Anomaly detection and performance assessment using a provider performance index

A system, comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured to:
• select a first target, wherein the first target includes a first target value;
• determine a first adjusted target value by combining the first target value with a first external weight generated by a first machine learning model,
• wherein the first external weight corresponds to the first target and corresponds to an impact of one or more features associated with the first target, and
• wherein the first machine learning model corresponds to the first target;
• determine a first error value based on the first target value and the first adjusted target value;
• normalize the first error value via a statistical measure;
• select a second target, wherein the second target includes a second target value;
• determining a second adjusted target value by combining the second target value with a second external weight generated by a second machine learning model,
• wherein the second external weight corresponds to the second target and corresponds to an impact of the one or more features associated with the second target, and
• wherein the second machine learning model corresponds to the second target;
• determine a second error based on the second target value and the second adjusted target value;
• normalize the second error value error via the statistical measure;
• and
• generate an index value based on the first normalized error and second normalized error.

A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

• selecting a first target, wherein the first target includes a first target value;
• determining a first adjusted target value by combining the first target value with a first external weight generated by a first machine learning model,
• wherein the first external weight corresponds to the first target and corresponds to an impact of one or more features associated with the first target,
• wherein the first machine learning model corresponds to the first target;
• determining a first error value based on the first target value and the first adjusted target value;
• normalizing the first error value via a statistical measure;
• selecting a second target, wherein the second target includes a second target value;
• determining a second target value by combining the second target value with a second external weight generated by a second machine learning model,
• wherein the second external weight corresponds to the second target and corresponds to an impact of the one or more features associated with the second target,
• wherein the second machine learning model corresponds to the second target;
• determining a second error based on the second target value and the second adjusted target value;
• normalizing the second error value error via the statistical measure;
• and
• generating an index value based on the first normalized error and second normalized error.