Patent Issued for Systems and methods for patient record matching (USPTO 11515018): Express Scripts StrategiC Development Inc.

2022 DEC 20 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Ahmad, Mateen (Iselin, NJ, US), Kreitzman, Elisa D. (Cherry Hill, NJ, US), Russo, Steven V. (North Caldwell, NJ, US), Sragow, Howard M. (West Orange, NJ, US), filed on February 28, 2022, was published online on November 29, 2022, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11515018 is assigned to Express Scripts StrategiC Development Inc. (St. Louis, Missouri, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Health care is provided to patients using health care records that are associated with patients throughout their lives. Information can be included in these records to try and associate each record with a different patient. In the United States, however, there is no unique identifier assigned to each patient to ensure that each health care record is correctly associated with the same patient. Each health care provider, processor, and payor make individual decisions about for whom each service is being performed, and whether the patient receiving the service is the same as another patient who received another service on a different day. With millions of providers; thousands of insurers; patients constantly moving between providers and insurers; and a constant stream of demographic volatility and typographical errors, it can be very difficult to confidently decide who is who and which record represents the health care provided to which patient.

“Patient-matching processes attempt to determine whether two or more patient health care records are associated with the same patient. For example, these processes attempt to determine whether patient records from the same or different health care provider, processor, and/or payor represent health care received by the same person. These processes try to avoid overmatching the records, which occurs when multiple records are determined to belong to (e.g., are associated with) the same person when the records belong to different people. These processes also try to avoid undermatching, which occurs when multiple records are determined to belong to two different people when the records belong to the same person.

“Both overmatching and undermatching can have significant consequences to health care decision-making. Overmatches can result in automated clinical decision-making using records that do not actually belong to the patient. Undermatches can result in automated clinical decision-making being performed without full view of the patient’s records. Either overmatching or undermatching can pose serious risks to the health of patients. For example, contraindicated medications and/or treatments may be administered to patients due to overmatching or undermatching patient records. Many health care organizations err on the side of undermatches because overmatches may result also in inappropriate disclosures. For example, an overmatch could result in private information in a patient record of one patient being exposed (without permission) to another patient. Consequently, currently known systems provide users with a dilemma of risking undermatching records (which causes clinical decision-making to miss relevant information, with potentially deadly consequences) or overmatching records (which causes clinical decision-making to be made on the basis of incorrect information, as well as inappropriately exposing private health information). An improved system is needed that avoids both undesirable outcomes of this dilemma. That is, a system is needed that correctly matches patient records to reduce clinical risks and avoid risking inappropriate exposure of private health information.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “Example systems and methods for matching patient records are described herein. These record matching systems and methods examine demographic information in patient records to determine which patient records are associated with (e.g., include the health care information of) the same patient or the same patient identifier (e.g., a unique identifier of a person). The systems and methods can use the demographic information available to health care processors, such as patient identifiers, patient names, mailing addresses, dates of birth, family associations, health care coverage information, and the like. The systems and methods can examine the demographic information in the records to determine whether records represent patients in the same household. If the records do not contain demographic information indicating that the records represent patients in the same household, then the systems and methods determine that the records represent different patients.

“If the system and method determine that the records represent patients in the same household, the system and method can then determine whether the records represent different persons in the same household. For example, the system and method can examine the demographic information to determine whether the records are associated with different people in the same households (e.g., twins living in the same household, a junior and senior relationship between parent and child, etc.). If the system and method determine that the records do not represent different patients within the same household, then the system and method determine that the records belong to the same patient or patient identifier.

“The present disclosure described herein uses this two-step approach to allow for a less stringent confidence requirements in the second step. For example, the second step of intra-household examination of the household-related records to determine whether the records are associated with different people in the same household can be performed with a lower certainty or reduced requirement or threshold of confidence. Additionally, the separation of these steps can allow for customization of kinds of connections for certain types of patients. Patients with Medicare coverage, for example, tend to appear with certain demographic patterns while patients with employer health care coverage tend to appear with different demographic patterns. The connection algorithms used by the systems and methods described herein can be customized to these types of patterns.

“The rules used by the systems and methods for individual record matching can ignore the record information used to make the family connection (e.g., the same household determination), yet account for pattern-matching within families. This allows the system or method to infer whether the data source for one or more of the records considers the distinct records to represent distinct persons. The system or method can use this consideration among the other information to determine (with increased confidence) that the records match (e.g., are associated with the same patient).

“The systems and methods described herein improve the functioning of known computerized systems that attempt to match records with patients. As described herein, various rules are used to determine which records represent different patients to avoid overmatching records to patients (and thereby unlawfully exposing a patient’s private health information to others) and avoid undermatching records to patients (and thereby risking serious health consequences such as death to a patient who is incorrectly associated with another person’s medical record). For example, without the present disclosure described herein, computers that operate to automatically associate records with patients are at increased risk of overmatching and an increased risk of undermatching records to patients. But, with the unique rules and processes described herein, these risks are significantly reduced. Consequently, the functioning of the computers in matching records to patients is significantly improved as these computers will not or are less likely to overmatch or undermatch records to patients. Use of the subject matter described herein also provides a practical application of the subject matter with meaningful limitations as the processes provide specific improvements with the meaningful limitations to operation of the computers that match records to patients in a more accurate and reliable manner than the known processes used to match records to patients. Stated differently, the subject matter does not merely state that records are more accurately connected with patients. Instead, the subject matter described herein provides meaningful limitations on how specific rules and analyses are performed to improve the functioning of the computers that match records to patients.”

The claims supplied by the inventors are:

“1. An artificial intelligence (AI) record matching system comprising: one or more processors at a healthcare management system that are configured to obtain patient records having demographic information, the one or more processors configured to compare the demographic information in the patient records and determine that the demographic information in the patient records does not match, responsive to determining that the demographic information in the patient records does not match, the one or more processors are configured to: determine whether the demographic information in the patient records that do not match are linked with a common household by comparing the patient records using artificial neurons connected with each other in different layers, compare a first model containing a first set of one or more rules, criteria, or parameters that include mathematical relationships between (a) the patient records that are input to the artificial neurons and (b) outputs from the artificial neurons that indicate whether the patient records match or do not match each other, the mathematical relationships including one or more of a limit on an edit distance on differences between the demographic information in the patient records, a value for a likelihood of affinity measurement between the demographic information in the patient records, or a threshold number or threshold percentage of times that the demographic information in the patient records match, compare the records using the first set of the one or more rules, criteria, or parameters to determine: © whether the demographic information in the patient records that do not match include a common first name and a same date of birth and whether the patient records indicate membership in a common health benefit plan, (d) whether the demographic information in the patient records that do not match include personal identifiers that share at least a designated length of a character string, (e) whether the demographic information in the patient records that do not match include a common street address name, a common postal code, and the common first name and include a combination of a street address number and the common street address name is used by no more than a designated number of people, or (f) whether the demographic information in the patient records that do not match include the common street address name, the street address number, and the common postal code, and the combination of the street address number and the common street address name is used by no more than the designated number of people, determine whether the demographic information in the patient records that do not match includes exclusionary intra-family overmatching data according to the first set of one or more rules, criteria, or parameters and using the artificial neurons responsive to determining that the demographic information in the patient records that do not match are linked to with the common household, determine whether the demographic information includes the exclusionary intra-family overmatching data by using the artificial neurons to determine: (g) whether the demographic information in the patient records that do not match include the common first name but different dates of birth that are within a designated time period of each other, (h) whether the demographic information in the patient records that do not match includes the same date of birth but different first names that include a designated nickname, a truncated variation of the first names, or initials of the first names, (i) whether the demographic information in the patient records that do not match include different character strings that have at least a designated length of identical characters, or (j) whether the demographic information in the patient records that do not match includes the different first names that differ by no more than a designated edit distance, determine that the patient records include medical information of a same person using the artificial neurons and responsive to: determining that the demographic information in the patient records that do not match are linked with the common household but do not include the exclusionary intra-family overmatching data or determining that the patient records do not all include the medical information of the same person responsive to determining that the demographic information in the patient records are not linked with the common household or include the exclusionary intra-family overmatching data, repeatedly receive feedback data indicative of one or more of overmatching or undermatching the patient records to each other using the first set of one or more rules, criteria, or parameters, and repeatedly train the artificial neurons based on the feedback data by repeatedly modifying the one or more rules, criteria, or parameters of the first set to change connections between the artificial neurons in the different layers into a modified second set of the one or more rules, criteria, or parameters that differs from the first set, the one or more processors configured to use the one or more rules, criteria, or parameters that are modified in the second set during repeated training of the connections between the artificial neurons to reduce the one or more of overmatching or undermatching of the patient records during successive iterations of the one or more processors examining the patient records.

“2. The AI record matching system of claim 1, wherein the one or more processors are configured to use the modified set of the one or more rules, criteria, or parameters as the first set of the one or more rules, criteria, or parameters during at least one of the successive iterations of the one or more processors examining the patient records, the one or more processors configured to be repeated re-trained by using the feedback data obtained following using the modified set of the one or more rules, criteria, or parameters as the first set of the one or more rules, criteria, or parameters and modifying the first set of the one or more rules, criteria, or parameters into another iteration of the modified set of the one or more rules, criteria, or parameters.

“3. The AI record matching system of claim 1, wherein the one or more processors are configured to create a database that organizes different portions of the demographic information of at least one of the records in the database, the database created by the one or more processors to include connecting data elements that each include a name and a value that indicate the patient records that match the same person, the database created by the one or more processors to indicate that the patient records that match include medical data of the same person without changing the demographic information in the patient records, the one or more processors configured to use the database that is created and the modified set of the one or more rules, criteria, or parameters during the successive iterations of examination of the patient records to determine that the patient records match the same person without repeating determining whether the demographic information in the patient records are linked with the common household and without repeating determining whether the demographic information in the patient records include the exclusionary intra-family overmatching data.

“4. The AI record matching system of claim 1, wherein the one or more processors are configured to determine whether the patient records are linked with the common household includes by identifying a first combination of the first name and the date of birth in the patient records, identifying a second combination of the first name and the date of birth in the patient records, determining whether the first combination is associated with a first address in the patient records, and determining whether the second combination is associated with a second address in the patient records.

“5. The AI record matching system of claim 4, wherein the first combination and the second combination both include the first name and the date of birth of a third person.

“6. The AI record matching system of claim 1, wherein the one or more processors also are configured to create a complete patient history by combining a clinical histories associated with the patient records that are determined to include the medical information of the same person, the one or more processors also configured to make one or more automated clinical decisions using the complete patient history and to enable clinicians to make one or more manual decisions using the complete patient history.

“7. The AI record matching system of claim 1, wherein the one or more processors are configured to obtain the patient records from different data sources.

“8. The AI record matching system of claim 1, wherein the one or more processors are configured to obtain the patient records from a same data source.

“9. The AI record matching system of claim 1, wherein the one or more processors are configured to modify at least one of the patient records by adding or changing one or more of data included in a first patient record of the patient records to match data included in a second patient record of the patient records, or the first patient record to match the second patient record.

“10. The AI record matching system of claim 1, wherein the one or more processors are configured to determine that a first record and a second record of the patient records have the demographic information that does not match but that include the medical information of the same person responsive to the first record being associated with continuing treatment of the same person and the second record not being associated with the continuing treatment of the same person.”

There are additional claims. Please visit full patent to read further.

URL and more information on this patent, see: Ahmad, Mateen. Systems and methods for patient record matching. U.S. Patent Number 11515018, filed February 28, 2022, and published online on November 29, 2022. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11515018)&db=USPAT&type=ids

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