Enhanced multiple resource planning and forecasting

• US 8,229,761 B2
• Filed: 11/09/2010
• Issued: 07/24/2012
• Est. Priority Date: 02/25/2005
• Status: Active Grant

• Current Assignee: Virtual Radiologic Corporation (Mednax Incorporated)
• Original Assignee: Virtual Radiologic Corporation (Mednax Incorporated)
• Inventors: Backhaus, Brent, Backhaus, Lorna, Ebesu, Dean, Van Arnem, Durand R.
• Primary Examiner(s): O'Connor, Gerald J.
• Assistant Examiner(s): PAULS, JOHN A
• Application Number:
  US12/942,355
• Publication Number:
  US 20110066449A1
• Time in Patent Office: 623 Days
• Field of Search: 705/2, 705/3, 705/4, 705/37, 707/10, 600/300, 600/437, 364/413.01
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 99/00 Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Abstract

A system configuration and techniques for optimizing schedules and associated use predictions of a multiple resource planning workflow are disclosed herein, applicable to environments such as radiologist scheduling in a teleradiology workflow. In one embodiment, a series of computing engines and components are provided to allow detailed forecasting and the generation of customized recommendations for scheduling and other resource usage scenarios. This forecasting can factor resource efficiencies, changes in resource demand volume, resource specialties, resource usage preferences, expected future events such as the removal or addition of resources at future times, and other resource availability or usage changes. The forecasts may be further enhanced through the use of historical data models and estimated future data models. Additionally, a calendar and other tools may be presented through a user interface to allow forecast and scenario customization based on selection of a series of future dates.

Claims

1. A method for forecasting radiology read demand and radiologist staffing within a future time period, comprising:

obtaining, at a computer system, historical demand information from historical data values of radiology read requests, the historical data values recorded during a past period of time wherein the past period of time provides one or more equivalent characteristics to the future period of time;

obtaining, at the computer system, future demand information relating to changes to radiologist availability and radiology read request volume expected to occur prior to or during the future period of time;

forecasting, with the computer system, based on the historical demand information and the future demand information, a probability and timing of the changes to radiologist availability and radiology read request volume expected to occur prior to or during the future period of time;

calculating, with the computer system, expected radiology availability for the future period of time by factoring the forecasted probability and timing of the changes to radiologist availability;

calculating, with the computer system, expected radiology read request volume for the future period of time by modifying the historical data values with the forecasted probability and timing of the changes to radiology read request volume; and

comparing, with the computer system, the expected radiology read request volume with the expected radiologist availability for the future period of time, thereby predicting any shortfall or excess in radiologist staffing levels;

wherein the method for forecasting is performed for predicting any shortfall or excess in radiologist staffing levels based on a plurality of constraints related to matching selected radiologists to demand from selected medical facilities, the constraints including one or more of radiologist specialties, radiology procedure types, radiologist credentialing, radiologist licensing, radiologist scheduling, radiologist availability, radiologist efficiencies, radiologist preferences and restrictions, medical facility preferences and restrictions, and medical facility location.

2. The method of claim 1, further comprising implementing a schedule based on the expected radiology read request volume and the expected radiologist availability for the future period of time.

3. The method of claim 1, further comprising presenting the expected radiology read request volume to one or more users.

4. The method of claim 1, further comprising predicting expected radiology read request volume per hour and available radiologist read capacity per hour for the future period of time.

5. The method of claim 1, further comprising forecasting radiologist staffing levels needed to fulfill the expected radiology read request volume.

6. The method of claim 1, wherein the historical demand information and future demand information relating to radiologist availability and radiology read request demand includes information for one or more of facility request volumes, request types, modality information, facility preferences, radiologist preferences, radiologist efficiencies, coverage hours for facilities, contract hours for radiologists, expected licensing of radiologists, and expected credentialing of radiologists.

7. The method of claim 1, wherein the historical demand information is selected from comparable medical facilities on the basis of similar factors between the comparable medical facilities and the selected medical facilities, the similar factors including one or more of size, geographic area, demographics, radiology procedure type, and radiology procedure volume.

8. The method of claim 1, wherein the forecasting method is performed for predicting any shortfall or excess in radiologist staffing levels for one or more new medical facilities, wherein none of the historical data values are provided from the new medical facilities.

9. The method of claim 1, wherein the forecasting method may be initiated by and presented to a user through a forecast period selection interface, a forecast range interface, and a selectable date calendar.

10. The method of claim 1, wherein information relating to radiologist availability and radiologist read request volume is aggregated into hourly chunks.

11. The method of claim 1, wherein outlier data from holidays and other dates with an abnormal volume of radiology read requests is removed from the historical data values.

12. The method of claim 1, wherein forecasting based on the historical demand information and the future demand information further comprises factoring variances of the historical data values.

13. The method of claim 1, wherein the prediction of any shortfall or excess in radiologist staffing levels is presented to a user as a range of values with an accompanying confidence level.

14. The method of claim 1, further comprising comparing expected radiologist availability and expected radiology read request volume to actual data after occurrence of the future time period.

15. A method for radiology read demand planning and forecasting, comprising:

obtaining, at a computer system, baseline data for a future time period by selecting historical data of radiology read activities occurring during a prior time period, the prior time period having common characteristics to the future time period, and the historical data being obtained from radiology read activities performed for a set of past medical facilities having common characteristics to a set of future medical facilities projected for the future time period;

predicting, with the computer system, radiology staffing and demand changes to the baseline data expected to occur by the future time period, the demand changes being predicted for the future set of medical facilities;

integrating, with the computer system, the baseline data for the future time period and the expected radiology staffing and demand changes to the baseline data in a data model for the future time period; and

to forecasting, with the computer system, expected radiology read demand for the future time period at the future set of medical facilities with results from the data model, to predict any shortfall or excess in radiologist staffing levels based on a plurality of constraints related to matching selected radiologists to demand from selected medical facilities, the constraints including one or more of radiologist specialties, radiology procedure types, radiologist credentialing, radiologist licensing, radiologist scheduling, radiologist availability, radiologist efficiencies, radiologist preferences and restrictions, medical facility preferences and restrictions, and medical facility location.

16. The method of claim 15, wherein forecasting the expected radiology read demand occurs through a regression on the data inputs and modeling projected orders for subsets of time within the future time period.

17. The method of claim 15, further comprising generating a forecast for radiology staffing levels for the future period of time based on the expected radiology read demand and estimated radiology staffing efficiency.

18. The method of claim 17, wherein the forecast provides the expected radiology read demand and the radiology staffing levels in estimated ranges with a confidence level.

19. The method of claim 15, wherein the common characteristics between the past time period and the future time period include one or more of a common day, a common day of week, a common month, a common season, and a proximity to one or more common dates or holidays.

20. The method of claim 15, wherein the common characteristics between the set of past medical facilities and the set of future medical facilities includes one or more facility size, geographic proximity, regional demographics, number of facility modalities, and requested subspecialties.

21. The method of claim 15, wherein demand changes to the baseline data are determined in part by either a volume of previous radiological reading requests occurring during the prior time period weighted by a calculated growth factor or a number of contractually obligated radiological reading requests per medical facility.

22. The method of claim 15, further comprising determining, based on the expected radiology read demand, a number of licenses, credentials, or radiologists needed to fulfill the expected radiology read demand during the future time period.

23. The method of claim 15, wherein obtaining baseline data and predicting radiology staffing and demand changes to the baseline data further comprises factoring qualifications of radiology staffing, including licenses, credentials, and specializations of radiologists.

24. A system for forecasting radiology read demand and radiologist staffing within a future time period, the system comprising:

at least one processor;

at least one memory

a data module implemented by the processor and the memory, the data module configured to obtain historical demand information and future demand information for a plurality of medical facilities, the historical demand information derived from historical data values of radiology read demand, the historical data values recorded during a past period of time wherein the past period of time provides one or more equivalent characteristics to the future period of time, and the future demand information relating to changes to radiologist availability and radiology read request volume expected to occur prior to or during the future period of time;

a forecast module implemented by the processor and the memory, the forecast module configured to;

compute a probability and timing of the changes to radiologist availability and radiology read request volume expected to occur prior to or during the future period of time based on the historical demand information and the future demand information;

calculate expected radiologist availability for the future period of time by factoring the forecasted probability and timing of the changes to radiologist availability;

calculate expected radiology read request volume for the future period of time by modifying the historical data values with the forecasted probability and timing of the changes to radiology read request volume; and

compare the expected radiology read request volume with the expected radiologist availability for the future period of time, thereby predicting any shortfall or excess in radiologist staffing levels; and

predict any shortfall or excess in radiologist staffing levels based on a plurality of constraints related to matching selected radiologists to demand from selected medical facilities, the constraints including one or more of radiologist specialties, radiology procedure types, radiologist credentialing, radiologist licensing, radiologist scheduling, radiologist availability, radiologist efficiencies, radiologist preferences and restrictions, medical facility preferences and restrictions, and medical facility location.

25. The system of claim 24, further comprising a scheduling component implemented by the processor and the memory, and being configured to generate an optimized assignment schedule based upon the future time period, the scheduling component configured for:

identifying a list of scheduled doctors associated with the future time period;

identifying an optimized assignment schedule, including;

(a) assigning each of the volume of expected radiological reading requests to one of the scheduled doctors; and

(b) creating a proposed assignment schedule based upon the assignments;

wherein identifying the optimized assignment schedule is repeated for a plurality of iterations, each iteration including a different combination of doctors and radiological reading requests.

26. The system of claim 25, further comprising a workflow module implemented by the processor and the memory, and being configured for:

determining, at the beginning of the future time period, whether a list of available doctors matches the scheduled doctors and a list of connected medical facilities matches the plurality of medical facilities; and

assigning one or more radiological reading requests using the optimized assignment schedule based upon the determination.

27. The system of claim 26, wherein assigning each of the radiological reading requests includes ranking, with the workflow module, the list of scheduled doctors, the ranking comprising:

determining a pending assignment volume for each in the list of scheduled doctors and applying a volume weight for doctors having low pending order volumes;

determining a radiological reading request quota for each in the list of scheduled doctors and applying a quota weight for doctors currently below quota;

combining the volume weight and the quota weight for each in the list of scheduled doctors into a total weight value; and

identifying a selected doctor based upon the total weight value.