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Romero N. Santiago

Mentors: Guy David, Ph.D. and Mark Neuman, M.D.
Motivation
                    
 Elements that distort efficient matching of patients to
  physicians may impact patient wellbeing.
    Perverse incentives to take or avoid difficult cases.
    For the same condition, recommendations may vary
     by specialty (supplier induced demand).
 Inefficient matching is potentially costly to the
  healthcare system.
 Hospital reputation and malpractice pressure may
  not provide sufficient incentives to induce efficient
  matching.
Strategy Used to Study
          Task-Talent Matching
                         
 Review previous research discussing current
  framework of task-talent matching
 Read through the literature to find various incentives
  that could help explain cause of inefficient matching
 Analyze task-talent matching in a specific region and
  specialty to observe degree of matching problem
Project Overview
               
 Are highly talented physicians performing the most
  difficult cases?
 Theoretical Framework
 Empirical Work
 Valuable insights about research experience
Garicano’s Pyramid Hierarchy is Not a
        Perfect Fit for Medicine
                            
 “Hierarchies and the Organization of Knowledge in
  Production” – Garicano
   “Knowledge-based hierarchy” – production workers and
    specialized problem solvers (industrial sector).
   Pyramidal structure with multiple levels, communication
    costs incurred with specialization
   Knowledge of problem solvers incorporates knowledge of
    those asking them for advice on solving a particular problem
   In medicine, no fine line between base level
    (production worker) and problem solver, levels overlap.
Specialization Creates Matching
     Issue Through Incentives
                         Santos
 “Referrals” – Garicano and
   Agent diagnosing opportunity/task incentivized to keep
    most valuable ones and refer least valuable
   Top-down diagnosis generates no inefficiency, unlike
    bottom-up arrangements
 “Specialization and Matching” – Epstein and colleagues
   Physicians in group partnerships specialize more than
    solo physicians, utilizing referral system
   Matching of specialists to patient heightened under firm
    or group practice.
Macroscopic View of Florida’s
    Cardiac Surgeon Population
                          of Health website
 Utilized the Florida Department
    Board-certified cardiac surgeons (162)
    Graduation Year from Medical School
    Residency and Fellowship Information
 Age of patient utilized as proxy for task difficulty
 Years of experience used as proxy for talent
 Data represents inpatient cases from 2005 to 2007
      Mean age of patient = 66 years
      Standard Deviation = 10.7 years
      Mean Experience for Surgeon = 28.8 years
      Standard Deviation = 7.9 years
Using Experience=15 as Cutoff
                            Experience<15                Experience>=15
Age<70                      233 (60%)                   24215 (57%)
Age>=70                     153 (40%)                    18130 (43%)


                           Experience<15                 Experience>=15
Age<80                     343 (89%)                     37359 (88%)
Age>=80                    43 (11%)                      4986 (12%)


                            Experience<15                 Experience>=15

Age<90                      380 (98%)                     41804 (99%)

Age>=90                     6 (2%)                        541 (1%)


    Chi-squared values were 0.21, 0.70, and 0.63 respectively
Using Experience = 20 as Cutoff

Age<70                     2815 (56%)
                                       
                           Experience<20                 Experience>=20
                                                         21633 (57%)
Age>=70                    2174 (44%)                    16109 (43%)


                           Experience<20                 Experience>=20
Age<80                     4393 (88%)                    33309 (88%)
Age>=80                    596 (12%)                     4433 (12%)


                           Experience<20                 Experience>=20
Age<90                     4923 (99%)                    37261 (99%)
Age>=90                    66 (1%)                       481 (1%)


    Chi-squared values were 0.23, 0.68, and 0.77 respectively
Using Experience=25 as cutoff

Age<70                      6962 (58%)
                                       
                            Experience<25                Experience>=25
                                                         17486 (57%)
Age>=70                     5044 (42%)                   13239 (43%)


                           Experience<25                 Experience>=25
Age<80                     10635 (89%)                   27067 (88%)
Age>=80                    1371 (11%)                    3658 (12%)


                            Experience<25                Experience>=25
Age<90                      11852 (99%)                  30332 (99%)
Age>=90                     154 (1%)                     393 (1%)


   Chi-squared values were 0.05, 0.16, and 0.98 respectively
Next Steps
                    
 Look for a more accurate way to define task and talent, as
  age and experience are very approximate proxies
 Analyzing various comorbidity indices to account for
  preexisting conditions among patients (task)
 Attempt to verify payment structure’s effect on task and
  talent matching in cardiac surgery
 Compare and contrast incentives and payment structures
  of various specialties
Interdisciplinary Mindset and
Communication are the Keys to Success
                          
  Thoroughly understanding the significance of
   assumptions is crucial.
  Health services research requires an interdisciplinary
   approach and mindset.
  Learning about prior research done in one’s topic is
   essential for future growth and advancement.
  Communication and conversation is vital.
  Combining Economics and Medicine
Special Recognition
             
 Leonard Davis Institute of Health Economics
 Anesthesiology Department at Penn Medicine
 Mentors: Guy David, Ph.D. and Mark Neuman, M.D.
 LDI Staff
     Joanne Levy,
     Elisabeth Madden,
     Hoag Levins,
     Megan Pellegrino
     Renee Zawacki


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Who is Assigned to Difficult Cases in the Hospital

  • 1. Romero N. Santiago Mentors: Guy David, Ph.D. and Mark Neuman, M.D.
  • 2. Motivation   Elements that distort efficient matching of patients to physicians may impact patient wellbeing.  Perverse incentives to take or avoid difficult cases.  For the same condition, recommendations may vary by specialty (supplier induced demand).  Inefficient matching is potentially costly to the healthcare system.  Hospital reputation and malpractice pressure may not provide sufficient incentives to induce efficient matching.
  • 3. Strategy Used to Study Task-Talent Matching   Review previous research discussing current framework of task-talent matching  Read through the literature to find various incentives that could help explain cause of inefficient matching  Analyze task-talent matching in a specific region and specialty to observe degree of matching problem
  • 4. Project Overview   Are highly talented physicians performing the most difficult cases?  Theoretical Framework  Empirical Work  Valuable insights about research experience
  • 5. Garicano’s Pyramid Hierarchy is Not a Perfect Fit for Medicine   “Hierarchies and the Organization of Knowledge in Production” – Garicano  “Knowledge-based hierarchy” – production workers and specialized problem solvers (industrial sector).  Pyramidal structure with multiple levels, communication costs incurred with specialization  Knowledge of problem solvers incorporates knowledge of those asking them for advice on solving a particular problem  In medicine, no fine line between base level (production worker) and problem solver, levels overlap.
  • 6. Specialization Creates Matching Issue Through Incentives Santos  “Referrals” – Garicano and  Agent diagnosing opportunity/task incentivized to keep most valuable ones and refer least valuable  Top-down diagnosis generates no inefficiency, unlike bottom-up arrangements  “Specialization and Matching” – Epstein and colleagues  Physicians in group partnerships specialize more than solo physicians, utilizing referral system  Matching of specialists to patient heightened under firm or group practice.
  • 7. Macroscopic View of Florida’s Cardiac Surgeon Population  of Health website  Utilized the Florida Department  Board-certified cardiac surgeons (162)  Graduation Year from Medical School  Residency and Fellowship Information  Age of patient utilized as proxy for task difficulty  Years of experience used as proxy for talent  Data represents inpatient cases from 2005 to 2007  Mean age of patient = 66 years  Standard Deviation = 10.7 years  Mean Experience for Surgeon = 28.8 years  Standard Deviation = 7.9 years
  • 8. Using Experience=15 as Cutoff Experience<15 Experience>=15 Age<70 233 (60%)  24215 (57%) Age>=70 153 (40%) 18130 (43%) Experience<15 Experience>=15 Age<80 343 (89%) 37359 (88%) Age>=80 43 (11%) 4986 (12%) Experience<15 Experience>=15 Age<90 380 (98%) 41804 (99%) Age>=90 6 (2%) 541 (1%) Chi-squared values were 0.21, 0.70, and 0.63 respectively
  • 9. Using Experience = 20 as Cutoff Age<70 2815 (56%)  Experience<20 Experience>=20 21633 (57%) Age>=70 2174 (44%) 16109 (43%) Experience<20 Experience>=20 Age<80 4393 (88%) 33309 (88%) Age>=80 596 (12%) 4433 (12%) Experience<20 Experience>=20 Age<90 4923 (99%) 37261 (99%) Age>=90 66 (1%) 481 (1%) Chi-squared values were 0.23, 0.68, and 0.77 respectively
  • 10. Using Experience=25 as cutoff Age<70 6962 (58%)  Experience<25 Experience>=25 17486 (57%) Age>=70 5044 (42%) 13239 (43%) Experience<25 Experience>=25 Age<80 10635 (89%) 27067 (88%) Age>=80 1371 (11%) 3658 (12%) Experience<25 Experience>=25 Age<90 11852 (99%) 30332 (99%) Age>=90 154 (1%) 393 (1%) Chi-squared values were 0.05, 0.16, and 0.98 respectively
  • 11. Next Steps   Look for a more accurate way to define task and talent, as age and experience are very approximate proxies  Analyzing various comorbidity indices to account for preexisting conditions among patients (task)  Attempt to verify payment structure’s effect on task and talent matching in cardiac surgery  Compare and contrast incentives and payment structures of various specialties
  • 12. Interdisciplinary Mindset and Communication are the Keys to Success   Thoroughly understanding the significance of assumptions is crucial.  Health services research requires an interdisciplinary approach and mindset.  Learning about prior research done in one’s topic is essential for future growth and advancement.  Communication and conversation is vital.  Combining Economics and Medicine
  • 13. Special Recognition   Leonard Davis Institute of Health Economics  Anesthesiology Department at Penn Medicine  Mentors: Guy David, Ph.D. and Mark Neuman, M.D.  LDI Staff  Joanne Levy,  Elisabeth Madden,  Hoag Levins,  Megan Pellegrino  Renee Zawacki
  • 14.