Running some tests: doctor-patient
demographic concordance and
diagnostic test orders
17/ 4/2014
Singapore Health Economics Association Conference
Sophie Joyce
Post-doctoral Research Fellow at SKBI, Singapore Management
University
Introduction
• Study association between doctor-patient
gender and ethnic group concordance and the
amount of diagnostic tests (laboratory and
radiology) ordered during a hospital stay
• Find a statistically significant reduction in
laboratory and radiology test orders with
demographic concordance.
Over-use of diagnostic tests
• Increased use of diagnostic tests:
- American College of Physicians publishes lists of unnecessary tests and
procedures (Cassel and Guest, 2012)
⁻ Costs of unnecessary diagnostic tests and procedures has been estimated
at USD 200 and 250 billion in the U.S. (Berwick and Hackbarth, 2012;
Thompson, 2011)
•
⁻
⁻
⁻
Risk to patient health:
exposes patients to harmful radiation
false positive
clinical abnormality - excessive medical treatment
‘Sifting through and assigning importance to vast quantities of unfiltered information
is a defining skill of our [medical] profession. One of the unintended yet unavoidable
consequences of increased diagnostic information is increased false positives.’ (Sutton,
2011, pg.1600)
Background
Demographic concordance in doctor-patient consultation:
Medical literature - improved communication and patient satisfaction in
demographically concordant consultations (LaVeist and Nuru-Jeter, 2002; Street Jr
et al., 2007, 2008; Sandhu et al., 2009, Van Ryn and Burke, 2000; Gordon et al.,
2006).
Economic literature - Godager (2012) patients prefer a GP of the same gender.
Balsa and McGuire (2005) model poorer patient communication in racially
discordant consultations to explain racial variation in medical treatments.
Data
• Data from a large metropolitan hospital in New Zealand
• Study period is 2004 to 2011, approximately 236,205 inpatient
events in regression analysis
• Ethnic and gender information on doctors obtained from HR
department – associate one of 7 ethnicity groupings to doctors
(European, Māori, Pacific Peoples, Asian, Indian, Middle Eastern,
and Latin American/other)
• 324 doctors with information on both gender and ethnic group:
- 82.4% are European ethnic origin, 7.4% are Asian ethnic origin,
4.9% are Indian ethnic origin
- 59.3% are Male
Empirical method
• Specify a fixed-effect on an inpatient event i’s primary doctor, j:
𝑑𝑖𝑎𝑔_𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒𝑖𝑗 = 𝛼 + 𝛽𝑿𝑖 + 𝛽1 𝑔𝑒𝑛𝑑𝑒𝑟𝑐𝑜𝑛𝑐𝑜𝑟𝑑𝑎𝑛𝑐𝑒 + 𝛽2 𝑒𝑡ℎ𝑛𝑖𝑐𝑐𝑜𝑛𝑐𝑜𝑟𝑑𝑎𝑛𝑐𝑒 +
𝛽3 𝑔𝑒𝑛𝑑𝑒𝑟_𝑎𝑛𝑑_𝑒𝑡ℎ𝑛𝑖𝑐 𝑐𝑜𝑛𝑐𝑜𝑟𝑑𝑎𝑛𝑐𝑒 + 𝛾𝑗 + 𝜖𝑖𝑗
• Demographic concordance is estimated with a set of dummy
variables for each type of concordance; ethnic only, gender only
and ethnic plus gender. The base category is no demographic
concordance.
• Dummy variables for patient’s gender and ethnic group control for
variation in average severity or complexity of illnesses across
patient demographic groups.
Assignment of patients to doctors in
hospital
Are variables for (doctor-patient) gender and/or ethnic group concordance exogenous?
• Use the primary doctor identified in hospital data to determine whether demographic
concordance occurred.
•
Patients are assigned to doctors on-call at patient’s arrival time to hospital, no policy for
demographic matching
•
Patient or doctor can request transfer to another doctor - therefore cannot guarantee
random assignment of doctors to patients
•
Argue any ‘informal’ sorting of patients to doctors, for demographic reasons, would not be
expected to explain my negative relationship:
-
Expect transfers, for demographic reasons, to achieve concordance rather than discordance > concordant patients treated by more doctors -> positive, rather than negative, bias in
diagnostic test orders.
•
In addition, I am also able to restrict my sample to patients that have the same doctor
recorded for their admittance, primary and discharge from hospital. This allows me to check
that patients who transfer doctors, for whatever reason, do not explain my results.
Demographic concordance variables
•
Mutually exclusive demographic concordance categories, base category is no
demographic concordance.
•
-
Of the patient population in study:
25.2% ethnic only concordance (e.g. European patient and doctor)
24.8% gender only concordance
31% ethnic plus gender concordance (e.g. European and male doctor and patient)
19% no demographic concordance
•
97.2% of ethnic concordance is European, 57% of gender concordance is male
•
A large proportion of ethnic concordance is European, this is a limitation of my
data. I further estimate the relationship between gender concordance and
diagnostic test orders for male and female doctors separately.
Laboratory resource outcomes
• Total cost and quantity of laboratory tests
Kdensity square root laboratory cost
.08
.04
.06
Density
.003
.002
0
0
.02
.001
Density
.004
.1
.005
Kdensity raw laboratory cost
0
500
1000
Raw laboratory cost
1500
2000
0
99th percentile of raw lab. cost excluded
Quantity of laboratory tests
20
30
square root laboratory cost
40
50
.2
0
.1
.05
.1
Density
.3
.15
.4
.2
Kdensity logarithm laboratory cost
0
Density
10
99th percentile of square root lab. cost excluded
0
50
100
Laboratory quantity
99th percentile of lab. quantity excluded
150
200
0
2
4
Logarithm laboratory cost
99th percentile of logarithm lab. cost excluded
6
8
Radiology resource outcomes
Total cost of radiology tests and a binary outcome for whether patient received
one or more radiology test during hospital stay
Kdensity square root radiology cost
Density
0
0
.002
.05
.004
Density
.1
.006
.15
.008
Kdensity raw radiology cost
0
1000
2000
Raw radiology cost
3000
4000
0
99th percentile of raw rad. cost excluded
20
40
square root radiology cost
60
99th percentile of square root rad. cost excluded
.2
0
0
.1
.2
.4
Density
.3
.6
.4
Quantity of radiology tests
.8
Kdensity logarithm radiology cost
Density
•
2
4
6
Logarithm radiology cost
99th percentile of logarithm rad. cost excluded
8
10
0
5
Radiology quantity
99th percentile of rads. quantity excluded
10
15
Econometric model
• Cost outcomes
- Fixed-effect Ordinary Least Squares (OLS) regression
on, raw, natural-logarithm transformed, square-root
transformed costs
• Quantity outcomes
- Radiology – binary – fixed-effect linear
- Laboratory – count – fixed-effect linear and poisson
• 99th percentile cost and quantity excluded, standard
errors clustered on doctor
Hospital Study Sample
• All hospital inpatient events during 2004-2011,
excluding:
- Entry to hospital from waiting list (i.e. for an
elective procedure)
- Aged under 5 years
- Discharged from a rehabilitation facility
Explanatory variables
•
Explanatory variables:
Patient Characteristics:
⁻
Male patient
⁻
Patient age
⁻
Socioeconomic scale
⁻
Patient ethnic group dummy variables
Timing of admission:
⁻
Day of week admission
⁻
Year of admission
⁻
Time of day of admission
Type of admission:
⁻
Transfer patient
⁻
Accident and Emergency Department (AED) entry
⁻
Acute admission (relative to base category of arranged admission)
⁻
Intended day patient admission
⁻
Accident as cause of hospital admission
Clinical condition:
⁻
Whether a patient had one or more inpatient events within the last 60 days
⁻
Length of hospital stay in days
⁻
The number of diagnoses
⁻
If patient had a surgical theatre event
⁻
Charlson’s comorbid conditions
⁻
Major Diagnostic Category of illness.
Laboratory test: results
Laboratory test: results
Radiology test: results
Robustness tests
•
•
•
Fixed-effect on doctor and Major Diagnostic Category (MDC)
Admit, primary and discharge doctors are the same
Including manually associated ethnic and gender groups for doctors
Robustness tests
•
Specific medical populations – most populous Major Diagnostic Categories in
hospital data (excluding pregnancy)
Robustness tests
•
Male and female doctors in MDCs of 11 (kidney), 6 (digestive), 5 (circulatory) and 4
(respiratory)
Explaining a negative relationship
• Find no evidence that demographic concordance is positively associated
with patient health outcomes of mortality within 60 days of hospital
admission and emergency readmission to hospital
Suggests patients
in demographically concordant consultations received adequate health
care.
• Suggest improved information on patient’s health condition from
consultation process itself (communication, physical exam) and/or
preferences for fewer diagnostic tests could explain a reduction in
diagnostic resources.
• Health care publicly provided and doctors not at risk of medical
malpractice lawsuits, therefore insurance arrangements or fear of
litigation is not expected to explain a negative relationship.
Conclusion
• Statistically significant reduction in laboratory and radiology
resources in demographically concordant pairs. Though,
estimated size of reduction in diagnostic resources is small.
• Gender concordance more robust than ethnic concordance
(limited variation in ethnic groups of doctors.)
• Reducing ‘unnecessary’ diagnostic test orders reduces
complexity in decision-making and risk of over-treatment.
Could occur through ‘improvements’ in doctor-patient
relationship.