MASTER FORMULATION GENERATION FOR DRUG COMPOUNDING

20240321422

18/611036

March 20, 2024

Disclosed herein are system, method, and computer program product embodiments for generating missing master formulation records. An embodiment operates by receiving recipe data for a compounded preparation or drug product. The embodiment compares using a first artificial intelligence (AI) engine the recipe data with stored recipe data to determine whether a master formulation record corresponding to the recipe data is available. The embodiment generates, using a second AI engine, the master formulation record for the compounded preparation or drug product based on a determination that the master formulation record corresponding to the recipe data is not available. The embodiment receives an input comprising an approval or a correction for the generated master formulation record. The embodiment retrains the second AI engine based on the input. The embodiment finally generates, using the retrained second AI engine, a missing master formulation record for another recipe data.

1. A computer implemented method for formulation generation, comprising: receiving, by at least one processor, recipe data for a compounded preparation or drug product; comparing, using a first artificial intelligence (AI) engine, the recipe data with stored recipe data to determine whether a master formulation record corresponding to the recipe data is available; generating, using a second AI engine, the master formulation record for the compounded preparation or drug product based on a determination that the master formulation record corresponding to the recipe data is not available; receiving an input comprising an approval or a correction for the generated master formulation record; retraining the second AI engine based on the input; and generating, using the retrained second AI engine, a missing master formulation record for another recipe data.

2. The computer implemented method of claim 1, further comprising: determining a match score between the recipe data and the stored recipe data; displaying a list of one or more recipe for a user to select on a user device, wherein the displayed one or more recipe have a respective match score above a threshold; receiving a selection from the user, wherein the selection indicates a correct match between the recipe data and the stored recipe data; and retraining the first AI engine based on the selection of the user.

3. The computer implemented method of claim 2, further comprising: determining that a first ingredient included in the recipe data and a second ingredient included in the stored recipe data are exchangeable based on the selection received from the user; adding the first ingredient and the second ingredient to a synonyms database; and retraining the first AI engine using the synonyms database.

4. The computer implemented method of claim 1, wherein the comparing further comprising: parsing the recipe data to extract an active ingredient; and assigning a higher weight to a feature corresponding to the active ingredient compared to weights corresponding to other features of the first AI engine.

5. The computer implemented method of claim 1, wherein the generating the master formulation record further comprising: predicting one or more attributes of the master formulation record, wherein the attributes comprise at least one of an ingredient list, an expiration date, an equipment, a set of guidelines, and instructions to compound the preparation or drug product.

6. The computer implemented method of claim 1, further comprising: automatically correcting one or more typographical errors in the recipe data before the comparing step.

7. The computer implemented method of claim 1, further comprising: receiving, from a user device, data that identifies a patient; retrieving the recipe data associated with the patient; and displaying one or more master formulation records associated with the patient.

8. The computer implemented method of claim 1, wherein the recipe data is received from an electronic medical record.

9. A system, comprising: a memory; and at least one processor coupled to the memory and configured to: receive recipe data for a compounded preparation or drug product; compare, using a first artificial intelligence (AI) engine, the recipe data with stored recipe data to determine whether a master formulation record corresponding to the recipe data is available; and generate, using a second AI engine, the master formulation record for the compounded preparation or drug product based on a determination that the master formulation record corresponding to the recipe data is not available, wherein to train the second AI engine the at least one processor is configured to: receive an input comprising an approval or a correction for a generated master formulation record corresponding to a missing master formulation record; and retrain the second AI engine based on the input.

10. The system of claim 9, wherein the at least one processor is further configured to: determine a match score between the recipe data and the stored recipe data; display a list of one or more recipe for a user to select on a user device, wherein the displayed one or more recipe have a respective match score above a threshold; receive a selection from the user, wherein the selection indicates a correct match between the recipe data and the stored recipe data; and retrain the first AI engine based on the selection of the user.

11. The system of claim 10, wherein the at least one processor is further configured to: determine that a first ingredient included in the recipe data and a second ingredient included in the stored recipe data are exchangeable based on the selection received from the user; add the first ingredient and the second ingredient to a synonyms database; and retrain the first AI engine using the synonyms database.

12. The system of claim 9, wherein to compare the recipe data with the stored recipe data, the at least one processor is further configured to: parse the recipe data to extract an active ingredient; and assign a higher weight to a feature corresponding to the active ingredient compared to weights corresponding to other features of the first AI engine.

13. The system of claim 9, wherein to generate the master formulation record, the at least one processor is further configured to: predict one or more attributes of the master formulation record, wherein the attributes comprise at least one of an ingredient list, an expiration date, an equipment, a set of guidelines, and instructions to compound the preparation or drug product.

14. The system of claim 9, wherein the at least one processor is further configured to: automatically correct one or more typographical errors in the recipe data before the comparing.

15. The system of claim 9, wherein the at least one processor is further configured to: receive, from a user device, data that identifies a patient; retrieve the recipe data associated with the patient; and display one or more master formulation records associated with the patient.

16. A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations comprising: receiving recipe data for a compounded preparation or drug product; comparing, using a first artificial intelligence (AI) engine, the recipe data with stored recipe data to determine whether a master formulation record corresponding to the recipe data is available; generating, using a second AI engine, the master formulation record for the compounded preparation or drug product based on a determination that the master formulation record corresponding to the recipe data is not available; receiving an input comprising an approval or a correction for the generated formulation record; retraining the second AI engine based on the input; and generating, using the retrained second AI engine, a missing master formulation record for another recipe data.

17. The non-transitory computer-readable device of claim 16, the operations further comprising: determining a match score between the recipe data and the stored recipe data; displaying a list of one or more recipe for a user to select on a user device, wherein the displayed one or more recipe have a respective match score above a threshold; receiving a selection from the user, wherein the selection indicates a correct match between the recipe data and the stored recipe data; and retraining the first AI engine based on the selection of the user.

18. The non-transitory computer-readable device of claim 17, the operations further comprising: determining that a first ingredient included in the recipe data and a second ingredient included in the stored recipe data are exchangeable based on the selection received from the user; adding the first ingredient and the second ingredient to a synonyms database; and retraining the first AI engine using the synonyms database.

19. The non-transitory computer-readable device of claim 16, the comparing comprising: parsing the recipe data to extract an active ingredient; and assigning a higher weight to a feature corresponding to the active ingredient compared to weights corresponding to other features of the first AI engine.

20. The non-transitory computer-readable device of claim 16, the operations further comprising: predicting one or more attributes of the master formulation record, wherein the attributes comprise at least one of an ingredient list, an expiration date, an equipment, a set of guidelines, and instructions to compound the compounded preparation or drug product.

16; 16

edspap.20240321422