technologies which include the functionality outlined in the EHR-S Functional Model will change the process of collection, maintenance, use, and distribution of coded data. In addition, the encoding of clinical documentation for direct patient care and assigning a code for payment purposes involve different work flows. Clinical data coded at the point of care using a controlled reference terminology becomes the source for the billing data through mappings. Therefore, the management of the revenue cycle is directly impacted by any changes involving the coding function. A key factor is the sequence of events changes, i.e., instead of a coding professional selecting billing codes at the conclusion of a patient’s encounter based on the review of the record, codes from a reference terminology are generated by the clinical application, mapped to an administrative terminology, and when necessary provided for human review and approval. Mapping maximizes the benefits of an EHR-S by utilizing the clinical data being entered through automated coding practices thereby avoiding duplication of data capture. Automated maps are efficient because they minimize duplicative data entry. They offer the “code once, use many times” functionality. Furthermore, mapping maximizes the value of clinical data contained in electronic health record systems and enables the use of the codified data for multiple purposes, including the generation of claims data. JHIM Kathy Giannangelo, MA, RHIA, CCS, CPHIMS, is a director of practice leadership for the American Health Information Management Association. Susan Fenton, PhD, RHIA, is director of research for the AHIMA Foundation of Research and Education.

Conclusion The EHR-S must use consistent, codified terminology to eliminate ambiguity, confusion, and ensure data correctness and www.himss.org

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References 1. Centers for Medicare & Medicaid Services. HIPAA Transaction and Code Set Standards; 2005. Washington, DC: US Department of Health and Human Services. Available at: http://www.cms.hhs.gov/TransactionCodeSetsStands/04_ CodeSets.asp#TopOfPage. Accessed September 5, 2007. 2. National Committee on Vital and Health Statistics. Letter to the Honorable Tommy G. Thompson. November 5, 2003. Washington, DC: US Department of Health and Human Services. Available at: www.ncvhs.dhhs.gov. Accessed September 5, 2007. 3. Office of the National Coordinator for Health Information Technology. Consolidated Health Informatics Initiative. December 19, 2006. Washington, DC: US Department of Health and Human Services. Available at: http://www.hhs. gov/healthit/chiinitiative.html. Accessed September 5, 2007. 4. Healthcare Information Technology Standards Panel. Chicago, IL: HIMSS. Available at: http://www.ansi.org/standards_activities/standards_boards_panels/ hisb/hitsp.aspx?menuid=3. Accessed September 5, 2007. 5. Addison K, Braden JH, Cupp JE, Emmert D, Hall LA, Hall T, Hess B, Kohn D, Kruse MT, McLendon K, McQueary J, Musa D, Olenik KL, Quinsey CA, Reynolds R, Servais C, Watters A, Wiedemann LA, Wilkins M, Wills M, Vogt NE. Update: Guidelines for Defining the Legal Health Record for Disclosure Purposes. J AHIMA. September 2006;76(8):64A-64G. 6. HL7. EHR-S Functional Model, Release 1. Available at: www.hl7.org/ehr/ index.asp. Accessed September 5, 2007. 7. Ibid. 8. Ibid.

12. Amatayakul, MK. Electronic Health Records: A Practical Guide for Professionals and Organizations. 3rd ed. Chicago, IL: American Health Information Management Association; 2007:370. 13. American Health Information Management Association. Delving into computer-assisted coding; 2004. Available at: http://www.ahima.org/e-him/. Accessed September 5, 2007. 14. US National Library of Medicine. UMLS Basic Mapping Project Assumptions; October 3, 2006. Bethesda, MD: National Institutes of Health. Available at: http://www.nlm.nih.gov/research/umls/mapping_projects/mapping_ assumptions.html. Accessed September 6, 2007. 15. Foley M, Hall C, Perron K, D’Andrea R. Translation Please: Mapping Translates Clinical Data between the Many Languages That Document It. J AHIMA. February 2007;78(2):34-38. 16. Foley MM, Garrett GS. Code Ahead: Key Issues Shaping Clinical Terminology and Classification. J AHIMA. July-August 2006;77(7):24-30. 17. Giannangelo K, Fenton S. Prespectives in Health Information Management. Foundation of Research and Education of the American Health Information Management Association. In press. 18. Foundation of Research and Education of AHIMA. Automated Coding Software: Development and Use to Enhance Anti-Fraud Activities; 2005. Available at: http://www.hhs.gov/healthit/documents/AutomaticCodingReport. pdf. Accessed September 7, 2007. 19. Shaffer V, Rishel W, Edwards J, Handler TJ, Hieb, BR, Lovelock JD, Runyon, B. Top 10 Actions for U.S. Healthcare CIOs to Conquer Complexity and Achieve IT Impact, 2007. Gartner Inc. February 26, 2007. 20. Conn J. Following the Money. Mod Healthc. July 2-9, 2007;37(27):28-30.

9. Ibid. 10. Biesboer P, Pace MA. Partnering with Revenue Cycle for Success. Presented at: IFHRO Congress and AHIMA National Convention and Exhibit; October 2004; Washington, DC. 11. American Health Information Management Association. Delving into computer-assisted coding; 2004. Available at: http://www.ahima.org/e-him/. Accessed September 5, 2007.

21. HL7 EHR-S Functional Model, Release 1. Available at www.hl7.org/ehr/ index.asp. Accessed September 6, 2007. 22. Levy B. Evolving to Clinical Terminology. J Healthc Inform Manag. Summer 2004;18(3):37-43.

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