| 摘要 |
A prescription association system and method for grouping together prescription-related transactions and creating groups or “clusters” of prescriptions having similar characteristics. Through an association process, the prescription cluster describes the events surrounding prescription activity. This includes prescribing patterns, payer influences, and patient acceptance of therapy. The same prescription transaction from one data provider may contain additional or different information that can enhance a corresponding duplicate transaction or set of claim lifecycle transactions from another provider. The disclosed processes create unique linking across claims, payers, and patients and form the basis for relating and measuring payer, patient, practitioner, and pharmaceutical promotion influences on healthcare utilization and treatment. |
| 主权项 |
1. Processor-implemented method for associating prescription-related data, comprising the steps of:
providing at least one processor configured to
receive incoming batch transaction data from a plurality of sources, said incoming batch transaction data being standardized and stored to an operational data store and comprising a plurality of classifications;process the classifications in order to obtain attributes for the classifications;process the attributes to determine matches among the attributes for a respective classification for a predetermined period of time,wherein the at least one processor matches the attributes via: a primary association pass, whereby the primary association pass compares attributes reflecting at least one of (1) prescription number, (ii) prescription fill date, or (iii) refill code of the incoming batch transaction data to attributes in existing event clusters; and a secondary association pass, whereby the secondary association pass compares attributes reflecting at least one of (i) pharmacy ID, (ii) prescription number, (iii) drug identification number, or (iv) prescription fill of the incoming batch transaction data to attributes in said existing event clusters; wherein, when no match has occurred in either the primary or secondary association pass, the at least one processor matches incoming attribute groups to attributes in existing event clusters via: a tertiary association pass, whereby the tertiary association pass compares attributes reflecting a tertiary incoming attribute group of the incoming batch transaction data to attributes in said existing event clusters; a quaternary association pass, whereby the quaternary association pass compares attributes reflecting a quaternary incoming attribute group of the incoming bath transaction data to attributes in said existing event clusters; a quinary association pass, whereby the quinary association pass compares attributes reflecting a quinary incoming attribute group of the incoming batch transaction data to attributes in said existing event clusters; and updating existing event clusters to include one more attributes of the incoming batch transaction data when a match with one or more existing event clusters has occurred in the primary, secondary, tertiary, quaternary, or quinary association passes, thereby identifying events associated with the prescription-related data; and
grouping into one or more event clusters the matches for each attribute during the predetermined period of time to identify events associates with the prescription-related data that do not match an existing event cluster. |
