DR. LILY C CHANG, M.D.
Radiology at 9 Ave, Seattle, WA
License number
Washington MD00036409
Category
Radiology
Type
Surgery
Address
Address
1100 9Th Ave, Seattle, WA 98101
Phone
(206) 341-1994
(206) 515-5886 (Fax)
(206) 625-7245 (Fax)
(206) 515-5886 (Fax)
(206) 625-7245 (Fax)
Personal information
See more information about LILY C CHANG at radaris.comName
Address
Phone
7717 17Th Ave NE, Seattle, WA 98115
1400 238Th Pl SW, Bothell, WA 98021
14875 SE 10Th Pl #336, Bellevue, WA 98007
14875 SE 10Th Pl, Bellevue, WA 98007
1205 16Th Ave E, Seattle, WA 98112
Organization information
See more information about LILY C CHANG at bizstanding.comLily C Chang MD
1100 9 Ave #G1-MSO, Seattle, WA 98101
Categories:
General Surgeons
Phone:
(206) 624-1144 (Phone)
Professional information
Lily C Chang, Seattle WA
Specialties:
Surgeon
Address:
1100 9Th Ave, Seattle, WA 98101
Board certifications:
American Board of Surgery Certification in Surgery
Lily Jei-Ho Chang, Seattle WA
Specialties:
Acupuncturist
Address:
2611 Ne 125Th St, Seattle, WA 98125
Skill Evaluation Using Spherical Motion Mechanism
US Patent:
2011002, Jan 27, 2011
Filed:
Jun 28, 2010
Appl. No.:
12/825236
Inventors:
Blake Hannaford - Seattle WA, US
Jacob Rosen - Santa Cruz CA, US
Jeffrey D. Brown - Palo Alto CA, US
Timothy Kowaleski - Tacoma WA, US
Mika N. Sinanan - Brier WA, US
Lily Chang - Seattle WA, US
Jacob Rosen - Santa Cruz CA, US
Jeffrey D. Brown - Palo Alto CA, US
Timothy Kowaleski - Tacoma WA, US
Mika N. Sinanan - Brier WA, US
Lily Chang - Seattle WA, US
Assignee:
University of Washington - Seattle WA
International Classification:
G09B 23/28, B25J 9/02, G06F 3/01
US Classification:
434262, 700255, 715702, 901 9
Abstract:
Software tools, methods and apparatus for objectively assessing surgical and medical procedural skills are described. Data corresponding to performance of a manipulative task by a subject is modeled using Markov modeling techniques and compared with stored models corresponding to each of a plurality of proficiency levels. A particular proficiency level is selected based on proximity of the subject data relative to each of the stored models.