BEGIN:VCALENDAR
PRODID:-//github.com/ical-org/ical.net//NONSGML ical.net 4.0//EN
VERSION:2.0
BEGIN:VTIMEZONE
TZID:America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:STANDARD
DTSTART:20241103T020000
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:EST
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20250309T020000
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:EDT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
DESCRIPTION:NOTE: BIS 525 students are required to attend in person. Other
 s are invited to attend in person\, but may also attend via Zoom. SPEAKER
  : Wenbo Wu\, Assistant Professor\, Department of Population Health\, New
  York University Grossman School of Medicine TITLE : “Versatile Deep Lear
 ning Provider Profiling: A Design-Based Approach” ABSTRACT: Provider prof
 iling is a quality assessment process in which the performance of hospita
 ls and clinicians is compared based on patient-centered outcomes\, and ou
 tlying providers with significantly subpar services are identified. Encom
 passing numerous initiatives in the United States\, provider profiling ha
 s evolved into a major health care undertaking with ubiquitous applicatio
 ns\, profound implications\, and high-stakes consequences. In line with s
 uch a significant profile\, the literature has accumulated an enormous co
 llection of articles dedicated to enhancing the statistical paradigm of p
 rovider profiling. Tackling wide-ranging profiling issues\, these methods
  typically adjust for risk factors using linear predictors. While this si
 mple approach generally leads to reasonable assessments\, it can be too r
 estrictive to characterize complex factor-outcome associations. Secondly\
 , conventional methods\, having been historically driven by the demand fo
 r controlling care expenditures\, tend to amalgamate all racial/ethnic gr
 oups without accounting for their socioeconomic diversity. Thirdly\, desp
 ite the paramount importance of distinguishing between cost-driven and eq
 uity-driven profiling\, a methodological framework capable of addressing 
 these different but related objectives is still lacking\, due in part to 
 the absence of a unified framework defining objective-oriented performanc
 e benchmarks. To address these issues\, we consider a versatile probabili
 stic method based on so-called provider comparators\, defined as hypothet
 ical reference providers that correspond to specific profiling objectives
 . In addition\, we develop a flexible deep learning approach that relaxes
  the linearity assumption underpinning existing profiling methods. The ad
 vantages of the proposed methods are demonstrated through simulation expe
 riments and the profiling of kidney dialysis facilities using 2020 Medica
 re claims. YSPH values inclusion and access for all participants. If you 
 have questions about accessibility or would like to request an accommodat
 ion\, please contact Charmila Fernandes at Charmila.fernandes@yale.edu . 
 We will try to provide accommodations requested by October 26\, 2023.\n\n
 Speaker:\nWenbo Wu\, PhD\n\nAdmission:\nFree\n\nDetails URL:\nhttps://med
 icine.yale.edu/event/ysph-biostatistics-seminar-tba-10-31-23-copy-4-copy-
 copy-copy-1-copy/\n
DTEND;TZID=America/New_York:20231031T125000
DTSTAMP:20260807T121133Z
DTSTART;TZID=America/New_York:20231031T120000
GEO:41.302961;-72.931638
LOCATION:106-A&B\, 47 College Street\, New Haven\, CT\, United States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:YSPH Biostatistics Seminar: “Versatile Deep Learning Provider Prof
 iling: A Design-Based Approach”
UID:870fdcde-cb62-4fea-96bf-c346b871ec6f
END:VEVENT
END:VCALENDAR