US2020372985A1PendingUtilityA1
Method and system for managing health care patient record data
Assignee: UNIV CENTRAL FLORIDA RES FOUND INCPriority: Sep 30, 2015Filed: Aug 7, 2020Published: Nov 26, 2020
Est. expirySep 30, 2035(~9.2 yrs left)· nominal 20-yr term from priority
G16H 10/60
53
PatentIndex Score
0
Cited by
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0
Claims
Abstract
A method for managing health care patient record data including: associating, by a processor, native health care patient record data of a first patient stored in a database to a plurality of data fields, wherein the native health care patient record data of the first patient represents the first patient's health care record; assigning, by a processor, a relative importance weight score to each of the plurality of data fields; and generating, by a processor, a record strength score of the first patient's health care record based on the relative importance weight score assignments. The record strength score indicates a percentage of the first patient's health care record that contains important native health care patient record data.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A method for quantitatively managing the recordation of health care patient data, the method comprising the steps of:
accessing a digital database using one or more processors; transforming native health care patient record data of a first patient stored in the database, using the one or more processors, into a plurality of parsed data fields; assigning, by the one or more processors, a relative importance weight score to each of the plurality of data fields, wherein the relative importance weight score is a numerical value corresponding to the relative importance of each of the plurality of data fields; generating, by the one or more processors, a numerical record strength score of the first patient's health care record, thereby converting the first patient's health care record from qualitative data to a numerical data set, the record strength determined based on the following formula:
RSS
(
%
)
=
(
IW
1
)
(
X
1
)
+
(
IW
2
)
(
X
2
)
+
(
IW
3
)
(
X
3
)
+
…
+
(
IW
n
)
(
X
n
)
∑
i
=
1
n
IW
1
×
100
wherein RSS is the record strength score which is a total strength of completeness for the first patient's health care record constrained from 0 to 100%; IW i is the importance weight of i th data field; and X i is a binary completeness variable for i th data field wherein 1 represents a complete data field and 0 represents an incomplete data field; and
displaying on the graphic user interface the numerical record strength score of the first patient's health care record, thereby conveying a quantitative analysis of a completeness of the first patient's health care record.
2 . The method of claim 1 , wherein transforming native health care patient data further includes transforming the native health care patient data to a standardized format based at least in part on concept mapping.
3 . The method of claim 1 , further comprising a step of:
generating, by the processor, a patient database score, wherein the patient database score is an average of record strength scores of all patient health care records in the database; wherein the patient database score indicates a percentage of all patient health care records in a database that contains important native health care patient record data.
4 . The method of claim 3 , wherein the patient database score is generated pursuant to the following formula:
PDS
(
%
)
=
RSS
1
+
RSS
2
+
⋯
+
RSS
n
n
wherein RSSi is the record strength score which is a total strength of completeness for an individual patient record constrained from 0 to 100% for an i th patient; and n is a total number of patient records in the database.
5 . The method of claim 1 , further comprising a step of:
generating, by the processor, a patient subgroup score based on the average of record strength scores of all patient health care records in a subgroup based on a subpopulation condition; wherein the patient subgroup score indicates a percentage of all patient health care records in the subgroup that contains important native health care patient record data.
6 . The method of claim 5 , wherein the patient subgroup score is generated pursuant to the following formula:
PSS
(
%
)
=
RSS
2
+
RSS
2
+
⋯
+
RSS
n
n
wherein RSSi is the record strength score which is a total strength of completeness for an individual patient record constrained from 0 to 100% for an i th patient that meets the subpopulation condition; and n is a total number of patient records that meet the subpopulation condition.
7 . The method of claim 1 , further comprising a step of:
comparing the strength of a selected data field across two or more databases using a data field completeness score based on an average of binary completeness variables of the selected data field in each database; wherein the data field completeness score indicates a percentage of all the selected data fields in each database that are complete.
8 . The method of claim 8 , wherein the data field completeness score is generated for all patients pursuant to the following formula:
DFCS
k
(
%
)
=
Σ
1
n
X
ik
n
×
100
wherein Xik is a binary completeness variable for an i th patient wherein 1 represents a complete data field and 0 represents an incomplete data field; a k th data field, wherein k remains constant and i iterates for individual patients; and n is a total number of patient records.
9 . The method of claim 1 , further comprising a step of:
comparing the strength of a selected data field for a subgroup across two or more databases using a data field completeness score based on an average of binary completeness variables of the selected data field in each database; wherein the data field completeness score indicates a percentage of all the selected data fields in each subgroup of each database that are complete.
10 . The method of claim 19 , wherein the data field completeness score is generated for a subgroup of patients pursuant to the following formula:
DFCS
k
(
%
)
=
Σ
1
n
X
ik
n
×
100
wherein Xik is a binary completeness variable for an i th patient wherein 1 represents a complete data field and 0 represents an incomplete data field; a k th data field, wherein k remains constant and i iterates for individual patients that meet a subpopulation condition; and n is a total number of patient records that meet the subpopulation condition.
11 . A non-transitory computer-readable storage medium containing program code for executing the following steps:
providing remote access to a database over a network through a graphical user interface, wherein a user can store health care patient record data in a native format dependent on a software platform used by the user; accessing, by one or more processors, the database containing native health care patient record data; transforming native health care patient record data of a first patient stored in a database to a plurality of data fields, wherein the native health care patient record data of the first patient represents the first patient's health care record; assigning a relative importance weight score to each of the plurality of data fields, wherein the relative importance weight score is a numerical value; and generating a numerical record strength score of the first patient's health care record based on the relative importance weight score assignments and a binary completeness variable for each field wherein 1 represents a complete data field and 0 represents an incomplete data field; and displaying on the graphic user interface the numerical record strength score of the first patient's health care record, thereby conveying a quantitative analysis of a completeness of the first patient's health care record.
12 . The non-transitory computer-readable storage medium of claim 11 , further comprising program code for generating a patient database score based on an average of record strength scores; wherein the patient database score indicates a percentage of all patient health care records in a database that contains important native health care patient record data.
13 . The non-transitory computer-readable storage medium of claim 12 , wherein the patient database score is generated pursuant to the following formula:
PDS
(
%
)
=
RSS
1
+
RSS
2
+
⋯
+
RSS
n
n
wherein RSSi is a record strength score which is a total strength of completeness for an individual patient record constrained from 0 to 100% for an i th patient; and n is a total number of patient records.
14 . The non-transitory computer-readable storage medium of claim 11 , further comprising program code for generating a patient subgroup score based on an average of all patient health care records in a subgroup based on a subpopulation condition; wherein the patient subgroup score indicates a percentage of all patient health care records in the subgroup that contains important native health care patient record data.
15 . The non-transitory computer-readable storage medium of claim 14 , wherein the patient subgroup score is generated pursuant to the following formula:
PSS
(
%
)
=
RSS
2
+
RSS
2
+
⋯
+
RSS
n
n
wherein RSSi is the record strength score which is a total strength of completeness for an individual patient record constrained from 0 to 100% for an i th patient that meets the subpopulation condition; and n is a total number of patient records that meet the subpopulation condition.
16 . The non-transitory computer-readable storage medium of claim 14 , wherein the subpopulation condition is selected from a group consisting of age, race, gender, and/or insurance status.
17 . The non-transitory computer-readable storage medium of claim 11 , wherein the record strength score of the first patient's health care record based on the relative importance weight score assignments, pursuant to the following formula:
RSS
(
%
)
=
(
IW
1
)
(
X
1
)
+
(
IW
2
)
(
X
2
)
+
(
IW
3
)
(
X
3
)
+
…
+
(
IW
n
)
(
X
n
)
∑
i
=
1
n
IW
1
×
100
wherein RSS is the record strength score which is a total strength of completeness for the first patient's health care record constrained from 0 to 100%; IWi is the importance weight of ith data field; and Xi is a binary completeness variable for ith data field wherein 1 represents a complete data field and 0 represents an incomplete data field.
18 . The non-transitory computer-readable storage medium of claim 11 , further comprising program code for generating a data field completeness score based on an average of binary completeness variables of a selected data field in a database;
wherein the data field completeness score indicates a percentage of all the selected data fields in the database that are complete; wherein the data field completeness score is generated for all patients pursuant to the following formula:
DFCS
k
(
%
)
=
Σ
1
n
X
ik
n
×
100
wherein Xik is a binary completeness variable for an i th patient wherein 1 represents a complete data field and 0 represents an incomplete data field; a k th data field, wherein k remains constant and i iterates for individual patients; and n is a total number of patient records.
19 . The non-transitory computer-readable storage medium of claim 11 , further comprising program code for comparing the strength of a selected data field for a subgroup across two or more databases using a data field completeness score based on an average of binary completeness variables of the selected data field in each database;
wherein the data field completeness score indicates a percentage of all the selected data fields in each subgroup of each database that are complete; wherein the data field completeness score is generated for the subgroup of patients pursuant to the following formula:
DFCS
k
(
%
)
=
Σ
1
n
X
ik
n
×
100
wherein Xik is a binary completeness variable for an i th patient wherein 1 represents a complete data field and 0 represents an incomplete data field; a k th data field, wherein k remains constant and i iterates for individual patients that meet a subpopulation condition; and n is a total number of patient records that meet the subpopulation condition.
20 . A method for managing health care patient record data from various independent healthcare facilities using independent healthcare software, the method comprising the steps of:
providing remote access to at least one database over a network through a graphical user interface, wherein each database stores health care patient record data in a native format dependent on a software platform used at each healthcare facility; accessing, by one or more processors, the at least one database containing native health care patient record data; transforming native health care patient record data, using the one or more processors, to a standardized format in which the native health care patient record data for one or more patients is converted into a plurality of parsed data fields; generating, by the one or more processors, a numerical record strength score for the native health care patient record data for each patient, thereby converting the native health care patient record data for each patient from qualitative data to a numerical data set, the record strength determined based on a binary completeness variable for a plurality of data fields; and displaying on the graphic user interface the numerical record strength score of the native health care patient record data for each patient, thereby conveying a quantitative analysis of a completeness of the native health care patient record data for each patient.Join the waitlist — get patent alerts
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