Automated medical coding
Systems and methods for improving reimbursement rates from payors for services or procedures provided by a medical care provider are disclosed herein. In some examples, reimbursement can be improved by improving the accuracy of medical coding of services and diagnosis codes through the use of machine learning techniques. In some examples, the system can suggest codes more likely to get reimbursed by a payor through analysis of patient data and/or insurance information. In some examples, the system can determine a likelihood of reimbursement through analysis of patient data and/or insurance information.
1. A system for improving accuracy of medical coding, the system comprising:
a patient monitoring device configured to communicate with at least one treatment device configured to administer a medical treatment to the patient;
a non-transitory memory configured to store a plurality of machine learning classifiers comprising at least a first machine learning classifier and a second machine learning classifier,
wherein the first machine learning classifier of the plurality of machine learning classifiers is configured to identify a service code associated with a threshold probability of reimbursement by a payor,
wherein the first machine learning classifier is trained using historical reimbursement data associated with the payor,
wherein the second machine learning classifier of the plurality of machine learning classifiers is configured to determine a likelihood that a diagnosis code corresponding to the service code is accurate,
wherein the second machine learning classifier is trained using diagnostic data associated with rejected and accepted medical service codes, and
wherein the first machine learning classifier is configured to transform the historical reimbursement data associated with the payor and information associated with the patient into the threshold probability of reimbursement by the payor; and
one or more hardware processors in communication with the non-transitory memory, the one or more hardware processors configured to:
receive identification information corresponding to the patient from a clinician device;
associate the patient monitoring device with the patient based on the identification information;
receive patient data from the patient monitoring device, wherein the patient data is representative of at least one or more symptoms of the patient and a diagnosis, and wherein the one or more symptoms and diagnosis are input by a clinician;
automatically store at least the patient data in an electronic medical record system based at least in part on the identification information;
determine insurance information of the patient;
analyze, using the second machine learning classifier, the patient data to determine a likelihood of accuracy for the diagnosis code, wherein the diagnosis code is input by a clinician;
determine, using the second machine learning classifier, that the diagnosis code is not accurate based on a determination that the diagnosis code input is not associated with the diagnosis input;
determine, using the second machine learning classifier, a plurality of candidate diagnosis codes based at least in part on the symptoms of the patient, wherein each candidate diagnosis code is associated with a disease that comprises said symptoms,
for each candidate diagnosis code, using the second machine learning classifier, analyze the patient data to determine a likelihood of accuracy for the candidate diagnosis code;
suggest, using the second machine learning classifier, at least some of the candidate diagnosis codes based on a determination that one or more candidate diagnosis codes are associated with the diagnosis input;
receive a selection of an updated diagnosis code from the clinician device, wherein the updated diagnosis code is selected from the suggested candidate diagnosis codes;
analyze, using the first machine learning classifier, the patient data, the updated diagnosis code, and the insurance information to determine at least one service code associated with a threshold probability of reimbursement by the payor and a confidence score associated with the at least one service code; and
output the at least one service code, based on the confidence score, to the clinician device, wherein the service code is a basis for the clinician in deciding a treatment plan for the patient.
2. The system of claim 1 , wherein the historical reimbursement data is associated with a plurality of patient records.
3. The system of claim 1 , wherein the historical reimbursement data is associated with a patient diagnosis or patient diagnosis code.
4. The system of claim 1 , wherein the insurance information comprises plan coverage associated with an insurance plan of the patient.
5. The system of claim 1 , wherein the clinician device comprises a computing device.
6. The system of claim 5 , wherein the clinician device comprises a mobile computing device.
7. The system of claim 5 , wherein the clinician device comprises a patient monitor.
8. The system of claim 1 , wherein the one or more hardware processors are configured to select a service code of a plurality of service code with a highest confidence score and wherein to output the at least one service code, the one or more hardware processors are configured to output the service code with the highest confidence score.
9. The system of claim 1 , wherein the one or more hardware processors are configured to output a plurality of service codes and confidence scores.
10. The system of claim 9 , wherein the one or more hardware processors are configured to accept a selection of a service code of the plurality of service codes and store the selection of the service code to a database.
11. The system of claim 10 , wherein the one or more hardware processors are configured to submit the selection of the service code to the payor.
12. A system for improving accuracy of medical coding, the system comprising:
a non-transitory memory configured to store at least one classifier,
wherein the at least one classifier is configured to determine a likelihood that a diagnosis code corresponding to a service code is accurate, and
wherein the at least one classifier is trained using machine learning with diagnostic data associated with rejected and accepted medical service codes; and
one or more hardware processors in communication with the non-transitory memory, the one or more hardware processors configured to:
receive identification information corresponding to a patient from a clinician device;
associate a patient monitoring device with the patient based on the identification information, the patient monitoring device configured to receive treatment information associated with a treatment device coupled to the patient, wherein the treatment information is representative of at least one or more symptoms of the patient and a diagnosis;
receive patient data from the patient monitoring device, wherein the patient data comprises at least the treatment information;
automatically store the patient data in an electronic medical record system based at least in part on the identification information;
determine insurance information of the patient;
receive a diagnosis code input from the clinician device;
analyze, using the at least one classifier, the patient data to determine a likelihood of accuracy for the diagnosis code input;
determine, using the at least one classifier, that the diagnosis code input is not accurate based on a determination that the diagnosis code input is not associated with the diagnosis;
determine, using the at least one classifier, a plurality of candidate diagnosis codes based at least in part on the symptoms of the patient, wherein each candidate diagnosis code is associated with a disease that comprises said symptoms,
for each candidate diagnosis code, using the at least one classifier, analyze the patient data to determine a likelihood of accuracy for the candidate diagnosis code;
suggest, using the at least one classifier, at least some of the candidate diagnosis codes based on a determination that one or more candidate diagnosis codes are associated with the diagnosis; and
receive a selection of an updated diagnosis code from the clinician device,
wherein the updated diagnosis code is selected from the suggested candidate diagnosis codes,
wherein the updated diagnosis code is used to determine a candidate service code that is output to the clinician device, the candidate service code associated with a threshold probability of reimbursement by a payor, and
wherein the candidate service code is a basis for the clinician in deciding a treatment plan for the patient.
13. The system of claim 12 , wherein the one or more hardware processors are configured to determine, using the at least one classifier, a plurality of candidate service codes and associated confidence scores.
14. The system of claim 13 , wherein the one or more hardware processors are configured to output at least some of the plurality of candidate service codes to the clinician device.
15. The system of claim 14 , wherein the one or more hardware processors are configured to receive, from the clinician device, an updated service code based on the plurality of candidate service codes.
16. The system of claim 15 , wherein the one or more hardware processors are configured to submit the updated service code to the payor.
17. The system of claim 12 , wherein the clinician input comprises diagnosis data or treatment data.
18. The system of claim 12 , wherein the insurance information comprises plan coverage associated with an insurance plan of the patient.
19. The system of claim 12 , wherein the clinician device comprises a computing device.