Abstract:
A computerized system and method for medical record coding is disclosed. The computerized system and method facilitates pre-screening of medical records and analyzes medical record data to identify potential coding errors. When potential coding errors are identified in a record, the suspect record is forwarded to a certified medical coder to confirm the presence or absence of the error. In an example embodiment, synonyms likely to be found in medical records are associated with standardized medical terms and codes and stored in a database. The computerized system and method searches electronic records using the synonyms. If a synonym is present in a record, the system identifies an appropriate code for the associated medical condition confirms that the appropriate medical code is associated with the record. If the code is not found, appropriate action is taken to correct the coding error. Multiple levels of review may be applied to each record.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    None. 
       BACKGROUND OF THE INVENTION 
       [0002]    To facilitate reimbursements to healthcare providers, organizations such as the Center for Medicare Services (CMS) and other payors require coding of medical records. Coding classifications, such as CMS&#39;s hierarchical condition categories (HCC), are used to identify numerous clinical diagnoses or medical conditions relevant to a patient&#39;s health. When healthcare claims coding was first adopted in the health benefits industry, the coding was typically performed by a certified medical record coder. The coder would review the patient&#39;s medical record data and apply codes to the record and/or correct codes that appear suspicious for a variety of reasons. For example, a code related to chronic pulmonary heart disease for a patient that has never been diagnosed with a heart problem may reflect a coding error. In other instances, a relevant code (e.g., code for a diagnosis of diabetes) may be absent. The record may be flagged with by the coder for further action to correct the coding problem. If the coder determined there were no additional conditions that needed to be identified on the medical record, the record was summarily dismissed (“No Action”). 
         [0003]    Certified medical coders review a substantial number of records every day but the volume of medical records that many health benefits providers need to process increases every year. Unless the health benefits provider is able to increase the number of coders in proportion to the increase in the volume of records, the percentage of records that are reviewed declines. As a result, claims processing, medical risk adjustment, and other functions involving medical records are delayed until the coding errors are uncovered and corrected. 
         [0004]    Although certified medical record coders are skilled at identifying coding errors within a record, it is impossible for them to review every medical record received by the health benefits provider. The volume of records—which increases every year—is simply too great to be reviewed manually. Therefore, there is a need for an automated system and method to identify records that may have coding errors and that could be forwarded to a medical record coder for review and further processing. There is a need for an automated system and method to distinguish medical records that are more likely to have coding errors from records that are less likely to have coding errors. There is a need for an automated system and method to pre-screen medical records and establish a priority for manual review of records by coders. There is further a need for an automated system and method that is easily modified to include additional information about codes and related health care information to facilitate pre-screening and review of records. 
       SUMMARY OF THE INVENTION 
       [0005]    The present disclosure is directed to a computerized system and method medical record coding. The computerized system and method facilitates pre-screening of medical records and analyzes medical record data to identify potential coding errors in records. When potential coding errors are identified in a record, a suspect code indicator may be associated with the record and the suspect record may be forwarded to a certified medical coder to confirm the presence or absence of the error. The computerized system and method facilitates the creation and editing of a library of information to further facilitate review and analysis of medical record data. 
         [0006]    In an example embodiment, the computerized system and method identifies records that have sufficient textual detail to further allow the detection of possible medical record coding errors. In an example embodiment, the computerized system and method applies a test for “overlooked” information within a record. Because the disclosed system and method employs computer technology, additional medical conditions that manual coders simply do not have time to locate can be considered in the initial review and analysis of a record. As a result, the number of medical records that can be reviewed and verified and/or corrected is increased substantially. Because the number of records that may be reviewed and corrected is increased substantially, fewer processing delays result from coding errors. Furthermore, records that are unlikely to have suspect coding are not forwarded to human coders thereby allowing the coders to spend more time on records that are likely to have errors. For health benefit providers that partner with CMS, the disclosed computerized system and method allows the providers to identify medical conditions of members that need to be reported to CMS to ensure proper calculation of the member&#39;s risk score. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]      FIG. 1  is a flow diagram of a medical records processing system with text mining features according to an example embodiment; 
           [0008]      FIGS. 2A and 2B  are an example of electronic data for developing a data dictionary according to an example embodiment; 
           [0009]      FIGS. 3A-3E  are examples of additional electronic data for the data dictionary according to an example embodiment; 
           [0010]      FIG. 4  is an example of nearness patterns involving a patient&#39;s family history. 
       
    
    
     DETAILED DESCRIPTION 
       [0011]    Referring to  FIG. 1 , a flow diagram of a medical records processing system with text mining features according to an example embodiment is shown. Electronic medical records are transmitted by hospitals, physician offices, and other healthcare providers and arrive at a text mining application site  100  where they are held for further processing. The electronic records typically arrive in an image format such as TIFF. The records, which may be one page or hundreds of pages in length, are processed through an optical character recognition application to facilitate parsing and searching of the records by the text mining pre-review application  102 . 
         [0012]    The pre-review application  102  searches the pages of each record to identify medical conditions the patient may have but have not been coded on the record. If the text mining pre-review application  102  identifies a suspect record  104  (e.g., incorrect code or absent code), the record is flagged as suspect or “prospective review positive-PRP”  106 . Flagged records may be maintained in a queue or otherwise made accessible to medical record coders for additional review. In an example embodiment, if the record does not appear to have a coding problem  108 , it is not flagged as suspect (“prospective review positive not-PRPN”). Suspect or flagged records, and in some instances, unflagged records, are then made available to a medical record coder for additional manual review to confirm the presence or absence of a coding problem. Unflagged records may also remain in a “no action” state. If the medical record coder confirms the error and the need for a new code  110 , the record may be submitted to an additional review process  112 . 
         [0013]    In an example embodiment, if the medical record coder does not agree with the outcome of the pre-review application  114  and concludes “no action” is required, the record is re-submitted to a text mining “no action” application  116  for additional analysis. In the second level of review, a search for “stronger” terms is completed. For example, a first level review may involve a search for the term “emphysema.” In a second level review, the text mining “no action” application  116  may search for the terms “patient has emphysema.” If the text mining application finds the stronger terms in the record  118 , the record is flagged again for review by a medical record coder  122 . If the stronger terms are not present in the record, the record remains in a “no action” state  120 . 
         [0014]    If the medical record coder confirms the need for a new code  124 , the record proceeds through the normal workflow process and an additional review is conducted  128 . If the medical record coder confirms there is no need for a new code or other coding change, the record remains in a “no action” state  126 . 
         [0015]    Referring to  FIGS. 2A and 2B , an example of electronic data for developing a data dictionary according to an example embodiment is shown. The example data comprises standardized text data (terms or phrases) for medical conditions  140  and related code and other data for each condition. In the example, textual descriptions of diabetes conditions are shown. The example data in the table also comprises a data source  162  and update date  164 . In an example embodiment, the electronic data of  FIGS. 2A and 2B  is organized according to the following fields: 
         [0000]    [Please confirm the following descriptions are correct or provide descriptions if blank.] 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Description of Data Elements 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Primary Terms or 
                 Brief description of medical condition 
               
               
                 Concepts (Phrases) 
               
               
                 140 
               
               
                 Type Name 
                 Code based name for medical condition 
               
               
                 142 
               
               
                 Non-Medical Type 
                 Non-medical type indicator for text data not 
               
               
                 144 
                 related to patient medical condition 
               
               
                 ICD9 Code 
                 Related code from International Classification 
               
               
                 146 
                 of Disease Codes 
               
               
                 HCC 
                 Related code from CMS Hierarchical Classification 
               
               
                 148 
                 Code for Risk Adjustments 
               
               
                 Forced 
                 Indicates this term exists in collocated dictionaries. 
               
               
                 150 
                 Typically application is to common language that 
               
               
                   
                 needs to be treated specially for medical records. 
               
               
                 Inflected 
                 Indicates is inflection rules should be applied during 
               
               
                 162 
                 extraction 
               
               
                 Match Type 
                 Indicator for type of match (e.g., entire, exact, 
               
               
                 154 
                 partial, etc.) 
               
               
                 Negated 
                 Flag to (temporarily) exclude term from extractions 
               
               
                 156 
               
               
                 Not Me 
                 Indicator for conditions related to patient 
               
               
                 158 
               
               
                 Selected 
                 If the term is forced, indicates if this dictionary&#39;s 
               
               
                 160 
                 term/type is selected. 
               
               
                 Data Source 
                 File identifying data source 
               
               
                 162 
               
               
                 Update Date 
                 Update date for data source 
               
               
                 164 
               
               
                   
               
             
          
         
       
     
         [0016]    Referring to  FIGS. 3A-3E , additional electronic data for the data dictionary is shown. The data in  FIGS. 3A-3D  lists for each standardized term  140  from  FIGS. 2A-2B  related synonyms. The synonyms are used in searching records and facilitate matching of medical records with proper codes. The synonyms comprise descriptions for medical conditions as they are typically recorded in medical records by healthcare providers and practitioners. The synonyms may be derived from actual medical records or other sources and therefore, reflect actual terminology and language found in healthcare records. As indicated in the synonym column  170  of the table, providers and practitioners often use abbreviations or shorthand terms to describe relevant medical conditions. For example, “DIAB NEUR NAMIF TYPE 2 UNCN; DIAB CIRC DIS TYPE 2 UNCONT” may be considered equivalent to a standardized term “diabetes with neurological manifestations, type ii or unspecified type, not stated as uncontrolled.” As the data in the table also indicates, “type ii” may be recorded as “type 11,” “type II,” or “type 2.” Each healthcare provider or practitioner that creates or updates medical records may adopt its own conventions for taking and recording patient and family histories, complaints, and symptoms as well as recording related medical conditions, diagnoses, treatments, prognoses, etc. The synonyms, therefore, account for the varied ways in which a single condition may be recorded and described in medical records from numerous providers. 
         [0017]    In addition to accounting for the various ways in which a single condition may be described and recorded, the synonyms facilitate location of information that is likely to be relevant to the patient&#39;s actual health status. Although each medical record may be very long—in some cases hundreds of pages—the relevant medical information may be contained within a proportionately small number of pages. Each record may comprise pages with patient contact information, demographic data, HIPAA forms, procedure consent forms, and a substantial amount of textual data not directly relevant to the patient&#39;s health status. Therefore, the presence of a medical term within the Record—“pneumonia,” for example—may or may not be indicative of the patient&#39;s health condition. Because many of the synonyms comprise phrases or terminology that is likely to be used by the physician or other individual providing the healthcare services to the patient, the likelihood of finding relevant medical conditions is increased. 
         [0018]    The text mining “pre-review” and “no action” processes of  FIG. 1  parse and search each incoming medical record to locate the presence of one or more synonyms. The presence of one or more synonyms may then be used to identify the related HCC code or codes that should be associated with the record. The synonym data may be updated as new terminology and language for known conditions is encountered as a result of the review process. 
         [0019]    In an example embodiment, a greedy lookahead tokenizer algorithm is applied to incoming records. Each record is examined for the presence of a plurality of medical conditions that may be applicable to the patient. The algorithm builds matches into an extraction for the record by extending the match according to terms from the applicable synonym. The algorithm continues searching the record for synonyms for each medical condition until it finds a match or concludes there are no matches and therefore, the condition is not present. As indicated in  FIGS. 3A-3E , a plurality of synonyms correspond to a standardized medical term. As portions of the record are matched with a synonym, the extraction is updated with supporting language from the record and the record is tokenized according to the match. In an example embodiment, the token is an ICD9 or HCC code for the standardized medical term identified in the match with a synonym. 
         [0020]    Referring to  FIG. 4 , the electronic data dictionary further comprises a collection of relationships, or “nearness patterns” used to identify when key concepts or terms are found adjacent to or near one another in the record. Tokenized records are analyzed according to the “nearness patterns.” Using the tokens in the records (e.g., the codes for one or more suspected medical conditions), the “nearness patterns” for identified conditions are applied to locate additional supporting language in the record for the suspected conditions. The “nearness” of key data elements is useful in determining whether a particular medical condition is actually relevant to a patient. For example, the words “assessment” or “assessed” in close proximity to the word “diabetes” suggests a positive diagnosis of diabetes for the patient. Analyzing records for the proximity of certain words, terms, or phrases within each record increases the accuracy of the system because medical records may contain many medical terms that are not relevant to a patient&#39;s health status. For example, many consent forms that are part of a medical record may list certain medical conditions that are side effects of a procedure (e.g., dizziness, nausea) rather than indicative of a patient&#39;s current condition. The nearness patterns may be used to discern whether certain medical conditions as identified in the tokenized record are actually relevant to the patient status. Alternatively, certain medical terms or phrases may simply be present in the record for other reasons. 
         [0021]    “Negative” nearness patterns may be used to confirm that patient does not have a medical condition. The example nearness patterns of  FIG. 4  relate to patterns involving a patient&#39;s family history. The proximity of the words “father,” “mother,” “brother,” or “sister” to a specified medical condition (e.g., diabetes) in the record may be an indication that another family member, and not the patient, has the medical condition. The absence of any other indicators that the patient actually has the condition may lead to a conclusion that the patient does not have the condition despite the presence of the terms for the medical condition within the record. Such information, therefore, may be used to determine the patient does not have the suspected medical condition. The “nearness patterns,” whether positive or negative, may further increase the accuracy of the system. 
         [0022]    Records that have been labeled “suspect” or otherwise flagged for manual review may be made accessible to a certified medical coder to determine whether the record should be coded with additional medical condition codes. The coder may review the results of the automated analysis in which suspect medical codes with supporting language are identified and confirm that the recorded should be coded as indicated or that the record does not support the addition of the suspect medical codes. 
         [0023]    The text mining functionality described herein may be implemented using IBM® SPSS® predictive analytics software. Data dictionary functionality may be provided using Microsoft® SQL Server. While certain embodiments of the present invention are described in detail above, the scope of the invention is not to be considered limited by such disclosure, and modifications are possible without departing from the spirit of the invention as evidenced by the claims. For example, medical term, synonym, and code data may be organized across tables in numerous ways and fall within the scope of the invention. Other aspects of the text mining functionality may be varied and fall within the scope of the claimed invention. Records may be coded automatically rather than flagged for review. One skilled in the art would recognize that such modifications are possible without departing from the scope of the claimed invention.