Systems and methods for electronic health records

1. A method of generating a clinically supplemented risk score by an electronic health record system having one or more processors, the method comprising:

collecting, via at least one of the one or more processors, data from a plurality of electronic health records of a patient;

parsing, via at least one of the one or more processors, the data into defined fields to generate parsed data;

determining a closed risk score in response to a documented diagnostic condition of the patient in the plurality of electronic health records;

comparing, via at least one of the one or more processors, the parsed data to at least one look-up table to generate an inferred diagnostic condition;

comparing, via at least one of the one or more processors, the inferred diagnostic condition to the documented diagnostic condition of the patient in the plurality of electronic health records, thereby determining the documented diagnostic condition is not associated with the inferred diagnostic condition;

mapping, via at least one of the one or more processors, the inferred diagnostic condition to at least one condition category to generate at least one mapped inferred diagnostic condition;

refining, via at least one of the one or more processors, the at least one mapped inferred diagnostic condition into a hierarchy to generate a hierarchal mapped conditioned category;

determining an open risk score in response to the hierarchal mapped condition category of the at least one mapped inferred diagnostic condition; and

determining, via at least one of the one or more processors, a risk score in response to open risk score and the closed risk score, the risk score representing an expected total cost of care for the patient relative to the average per-patient cost of care over an entire population.

2. The method as recited in claim 1 , further comprising identifying a physician associated with determining the documented diagnostic condition.

3. The method as recited in claim 1 , further comprising identifying that the inferred diagnostic condition is associated with a medication.

4. The method as recited in claim 1 , wherein the inferred diagnostic condition is determined retroactively.

5. The method as recited in claim 1 , wherein the parsed data is based on a presence of a diagnosis code.

6. The method as recited in claim 5 , wherein comparing the parsed data includes processing text notations to identify phrases that are then mapped to the diagnosis code.

7. The method as recited in claim 6 , wherein comparing the parsed data includes identifying a previously un-notated condition.

8. The method as recited in claim 1 , wherein the inferred diagnostic condition is inferred from medication records.

9. The method as recited in claim 1 , wherein the inferred diagnostic condition is inferred from laboratory results.

10. The method as recited in claim 1 , wherein the inferred diagnostic condition is inferred from patient vitals over a time period.

11. The method as recited in claim 1 , further comprising:

determining the inferred diagnostic condition from a subset of the data;

determining the lack of an associated diagnosis code for the inferred diagnostic condition in the plurality of electronic health records of a patient; and

identifying the inferred diagnostic condition as a risk.

12. The method as recited in claim 11 , wherein the subset of the data is from at least one of medication records, laboratory results and patient vitals over a time period.

13. The method of claim 1 , further comprising calculating, via at least one of the one or more processors, measures of personnel activity with reference to personnel type, time of personnel activity and an electronic health record module of the personnel activity to demonstrate workflow distribution among support staff and providers, wherein at least one measure of personnel activity is determined by calculating a number of manipulations of user input devices and a time period spent manipulating the user input devices within a field of any of the plurality of electronic health records; and wherein the step of determining the risk score utilizes the measures of personnel activity as a source of data for the risk score.

14. The method of claim 1 , wherein the inferred diagnostic condition is determined based on a calendar time interval.

15. The method of claim 14 , wherein the risk score is updated for the patient based on the calendar time interval, and for a population of interest, wherein the population of interest includes the patient.

16. A method of generating a clinically supplemented risk score by an electronic health record system having one or more processors, the method comprising:

collecting data from a plurality of electronic health records of a patient via an electronic health record system having one or more processors;

determining a closed risk score based on a documented diagnostic condition from the plurality of electronic health records;

determining, via at least one of the one or more processors, an inferred diagnostic condition from the plurality of electronic health records;

determining, via at least one of the one or more processors, the lack of an associated documented diagnostic condition for the inferred diagnostic condition;

identifying, via at least one of the one or more processors, the inferred diagnostic condition as a risk;

determining an open risk score in response to the identified inferred diagnostic condition; and

determining, via at least one of the one or more processors, a risk score in response to the open risk score and the closed risk score for the patient, the risk score representing an expected total cost of care for the patient relative to the average per-patient cost of care over an entire population.

17. The method as recited in claim 16 , wherein the documented diagnostic condition is based on a diagnosis code.

18. The method as recited in claim 17 , further composing processing text notations to identify phrases that are then mapped to the diagnosis code.

19. The method as recited in claim 16 , wherein determining the inferred diagnostic condition is from parsed data.

20. The method as recited in claim 19 , wherein the parsed data includes at least one of medication records, laboratory results, and patient vitals over a time period.