SYSTEMS, MEDIA, AND METHODS FOR MEASURING HEALTH CARE PROVIDER PERFORMANCE AND TO OPTIMIZE PROVISION OF HEALTH CARE SERVICES
Systems, methods, and media for measuring health care provider performance and optimizing provision of health care services are provided. A method may include identifying potential chronic conditions a patient has based on a chronic conditions mapping. The method may also comprise determining a cost score. The method may further comprise determining an outcome score. The method may additionally comprise obtaining a final outcome score for each of a plurality of providers based on a variance weighted average of probabilities for groups of metrics pertaining to each provider. The method additionally include generating a final quality index based upon the final outcome score for each provider.
1 . A method comprising:
identifying potential chronic conditions a patient has based on a chronic conditions mapping;
determining a cost score;
determining an outcome score;
obtaining a final outcome score for each of a plurality of providers based on a variance weighted average of probabilities for groups of metrics pertaining to each provider; and
generating a final quality index based upon the final outcome score for each provider.
2 . The method of claim 1 wherein the cost score comprises:
identifying a plurality of episodes;
attributing episodes to providers using allowed amounts;
calculating a total spend amount for each episode;
determining an expected cost of each episode;
calculating a composite score for each provider; and
calculating a final cost index score.
3 . The method of claim 1 , wherein the outcome score comprises:
determining attributed episodes and a patient population for each provider;
identifying a plurality of outcome metrics for the attributed episodes and the patient population;
calculating an expected probability for each outcome metric for each episode;
aggregating each outcome metric for each provider;
calculating a binomial probability of performing as well or better than the provider actually performed on each metric; and
combining outcome metrics into one score.
4 . The method of claim 3 wherein the binomial probability is P(x) as defined by
P
(
x
)
(
n
x
)
p
x
q
n
-
x
=
n
!
(
n
-
x
)
!
x
!
p
x
q
n
-
x
where n is a quantity of total observations, x is a quantity metric occurrences, p is an expected metric outcome probability, and q=1−p, where q is a probability of not seeing a metric outcome, numerator n! is a quantity of Emergency Department (ED) visits that were avoidable, and denominator (n−x)!x! is a quantity of total ED visits.
5 . The method of claim 1 further comprising utilizing a hierarchical logistic regression model that takes patient risk factors into account to factor into an expected episode cost based upon a ratio of observed cost over expected cost.
6 . The method of claim 5 , wherein different hierarchical logistic regression models are utilized for each of primary care episodes, chronic condition episodes, acute inpatient episodes, and acute outpatient episodes.
7 . The method of claim 1 further comprising mapping each chronic condition associated with a patient to an internal hierarchy wherein only a chronic condition with a highest average cost among all chronic conditions associated with the patient becomes an episode.
8 . A system comprising:
memory and a processor coupled to the memory, wherein the processor is configured to:
identify potential chronic conditions a patient has based on a chronic conditions mapping;
determine a cost score;
determine an outcome score;
obtain a final outcome score for each of a plurality of providers based on a variance weighted average of probabilities for groups of metrics pertaining to each provider; and
generate a final quality index based upon the final outcome score for each provider.
9 . The system of claim 8 wherein the processor is further configured to determine the outcome score by:
determining attributed episodes and a patient population for each provider;
identifying a plurality of outcome metrics for the attributed episodes and the patient population;
calculating an expected probability for each outcome metric for each episode;
aggregating each outcome metric for each provider;
calculating a binomial probability of performing as well or better than the provider actually performed on each metric; and
combining outcome metrics into one score.
10 . The system of claim 8 wherein the processor is further configured to determine the outcome score by:
determining attributed episodes and a patient population for each provider;
identifying a plurality of outcome metrics for the attributed episodes and the patient population;
calculating an expected probability for each outcome metric for each episode;
aggregating each outcome metric for each provider;
calculating a binomial probability of performing as well or better than the provider actually performed on each metric; and
combining outcome metrics into one score.
11 . The system of claim 10 , wherein the binomial probability is P(x) as defined by
P
(
x
)
(
n
x
)
p
x
q
n
-
x
=
n
!
(
n
-
x
)
!
x
!
p
x
q
n
-
x
where n is a quantity of total observations, x is a quantity metric occurrences, p is an expected metric outcome probability, and q=1−p, where q is a probability of not seeing a metric outcome, numerator n! is a quantity of Emergency Department (ED) visits that were avoidable, and denominator (n−x)!x! is a quantity of total ED visits.
12 . The system of claim 8 , wherein the processor is further configured to utilize a hierarchical logistic regression model that takes patient risk factors into account to factor into an expected episode cost based upon a ratio of observed cost over expected cost.
13 . The system of claim 12 , wherein different hierarchical logistic regression models are utilized for each of primary care episodes, chronic condition episodes, acute inpatient episodes, and acute outpatient episodes.
14 . The system of claim 8 , wherein the processor is further configured to map each chronic condition associated with a patient to an internal hierarchy, wherein only a chronic condition with a highest average cost among all chronic conditions associated with the patient becomes an episode.
15 . A non-transitory computer readable medium embodying computer-executable instructions, that when executed by a processor, cause the processor to execute operations comprising:
identifying potential chronic conditions a patient has based on a chronic conditions mapping;
determining a cost score;
determining an outcome score;
obtaining a final outcome score for each of a plurality of providers based on a variance weighted average of probabilities for groups of metrics pertaining to each provider; and
generating a final quality index based upon the final outcome score for each provider.
16 . The non-transitory computer readable medium of claim 15 embodying further computer-executable instructions wherein the cost score comprises:
identifying a plurality of episodes;
attributing episodes to providers using allowed amounts;
calculating a total spend amount for each episode;
determining an expected cost of each episode;
calculating a composite score for each provider; and
calculating a final cost index score.
17 . The non-transitory computer readable medium of claim 15 embodying further computer-executable instructions wherein the outcome score comprises:
determining attributed episodes and a patient population for each provider;
identifying a plurality of outcome metrics for the attributed episodes and the patient population;
calculating an expected probability for each outcome metric for each episode;
aggregating each outcome metric for each provider;
calculating a binomial probability of performing as well or better than the provider actually performed on each metric; and
combining outcome metrics into one score.
18 . The non-transitory computer readable medium of claim 17 embodying further computer-executable instructions wherein the binomial probability is P(x) as defined by
P
(
x
)
(
n
x
)
p
x
q
n
-
x
=
n
!
(
n
-
x
)
!
x
!
p
x
q
n
-
x
where n is a quantity of total observations, x is a quantity metric occurrences, p is an expected metric outcome probability, and q=1−p, where q is a probability of not seeing a metric outcome, numerator n! is a quantity of Emergency Department (ED) visits that were avoidable, and denominator (n−x)!x! is a quantity of total ED visits.
19 . The non-transitory computer readable medium of claim 15 embodying further computer-executable instructions that comprise utilizing a hierarchical logistic regression model that takes patient risk factors into account to factor into an expected episode cost based upon a ratio of observed cost over expected cost.
20 . The non-transitory computer readable medium of claim 19 embodying further computer-executable instructions such that different hierarchical logistic regression models are utilized for each of primary care episodes, chronic condition episodes, acute inpatient episodes, and acute outpatient episodes.