Clinical decision-making artificial intelligence object oriented system and method
View Patent ↗The present invention involves a system and method of providing decision support for assisting medical treatment decision-making. A patient agent software module processes information about a particular patient. A doctor agent software module processes information about a health status of a particular patient, beliefs relating to patient treatments, and the actual effects of treatment decisions. By filtering information over time from the patient agent into the doctor agent, a plurality of decision-outcome nodes are created and formed into a patient-specific outcome tree with the plurality of decision-outcome nodes. An optimal treatment is determined by evaluating the plurality of decision-outcome nodes with a cost per unit change function to output the optimal treatment. When additional information is available from at least one of the patient agent and the doctor agent, the filtering, creating, and determining steps are repeated thus allowing for the system to “reason over time”, continuously updating and learning as new information is received.
1. A method of providing decision support for assisting medical treatment decision-making, comprising:
receiving, by a health care computing device and from a first device operating an agent software module, evidence based information corresponding to a health status of a particular patient and patient treatment decisions;
filtering, by the health care computing device, the evidence based information to create a plurality of decision-outcome nodes comprising a plurality of belief state nodes reflecting potential future effects of different treatment choices;
determining an optimal treatment for the particular patient by evaluating the plurality of decision-outcome nodes with a scoring function, wherein the scoring function comprises a cost per unit change function and includes calculating the cost in an amount it takes to obtain one unit of outcome change (delta) on a given outcome;
transmitting, by the health care computing device and to the first device, one or more first messages describing the determined optimal treatment;
receiving, by the health care computing device, observation data corresponding to the particular patient after the patient was physically administered the determined optimal treatment;
updating at least one of the plurality of belief state nodes using the received observation data;
determining a new optimal treatment for the particular patient by evaluating the updated plurality of decision-outcome nodes with the scoring function; and
transmitting, by the health care computing device and to the first device, one or more second messages describing the determined new optimal treatment.
2. The method of claim 1 wherein the calculation involves the following equation:
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=
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min
E
π
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C
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where CPS=cost per service for a given session, CT is a random variable denoting accumulated cost at the end of treatment, E indicates expected value, and ACDOI(it) is calculated as:
ΔCDOI(π)=CDOI T (π)−CDOI 0
where CDOI T is a random variable denoting the CDOI-ORS value at the end of treatment.
3. The method of claim 1 wherein the evidence based information includes a plurality of health status information at a plurality of times.
4. The method of claim 1 wherein software creating the decision-outcome nodes includes a module that receives rewards/utilities, and a module to select patient treatments in order to maximize overall utilities.
5. The method of claim 1 wherein the decision-outcome nodes are updated according to a transition model.
6. The method of claim 1 the plurality of decision-output nodes are configured as a multi-level tree.
7. The method of claim 1 wherein the evidence based information includes at least one of clinical data, electronic health record information, and genetic data.
8. The method of claim 1 wherein upon receiving additional evidence based information, each of the decision-output nodes are recalculated.
9. A decision support system for assisting medical treatment decision-making, said system comprising:
a processor and associated memory, said memory including program memory configured to store instructions for enabling said processor to perform operations, and said memory including storage memory configured to store data upon which said processor performs operations;
said storage memory including data relating to a particular patient;
said program memory including a plurality of instructions that when executed by said processor enables said process to execute the following steps:
receive, from a first device operating a patient agent software module, information about a particular patient;
receive, from a second device operating a doctor agent software module, information about a health status of the particular patient, doctor beliefs relating to at least one of patient treatments and treatment effects, and evidence based information relating to effects of patient treatments;
filter the information from the patient agent and the information from the doctor agent to create a plurality of decision-outcome nodes comprising a plurality of belief state nodes reflecting potential future effects of different treatment choices;
create a patient-specific outcome based on the plurality of decision-outcome nodes;
determine an optimal treatment by evaluating the plurality of decision-outcome nodes with a scoring function and outputting the optimal treatment, wherein the scoring function comprises a cost per unit change function and includes calculating the cost in an amount it takes to obtain one unit of outcome change (delta) on a given outcome;
cause transmission of one or more first messages describing the determined optimal treatment;
receive observation data corresponding to the particular patient after the patient was physically administered the determined optimal treatment;
update at least one of the plurality of belief state nodes using the received observation data;
determine a new optimal treatment for the particular patient by evaluating the updated plurality of decision-outcome nodes with the scoring function; and
cause transmission of one or more second messages describing the determined new optimal treatment.
10. The decision support system of claim 9 wherein the calculation involves the following equation:
π
*
=
arg
min
π
C
P
U
C
(
π
)
=
arg
min
E
π
{
C
T
(
π
)
Δ
CDOI
(
π
)
,
if
Δ
CDOI
(
π
)
≥
1
C
T
(
π
)
1
+
(
1
-
Δ
CDOI
(
π
)
)
×
C
P
S
,
else
where CPS=cost per service for a given session, CT is a random variable denoting accumulated cost at the end of treatment, E indicates expected value, and ACDOI(it) is calculated as:
ΔCDOI(π)=CDOI T (π)−CDOI 0
where CDOI T is a random variable denoting the CDOI-ORS value at the end of treatment.
11. The decision support system of claim 9 wherein the patient agent includes a plurality of health status information at a plurality of times.
12. The decision support system of claim 9 wherein the doctor agent includes a module that receives rewards/utilities, and a module to select patient treatments in order to maximize overall utilities.
13. The decision support system of claim 9 wherein the decision-outcome nodes are updated according to a transition model.
14. The decision support system of claim 9 further including a learning software module with a knowledge base wherein when additional information is available, such information is included in the knowledge base used by at least one of the patient agent software module, the doctor agent software module, and the determining optimal treatment step.
15. The decision support system of claim 9 the doctor agent software has evidence based information from at least one of clinical information, electronic health record information, and genetic information.
16. The decision support system of claim 9 wherein said plurality of decision-outcome nodes are configured as a multi-level tree.
17. A server for providing decision support for medical treatment decision-making, said system comprising:
a processor and associated memory, said memory including program memory configured to store instructions for enabling said processor to perform operations, and said memory including storage memory configured to store data upon which said processor performs operations;
said storage memory including data relating to a particular patient;
said program memory including a plurality of instructions that when executed by said processor enables said processor to execute the following steps:
receive, from a first device, evidence based information about a health status of the particular patient, doctor beliefs relating to at least one of patient treatments and treatment effects, and patient treatment decisions;
filter the evidence based information to create a plurality of decision-outcome nodes comprising a plurality of belief state nodes reflecting potential future effects of different treatment choices;
determine an optimal treatment by evaluating the plurality of decision-outcome nodes with a scoring function and outputting the optimal treatment, wherein the scoring function comprises a cost per unit change function and includes calculating the cost in an amount it takes to obtain one unit of outcome change (delta) on a given outcome;
cause transmission of one or more first messages describing the determined optimal treatment;
receive observation data corresponding to the particular patient after the patient was physically administered the determined optimal treatment;
update at least one of the plurality of belief state nodes using the received observation data;
determine a new optimal treatment for the particular patient by evaluating the updated plurality of decision-outcome nodes with the scoring function; and
cause transmission of one or more second messages describing the determined new optimal treatment.