Abstract:
A system and method for creating a unique alias associated with an individual identified in a health care database such that health care data, and particularly pharmaceutical-related data, can be efficiently gathered and analyzed. The system has a first data store for storing at least one record where each record includes a plurality of identification fields which when concatenated uniquely identify an individual, and at least one health care field corresponding to health care data associated with the individual. The system also has a second data store, and a processor. The processor selects a record of the first data store, then selects a subset of the plurality of identification fields within the selected record, and concatenates the selected subset of identification fields. Then the processor stores the concatenated identification fields in a record in the second data store with the at least one health care field from the selected record of the first data store.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of and claims priority to U.S. application Ser. No. 09/665,752, filed on Sep. 20, 2000, which application claims the benefit of U.S. Provisional Application No. 60/154,726, filed Sep. 20, 1999, the entireties of which applications are incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention generally relates to computer systems and databases. More particularly, the present invention relates to a system and method for the gathering and analysis of health-care related data, and specifically the gathering and analysis of information regarding the use of pharmaceuticals by individuals. The present invention also relates to techniques for de-identifying the individuals from such pharmaceutical data, in order to maintain privacy. 
     2. Description of the Related Art 
     In the medical information field, pharmaceutical claims are processed on large computer systems which receive claims data for patients who have been prescribed one or more medications and have filed claims with insurance companies (or government entities) in order to have the claim paid by the company or entity. The claims data includes very specific details and attributes about the individuals making the claims. For example, attributes can include name, gender, birth date, address, medical diagnosis, specific drug prescribed, and other drugs the patient is using. Consequently, this data is very useful in assisting marketing research relative to usage of a specific drug and identifying various attributes that impact the usage. 
     The claims data is typically received at a data “clearinghouse” which can be a database for a specific insurance company or a larger database providing the claim processing service for many insurance companies. Moreover, the claims data that are produced by claimants include a significant amount of data, with millions of new claims being entered into the system each month. Several of the claims data clearinghouses have systems handling many terabytes of claims data. Because of the large size of the data being produced and the large amount of attributes, the data is in an inadequate format for efficient search, retrieval and analysis of specific attributes. 
     Recently, there have been laws passed that prevent the transmission of personal information associated with individuals, within health care claims data. This legislation particularly prohibits the transfer of specific personal data such as names, addresses and social security numbers. Thus, the claims data is no longer allowed to be transmitted from the clearinghouse to others in raw form with the personal data. Without the personal information to segregate the claims data, it becomes much harder to generate valuable research and market data based upon the unique attributes for specific individuals, such as age, gender and geographic distribution. 
     It is therefore desirous to provide the ability to efficiently gather information from the claims databases to allow research and analysis of the attributes that effect the pharmaceutical industry. Accordingly, the present invention is primarily directed to systems and methods for overcoming the problems discussed above, as well as related limitations of the prior art. 
     SUMMARY OF THE INVENTION 
     In one embodiment, the present invention is directed to a system and method for creating a unique alias associated with an individual identified in a health care database, that allows the aggregation of segregated data for marketing research. The system may include a first data store for storing at least one record where each record has a plurality of identification fields, such as name and birth date, which when concatenated uniquely identify an individual, and at least one health care field corresponding to health care data associated with the individual, such as a medication type. The system may also have a second data store and a processor that selects a record of the first data store, selects a subset of the plurality of identification fields within the selected record, concatenates the selected subset of identification fields, and stores the concatenated identification fields in a record in the second data store along with at least one health care field from the selected record of the first data store. The first data store and the second data store can either be located within the same database or in separate databases. 
     The health care data stored within the first data store may, in one embodiment, correspond to pharmaceutical claims data. The selected subset may correspond to a specific person in the healthcare database, and the person&#39;s last name, birthday, and gender are concatenated to form a unique identifier for that record. The processor may analyze longitudinal and historical records of individuals using individual-level linking methodologies based on the concatenated identification fields and the at least one health care field of each record of the second data store. The health care data also can have personal data removed from the various records such that only medically significant information remains, and the identifier allows the medical information to be segregated such that the individual records are still identifiable. 
     In order to more efficiently process the tremendous amount of data of the health care records, the processor may perform the further steps of selectively gathering the records from the first data store and selectively manipulating the records into a data cube. The records of the first data store are typically in tabular form, and the process of manipulating the records comprises selectively joining and projecting records from the various tabular records in the first data store to ultimately form a data cube comprised of a table of records. The data cube format allows the processor to more easily perform a search of the health care records, and also generate a report by displaying the records 25 of a specific data cube. 
     The present invention thus provides a method for creating a unique alias associated with an individual identified in a health care database, wherein the health care database stores at least one record, and each record has a plurality of identification fields which when taken together uniquely identify an individual, and at least one health care field may correspond to health care data associated with the individual. The method includes the steps of selecting a record within the health care database, selecting a subset of the plurality of identification fields within the selected record, concatenating the selected subset of identification fields, and storing the concatenated identification fields in a record in a second database with the at least one health care field from the selected record of the first data store. The method preferably includes the step of analyzing longitudinal, historical records of individuals using individual level linking methodologies based on the concatenated identification fields and the at least one health care field of each record of the second database. 
     The step of selecting a record within the health care database may comprise selecting a record from pharmaceutical claims data. Further, the step of concatenating the selected subset of identification fields may comprise, for example, concatenating, for a specific person in the healthcare database, that person&#39;s last name, birthday, and gender. Thus, based on the concatenated identification fields and the at least one health care field of each record of the second data store, the method may include the step of analyzing longitudinal, historical records of individuals using individual-level linking methodologies. 
     As discussed above, the method further may include the steps of selectively gathering the records from the first data store, and selectively manipulating the records into a data cube. The step of selecting a record within the health care database may comprise selecting records of the first data store that are in tabular form, and the step of selectively manipulating the records into a data cube may comprise selectively joining and projecting records from the first data store and creating a data cube comprising a table of records. 
     The data cube allows the present system to aggregate the records in an efficient format such that all new records can be viewed shortly after posting. Further, the unique population identifiers allow users to follow patients over time yielding important results unavailable in other databases, such as patient drug switching behavior. By linking medical and pharmacy transactions at the patient level, new insights such as indication specific use of drugs and patient comorbidities can be determined. 
     The report displayed by the system may contain several attributes, such as: market shares geographic information at the national, regional, state and MSA levels; trends over time including annual, quarterly, monthly, and weekly periods; traditional measures such as total, new and refilled prescription counts; source of business such as new prescription starts, switches, and continuing patients; prescriber specialty; patient demographics for age and gender; indication specific use; and patient comorbidities. The system can therefore be used in a number of ways to help make business decisions, such as monitoring new drug launches and marketing campaigns, enhanced sales force targeting, and micro-marketing in select geographic areas or to select customers. Furthermore, the system can be used for forecasting and development of a pharmaceutical marketing strategy including indication-specific product positioning, early warning market share shifts, clinical trial site selection, investigator recruiting, and accurate intelligence on market size and demand. 
     Other objects, features, and advantages of the present invention will become apparent from the drawings, detailed description of the invention, and the claims, below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1A ,  1 B,  1 C,  1 D and  1 E are block and flow diagrams showing the overall structure and overall flow of the present invention in one embodiment. 
         FIGS. 2A ,  2 B, and  2 C are block and flow diagrams showing the overall structure and overall flow of the present invention in one embodiment. 
         FIGS. 3A ,  3 B, and  3 C illustrate a flow and relationship diagram, showing the overall flow and data relationship of the present invention. 
         FIG. 4  illustrates the operation of the Daily Rx Load Process of the present invention. 
         FIG. 5  illustrates the operation of the Monthly Mx Load Process of the present invention. 
         FIG. 6  illustrates the operation of the Monthly Hx Load Process of the present invention. 
         FIG. 7  illustrates the operation of Quarter Monthly Rx Merge Process of the present invention. 
         FIG. 8  illustrates the operation of the Prepare Mx Data Process of the present invention. 
         FIG. 9  illustrates the operation of the Produce Patient Data Process of the present invention. 
         FIG. 9A  illustrates the operation of the RSTRANSFORMER process of the present invention. 
         FIG. 10  illustrates the operation of the Pull Cube Data Process of the present invention. 
         FIG. 11  illustrates the operation of the Generate TC Cube Data Process of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     With reference to the drawings, in which like numerals represent like elements throughout,  FIGS. 1A ,  1 B,  1 C,  1 D,  1 E,  2 A,  2 B, and  2 C illustrate a high-level combined block/flow diagram for the present invention. These figures represent both the elements of a block diagram for, as well as the steps performed by the system of, the present invention. 
     Referring to  FIGS. 1A ,  1 B,  1 C,  1 D,  1 E,  2 A,  2 B, and  2 C, the primary processing that takes place in the present invention may be performed by, for example, a high-performance computing system, such as a Sun Microsystems ES10000 computer (at SITE  2 ). On a periodic basis, such as each day, seven days per week, a computing system at SITE  1  places healthcare claims data at step  103  via a secure connection  190  onto a computer system at SITE  1 . This healthcare claims data may include, for example, pharmaceutical, 20 medical, and hospital claims  101  that have been “de-identified” at step  102  (explained in further detail below). 
     The claims data is de-identified at step  102  before it is sent to SITE  2 , which includes applying a unique identifier, encrypting this identifier, and removing specific patient identifying fields. Data is then loaded into database tables (such as an Oracle database) at step  104  that also reside on SITE  2 . At step  105 , SITE  2  runs all processes for analyzing and consolidating the data and for transforming the resulting Oracle tables into OLAP cubes. 
     The cube building process may run on a different computer (such as SITE  2 ). Cubes are modeled using an OLAP product on a desktop computer under, for example, the Windows NT operating system. 
     The cube deployment process may run on a different computer (such as SITE  3 ). A computing system at SITE  2  places cubes and metadata files at step  106  via a secure connection to SITE  3 . Processes run at step  107  at SITE  3  to place the cube on the production web site and to update the web site pages with the associated metadata. 
     The present process performed at SITE  2  after obtaining data from the SITE  1  computer, making data ready for cube transformers, and then displaying it on the web  5  at SITE  3  can be logically divided into six major steps, as shown in  FIGS. 3A ,  3 B, and  3 C.
         1. Load Oracle Tables (step  301 )   2. Produce Patient Data (step  302 )   3. Pull Cube Data (step  303 )   4. Generate Cube Data (step  304 )   5. Build Cube (step  305 )   6. Automated Cube Deployment and Metadata Update Process       

     All these processes are handled, maintained and executed at regular daily, weekly and monthly intervals. There are some processes which are done out of the routine process, such as generation of DOI, zip-state-region, ICD9, etc. tables.  FIGS. 3A ,  3 B, and  3 C show a high level overview of the processes used to create cubes. 
     1. Load Oracle Tables (Step  301 ) 
     The Load Oracle Tables process (step  301 ) can be divided into two logically different steps, daily and monthly processes, described in further detail below with respect to  FIGS. 4-8 . The daily routines convert the text format data supplied from SITE  1  into “RX” and “Do Not Use Company Name (DNU)” daily Oracle tables. The monthly processes convert Hospital (HX) and Medical (MX) data into monthly Oracle tables. Note that all run times provided below correspond to approximate run times. 
     1.1 Daily Rx Load Process  401   
     The Daily Rx Load Process  401  is described below with respect to  FIG. 4 : 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Script Use 
                 Loaddaily.sh is the unix shell script that uses the SQL Loader 
               
               
                   
                 Rx Control File to Convert the Rx Text File from SITE 1 
               
               
                   
                 into LOAD_YYYYMMDD, 
               
               
                   
                 {DNU}_YYYY MMDD Oracle tables after doing all the 
               
               
                   
                 necessary number, char and date conversions. The {DNU} 
               
               
                   
                 list contains BLUFFCREEK, KROGER, OMNI, PCN, 
               
               
                   
                 VIP and WALGREENS. 
               
               
                 Input 
                 YYYYMMDD.synergy.log.gz, 
               
               
                   
                 RX Control file, YYYYMMDD.ctl. 
               
               
                 Output 
                 LOAD_19991123, 24 etc. tables for each day of a month. 
               
               
                   
                 WALGREENS_YYYYMMDD etc. tables for each DNU 
               
               
                   
                 company. 
               
               
                   
                 ../log/YYYYMMDD.log 
               
               
                   
                 ../data.YYYYMMDD.synergy.log 
               
               
                   
                 ../bad/YYYYMMDD.synergy.bad 
               
               
                   
                 ../discard/YYYYMMDD.synergy.discard. 
               
               
                 Frequency 
                 Daily 
               
               
                 Run Time 
                 ~4 hours 
               
               
                   
               
             
          
         
       
     
     1.2 Monthly Mx Load Process  501   
     The Monthly Mx Load Process  501  is described below with respect to  FIG. 5 : 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Script Use 
                 SQL LOADER process reads MXDaily.MMDDYY text file 
               
               
                   
                 and control file to convert it into LOAD_MX_YYYYMM 
               
               
                   
                 tables. 
               
               
                 Input 
                 /raid/4011/envoydata/mx/oct1999/data/MXDaily.100199 
               
               
                   
                 MX Control file, yyyymmdd.ctl. 
               
               
                 Output 
                 LOAD_MX_YYYYMM table 
               
               
                 Frequency 
                 Monthly 
               
               
                 Run Time 
                 ~8 hours 
               
               
                   
               
             
          
         
       
     
     1.3 Load HX Text Data  601   
     The Load HX Text Data Process  601  is described below with respect to  FIG. 6 : 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Script Use 
                 SQL LOADER process reads MX text file and Control File to 
               
               
                   
                 convert it into WH_ENVOY_HX_SEP99 tables. 
               
               
                 Input 
                 /raid/4011/envoydata/hx/sep1999/data/ 
               
               
                   
                 MCDS.DPRET60.090199, 
               
               
                   
                 HX Control file, yyyymmdd.ctl. 
               
               
                 Output 
                 WH_ENVOY_HX_SEP99_10..20..30..36..40..46..50.. 
               
               
                   
                 60..61..66..70..80..90 tables for HX. 
               
               
                 Frequency 
                 Monthly 
               
               
                 Run Time 
                 ~8 hours 
               
               
                   
               
             
          
         
       
     
     1.4 Quarter-Monthly Rx Merge  701   
     The Quarter-Monthly Rx Merge Process  701  is described below with respect to  FIG. 7 : 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Script Use 
                 This process uses RX_Weekly_New.sql SQL script to 
               
               
                   
                 combine all the daily (approx.. 8 days of tables) RX and DNU 
               
               
                   
                 tables into quarter-monthly tables. 
               
               
                 Input 
                 LOAD_19991123..24 etc. tables for each day of a month 
               
               
                   
                 WALGREENS_YYYYMMDD etc. tables for each “DNU” 
               
               
                   
                 company 
               
               
                 Output 
                 WH_ENVOY_9911A..B..C..D etc. 4 tables for a month. 
               
               
                   
                 WALGREENS_9911A..B..C..D like tables for each “DNU” 
               
               
                   
                 company for a month. 
               
               
                 Frequency 
                 Monthly 
               
               
                 Run Time 
                 ~6 hours 
               
               
                   
               
             
          
         
       
     
     1.5 Prepare Mx Data ( 801 ) 
     The Prepare mx Data Process  801  is described below with respect to  FIG. 8 : 
                                     Script Use   This process uses Process_Monthly_MX.sql SQL script to           validate and convert LOAD_MX_YYYYMM table           data into required date, char and numbers.       Input   LOAD_MX_YYYYMM,           WHREF_DONOT_USE_MX       Output   WH_ENVOY_YYYYMM           BAD_PAYER_ID_YYYYMM       Frequency   Monthly       Run Time                    
2. Produce Patient Data (Step  302 )
 
     The Produce Patient Data Process of step  302  ( FIG. 3B ) is described below in further detail with respect to  FIG. 9 : 
                                     Script Use   This process uses Master_PXpgmv1b_9910.sql SQL script to           combine weekly RX and monthly MX, HX tables to create a           relational           WHREF_ENVOY_PXYYMM table.       Input   WH_ENVOY_YYMMA..B etc.,           WH_ENVOY_MX_YYYYMM,           WH_ENVOY_HX_MMMYY_20 tables.       Output   WHREF_PATIENT REPOSITORY RXMM,           WHREF_MXTEMP_YYMM,           WHREF_HXTEMP_YYMM,           WHREF_ENVOY_PXYYMM tables.       Frequency   Monthly       Run Time   ~13 hours                    
3. Pull Cube Data (Step  303 )
 
     The Produce Patient Data Process of step  303  ( FIG. 3B ) is described below in further detail with respect to  FIG. 10 : 
     This process uses a series of Oracle stored procedures to allow for error checking and audit logging. Logging for these procedures uses the MM_LOG table. These stored procedures are called from the Unix shell using shell script wrappers that input the necessary variable values. The stored procedures used are as follows:
         mm00_init   mm01_set ears   mm02_weekly_data_pull   mm03_memids   mm04_mx_diags       

     3.1 Audit Logging in Oracle Table MM_LOG 
     Structure of the MM_LOG table. 
     
       
         
               
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                   
               
             
             
               
                 RUN_DATE 
                 START_TIME 
                 STOP_TIME 
                 CUBE_NAME 
                 PROCESS 
                 RETURN_CODE 
               
               
                   
               
               
                 10-Jul-00 
                 8:26:37 
                  8:26:38 
                   
                 mm00_init( ) 
                 0 
               
               
                 10-Jul-00 
                 8:26:38 
                  8:26:;38 
                   
                 mm01_set_vars( ) 
                 0 
               
               
                 11-Jul-00 
                 8:26:38 
                 12:35:49 
                   
                 mm02_weekly_data_pull( ) 
                 0 
               
               
                 11-Jul-00 
                 2:04:59 
                 12:11:57 
                   
                 mm03_memids( ) 
                 0 
               
               
                 11-Jul-00 
                 1:07:32 
                 11:23:46 
                   
                 mm04_mx_diags( ) 
                 1 
               
               
                   
               
             
          
           
               
                   
                 RUN_DATE 
                 START_TIME 
                 ERROR_CODE 
                 DESCR 
               
               
                   
                   
               
               
                   
                 10-Jul-00 
                 8:26:37 
                 Completed 
                 Procedure mm00_init( ) completed 
               
               
                   
                   
                   
                   
                 successfully 
               
               
                   
                 10-Jul-00 
                 8:26:38 
                 Completed 
                 Procedure mm01_set_vars( ) completed 
               
               
                   
                   
                   
                   
                 successfully 
               
               
                   
                 11-Jul-00 
                 8:26:38 
                 Completed 
                 Procedure mm02_weekly_data_pull( ) 
               
               
                   
                   
                   
                   
                 completed successfully. 
               
               
                   
                 11-Jul-00 
                 2:04:59 
                 Completed 
                 Procedure m03_memids( ) completed 
               
               
                   
                   
                   
                   
                 successfully 
               
               
                   
                 11-Jul-00 
                 1:07:32 
                 −904 
                 ORA-00904: invalid column name 
               
               
                   
                   
               
             
          
         
       
     
     A record is added to MM_LOG for each process. The name of the process is in the PROCESS column. For cube specific processes, the name of the cube is in the CUBE_NAME column. When a process successfully completes, the RETURN_CODE column contains a 0; when there is an error, the RETURN_CODE column contains a 1. 
     3.2 Initialization 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Script 
                 The mm00_init procedure initializes the environment for weekly 
               
               
                 Use 
                 Market Monitory cube processing. The mmOO.sh shell script 
               
               
                   
                 calls the mm00_init procedure. 
               
               
                 Input 
                 None 
               
               
                 Output 
                 MM_LOG table truncated. 
               
               
                   
                 MM_VARS table truncated. 
               
               
                   
                 CUBE_DATA_TEXT table truncated. 
               
               
                   
                 MM_LOG table—row inserted showing successful 
               
               
                   
                 completion or error condition. 
               
               
                   
               
             
          
         
       
     
     3.3 Set Variables 
     
       
         
               
               
             
               
               
               
             
               
               
             
           
               
                   
               
             
             
               
                 Script Use 
                 The mm01_set_vars procedure sets variables for the Rx Market Monitor 
               
               
                   
                 weekly cube processing. The mm01.sh shell script calls the 
               
               
                   
                 mm01_set_vars procedure with input variables set as text. The 
               
               
                   
                 mm00_init procedure must already have been run. 
               
             
          
           
               
                 Input 
                 p_run_date 
                 Run date of pull as text ‘YYYYMMDD’. 
               
               
                   
                 p_start_date 
                 Start date of pull as text ‘YYYYMMDD’. 
               
               
                   
                 p_end_date 
                 End date of pull as text ‘YYYYMMDD’. 
               
               
                   
                 p_post_date 
                 Post date of pull as text ‘YYYYMMDD. 
               
               
                   
                 p_acute_lookback 
                 Acute lookback date as text ‘YYYYMMDD’. 
               
               
                   
                 p_chronic_lookback 
                 Chronic lookback date as text ‘YYYYMMDD’’. 
               
             
          
           
               
                 Output 
                 MM_VARS table - row inserted with this week&#39;s values as DATE 
               
               
                   
                 datatype. 
               
               
                   
                 MM_VARS_HIST table - row inserted with this week&#39;s values as DATE 
               
               
                   
                 datatype. 
               
               
                   
                 MM_LOG - row inserted showing successful completion or error 
               
               
                   
                 condition. 
               
               
                   
               
             
          
         
       
     
     3.4 Pull Weekly Data 
     
       
         
               
               
               
             
               
               
               
             
               
               
             
           
               
                   
               
             
             
               
                 Script 
                 The mm02_weekly_data_pull procedure pulls one week of 
                   
               
               
                 Use 
                 Rx data for weekly Rx Market Monitor cube processing. 
               
               
                   
                 The mm02.sh shell script calls this procedure with the tablespace 
               
               
                   
                 variable input set. The mm00_init and mm01_set_vars 
               
               
                   
                 procedures must already have been run. 
               
             
          
           
               
                 Input 
                 p_tablespace 
                 Tablespace name as text. 
               
               
                   
                 MM_VARS table 
               
             
          
           
               
                   
                 WH_ENVOY_YYMM where YYMM is the two character 
               
               
                   
                 year and month from start_date in MM_VARS table. 
               
               
                   
                 WWW_MASTER_DOI 
               
               
                 Output 
                 Last week&#39;s WEB_DATA_WEEK_PULL table is renamed to 
               
               
                   
                 WEB_DATA_WEEK_PULL_YYYYMMDD where 
               
               
                   
                 YYYYMMDD is one day before the start_date in 
               
               
                   
                 MM_VARS table. New WEB-DATA-WEEK-PULL table is 
               
               
                   
                 created in the WEB schema in the tablespace named 
               
               
                   
                 in the p_tablespace parameter. The 
               
               
                   
                 WEB_DATA_WEEK_PULL table contains Rx data from 
               
               
                   
                 the start and end dates in the MM_VARS table. 
               
               
                   
                 MM_LOG—a row is inserted to indicate either successful 
               
               
                   
                 completion or error condition. 
               
               
                   
               
             
          
         
       
     
     3.5 Get Memids 
     
       
         
               
               
             
               
               
               
             
               
               
             
           
               
                   
               
             
             
               
                 Script 
                 The mm03_memids procedure accumulates six weeks of 
               
               
                 Use 
                 memids. The mm03.sh shell script calls this procedure and 
               
               
                   
                 inputs the tablespace parameter. The mm00_init, 
               
               
                   
                 mm01_set_vars, and 
               
               
                   
                 mm02_weekly_data_pull procedures must 
               
               
                   
                 already have been run. 
               
             
          
           
               
                 Input 
                 p_tablespace 
                 Tablespace name as text. 
               
               
                   
                 MM_VARS table 
               
             
          
           
               
                   
                 ALL_MEM_TO CONVERT table 
               
               
                   
                 WEB_DATA_WEEK_PULL_V2 table 
               
               
                   
                 WEB_UMEMS_WEEK_V2_MONDD table where 
               
               
                   
                 MONDD is the end_date from MM_VARS table as text. 
               
               
                   
                 WEB_UMEMS_WEEK_V2_MONDD[1-5] tables where 
               
               
                   
                 MONDD[1-5] are the previous five weeks of data. 
               
               
                   
                 RXMEMID SEQ table 
               
               
                 Output 
                 WEB_UMEMS_WEEK_V2_MONDD table is created where 
               
               
                   
                 MONDD is start_date in MM_VARS table minus one day. 
               
               
                   
                 WEB_UMEMS_WEEK_PULL is created with data for current 
               
               
                   
                 week and previous 5 weeks. 
               
               
                   
                 MM_LOG—a row is inserted to indicate either successful 
               
               
                   
                 completion or error condition. 
               
               
                   
               
             
          
         
       
     
     3.6 Get Mx Diagnoses 
                                                             Script   The mm04_mx_diags procedure gets diagnoses information       Use   from Mx tables for weekly processing. The mm04.sh shell script           executes this procedure with the tablespace input variable set.           The mm00_init, mm01_set_vars,           mm02_weekly_data_pull, and mm03_memids           procedures must already have been run.            Input   p_tablespace   Tablespace name as text.           MM_VARS table                WEB_UMEMS_WEEK_PULL_V2 table           MASTER PX table           WEB_MXPTS_WEEK_PULL_V2 table           WH_ENVOY_MX_YYYYMM[1-3] tables where           YYYYMM[1-3] are the current month and two prior months           of data.           WEB_RXMX_WEEK_PULL_V2 table           RXMEMID_SEQ table       Output   ACUTE_RXMX table—records from the week are appended.           CHRONIC_RXMX table—records from the week are           appended.           MM_LOG table—row inserted to indicate either successful           completion or error condition.                    
4. Generate TC Cube Data (Step  304 )
 
     The Generate TC Cube Data Process of step  304  ( FIG. 3C ) is described below in further detail with respect to  FIG. 11 : 
     The Generate TC Cube Data Process  304  uses three Oracle stored procedures to generate a cube table which will be further used by data transformers to build a COGNOS readable multi-dimensional formatted cube structure. The last stored procedure updates statistics for each cube. The stored procedures are as follows:
         mm05_step1   mm06_step2   mm07_step3   mm08_cube metadata       

     4.1 Process Step  1101  (Step 1) 
     
       
         
               
               
             
               
               
               
             
               
               
             
           
               
                   
               
             
             
               
                 Script Use 
                 The mm05_stepl procedure must be run for each therapeutic 
               
               
                   
                 class. This procedure inserts records into the 
               
               
                   
                 CMID_V2_CLASS table where CLASS is the p_class 
               
               
                   
                 specified. The mm00_init, mm01_set_vars, 
               
               
                   
                 mm02_weekly_data_pull, mm03_memids, and 
               
               
                   
                 mm04_mx_diags procedures must already have been run. 
               
             
          
           
               
                 Input 
                 p_class 
                 Class name. 
               
               
                   
                 p_tablespace 
                 Tablespace name as text. 
               
               
                   
                 p_lookback 
                 Number of days of lookback 
               
               
                   
                 p_condition 
                 “ACUTE” or “CHRONIC” 
               
             
          
           
               
                   
                 MM_VARS table 
               
               
                   
                 WEB_DATA_WEEK_PULL_V2 table 
               
               
                   
                 CUBE_V2_LIST table 
               
               
                   
                 CMID_V2_CLASS table where CLASS is the p_class. 
               
               
                 Output 
                 Records inserted into CMID_V2_CLASS table where 
               
               
                   
                 CLASS is the p_class. 
               
               
                   
                 New CMID_V2_CLASS_TMP table is created where 
               
               
                   
                 CLASS is the p_class. 
               
               
                   
                 MM_LOG table—row inserted to indicate either successful 
               
               
                   
                 completion or error condition. 
               
               
                   
               
             
          
         
       
     
     4.2 Process Step  1102  (Step 2) 
     
       
         
               
               
             
               
               
               
             
               
               
             
           
               
                   
               
             
             
               
                 Script 
                 The mm06_step2 procedure must be run for each therapeutic 
               
               
                 Use 
                 class. This procedure inserts records into the 
               
               
                   
                 RX_RESULT_V2_CLASS table where CLASS is the 
               
               
                   
                 p_class specified when the procedure is called. The 
               
               
                   
                 mm00_init, mm01_set_vars, mm02_weekly_data_pull, 
               
               
                   
                 mm03_memids, mm04_mx_diags, and 
               
               
                   
                 mm05_step1 procedures must already have been run. 
               
             
          
           
               
                 Input 
                 p_class 
                 Class name. 
               
               
                   
                 p_tablespace 
                 Tablespace name as text. 
               
               
                   
                 p_lookback 
                 Number of days of look back 
               
               
                   
                 p_condition 
                 “ACUTE” or “CHRONIC” 
               
               
                   
                 MM_VARS table 
               
             
          
           
               
                   
                 CMID_V2_CLASS_TMP table where CLASS is the p_class. 
               
               
                   
                 CMID_V2_CLASS table where CLASS is the p_class. 
               
               
                   
                 RX_RESULT_TEMP_CLASS table where CLASS is the 
               
               
                   
                 p_class. 
               
               
                   
                 ZIP_ST_MSA_REG_DIV table 
               
               
                   
                 WEB_DEA_TO_SPEC_U 
               
               
                 Output 
                 New records are inserted into RX_RESULT_V2_CLASS 
               
               
                   
                 where CLASS is p_class. 
               
               
                   
                 MM_LOG table—row inserted to indicate either successful 
               
               
                   
                 completion or error condition. 
               
               
                   
               
             
          
         
       
     
     4.3 Process Step  1103  (Step 3) 
     
       
         
               
               
             
               
               
               
             
               
               
             
           
               
                   
               
             
             
               
                 Script 
                 The mm07 step3 procedure must be run for each therapeutic 
               
               
                 Use 
                 class. This procedure creates a new 
               
               
                   
                 RXMX_CUBE_V2_CLASS table where class is 
               
               
                   
                 the p_class specified. The mm00_init, mm01_set_vars, 
               
               
                   
                 mm02_weekly_data_pull, mm03_memids, mm04_mx_diags, 
               
               
                   
                 mm05_step1, and mm06_step2 procedures must already 
               
               
                   
                 have been run. 
               
             
          
           
               
                 Input 
                 p_class 
                 Class name. 
               
               
                   
                 p_tablespace 
                 Tablespace name as text. 
               
               
                   
                 p_lookback 
                 Number of days of lookback 
               
               
                   
                 p condition 
                 “ACUTE” or “CHRONIC” 
               
             
          
           
               
                   
                 RXMX_CONDITION_V2 table where CONDITION is the 
               
               
                   
                 p_condition. 
               
               
                   
                 RX_RESULT_V2_CLASS table where CLASS is the p_class. 
               
               
                   
                 ICD9 V2_CLASS table where CLASS is the p_class. 
               
               
                   
                 RX_RESULT_V2_CLASS_M table where CLASS is the 
               
               
                   
                 p_class. 
               
               
                 Output 
                 New RXMX_CUBE_V2_CLASS table is created where 
               
               
                   
                 CLASS is p_class. 
               
               
                   
                 MM_LOG table—row inserted to indicate either successful 
               
               
                   
                 completion or error condition. 
               
               
                   
               
             
          
         
       
     
     4.4 Generate Cube Metadata 
                                                             Script Use   The mm08 cube_metadata procedure must be run for each           therapeutic class. This procedure updates the CUBE_DATA           table for each cube. The mm00_init, mm01_set_vars,           mm02_weekly_data_pull, mm03_memids,           mm04_mx_diags, mm05_step1, mm06_step2 and           mm07 step3 procedures must already have been run.            Input   p_class   Class name.           MM_VARS table                CUBE_DATA table where CLASS is the p_class.       Output   CUBE_DATA_TEXT table is appended where CLASS           is p_class.           MM_LOG table—row inserted to indicate either successful           completion or error condition                    
5. Build Cube (Step  305 )
 
     The Build Cube Process of step  305  ( FIG. 3C ) is described below in further detail with respect to  FIG. 9A : 
     This process uses a C program to create a cube for each therapeutic class. Each cube is FTP&#39;d to the server of SITE  3 . Metadata for each cube is spooled to a text file and FTP&#39;d to the SITE  3  server. The same text files may be concatenated and sent via email to the web developer of SITE  2 . 
     5.1 Build Cube 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Script Use 
                 The program RSSERVER is repeated for each of the 
               
               
                   
                 therapeutic classes and called by the script 
               
               
                   
                 mmv2_‘class name’.sh. Data Transformers uses Model 
               
               
                   
                 Structure and OBES_RX_CUBE Oracle table (built in above 
               
               
                   
                 process) to finally build a CUBE for each of the therapeutic 
               
               
                   
                 class. This Cube is then used by COGNOS to show requested 
               
               
                   
                 information on the Web. 
               
               
                 Input 
                 OBES_RXMX_CUBE 
               
               
                 Output 
                 Cube for a OBES Class 
               
               
                 Frequency 
                 On Request 
               
               
                 Run Time 
                 ~8 hours 
               
               
                   
               
             
          
         
       
     
     5.2 FTP Cube to SITE  3  server 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Script Use 
                 The transfer_mm_cube.sh script renames a cube and puts a 
               
               
                   
                 copy into directory /raid4011/cubes/transfer where it will 
               
               
                   
                 automatically be FTP&#39;d to the SITE 3 server. This script is run 
               
               
                   
                 in parallel for each class (class corresponds to cube). 
               
               
                   
               
             
          
         
       
     
     5.3 Approve Cube 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Script Use 
                 The approve_cube script is run manually for each cube after 
               
               
                   
                 quality assurance has been performed. This script is run in 
               
               
                   
                 parallel for each class (class responds to cube). 
               
               
                   
               
             
          
         
       
     
     5.4 Create Metadata text file/FTP to SITE  3  server 
     
       
         
               
               
             
           
               
                   
               
             
             
               
                 Script Use 
                 The process gen_mm_file.sql is called by gen_mm_file.sh 
               
               
                   
                 to spool metadata for a cube to a text file. The text file is 
               
               
                   
                 put into a directory where it is automatically FTP&#39;d to the 
               
               
                   
                 SITE 3 server. This script is run in parallel for each class. 
               
               
                   
                 All procedures to pull cube data must have been successfully 
               
               
                   
                 completed and the metadata must exist in the Oracle tables 
               
               
                   
                 cube_data and cube_data_text. 
               
               
                   
               
             
          
         
       
     
     5.5 E-mail Metadata to Web Developer 
                                     Script Use   The email_meta.sh script will e-mail metadata to a SITE 2           Web Developer. This script is run in parallel for each class           (class corresponds to cube).                    
6. Automated Cube Deployment and MetaData Update Process
 
     Automated processes exist on the OnLine Analytical Processing (OLAP) host machine to deploy data cubes (such as QUINTERNET™ Series, from Quintiles Transnational Corp.) to the production web site, cubes ready for Quality Assurance (QA) verification, as well as to automatically update “metadata” on production web pages. This enables production cube deployments and web page updates to occur during off-peak hours without any manual intervention. 
     As a QUINTERNET™ Series data cube is created, the cube is sent via a secure connection to the host machine. The cube is then automatically “served up” to the QA location on the web, to which only authorized personnel have access. 
     For each cube approval, a “metadata” file is transmitted from the SITE  2  server, via a secure connection, to the host machine in a specific location (directory within a file system). This secure transmission may occur after a data cube has passed the QA verification. 
     The metadata file contains statistical information about the specific cube (e.g.—date that cube contains data through, number of records, number of patients, etc.). Several times each night, an automated process may be initiated which checks for the presence of a metadata file and a corresponding data cube file. If matching files for a specific cube exist, the process automatically “serves” up this cube into production for access via the web pages. In addition, the HTML page which contains the metadata for the cube is updated with the metadata contained in the metadata file. 
     The server at, for example, SITE  3  may prepare and maintain HTML template files for each QUINTERNET™ Series cube. These files contain the base HTML used to create each cube&#39;s web page. Instead of the actual metadata values that will populate the cubes&#39; web pages, the HTML template files may contain placeholder tags. These placeholder tags are replaced by data values supplied by SITE  2  in metadata files. 
     SITE  2  transfers the template files and the metadata files to a host via FTP. The metadata files are transferred to the host each time a cube is approved. Template files are maintained for each QUINTERNET™ Series cube and are updated by SITE  2  as necessary so that a current version of each cube&#39;s template file is always available for processing on the host. 
     After a cube has been updated, reviewed for quality and approved by the operator of SITE  2 , SITE  2  transfers a metadata file for that cube to the host via FTP. The metadata files contains the same tags found in the HTML template file for each cube. Each of these tags is coupled with a value that will be substituted for the placeholder tag in the HTML template file. 
     An event-driven file processing script runs periodically via cron, a unix scheduling system, on the host. If the file processing script detects the existence of a designated flag file, a script called enable_cube.ksh is run. The enable_cube.ksh script calls a Perl script, replaceHtmlMetaTags.pl, passing it the name of the cube being processed and the name of the related metadata file. The enable_cube.ksh script also updates the metadata file with a tag/value pair representing the date the updated cube is being deployed. 
     The purpose of the replaceHtmlMetaTags.pl script is to automatically generate HTML pages for the QUINTERNET™ Series products. The replaceHtmlMetaTags.pl script substitutes the values in the metadata file for the placeholder tags in the template and saves the resulting output in an HTML file. Referring to  FIG. 1E , the enable_cube.ksh script then promotes the updated HTML file(s) to SITE  3 &#39;s web server  181  thus making it available via, for example, the Internet  183 , to users of web browsers  182  operating on client computers. 
     The present invention may be implemented with a processing schedule defined in many ways. For example, the schedule may be on a weekly or monthly basis, depending upon the needs of the implementation. At times, special requests may be required and the ability to process data and create cubes on an ad hoc basis exists. 
     While there has been shown the preferred embodiment of the present invention, it is to be understood that certain changes can be made in the forms and arrangements of the elements of the system and the steps of the method without departing from the spirit and scope of the invention as is set forth in the Claims. 
     A system for analyzing de-personalized health care data includes health care databases and a processor connected to the health care databases. The health care data bases each include at least one record, and each record includes a depersonalized yet unique patient identifier associated with a patient. The de-personalized patient identifier is common for a specific patient across several health care databases. According to one embodiment, the de-personalized health care data can be pharmaceutical claims data. 
     The processor performs the steps of: (i) receiving records from the health care databases; (ii) querying the records received from the health care databases, based upon selected person-level criteria; and (iii) generating at least one report based upon the results of the querying step. In another embodiment, the generating step performed by the processor can include the step of manipulating the results for display in specific views that satisfy specific analytical goals. In yet another embodiment, the generating step performed by the processor can include the step of displaying the report in a tabular format. 
     A method for analyzing de-personalized health care data within health care databases, wherein the health care databases each include at least one record. Each record includes a depersonalized yet unique patient identifier associated with a patient. The de-personalized patient identifier is common for a specific patient across the health care databases. According to one embodiment, the de-personalized health care data includes pharmaceutical claims data. The method includes the steps of: (a) receiving records from the health care databases; (b) querying the records received from the health care databases; and (c) generating at least one record based upon the results of the querying step. In one embodiment, the generating step includes the step of manipulating the results for display in specific views that satisfy specific analytical goals. In another embodiment, the generating step includes the step of displaying the report in a tabular format.