Computer Assisted Coding & Natural Language Processing

Transcription

1 Northwestern University MED INF 408 Fall 2014 Computer Assisted Coding & Natural Language Processing Scope of Work Russ Abercrombie, William Dailey, Jeremy Lutz 10/19/2014

2 Document Control Sheet General Information Project Name Project Manager Business Owner (Key Sponsor) Provider Single Point of Contact NLP/CAC CMIO Director of IT Coding Manager Document Preparation Information Author Date Organization Name File Location (link) CMIO 10/16/2014 GVMH Phone Number (660) N/A Distribution and Approvals Name Title and Organization Signature Approval Date Change History Date Change Description Approved By 2

3 Background Golden Valley Memorial Healthcare (GVMH) is a healthcare system comprised of one hospital and a multispecialty clinic with 4 clinic locations. The multispecialty clinic has 30 providers, 6 of which are specialists and 24 are primary care providers. Inpatient volumes continue to decrease at approximately 4-5% per year while outpatient volumes continue to grow. Over the past 5 years, the healthcare system profit margin has been driven by this increased outpatient revenue. Optimizing efficiency with regard to this revenue stream is critical to the continued success of GVMH. Increased pressures on coding accuracy coupled with this increase in volume have created a large opportunity for improvement in this revenue stream. There are significant bottle-necks in the processes that currently exist between the point of care and the resulting reimbursement. The methods previously in place for coding, billing and subsequent reimbursement have not scaled well in response to increased outpatient volumes and the looming ICD-10 transition will only make matters worse. The impending requirement for all healthcare organizations to transition from the ICD-9 code set to the ICD-10 code set in October of 2015 will create many challenges for HIM and coding professionals. First and foremost, the transition from ICD-9 to ICD-10 will expand the code set dramatically, from the roughly 14,000 codes in ICD-9-CM to about 68,000 codes in ICD-10-CM and from roughly 4,000 in ICD-9-PCS to about 87,000 in ICD-10-PCS (American Medical Association, n.d.). The significant expansion of the code set allows for more specific and granular coding, but also brings with it more complexity. This complexity, along with the need 3

4 for coders to learn, essentially, a brand new code set is, will require more time and effort for the coding process. An increase in the time and effort required for coding can have several negative effects on an organization, including (Cassidy, 2013): An overall in decrease in coder productivity and throughput, which could lead to a need to increase the coder workforce. This could be further complicated by a possible shortage in certified coders (Selvam, 2013). Delays in claims submissions and billing processes due to coding backlogs and decreases in coding accuracy and compliance. Negative long-term effects on the overall quality and standardization of care due to inefficient and inaccurate coding. The conversion to ICD-10 has the potential to increase data quality, which will allow healthcare organizations to increase adherence to evidence-based care, analyze gaps in care, improve clinical and billing processes, and facilitate quality reporting (Dougherty, 2013). But in order to fully realize this potential, and avoid the possible negative effects of the ICD-10 conversion, organizations will need to take advantage of technologies that will help ensure coding accuracy and efficiency. These issues create a great impetus to change the approach to coding. With this in mind, it is helpful to review the current processes for a basis in understanding the impact of potential solutions. 4

5 Status Quo The current system can be broken down into several base components that are important with regard to optimization. These components begin at the patient encounter with the electronic medical record (EMR), traverse coding and billing prior to submission to the payer for reimbursement. Descriptions of each of these base components are outlined below. EMR The electronic medical record system used in the outpatient setting is McKesson IC-Chart (formerly Med-3000/InteGreat). This system allows the providers to document both discrete and free text items either concurrently or subsequent to the encounter. EMR functionality and individual provider preference, efficiency, and thoroughness are the cause for variation from provider to provider regarding granular versus free text data entry for each encounter. All fixed historical data are fully granular within the EMR. Free text entries are classified in the typical fashion of the SOAP format within the note and as such are actually classified free text. Examples of this would be a free text blocks classified as History of Present Illness, Review of Systems broken down by system, Exam broken down by region or specialty, Diagnosis and Plan. Each of these can be discretely selected and coded by MEDCIN ID also via radio buttons and selection trees. Generally, each progress note contains a blend of discrete items and these classified free text items, again efficiency determines this blend. Once documentation on the encounter is complete, the provider finalizes the note within the EMR and an E&M code (level) is automatically determined based on the discrete data alone. The provider has the opportunity at this time to change that level to a more appropriate level based on what they have done within the free text portions of the note. Some providers do this well and some leave the code as selected based on the discrete data alone resulting in under- 5

6 coding. The note is then approved and is forwarded to coding. The average note is completed and forwarded within 2 days with the vast majority completed concurrently with the patient encounter. Coding The next step is for coding to manually extract and assign ICD-9/10 codes for all free text diagnoses and do all required compliance checks to ensure each encounter is appropriately coded and has the proper E&M level as supported by the entirety of the documentation (discrete and free text). This is a computer assisted (Encoder Pro) manual process that has resulted in an increasing backlog within the coding department as volumes have increased. The current average backlog within coding is 21 days. Once this is complete the appropriately coded and checked super-bill is forwarded to the business office for billing. Billing The business office receives the super-bill, verifies payer information and submits the claim to the appropriate payer. There are other business processes involved resulting in a 10 days average delay in charge submission. Payer Various payers have different turn around times for reimbursement. These reimbursement times vary from 14 days for private payers, to 30 days for Medicaid and Medicare. The average time to reimbursement is approximately 24 days. This results in average account receivable (AR) days in billing of approximately 34. Cost Each day of delay in these processes has a cost associated with it. Figure 1 shows the overall process and associated cost. 6

7 Figure 1. Process and Cost Figure 1 demonstrates the process starting at the date of Service (DOS) and progressing through reimbursement ($). Discussions with the clinic administrator reveal the annual cost per average day delay attributed to each portion of the process. Example, if average AR days were shortened by 1 day, this would result in an increase of $100,000 income for an entire year. These numbers are determined based on the volume, E&M/CPT level and payer-mix specific to GVMH. There is a nominal cost to recoding that is calculated via the contribution to average AR days. This <5% reject resulted in an additional 7 day delay which contributes approximately 0.35 days to the average AR days. The bottom line is there is an annual $2.3 million cost associated with delays in the outpatient coding and billing cycle that can be avoided at GVMH. This is a significant cost and there is great interest in increasing the efficiency of this process. Note: The payer delay is essentially fixed (although there may be some short-cycle potential if direct to bill were utilized). 7

8 Objectives The overarching objective of this project would be to increase coding and billing efficiency to shorten the time between DOS and reimbursement to recoup a portion of the cost outlined. The calculations are straightforward -- each day saved in the coding process results in $65,000 savings over the year. Similarly, each day saved in billing results in $100,000 savings over the year. The overall cost being $2.3 Million, the goal is to reduce the cost by 10% resulting in an increase in revenue of $230,000 annually. This goal could be accomplished by leveraging Natural Language Processing (NLP) and Computer Assisted Coding (CAC) technologies to reduce coding costs, increase coder efficiency, and increase coding accuracy. CAC systems use an NLP engine to scan electronic clinical documentation to facilitate, streamline, and speed up the coding process. In short, the CAC system interfaces with an organization s EHR and various other systems to gather all the necessary documentation, then scans the documentation to produce appropriate lists of ICD-X codes (many systems will produce both ICD-9 and ICD-10 codes) for coders to review and, if necessary, edit (Crawford, 2013). There is strong evidence to suggest that CAC systems cannot only overcome the challenges presented by the implementation of ICD-10, but also improve the throughput of coders with no decline in accuracy. One study performed at Cleveland Clinic showed that coder efficiency improved by as much as 22%, with coder accuracy levels remaining above Cleveland Clinic s credentialing threshold 95% (Dougherty, 2013). In addition, many NLP engines are designed to learn over time. This process of learning/tuning enables the system to become even more 8

9 accurate the more it is used (Dougherty, 2013). Some people even see the potential for the CAC system to possibly run in a completely automated fashion, eliminating the need for coders to review/audit the results (Crawford, 2013). The overall goal of increasing coding efficiency and accuracy will help the organization meet the following objectives: Capture lost revenue by improving efficiencies in the coding life cycle. Minimize or eliminate the risk of reductions in coder productivity or accuracy due to the impending conversion to ICD-10. Minimize or eliminate coding backlogs. Protect the organization from potential shortages of certified coders if some level of automation is implemented. Increase efficiency in claim submission and billing processes. Provide valuable data and insights for any current or future efforts to improve evidencebased care and standardization of care. Natural Language Processing (NLP) technology is relatively new as applied to medicine but has been around for quite some time in other applications (Wolniewicz, 2013). The basics of NLP are that free text broken into sentences, analyzed by a natural language engine and is tokenized to classify each word as a particular part of speech (noun, verb, pronoun, adjective etc) via specific training corpus. The next step removes stop words ; these are words that add nothing to the meaning therefore if removed the meaning remains unaffected. A whole host of algorithms can then be applied to reveal the meaning of the particular set of classified words 9

10 (heuristics, trees etc). The meaning is then converted into the pertinent ontological code that can then be consumed in combination with existing discrete data by the CAC engine (Bird et al, 2009). Figure 2 shows the basic concept (Wolniewicz, 2013). Figure 2. NLP and CAC overview The basics are that the free text entries are read and mapped into the appropriate medical ontologies (SNOMED-CT, ICD-9/10, RxNorm). These are then combined with the already discrete data captured in the balance of the note, creating a fully-discrete version that can then be fed into the encoder. This new CAC system will streamline the coding process and transform coders into higher-level coding auditors. This next-generation coding solution completely automates what was previously a resource-intensive process requiring manual interpretation of clinical documentation and coding input prior to billing. (Crawford, 2013). Productivity increases from 20-89% have been realized in outpatient settings (Morsch & Martin, 2013; Nuance, 2013). 10

11 CAC systems rely on discrete data in standard formats. Unstructured data cannot be processed by a CAC system unless there is an underlying NLP engine to create discrete data from unstructured text documents (Morsch, 2010). And because NLP engines are absolutely necessary for CAC systems to process data stored in unstructured formats, maximizing the accuracy of the NLP engine is critical to the success of the CAC system (Kohn, 2013). It s also important to note that NLP has benefits beyond the CAC system, including: Up to 60% of clinical documentation is stored as narrative or unstructured text, which cannot be understood by computers without an NLP tool (Harris, 2012). NLP systems can improve clinical documentation improvement (CDI) efforts by identifying missing, incomplete, or inconsistent information in clinical documentation (Harris, 2012). Structured, discrete data (as opposed to narrative or unstructured data) can be used by other computer-based systems, like EHRs and CDSS (Harris, 2012). Structured data also enables improvements in interoperability (Harris, 2012). For clinicians, NLP tools ease the burden of entering structured data into EHRs or other clinical systems and allow them to use more efficient workflows like entering narrative text and/or using dictation (Terry, 2012). Minimally, NLP and CAC should be capable of delivering on at least the low end of that spectrum (20%). Applied to this case that would allow for a potential shortening of the current 21 day coding delay to around 19 days. It is likely that some direct to billing would also occur 11

12 over time, reducing some of the time in billing (conservative estimate of a 1 day equivalent). Additional reductions would come in the form of eliminating or reducing the needs for recoding (0.35 days). The potential annual savings would be: 2 x $65,000 = $130, x $100,000 = $135,000 Total Annual Savings estimate: $265,000 Using the lowest quoted improvements in efficiency is very conservative and compares favorably to the overall goal of a $230,000 savings annually. The actual efficiency and improvements have the potential to be much higher. Even using a 40% improvement would double this result and that is still half of quoted maximum improvements (89% Jamaica Nuance). Constraints The following summarizes the time and monetary constraints for purchasing and implementing the proposed system: Initial Cost the initial cost of the system should be set at a breakeven for year 1. This results in an initial budget of $230,000 for purchase/implementation. Implementation interfaces, hardware and training would have to be included in the cost. ICD- 10 Timeline the current deadline set by the Department of Health and Human Services (HHS) is October 1, The CAC system will need to be implemented, tested, and in use in the live environment prior to the compliance deadline. Assuming 12

13 that the selected CAC system can support both ICD-9 and ICD-10, there is currently no reason to prevent earlier implementation of the system if possible. Ongoing Costs the ongoing support and licensing should be less than the differential between the goal and minimum anticipated ($35k) but typical licensing would place it at 15% or < $23,000 annually. Scope The following will define the scope of the project, including specific items regarding what is clearly in scope and what is clearly out of scope. Timeline Evaluate vendor solutions meeting business and budgetary requirements within 2 months. Get acceptance guarantee regarding efficiency gains (20%) from vendor as part of final selection. Complete acquisition via contract within 6 months. Implement solution within 3 months of executing contract (interfaces, installation, training). Evaluate efficiency monthly as an in-process measure. Seek solutions/remedy from vendor on an ongoing basis for 1 year (approaching or exceeding 20%). Included In order to meet the goals to increase coding efficiency and accuracy, while at the same time decreasing coding costs, our organization will procure a commercial-off-the-shelf (COTS) CAC system that leverages NLP technology. The system will be installed and maintained in the GVMH data center. We will form a steering committee comprised of key stakeholders from 13

14 within the organization, including representatives from finance, HIM management and coders, IT, care management, quality, and a representative from the clinical staff (Hartman, 2012). This steering committee will be responsible for the following: Creating RFIs and evaluating responses Creating RFPs and evaluating responses Attending and evaluating product demonstrations Awarding of contract to the chosen vendor The IT organization will manage the vendor and oversee the project using established project management methodology. Once the vendor has been chosen and the contract has been awarded, the selected vendor will: Install and configure the software on an appropriate platform within our network. Work with our internal IT team on integration with our existing systems. Perform thorough system testing and report the results of that testing. Provide training materials and conduct on-site training sessions. Provide post-implementation support and documentation. The features of software provided by the vendor will include, but not be limited to, the following (Cassidy, 2013): A heuristics based NLP engine that will learn over time and improve its coding accuracy. Support for both ICD-9 and ICD-10 Interfaces for our organization s existing EHR system and any other existing systems that are necessary to provide all relevant clinical documentation for coding purposes. Reporting tools to provide clear data regarding the performance and utilization of the system, as well as the ability to run custom reports. 14

15 Present all necessary clinical documentation and relevant ICD-9 and ICD-10 codes to the end user in a clear and easy to navigate view/screen Once the system has been in use for 1 year, we will evaluate the coding efficiency to see if we have achieved a 20% increase in efficiency. Excluded It is important to note that the implementation of the ICD-10 code set in other GVMH systems is beyond the scope of this project. Compliance with the ICD-10 code set will likely require modifications to many existing HIT systems within the GVMH environment. The new CAC system will support both ICD-9 and ICD-10 code sets, so implementation is not contingent upon the ICD-10 code set being installed on other non-related systems. One of the objectives of the CAC implementation is to provide valuable data for programs and initiatives aimed at improving the overall quality of care within our organization or efforts to improve clinical documentation. But any clinical programs or initiatives such as these that will utilize data provided by the CAC system are also beyond the scope of this project. Assumptions The CAC system will rely on clinical documentation to generate appropriate codes. It is assumed that the organization s EHR and other systems will provide a sufficient volume of clinical documentation in electronic format and that the quality of that documentation is also sufficient to produce accurate codes (Crawford, 2013). 15

16 Deliverables Deliverable Responsible Criteria Due Approver RFI Steering Within 1 month 10/5/14 CMIO Committee RFP CMIO Within 2 weeks 10/19/14 CMIO Proposals Vendors Within 2 weeks of RFP 11/2/14 CMIO Decision Selection Within 2 weeks 11/16/14 CMIO Group/CMIO Contracting CMIO 1 month 12/16/14 CMIO Product Interfacing Vendor 1 month 1/16/15 Director IT Hardware IT 1 month 1/16/15 Director IT Software install Training Vendor/IT 2 months 3/16/15 Director IT Go Live ALL Conclusion of training 3/16/15 CMIO Evaluation/ Holdback Payment CMIO Ongoing 3/16/15 CMIO Commitments Commitment Responsible Target Start Date Target End Date Coding Champion Director Business 1/16/15 Ongoing Coders Coding Champion 1/16/15 Ongoing IT technical Staff Director IT 12/16/14 1/16/14 Vendor Support/train Vendor 1/16/15 3/16/15 Vendor Evaluation/retrain Vendor 3/16/15 4/16/16 Final acceptance CMIO 4/16/16 4/16/16 Roles and responsibilities The steering committee will collaboratively produce a request for information specifying needs regarding NLP and CAC. This will include specifications and contacts with current EMR 16

17 vendor. Based on information received from RFI, an RFP will be constructed outlining the project requirements, timelines, expectations and acceptance criteria. The group will then negotiate a contract, including price, acceptance criteria, training, functionality, support and maintenance, upgrades and ongoing licensing fees. The CMIO and IT Director will oversee the install, training and implementation of the product. An instrument will be created to monitor use compliance and to ensure acceptance criteria are met on-budget, ontime; meeting the expected objective of > 20% improvement in coder productivity. If measurements indicate the threshold is not being met, the CMIO and IT Director will work with vendor regarding contractual remedies to the resulting deficiency. It is not enough that the product be installed; it must meet acceptance criteria. Final payments will be made upon meeting acceptance criteria (holdback in contract). This is a very important aspect of the final negotiated contract; it is important that the vendor has a stake in improving our production environment. 17

18 References Bird, S., Klein, E., Loper, E. (2009). Natural Language Processing with Python. O Reilly Media, Inc. Sebastopol, CA. Cassidy, B. (2013). Ten More Questions for CAC Vendors. Journal of AHIMA. Retrieved from Crawford, M. (2013). The truth about computer-assisted coding. Journal of AHIMA / American Health Information Management Association, 84(7): Dimick, Chris. (2013). ICD-10 Part of Achieving Healthcare s Triple Aim. Journal of AHIMA. Retrieved from: Dougherty, M., Seabold, S., and White, S. E. (2013). Study reveals hard facts on CAC. Journal of AHIMA / American Health Information Management Association, 84(7): Harris, B. (2012). 5 benefits of natural language understanding for healthcare. Healthcare IT News. Retrieved from: Hartman, K., Phillips, S. C. C., and Sornberger, L. (2012). Computer-assisted coding at the cleveland clinic: a strategic solution. addressing clinical documentation improvement, ICD-10-CM/PCS implementation, and more. Journal of AHIMA / American Health Information Management Association, 83(7): ICD-10 Code Set to Replace ICD-9. (n.d.). American Medical Association. Retrieved from: ICD-10 Changes from ICD-9. (n.d.). Medicaid.gov. Retrieved from Systems/ICD-Coding/ICD-10-Changes-from-ICD-9.html ICD-10. (2014). CMS.gov. Retrieved from: Kohn, D. (2013). Computer-Assisted Coding (CAC) Solutions. HIMSS News. Retrieved from: Morsch, M. (2010). Computer-assisted coding (CAC): the secret weapon. Health Management Technology. Retrieved from: 18

19 Morsch, M & Martin, C. (2013). Not all NLP is Created Equal: CAC Technology Underpinnings that Drive Accuracy, Experience, and Overall Revenue. Healthcare Financial Management Association. Retrieved from: Not%20all%20NLP%20is%20Created%20Equal_FINAL_ pdf Nuance Clintegrity 360 Computer-Assisted Coding Gains Rapid Market Adoption with more than 40 New Customer Wins. (2013). Nuance.com. Retrieved from: Rouse, M. (2011). Computer Assisted Coding System (CACS). SearchHealthIT.com. Retrieved from: Selvam, A. (2013). Numbers game. Modern Healthcare. Retrieved from: Transforming Medical Coding. (n.d.). Nuance.com. Retrieved from: Terry, K. (2012). Natural Language Processing Takes Center Stage in EHRs. InformationWeek - Healthcare. Retrieved from: Wolniewicz, R. (2013). Auto-coding and Natural Language Processing. 3M. Retrieved from: GevUqe17zHvTSevTSeSSSSSS--&fn=3M_NLP_white_paper.pdf 19

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