# Patent

## Title
System and method for analyzing de-identified health care data

## 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 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 store with at least one health care field from the selected record of the first data store.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation application of and claims priority to U.S. application Ser. No. 13/925,243, filed on Jun. 24, 2013, which application is a continuation application 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 Ser. 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'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'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, 1B, 1C, 1D and 1E are block and flow diagrams showing the overall structure and overall flow of the present invention in one embodiment.
FIGS. 2A, 2B, and 2C are block and flow diagrams showing the overall structure and overall flow of the present invention in one embodiment.
FIGS. 3A, 3B, and 3C 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, 1B, 1C, 1D, 1E, 2A, 2B, and 2C 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, 1B, 1C, 1D, 1E, 2A, 2B, and 2C, 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, 3B, and 3C.

    
    
        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, 3B, and 3C 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_vars
        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_
START_
STOP_
CUBE_

RETURN_
ERROR_



DATE
TIME 
TIME
NAME
PROCESS
CODE
CODE 
DESCR







10 Jul. 
8:26:37
 8:26:38

mm00_init( )
0
Completed
Procedure mm00_init( ) 


2000

 




completed successfully


10 Jul. 
8:26:38
 8:26:38

mm01_set_vars( )
0
Completed
Procedure mm01_set_vars( ) 


2000






completed successfully


11 Jul. 
8:26:38
12:35:49

mm02_weekly_data_pull( )
0
Completed
Procedure mm02_weekly_data_


2000






pull( ) completed successfully.


11 Jul. 
2:04:59
12:11:57

mm03_memids( )
0
Completed
Procedure m03_memids( ) 


2000






completed successfully


11 Jul. 
1:07:32
11:23:46

mm04_mx_diags( )
1
−904
ORA-00904: invalid 


2000






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 Use
The mm00_init procedure initializes the 



environment for weekly 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's values as DATE datatype.



MM_VARS_HIST table-row inserted with this week's values as DATE datatype.



MM_LOG-row inserted showing successful completion or error condition.









3.4 Pull Weekly Data













Script Use
The mm02_weekly_data_pull procedure pulls one week of 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'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 Use
The mm03_memids procedure accumulates six weeks of 



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 



whereMONDD[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 Use
The mm04_mx_diags procedure gets diagnoses information



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_step1 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 Use
The mm06_step2 procedure must be run for each 



therapeutic 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 Use
The mm07 step3 procedure must be run for each 



therapeutic 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'd to the server of SITE 3. Metadata for each cube is spooled to a text file and FTP'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’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’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 QUINTERNETTM 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 QUINTERNETTM 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 QUINTERNETTM Series cube. These files contain the base HTML used to create each cube's web page. Instead of the actual metadata values that will populate the cubes' 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 QUINTERNETTM Series cube and are updated by SITE 2 as necessary so that a current version of each cube'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 QUINTERNETTM 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'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.

## Claims

1. A system for analyzing de-personalized health care data, comprising:
(a) a plurality of health care databases, the plurality of health care databases each including at least one record, each record including a de-personalized yet unique patient identifier associated with a patient, whereby the de-personalized patient identifier is a common value identifying a specific patient across a plurality of health care databases that are associated with distinct database systems of different organizations;
(b) one or more processors in communication with a first of the health care databases, and one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
(i) receiving a new record from at least one of the plurality of healthcare databases, the new record comprising a plurality of identification fields uniquely identifying a patient associated with the new record;
(ii) concatenating, into a combined textual representation, text from each of the accessed plurality of identification fields included in the new record,
(iii) generating at least one de-personalized yet unique patient identifier associated with the patient by encrypting the concatenated text of the combined textual representation; and
(iv) storing, in a first of the health care databases, at least a portion of the received new record in association with the least one de-personalized yet unique patient identifier.

2. A method for de-identifying health care data comprising:
receiving a new record from at least one of a plurality of health care databases, the new record comprising a plurality of identification fields uniquely identifying a patient associated with the new record;
concatenating, into a combined textual representation, text from each of the plurality of identification fields included in the new record,
generating at least one de-personalized yet unique patient identifier associated with the patient by encrypting the concatenated text of the combined textual representation, the at least one de-personalized yet unique patient identifier representing a common value identifying the patient across a plurality of health care databases that are associated with distinct database systems of different organizations; and
storing, in a first of the health care databases, at least a portion of the received new record in association with the least one de-personalized yet unique patient identifier.

3. A non-transitory computer-readable medium encoded with instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving a new record from at least one of a plurality of health care databases, the new record comprising a plurality of identification fields uniquely identifying a patient associated with the new record;
concatenating, into a combined textual representation, text from each of the plurality of identification fields included in the new record,
generating at least one de-personalized yet unique patient identifier associated with the patient by encrypting the concatenated text of the combined textual representation, the at least one de-personalized yet unique patient identifier representing a common value identifying the patient across a plurality of health care databases that are associated with distinct database systems of different organizations; and
storing, in a first of the health care databases, at least a portion of the received new record in association with the least one de-personalized yet unique patient identifier.

4. The system of claim 1, wherein the plurality of identification fields included in the new record comprise one or more of a patient's name, address, social security, contact number, date of birth, or gender.

5. The system of claim 1, wherein the one or more processors execute instructions that cause the one or more processors to perform operations comprising:
identifying one or more records stored in the first of the health care databases that include the at least one de-personalized yet unique patient identifiers; and
generating a report based on data included in the identified one or more records.

6. The system of claim 1, wherein the new record comprises one or more healthcare fields that include at least one of pharmaceutical claims data, medical claims data, or hospital claims data.

7. The system of claim 1, wherein the operations further comprise:
removing, from the portion of the new record that is stored in associated with the at least one de-personalized yet unique patient identifier, information other than the at least one de-personalized yet unique patient identifier that identifies the patient.

8. The system of claim 1, wherein:
the plurality of identification fields comprises a patient last name field and a patient birthdate field; and
concatenating the text from each of the plurality of identification fields included in the new record comprises concatenating, into the combined textual representation, a first text segment corresponding to a value stored in the patient name field and a second text segment corresponding to a value stored in the patient birthdate field.

9. The method of claim 2, wherein the plurality of identification fields included in the new record comprise one or more of a patient's name, address, social security, contact number, date of birth, or gender.

10. The method of claim 2, comprising:
identifying one or more records stored in the first of the health care databases that include the at least one de-personalized yet unique patient identifiers; and
generating a report based on data included in the identified one or more records.

11. The method of claim 2, wherein the new record comprises one or more healthcare fields that include at least one of pharmaceutical claims data, medical claims data, or hospital claims data.

12. The computer-readable medium of claim 3, wherein the plurality of identification fields included in the new record comprise one or more of a patient's name, address, social security, contact number, date of birth, or gender.

13. The computer-readable medium of claim 3, wherein the operations comprise:
identifying one or more records stored in the first of the health care databases that include the at least one de-personalized yet unique patient identifiers; and
generating a report based on data included in the identified one or more records.

14. The computer-readable medium of claim 3, wherein the new record comprises one or more healthcare fields that include at least one of pharmaceutical claims data, medical claims data, or hospital claims data.