July 26, 2005
Chairmen Blair and Reynolds, members of the Standards and Security Subcommittee, and ladies and gentlemen, good afternoon. I am Valerie Watzlaf, associate professor within the Department of Health Information Management in the School of Health and Rehabilitation Sciences at the University of Pittsburgh. Joining me this afternoon is Mary Stanfill, professional practice manager for the American Health Information Management Association (AHIMA). On behalf of the Association and its members, thank you for allowing us this opportunity to provide input on issues related to computer-assisted coding.
The American Health Information Management Association (AHIMA) is a not-for-profit, professional association representing 50,000 educated health information management (HIM) professionals who work throughout the healthcare industry. HIM professionals serve the healthcare industry and the public by managing, analyzing, and utilizing data and records vital for patient care and making it accessible to healthcare providers and appropriate researchers when it is needed most. Managing the records for healthcare has been a role for HIM professionals for over 75 years, and AHIMA members are now working diligently to ensure that we soon have standard, interoperable electronic health records to improve the quality and safety of patient care. Our e-HIM™ (electronic health information management) initiative represents AHIMA’s commitment to:
- Promote the migration from paper to an electronic health record (EHR) information infrastructure,
- Reinvent how institutional and personal health information and health records are managed, and
- Deliver measurable cost and quality results from improved information management.
As part of this e-HIM initiative, in 2004, AHIMA convened an e-HIM Workgroup on computer-assisted coding (CAC). Although AHIMA had previously published articles in its Journal and provided presentations on the topic of CAC, the goals of this work group represented a much more extensive exploration of the use of CAC technology. This group was charged with researching CAC technology and related emerging roles for HIM professionals. Using this research, they were asked to identify the best practices for evaluating and applying this technology, use cases, and required skill sets for emerging HIM roles. The outcome of the work group’s efforts was a set of practice guidelines, or a practice brief, to assist healthcare organizations in preparing for the expanding role of this technology in the coding and billing process. We have attached a copy of this practice brief to our written testimony. This practice brief discusses computerized tools available to automate the assignment of certain medical or surgical codes (ICD-9-CM and CPT/HCPCS) from clinical documentation that are traditionally assigned by coding or HIM professionals as well as clinical providers. It also outlines the driving forces shaping the current and future applications of this technology, examines application of the technology, and provides guidance about the steps necessary to position coding professionals for the coming coding revolution.
It is appropriate and necessary at this time to disclose two pertinent current projects with which AHIMA is involved.
AHIMA’s Foundation of Research and Education (FORE) has a contract with the Office of the National Coordinator for Health Information Technology (ONCHIT), Department of Health and Human Services (HHS) to conduct a study to look at how automated coding software and a nationwide interoperable health information technology infrastructure can address healthcare fraud issues. This project is comprised of two tasks. The project’s first task is a descriptive study of the issues and steps in the development and use of automated coding or CAC software that will enhance healthcare anti-fraud activities. I am one of the co-principal investigators on this task. The second task will identify best practices to enhance the capabilities of a nationwide interoperable health information technology infrastructure to assist in healthcare fraud prevention, detection, and prosecution.
The second project is work AHIMA is conducting under contract with the National Library of Medicine (NLM). This task is to assist in the development, review, and testing of mappings between SNOMED-CT® and ICD-9-CM and any successor HIPAA standards to ICD-9-CM. Ms. Stanfill is part of the team working on this project.
Computer-Assisted Coding Technology
I would like to begin by clarifying some of the terms that we will be using today. Electronic health record (EHR) is the term we use to refer to computerization of health record content and associated processes. This is in contrast to the term electronic medical record (EMR), which is a computerized system of files (often scanned via a document imaging system) rather than individual data elements. Today when we mention an EHR we are referring to a system that captures, manages, and maintains discrete healthcare data elements.
AHIMA defines computer-assisted coding (CAC) as the use of computer software that automatically generates a set of medical codes for review and validation and/or use based upon clinical documentation provided by healthcare practitioners. CAC is often referred to as “automated coding” – but this terminology can be confusing as it implies a fully automated process with no human involvement, when in fact these applications require human review and validation for final code assignment for administrative purposes. We prefer including the term “assisted” when discussing these applications as this more closely characterizes how they are employed.
I would also like to make a distinction between CAC applications and other computerized tools currently utilized in the coding process. There are many tools available today to assist coding professionals in manual code assignment, including: pick or look-up lists, automated super-bills, logic or rules-based encoders, groupers, and imaged/remote coding applications. All these tools serve to assist a person in manually assigning correct codes. They do not fundamentally change the coding process. They simply facilitate the manual coding process. In contrast, CAC applications significantly alter the coding process through automatic generation of codes for review by a coding expert who validates and edits the codes rather than manually selects them.
In our research we found two approaches to CAC applications employed today: structured input, and natural language processing (NLP). Structured input or text, or codified input, is a form of data entry that captures data in a structured manner (for example, point-and-click fields, pull-down menus, structured templates, macros). Structured input CAC applications are essentially a documentation system where predefined clinical documentation is linked to applicable codes. As the clinical documentation is created, via the caregiver selecting applicable clinical phrases, the linked codes are automatically suggested.
Natural Language Processing (NLP) is essentially a form of artificial intelligence that emulates the way people read and understand so that it can extrapolate information from the written language the way the human brain does. This software technology is applied to a text-based document and uses computational linguistics to extract pertinent data and terms and convert them into discrete data, in this case medical codes. NLP-based CAC applications may use either a statistics-based (aka data-driven) or rules-based (aka knowledge-driven) approach to assign the code. Often a hybrid, or combination of both is employed in the NLP system architecture. With a statistics-based approach, the software predicts which code might apply for a given word or phrase based on past statistical experience. The rules-based approach uses programmed rules, or algorithms.
There is an entirely different mechanism to assist the coding process via automation – and that is the concept of mapping from a reference terminology embedded in an EHR to a classification system. Theoretically, once an electronic health record (EHR) containing a clinical reference terminology (for example, SNOMED-CT) has been implemented, information captured in the EHR during the course of patient care can be codified using the reference terminology and an automated mapping process may be employed to link the content of the terminology to the desired code sets for secondary uses.
Thus there are potentially three different ways to accomplish the medical coding process:
- Manual code assignment (with or without encoding tools),
- Use of a CAC application, or
- Mapping from a reference terminology embedded in an EHR to a classification system.
We would like to compare and contrast these three methodologies for you. First, I will compare the manual coding process with the use of CAC applications and then Mary Stanfill will address mapping from a reference terminology embedded in an EHR.
The Manual Coding Process
Medical coding involves manual evaluation and review of clinical documentation and application of coding and reporting rules to assign administrative codes. This process may include use of code books or computerized tools (for example, encoders, automated super-bills, pick lists) and it may be performed by multiple individuals ranging from noncredentialed or credentialed coding professionals to physicians.
Coding Process Using CAC Applications
In contrast, the coding process utilizing a CAC application is very different. Structured input CAC applications are used by the caregiver, often at the point of care, as a data entry mechanism. The clinician captures clinical information by adhering to the software application’s pre-defined structure for input. For example, he or she may select applicable words/phrases from menus or utilize multiple point and click fields to store the information. If a menu or field is skipped, the application may prompt the caregiver for the missing documentation.
Implementation of a structured input CAC application first involves developing, or tailoring, the structure for data input so that it closely matches the clinical information that will be stored and maintained as health record documentation. Once clinical information is set up in a structured format, codes are assigned to the clinical words/phrases where applicable. The software links these codes with the correct phrases so that codes may be captured as the documentation is created. The list of codes that correspond to the documentation is presented to the caregiver for review and validation and is subsequently presented to the coding professional for review.
The coding process using a NLP-based CAC application is a little different. This software undertakes the following processes almost simultaneously:
- It evaluates the documentation resulting from a patient-provider encounter to suggest codes that reflect the written word as documented (employing the computational linguistic analysis characteristic of natural language processing).
- Most NLP-based CAC applications use a combination of the statistics-based and rules-based approaches. In most cases, the statistics-based approach is applied first, and if errors are detected the rules-based approach is applied. Then an extensive quality check is usually performed.
- As the software performs this analysis it may also evaluate patterns of documentation that are statistically different from the average documentation for similar cases. In this manner it identifies potentially incomplete documentation so that physicians can be queried. Physicians are provided with feedback regarding documentation variances to help improve documentation practices.
- A list of suggested codes is sent to the appropriate coding personnel to verify the codes. Code output, from an NLP-based CAC application, is ranked based on the computer’s level of confidence in the accuracy of codes generated. Typically three levels are used to indicate: a high degree of confidence, more questionable codes, or the inability of the software to determine potential codes.
In both the manual coding process and the coding process using a type of CAC application, the final code set is determined by a person. However, the process using CAC differs from the manual process in a significant way – CAC applications suggest potentially applicable codes, rather than a coding professional being entirely responsible for code selection. Thus, CAC tools can significantly improve productivity.
It is important to note that CAC applications today are not capable of generating codes on every single case. Manual coding must still be performed on cases that do not readily fit the defined input structure or on types of cases that the NLP system has not previously encountered and thus has no framework from which to suggest applicable codes. So, with both structured input and NLP-based CAC applications, humans perform some manual code assignment, but to a lesser degree.
Editing and validation of computer-generated codes may involve use of coding tools such as up-to-date code books, coding references, and encoders that assist in determining the correct code assignment through text prompts. The expectation is that, as the coding professional becomes an expert editor, editing codes generated by the software is much less time consuming than an entirely manual process. In determining the final code set, the expert editor also applies modifiers and other payer reporting requirements that often require contextual information to be accurate (for example, Medicare’s Correct Coding Initiative edits). When CAC tools are employed, coding professionals’ roles evolve to include the following:
- Documentation specialists
- Revenue cycle specialists
- Data quality experts – assist in ensuring data input accuracy and monitor data output for accuracy
- Prepare utilization analysis and provider profiles
Benefits of Employing CAC
Even though CAC applications do not fully automate the coding process (human review for final code assignment is still necessary) these applications are beneficial for several reasons. They increase coder productivity and make the coding process more efficient. Thus, there is a return on investment. Equally important the availability of CAC applications relieves the shortage of expert coders and enable them to perform other critical data management roles in the electronic health information management (HIM) environment. AHIMA’s practice brief: Delving into Computer-assisted coding, includes a comprehensive exploration of the advantages and disadvantages of CAC.[i] Reported improvements in the coding process include:
- Improved coding consistency,
- More comprehensive coding,
- Enhanced coding compliance,
- Decreased coding/billing costs,
- Faster turnaround time (resulting in decreased accounts receivable days)
- Enhanced workflow
Benefits unique to structured input are related to the documentation process. Structured input creates consistent and potentially more complete documentation. The physician is prompted to add specificity to better reflect clinical details in the ICD system. This potentially eliminates physician queries. Also, structured input systems replace some dictation/transcription, thus reducing associated costs. A significant benefit unique to NLP-based CAC is that physicians may continue to document using preferred terms.
Lastly, many CAC applications offer mechanisms to query data from their systems. Thus we anticipate improved ability to analyze administrative data. The use of CAC data for such purposes as JCAHO auditing, QA measures, performance studies, credentialing and research is an attractive feature of this technology. CAC applications may be characterized as bridge technologies that serve the pressing need to improve today’s manual coding process.
Anticipated Impact of CAC
CAC has a significant impact on the coding workflow. With CAC that uses structured input, the entire coding workflow from the point of documentation through claim submission is affected. Physicians are directly impacted as they must document using the predefined structure (tailored to his/her practice). Cost savings are reported from elimination of transcription/dictation. But there are reports that some systems increase physicians’ documentation time causing decrease in throughput of patients and increasing waiting times, in these cases. However, when CAC works well, it does provide a closer relationship between data capture in real time and code assignment. With NLP-based CAC the documentation process does not have to change. In fact, this type of CAC may be transparent to the physician.
With both structured input and NLP-based CAC the responsibilities of the coding staff are significantly different. The coding professional must shift from a production role to the role of an expert editor. This means that they spend the majority of their time coding complex or unique cases, which can be more tiring. But it also means that the routine, mundane coding does not distract them from being able to focus on delving into the nuances of the coding and reporting guidelines as they apply to a complex case.
CAC also impacts management of the coding process. The workload can be managed by routing work to queues based on specific parameters such as the report type, the particular codes suggested, or the CAC application’s confidence level. This creates a much smoother workflow and allows coding staff to focus and become expert in certain specialties. For example, one coder could be designated to code all interventional radiology cases, or the particular staff member who had an opportunity to discuss a new surgical technique with the surgeon may be designated to code those procedures until cross-training can be provided to the rest of the coding staff. CAC applications also enable management of the coding process through tracking and administrative reporting. In short, incorporating a CAC application in the coding workflow has a profound impact on the coding staff.
It is more difficult to generalize the effect in terms of staffing changes. I noted that CAC has demonstrated improved productivity. However, specific performance in terms of coding accuracy for correct reimbursement is largely unknown. In our recent research, we found that the coding quality in many of the systems employed has not been assessed in actual practice. Overall, no CAC application, to date, has an accuracy rate that meets the existing industry standard of 95 percent that coding professionals are expected to meet.[ii] Thus CAC applications have not reached the point where large displacement of the coding workforce can occur.
It should be noted that multiple vendors’ marketing materials claim that their CAC products will result in reduction in FTEs and certainly increased productivity always carries this potential. However, there are no empirical studies from which to estimate overall staff reduction verses shift in responsibilities or simply relief from the coder shortage. One user reported that implementation of CAC allowed their coders to work remotely which has significantly improved morale and retention. Others specifically reported no reduction in FTEs but rather reassigned tasks. This is not surprising as CAC applications do not address specific reporting requirements and have been only minimally deployed for reimbursement use cases in the inpatient setting (where the real potential for staffing reduction exists).
Status of Deployment of CAC
I will now address the status of deployment of CAC technology. Widespread adoption of these technologies has not yet occurred. There are only a minimal number of CAC applications that address inpatient coding for reimbursement purposes. CAC applications are most commonly found in outpatient settings, such as physician practice and hospital outpatient ancillary departments or emergency departments. Structured input CAC is deployed in procedurally driven domains where documentation is predictable and repetitive (for example gastroenterology, orthopedics, urology, pulmonary medicine). NLP-based CAC is deployed in specific specialties where the vocabulary is more limited and source documentation is both limited and available in electronic text format (for example, radiology, cardiology, emergency medicine).
As I noted earlier, CAC applications are bridge technologies that serve the pressing need to improve today’s manual coding process. Mapping from a clinical terminology to a classification system is ideal for secondary uses of data. Thus, before we discuss catalysts and barriers affecting deployment of CAC applications. Mary Stanfill will now describe the coding process when reference terminology embedded in an EHR is mapped to classification systems.
Mary H. Stanfill, RHIA, CCS, CCS-P
Good afternoon, I am now going to compare and contrast the process of mapping from a reference terminology embedded in an EHR to a classification system with the work process utilizing a CAC application that Dr. Watzlaf described.
Coding Process When Reference Terminology Embedded in EHR Is Mapped to Classification Systems
Together, terminologies, such as SNOMED-CT, and classification systems, such as ICD-9-CM, ICD-10-CM, and ICD-10-PCS, provide the common medical language necessary for interoperability and the effective sharing of clinical data in an EHR environment. The benefits of using a reference terminology such as SNOMED-CT in an EHR increase exponentially if the reference terminology is linked to modern, standard classification systems for the purpose of generating health information necessary for secondary uses. This linkage from the reference terminology to a classification system is accomplished through mapping.
Mapping is the process of linking content from one terminology to another or to a classification. It provides a link between terminologies and classifications in order to:
- Use data collected for one purpose for another purpose
- Retain the value of data when migrating to newer database formats and schemas
- Avoid entering data multiple times and the associated risk of increased cost and errors.
Clinical data captured at the point of care can be efficiently and effectively used for a variety of administrative and secondary purposes such as reimbursement, quality measurement, medical error or public health reporting, vital and health statistics trending, health policy decision-making, and so on. Driven by a philosophy of “code once, use many times,” after clinical care is recorded in an EHR using SNOMED-CT, mapping tables can be used to identify the related code(s) in ICD. This process allows data encoded in SNOMED-CT to be aggregated into groupings for data reporting and analysis. Mapping from the reference terminology to classification systems avoids duplicate data capture, while facilitating enhanced health reporting, billing, and statistical analysis.
The mapping process employs a standard method. It begins with the development of heuristics (rules of thumb used for solving problems) and guidelines that support the use case or purpose of the map, respecting the conventions of the source and target to preserve the granularity and flexibility of both. Defined mapping rules must be developed and consistently applied to minimize incompatibilities without compromising clinical integrity. To do this the map must remain context free, meaning care must be taken not to introduce any assumptions or assertions. In order for diagnosis and procedure codes resulting from a map to be appropriate for use in meeting reimbursement requirements, algorithms that consider coding rules and conventions and reporting requirements (such as adhering to coding guidelines and identifying the principal diagnosis) need to be developed and applied to the mapping process.
The development of maps between terminologies and classifications will not eliminate administrative coding or the need for expertise in code selection. Fully automating the process of mapping from a reference terminology to a classification system is challenging because of the inherent differences between them. The mapping process is straightforward when the source terminology and the target match up. When more information is needed to express the concept in the target a CAC application can be used to bring in contextual information to further refine the map output. These slides (below) illustrate the mapping process from SNOMED-CT to ICD-9-CM for a few concepts at varying levels of detail. On the left is the SNOMED CT concept ID for the clinical finding. On the right is the mapped ICD-9-CM diagnosis code. When there is a direct match between the concept and the code- the mapping is very straightforward. For example, the concept of hypertension (without any further specificity) is fully reflected in one ICD-9-CM code (401.9). This happens fairly often.
This second slide represents an instance where mapping is more complex. This is an example where variance between the systems requires additional information in order to determine the target code. The concept esophageal reflux cannot be assigned an ICD-9-CM code without some additional information because it is classified in ICD-9-CM as either with or without esophagitis. Ulcer of esophagus is another example where contextual information is needed to complete the map because ICD-9-CM classified an esophageal ulcer as with or without bleeding. The default code is without bleeding but you would not want the automated map to always default to that. An IFA rule is defined in the map to allow for obtaining this contextual information. The map output is 530.21 if bleeding, or 530.20 if no bleeding. Somehow the correct code for the case must be determined from this output. This could be done by human review- or perhaps a CAC application.
In an EHR with automated mapping from reference terminology to administrative code sets the coding professional’s knowledge will expand to include expertise in clinical terminologies, medical vocabularies, as well as classification systems. Rather than focusing on code assignment, coding professionals will focus on management and use of the data. We anticipate their role will include many of the functions Dr. Watzlaf described with use of CAC applications, such as documentation specialist and revenue cycle specialist, but their role will also includes functions such as:[iii]
- Creation, maintenance, and implementation of terminologies, validation files, and maps for a variety of use cases in the EHR
- Ongoing review of the auto and manual encoder systems for terminology and classification systems, including methods and processes, and implementation of recommendations for improving and optimizing the encoding process
- Assist in the analysis of the enterprise’s classification and grouping system assignment trends and use data from classification and grouping systems to assist in decision making
- Proactively monitor developments in the field of clinical terminologies and medical vocabularies
- Recommend the most appropriate classification or terminology systems to meet all required information reporting needs
Benefits of Mapping from Terminology to Classification System
Dr. Watzlaf stated that current CAC applications are really just an interim step during the transition to fully-implemented EHR systems. Mapping is the ideal goal for a couple of key reasons. While use of CAC applications can increase productivity and create a more efficient coding workflow, the use of a standard terminology has the potential to further increase the accuracy of automated coding, and thus further increase efficiency. In addition, it is possible to more fully automate the coding process in an EHR with embedded clinical reference terminology mapped to a classification code set than with use of a CAC application. Structured input systems essentially employ manual coding, albeit coded once at the time the structure is set up, but this set-up is time consuming and does not lend itself readily to all the nuances of clinical practice. NLP-based CAC has improved dramatically over the last four years or so, but more research is needed on the accuracy of these systems and expansion into clinical domains with broader clinical vocabularies is difficult to achieve.
Anticipated Impact of Mapping
The coding process when mapping from a reference terminology in an EHR is entirely different than the process using a CAC application, particularly in terms of the computing process that actually generates the suggested medical codes. As with CAC applications, human review is still necessary before reporting a code resulting from a map, in order to ensure accuracy with regard to the context of a specific patient encounter and compliance with applicable coding guidelines and reimbursement policies. As rules-based maps are developed for multiple-use cases and become increasingly sophisticated, the level of human review at the individual code level will diminish. Workplace roles will focus on the development and maintenance (including quality control) of maps for a variety of use cases and the development of algorithmic translation and concept representation. Reduced staffing is expected.[iv]
The majority of our comments related to the impact of these technologies has focused on workflow and staffing. I would also like to address the impact on data quality.
If clinical data, captured in an EHR at the point of care, is to be useful for whatever secondary purposes one might find appropriate – data quality is critical. A common concern with data quality is manipulation of documentation to affect billing codes. Boundaries between clinical data capture and reimbursement are necessary to ensure data integrity. A clinical terminology intended to support clinical care processes should not be manipulated to meet reimbursement and other external reporting requirements, as such manipulation would have an adverse effect on patient care, the development and use of decision support tools, and the practice of evidence-based medicine. Use of a reference terminology embedded in an EHR separates clinical data capture management from reimbursement. This is expected to improve data quality, resulting in more accurate reimbursement.
Status of Deployment of Mapping
Today, an EHR with mapping from a reference terminology to a classification system is rare. A map from SNOMED-CT to ICD-9-CM already exists, and, according to SNOMED International, the purpose of this crossmapping is to support the process of deriving an ICD-9-CM code from patient data.[v] The map provides users with an approximation of the closest ICD-9-CM code(s). However, the mapping table is not intended for direct billing or reimbursement activities without additional authoritative review.[vi] Since SNOMED-CT’s scope of content is much broader than ICD-9-CM, less than 30 percent of the content of SNOMED-CT can be mapped to ICD-9-CM.[vii]
Barriers to Adoption of CAC and Mapping Technologies
Lack of widespread adoption of EHRs is a barrier to adoption of these technologies. Without an EHR, the complexity, quality and format of health record documentation makes it difficult to integrate CAC applications in the coding process. There are other barriers as well. A significant barrier is the complexity of federal and state regulations impacting administrative clinical data reporting and our national reimbursement structure, resulting in variable and conflicting reporting requirements.
Today, many administrative coding practices are driven by individual health plans or payer reimbursement contracts or policies requiring healthcare providers to add, modify, omit, or re-sequence reported diagnosis and procedure codes to reflect specific payer coverage policies or regulatory requirements, contrary to code set reporting standards. In the AHIMA practice brief “Internet Resources for Accurate Coding and Reimbursement Practices” over 100 Internet resources are compiled to assist with the coding process.[viii] This illustrates the complexity of the rules impacting the coding process.
Not only does the variability in reporting requirements undermine the integrity and comparability of healthcare data, it significantly complicates the development of map rules and algorithms in CAC applications, hampers advances in CAC application, and increases the extent of human review required in both CAC and mapping technologies. Current CAC applications rely on human intervention to apply these rules – limiting the degree of automation and thus the potential return on investment (ROI). The integrity of coded data and the ability to turn it into functional information require the use of uniform coding standards, including consistent application of standard codes, code definitions, and reporting requirements. In addition, variable code set update schedules increase the cost and complexity of ensuring CAC applications, and maps are accurate and up-to-date.
The failure to implement ICD-10-CM and ICD-10-PCS is also a barrier. It is extremely difficult to develop valid maps from current clinical reference terminology to an obsolete administrative code set. It makes no sense to map a robust terminology such as SNOMED-CT to an outdated classification system such as ICD-9-CM. The anticipated benefits of an EHR cannot be achieved if the reference terminology employed in the EHR, such as SNOMED-CT, is aggregated into a 30-year-old classification system such as ICD-9-CM for administrative use and indexing. When an up-to-date clinical terminology is mapped to an outdated classification system, the map is less reliable and meaningful information is lost. [ix] Furthermore, extensive guidelines and instructions have been created to compensate for the difficulties in using the obsolete ICD-9-CM coding system. This simply adds complexity in developing map rules and/or algorithms for CAC applications. In contrast the detailed and logical structure of ICD-10-CM and ICD-10-PCS simplify this development.
There are information technology barriers to adoption of CAC as well. This subcommittee is well aware of the lack of industry standards that makes integration of software applications difficult. In addition, this technology itself has limitations. As Dr. Watzlaf noted, structured input is best suited to procedurally driven domains and NLP is limited to electronic text-based documents. Also, performance of CAC applications, in terms of quality, is unknown. Available research evaluating existing CAC applications is insufficient. We need research designed to assess the usefulness of these applications in the administrative coding process. For example, where ICD guidelines are taken into consideration.
In relation to mapping, heuristics are extremely difficult to define given the various reimbursement rules, plus the inherent differences between reference terminologies and classification systems. Thus mapping between SNOMED-CT and ICD is an imperfect science. It is very difficult to adequately represent some of the ICD coding conventions for a computer’s purposes. The codes produced by the crossmap must be evaluated in the context of the complete medical record, as well as applicable reporting rules and reimbursement requirements before being submitted to payers and other external entities. Reliance on the technology alone carries the potential for increased errors in the coding process and associated compliance concerns.
Concerns of those involved in the coding process, the potential users of CAC and mapping technologies, can also be a barrier. These technologies involve significant change, thus user resistance to change is a very real factor. Physicians often resist structured input: coding professionals often resist re-engineering the coding workflow. Other concerns are more concrete, such as the cost of CAC hardware and software, and the pressure to meet healthcare compliance requirements. The healthcare industry today is very sensitive to issues that may result in allegations of fraud or abuse. If not carefully designed and used with caution, documentation generated via structured templates may justify more reimbursement than deserved for the services rendered. Physicians and coding professionals express concern as to whether the HHS Office of Inspector General (OIG) will embrace or even allow structured input systems.
Catalysts for Adoption of These Technologies
This lengthy discussion of barriers may sound daunting, but it is really not surprising when you consider that CAC is a disruptive technology.[x] So what are the drivers and trends that will produce a natural rate of diffusion?
There are many factors within the healthcare industry driving this technology, including the movement to adopt EHRs and create a national health information network (NHIN). The continued trend of increases in administrative costs within healthcare is also a factor. The manual coding process widely employed today is expensive and inefficient, thus there is a recognized need to improve the coding process. Also the shortage of qualified coders and increased outsourcing and remote work sites encourages use of CAC for productivity and consistency gains.
Deployment of EHRs with data input codified in a clinical reference terminology is a catalyst that will cause innovative computer assisted coding to become a necessity. Other catalysts that will enable this technology include:
- Simplification of reimbursement regulations, so that algorithms can be designed and more readily deployed and maintained.
- Adoption of ICD-10-CM and ICD-10-PCS to facilitate the development of automated maps between clinical terminologies and classification systems.
- Validation and availability of reimbursement use case maps from reference terminology to administrative code sets (for instance, SNOMED-CT to ICD-9-CM, SNOMED-CT to CPT, and LOINC to CPT).
As this committee knows, the NLM, through the unified medical language system (UMLS), has played a large role in emerging national standards for the electronic health record. They are committed to facilitate development of systems that can retrieve and integrate electronic biomedical information from a variety of sources. Toward that aim they are prepared to assist in the development, review and testing of mappings between reference terminologies and classification systems. Indeed, they have already committed resources and this work is underway.
Dr. Watzlaf will now provide recommendations and closing remarks.
Valerie J.M. Watzlaf, PhD, RHIA, FAHIMA
As we have discussed, CAC applications, presently, are not widely deployed, and an EHR with mapping from a reference terminology to a classification system is rare. We believe the subcommittee can speed adoption of CAC if you support:
- Continued efforts to encourage widespread adoption of EHRs
- Efforts to simplify and standardize the reimbursement framework
- Expeditious adoption of ICD-10-CM and ICD-10-PCS
In addition, I would like to describe some specific areas where the NCVHS could recommend further research to affect the development and adoption of CAC. Research is needed to:
- Evaluate the use of CAC technologies in production EHR settings. Compare and contrast the benefits in terms of data integrity, productivity, and compliance monitoring for EHRs that feature structured versus unstructured text and those based on a reference terminology.
- Create use cases and test databases on which to evaluate the capability of CAC technologies to assign codes according to standard coding guidelines and rules. Many of these tools are new and not widely used in production settings. Early laboratory-based research could provide useful insight while broad-based field research is not feasible. This will permit assessing how best to certify these technologies in the future.
- Evaluate the potential of automated coding software used in conjunction with the EHR to relieve coding workforce shortages. Research is needed to better understand this potential and what skills and competencies are required by coding experts of the future.
Other tangential, long-term research questions include:
- What is the statistical impact of CAC on errors and fraudulent claims?
- Are there statistically significant differences in error rates based on the type of CAC method?
- Is there a statistically significant difference in the number of claim errors in terms of a pre- and post- CAC usage?
- What is the best methodology to evaluate error rates of CAC in various healthcare settings?
- Is there a statistically significant difference in error rates in diagnostic, procedural, and evaluation and management code assignment?
- What are the known economic impacts or potential benefits through cost savings and accuracy improvements associated with CAC software?
- What is the impact of CAC on various constituents including users, beneficiaries, payers, and law enforcement?
- What is the impact of CAC on users?
- What is the impact of CAC on beneficiaries, and does it affect the interaction with providers positively or negatively?
- What is the impact of CAC on payers and law enforcement personnel?
- What is the prevalence rate of CAC products across the healthcare industry, including hospitals, outpatient facilities, physician offices, and so forth?
AHIMA and its members are pleased the NCVHS is taking a concerted interest in issues related to CAC and mapping, and we are ready to work with the Subcommittee and the full committee to help our industry move forward with its understanding and use of these significant tools. Thank you again for the opportunity to contribute to your discussion here today. Ms. Stanfill and I will be happy to answer any questions the subcommittee may have.
|Valerie J.M. Watzlaf, PhD, RHIA, FAHIMA
Department of Health Information Management
School of Health and Rehabilitation Sciences
University of Pittsburgh
Pittsburgh, PA 15260
Telephone: (412) 383-6647
|Mary H. Stanfill, RHIA, CCS, CCS-P
Professional Practice Manager
American Health Information Management Association
233 North Michigan Avenue, Suite 2150
Chicago, IL 60601-5800
Telephone: (312) 233-1525
[i] AHIMA practice brief: “ Delving into Computer-Assisted Coding.” November-December, 2004.
[ii] CAC fraud research project for ONCHIT: “Health Information Technology and Health Care Antifraud”
[iii] AHIMA practice brief: “EHR Career Opportunities.” June 2005.
[iv] AHIMA practice brief: “HIM Practice Transformation.” May 2005
[v] Imel, Margo and James Campbell. “Mapping from a Clinical Terminology to a Classification.”
[vi] Imel, Margo. “A Closer Look: the SNOMED Clinical Terms to ICD-9-Cm Mapping.” Journal of AHIMA 73, no. 6(2002):66-69.
[vii] Imel, Margo and James Campbell. “Mapping from a Clinical Terminology to a Classification.”
[viii] AHIMA practice brief: “Internet Resources for Accurate Coding and Reimbursement Practices”
[ix] Bowman, Sue. “Coordination of SNOMED-CT and ICD-10: “Getting the most out of electronic health record systems.” Perspectives in Health Information.
[x] Coye, Molly Joel, MD, MPH, Wade Aubry, Wil Yu. “The “Tipping Point” and Health Care Innovations: Advancing the Adoption of Beneficial Technologies.” Presentation at the accelerating quality improvement in health care conference held in Washington DC, January 27-28, 2003.