US 11,887,723 B2
Dental practice scheduling efficiencies and operational issue trainings
Matthew M. Crego, Peoria, AZ (US); Mitchell Ward Ellingson, Scottsdale, AZ (US); and Yonas Yohannes, Peoria, AZ (US)
Assigned to SPEAR EDUCATION, LLC, Scottsdale, AZ (US)
Filed by Spear Education, LLC, Scottsdale, AZ (US)
Filed on Apr. 5, 2022, as Appl. No. 17/713,557.
Application 17/713,557 is a continuation of application No. 16/503,192, filed on Jul. 3, 2019, granted, now 11,328,816.
Application 16/503,192 is a continuation of application No. PCT/US2018/012378, filed on Jan. 4, 2018.
Claims priority of provisional application 62/442,907, filed on Jan. 5, 2017.
Prior Publication US 2022/0238214 A1, Jul. 28, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/00 (2023.01); G16H 40/20 (2018.01); G06Q 10/10 (2023.01); G16H 50/20 (2018.01); G16H 40/40 (2018.01); G06Q 50/10 (2012.01)
CPC G16H 40/20 (2018.01) [G06Q 10/10 (2013.01); G06Q 50/10 (2013.01); G16H 40/40 (2018.01); G16H 50/20 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
comparing, by a processor, potential revenue from dental treatment plan data for a dental practice with at least one of total planned dental treatments, total accepted dental treatments, total scheduled dental treatments or total completed dental treatments, wherein the dental treatment plan data includes financial data, patient data, dental hygiene procedures, recommended dental treatments and dates for scheduling the recommended dental treatments;
determining, by the processor, scheduling efficiency data based on the comparing;
converting, by a processor, the dental treatment plan data in a first format to a second format that is a common format with a plurality of dental practices;
creating, by the processor, a schedule model based on the dental treatment plan data of a plurality of dental practices sharing one or more characteristics with the dental practice;
comparing, by the processor, the dental treatment plan data and the scheduling efficiency data with the schedule model;
generating, by the processor, financial projections for the dental practice based on the financial data and the recommended dental treatments;
generating, by the processor, a financial model from the financial projections based on the plurality of dental practices in a local geographic area to the dental practice;
selecting, by the processor, and based on the schedule model and the financial model, an operational issue with a quantifiable metric being below a threshold value;
obtaining, by the processor, an article of content related to the operational issue, wherein the article of content includes dental clinical content;
identifying, by the processor, a solution to the operational issue;
evaluating, by the processor using a machine learning module, an effectiveness of the solution by analyzing trend data for the quantifiable metric for a time period after the solution is implemented in the plurality of dental practices sharing one or more characteristics with the dental practice; and
modifying, by the processor, the solution based on the trend data and from evaluating the effectiveness of the solution.