Heuristic thinking in the workplace: Evidence from primary care

Abstract

We study whether primary care physicians (PCPs) exercise left digit bias with respect to patients' age. Relying on a comprehensive administrative visit level data from a large Israeli HMO, we measure the intensity of patients' medical examination in visits that take place around a decadal birthday—a birthday that ends with zero—within a regression discontinuity framework. We find that in standard settings with clear patient information there is no evidence that PCPs exhibit left digit bias. However, when PCPs meet unfamiliar patients seeking immediate care, they are more likely to use basic diagnostic tests just above the decadal birthday threshold, indicating that under these circumstances, PCPs do use left digit bias.

Full text

1 | INTRODUCTION
What role does inattention play in physician decision-making? Understanding physician decision-making is key to high-quality
healthcare. This issue has been the focus of a fast-growing recent line of research. 1 In settings with decision-relevant attributes
that are not entirely salient, given scarce cognitive resources, individuals tend to pay attention to components of the relevant
information as part of their decision-making process, that is, use heuristics. 2 It is not well-understood, however, to what extent
physicians use heuristic thinking. In this study, we ask: do primary care physicians (or “PCPs”) use simplifying heuristics
in their clinical decision making, and if so, under what circumstances? Concretely, we examine whether PCPs exercise the
so-called left digit bias with respect to the patients' age.
PCPs are a particularly important group of physicians. They play a central role in the delivery of health care in developed
countries, accounting for a substantial portion of health care cost—around 14% of total health spending in OECD countries. 3
As part of their practice, PCPs provide first contact health care, disease diagnosis, maintenance of continuity of care, manage-
ment of chronic conditions and coordination with other health care providers (Starfield etal.,2005). PCPs typically perform
their tasks during short office appointments, spending limited amount of time to each specific topic they address (Tai-Seale
etal.,2007), at a fast work pace (Linzer etal.,2009). It is therefore interesting to examine primary care physicians' heuristic
thinking.
Using a simple conceptual framework (Chetty etal.,2009; DellaVigna,2009; Lacetera etal.,2012), we show that if PCPs
exercise left digit bias with respect to the patient's age, namely, if they factor a truncated value of the patient's age into the diag-
nosis process, the patient's medical examination would be discontinuously more intensive above a decadal birthday—a birthday
that ends with zero. To examine this issue, we rely on comprehensive administrative visit level data from a large Israeli HMO
(“the HMO”). Using these data, we measure the intensity of patients' medical examination in visits around a decadal birthday
by analyzing the utilization of basic diagnostic tests. Diagnostic tests are a commonly used measure of diagnosis intensity and
Department of Economics, Ben-Gurion
University of the Negev, Beer-Sheva, Israel
Correspondence
Ity Shurtz, Department of Economics,
Ben-Gurion University of the Negev, Beer-
Sheva 8410501, Israel.
Email: shurtz@bgu.ac.il*
Abstract
We study whether primary care physicians (PCPs) exercise left digit bias with
respect to patients' age. Relying on a comprehensive administrative visit level data
from a large Israeli HMO, we measure the intensity of patients' medical examina-
tion in visits that take place around a decadal birthday—a birthday that ends with
zero—within a regression discontinuity framework. We find that in standard settings
with clear patient information there is no evidence that PCPs exhibit left digit bias.
However, when PCPs meet unfamiliar patients seeking immediate care, they are
more likely to use basic diagnostic tests just above the decadal birthday threshold,
indicating that under these circumstances, PCPs do use left digit bias.
KEYWORDS
attention, heuristic thinking, left digit bias, physician behavior
RESEARCH ARTICLE
Heuristic thinking in the workplace: Evidence from primary care
Ity Shurtz
DOI: 10.1002/hec.4534
Received: 1 February 2021 Revised: 12 September 2021 Accepted: 2 May 2022
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original
work is properly cited.
© 2022 The Authors. Health Economics published by John Wiley & Sons Ltd.
wileyonlinelibrary.com/journal/hecHealth Economics. 2022;31:1713–1729. 1713

their utilization in the primary care setting is very prevalent—diagnostic tests are needed to establish a diagnosis in over 20% of
primary care consultations (Epner etal.,2013; O’Sullivan etal.,2018). In essence, we examine, within a regression discontinu-
ity framework (RDD), if there is evidence that visits that occur just after a decadal birthday are associated with more utilization
of basic diagnostic tests. Notably, we are not aware of age-based guidelines that target populations above decadal birthdays
concerning the utilization of the basic diagnostic tests that we analyze here. We explicitly examine this issue below and find no
indication that this issue drives our results.
In the HMO, patients are enrolled with a regular primary care physician, with whom they often have a longstanding physi-
cian-patient relationship.Normally, a primary care visit is scheduled with the patient's regular physician. However, if patients
request care outside of their physician's regular office hours or when their physician is absent, they are referred to another physi-
cian at the clinic, who typically meets them for the first time. 4 We refer to these two very different, naturally occurring settings
as visits with familiar and unfamiliar patients. As we explain in more detail below, relative to visits with familiar patients, visits
with unfamiliar patients are held with relatively opaque patient information. We, therefore, hypothesize that these visits are
more likely to give rise to heuristic thinking.
The RDD analysis shows that physicians are more likely to use diagnostic tools when they meet patients short after a
decadal birthday in visits with unfamiliar patients. By contrast, in visits with familiar patients, the effect is small and statisti-
cally insignificant. Given statistical power limitations, this difference is not highly significant, yet it shows that the two groups
differ in a statistically meaningful sense. These findings support the view that decision-making context, and specifically back-
ground information, play a key role in inducing heuristic thinking among primary care physicians. 5
We assess whether the increased use of diagnostic tests in visits with unfamiliar patients around decadal birthdays impacted
a set of subsequent treatment outcomes which correspond to potential physicians' responses to informative diagnostic tests
result. None of the treatment aspects we examine changed around the decadal birthday threshold, suggesting that the increase in
the utilization of diagnostic tests around decadal birthdays did not induce overall changes in the course of treatment of affected
patients.
Finally, we analyze non-decadal birthdays—birthdays that do not end with zero. If our former results reflect physicians' left
digit bias, we expect to find smaller effects around non-decadal birthdays. We find no evidence of a significant increase in the
utilization of diagnostic tests around all non-decadal birthdays pooled together nor around any of the non-decadal birthdays
separately. These results indicate that the decadal birthdays' effect is unique, supporting the interpretation of the results as aris-
ing from the left digit bias heuristic.
Interpretation and policy implications. The evidence suggests two main takeaways. First, we find that in settings with
longitudinal physician-patient relationships and clear patient information, there is no evidence that PCPs exhibit left digit bias.
However, when PCPs meet sporadically unfamiliar patients seeking immediate care, they exercise left digit bias with respect
to the patients' age. Hence, the analysis of PCP behavior suggests that decision-making settings matter for the use of heuristic
thinking. Particularly, it shows that heuristic thinking tends to emerge in relatively confusing and opaque circumstances when
less background information is readily available. This result sheds light on the impact of decision-making settings on expert
choices in medicine and beyond.
Second, a priori, it is not clear if the increase in diagnostic tests is efficient or not. The decadal age threshold may operate as
a naturally occurring reminder about the patient's age inducing the physician to order the “age-appropriate” amount of diagnos-
tic tests. On the other hand, it may prompt unnecessary additional tests. While we cannot determine conclusively whether the
additional diagnostic tests indicate “waste”, we find no evidence of impact on subsequent treatment. If one is willing to take our
results at face value, then from a health care policy perspective, our findings can be viewed as evidence in favor of developing
physician-patient relationships and care continuity in the primary care setting. The longitudinal physician-patient relationship
appears to reduce the tendency to use heuristic thinking, perhaps because they alleviate some physician cognitive load.
Literature. A growing body of literature studies the consequences of heuristics and limited attention in healthcare. While
health care consumers' inattention has been studied quite extensively, much less is known about the role of limited attention
and heuristic thinking in physician decision making. Existing work on this issue include Rizzo and Zeckhauser(2003) that find
that physicians respond to loss aversion with respect to reference income by engaging in income generating activities, including
changing practice style. Frank and Zeckhauser(2007) show that drug prescribing patterns are consistent with the use of ready-
to-wear treatments, namely, physicians have a small number of favorite drugs that they prescribe to most patients with a given
condition. 6
Two recent studies examine similar issues in the hospital setting. 7 Olenski etal.(2020) study physicians' left digit bias in the
context of Coronary-Artery Bypass Graft Surgery (CABG). They show that patients admitted within 2 weeks after their 80th
birthday were significantly less likely to undergo CABG than those admitted 2 weeks or less before their 80th birthday. Cous-
sens(2018) examines the use of heuristics in the emergency department. He finds evidence that patients arriving in the ED just
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after their 40th birthday are 20% more likely to be diagnosed with ischemic heart disease relative to patients arriving just before
it, leading to a reduction in the number of missed IHD diagnoses. These studies show that in the context of the ED and CABG,
heuristic thinking may have important consequences for patient health. These results differ from our findings for the primary
care setting, but these differences are not surprising and the overall picture reinforces the takeaway of this paper. In more “diffi-
cult” settings such as the hospital environment, or, in our case, visits with unfamiliar patients, where a prior physician-patient
relationship does not typically exist, and background information is scarce, heuristic thinking is more likely to arise.
The remainder of the paper is structured as follows. Section2 lays out a simple framework and describes the empirical
strategy, Section3 describes the data and presents the empirical analysis, and Section4 concludes.
2 | FRAMEWORK AND EMPIRICAL STRATEGY
The physician interface at the HMO shows the patient's age at the date of the visit on the header of the patient's electronic medi-
cal record. Age is represented in a somewhat unique format in terms of years and months separated by a dot. 8 For example, the
age of a patient that was born on August 15, 1962, and visits the physician on August 1, 2012, would be 49.11, that is, 49 and
11months 9 If that same patient were to visit the physician 1month later, on September 1, 2012, her age, as it appears on the
header, would be 50.00.
We use a simple framework to describe physician inattention to the patient's exact age [see Lacetera etal.(2012) and Chetty
etal. (2009)]. The underlying assumption here is that the physician focuses on the leftmost digit of the patient's age and is
inattentive to the digits farther to the right. Let age∈[29.00, 89.11] be a patient's age—as it appears in the patient's electronic
medical record. Assume that physician perceives the patient's age as:
  
=1⋅10 + (1 − )⋅
(
2+3⋅10 + 4
12
)
(1)
where di is the value of the digit at location i of age and θ∈[0, 1] is the inattention parameter. Hence, physicians take into
account the truncated value of the patient's age in full and are less attentive to the exact patient's age. The physician perceives
a 1-year change in age that does not involve a change in the left digit as a change of 1−θ in the patient's age. A 1-year change
in age that involves a change in the left digit aligns the perceived age,
 𝐴  
, and the actual age creating a discontinuous jump in
the perceived age. Suppose that θ=1. The physician perceives a 69.11years old patient as a patient in his “sixties”, and the
perception of a 70.00 year-old patient, who is roughly the same age, jumps to be that of a patient in his “seventies”.
Consider a physician that chooses an action a given a patient's perceived age denoted by
 𝐴  
max


(
, 
)
(2)
Assuming that other things being equal, older age is associated with more conservative medical examination, this simple
framework predicts that a 1-year age increase when only one digit changes (e.g., 68.11–69.00) would induce a smaller effect on
physician behavior than that of a 1-year age increase around a decadal birthday (e.g., 69.11–70.00). A change in the left digit of
a patient's age would induce a discontinuously more conservative physician examination behavior. Specifically, we hypothesize
that there would be a discontinuous increase in the amount of basic diagnostic tests that physicians use as part of the medical
examination of a patient just above a decadal age relative to a patient just below it.
In order to empirically examine this hypothesis we implement an RDD approach. Let τ be the number of days relative to
the closest birthday (in absolute terms) at the time of the visit to the clinic. Let the treatment indicator D equal 1 if the visit
took place less than 6months after the patient's birthday (in which case τ≥0), and 0 otherwise. Consider the following model
(Angrist and Pischke(2008)):
=0+0+()+
(3)
where y is an outcome variable that measures the intensity of the physical examination, such as an indicator for using basic diag-
nostic tests during a visit at the clinic. f(τ) is a completely flexible control function, and is continuous at τ=0. The parameter of
interest in this model is the coefficient β0, which measures the causal effect of visiting the clinic just after the patient's birthday
rather than right before it on the intensity of the medical examination. Intuitively, given that f(τ) absorbs any continuous rela-
tionship between the timing of the visit relative to the patient's birthday and the outcome variable, the coefficient β0 estimates
the discontinuous relations between visiting the clinic after the patient's birthday and the outcome variable. Therefore, we may
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attribute its estimates to the causal effect of a (decadal) birthday on the intensity of the medical examination. We estimate the
model applying standard regression discontinuity design methods as we describe in detail below.
3 | EMPIRICAL ANALYSIS
Our analysis draws on data from a detailed administrative visit-level database covering all primary care visits in 11 clinics in the
Jerusalem area of the HMO—one of four HMOs that provide the vast majority of primary care in the country—in 2011–2014. 10
The HMO's patients are enrolled with a regular primary care physician at their local clinic. Patients may choose their regular
PCP every quarter, but in practice, there is little movement across physicians. By default, the regular PCP is the main point of
contact with the healthcare system. However, patients may meet other physicians at the clinic if they drop in or contact the clinic
with urgent medical issues when their regular physician is unavailable.
We distinguish between two very different, naturally occurring settings. The first is visits with familiar patients—patients
who meet the physician they are enrolled with—the standard and more common visit type. About 74% of the visits belong to
this visit type. Patients meet their regular physician quite frequently, and they often have a longstanding physician-patient rela-
tionship.In the clinics we study, the median number of times a patient meets her physician in a year is three. These visits are
arguably held in a clear and well-understood context. Due to the longitudinal physician-patient relationship, background infor-
mation about the patient is readily available to the physician. Furthermore, the physician-patient interaction is characterized by
trust and a sense of responsibility (Saultz,2003). The second is visits with unfamiliar patients, where patients who seek imme-
diate care meet, usually at their local clinic, physicians with whom they typically had no previous contact. The absence of prior
physician-patient contact and the sporadic nature of the visit limit the availability of patient information and naturally eliminate
the preexisting sense of physician-patient bonding. Relative to visits with familiar patients, the context of this visit type is less
clear and standard with relatively opaque patient information. We examine whether heuristic thinking arises in these two very
different, naturally occurring settings. We hypothesize that the latter visit type is more likely to give rise to heuristic thinking.
The two visit types differ in other ways because patients self-select to visits with physicians they are not enrolled with. By
definition, such visits occur when patients require care and their physician is unavailable. Therefore, the two visit types may
differ from one another in terms of patient characteristics and condition, as shown below. Nonetheless, our identification strat-
egy relies on the assumption that while patients self-select to visits with physicians they are not enrolled with, the timing of their
visit relative to their exact decadal birthday is random. Namely, patients do not systematically time their visit to an unfamiliar
physician to the weeks before and after a decadal birthday. This assumption cannot be fully tested. However, in what follows,
we perform a thorough examination of its validity.
3.1 | Data
The visit-level data include physician and patient identifiers, patient characteristics such as gender, age, country of origin,
chronic conditions, and a full summary of the visit, including referrals to laboratory tests, imaging, and prescriptions. Table1
provides descriptive statistics regarding these face-to-face visits by visit type. In the final sample, we include visits within
180days relative to the closest decadal birthday. To exclude follow-up visits, we keep only visits that occurred more than
30days since the previous visit to the clinic. There are 7098 (25,695) such office visits with unfamiliar (familiar) patients made
by 5532 (12,290) patients to 96 (77) physicians. 11 In 78% of visits with familiar patients, patients meet the physician at least
once in the 360days before the visit, relative to 22% in visits with unfamiliar patients. Namely, as expected, in the familiar
patients' visits, patients are much more likely to have prior contact with the physician. 12 Additionally, patients in the unfamiliar
group are less likely to meet any physician in the 90days before the visit.
The table further indicates that visits with unfamiliar patients involve younger and healthier patients. These visits tend to
have a higher share of acute conditions like upper respiratory or viral infections. There is also less utilization of diagnostic tests
in these visits. This is consistent with a selection process whereby younger and healthier patients with acute medical issues are
less flexible about the timing of the office visit and care less about the identity of the available physician, while sicker patients
are more flexible toward office visit timing and care more about meeting their “own” physician.
The main outcome variable we analyze in order to measure the intensity of patients' medical examination is an indicator
for using any of the three most common visit-level basic diagnostic tests: blood test, X-ray, and urine test. These tests are rela-
tively inexpensive, often available at the clinic, and give same-day results. This is not true, for example, in ultrasound scans
that often involve several weeks of waiting. Other advanced imaging tools such as CT and MRI scans are rarely used in our
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setting, and including them does not affect the results. As Table1 shows, the likelihood of using any of the basic diagnostic
tests in visits with unfamiliar (familiar) patients is 12 (23) percent. Figure1 displays the likelihood of using basic diagnostic
tests during office visits by patient age. 13 Interestingly, the use of diagnostic tests is not monotonic. It increases with age until
around age 65, and it starts to decline afterward, forming an “inverse u” shape. The raw data does not exhibit apparent jumps
in the use of diagnostics around each of the decadal birthdays separately. Our empirical approach, however, pools together the
information from the multiple decadal birthday thresholds to estimate the decadal birthdays' combined effect more precisely
(Cattaneo etal.,2016).
Before turning to the RDD analysis, Figure2 provides some descriptive analysis of the data. The figure displays the differ-
ence in the average of our main outcome variable, basic diagnostic tests, between visits that occur 29days after a patient's
birthday and 29days before it. The hollow gray circles and pink x's display this difference for visits with unfamiliar and famil-
iar patients, respectively. We include only physicians that see regular patients at least 25% of the time to avoid differences in
physician composition between the 2 groups. The solid (gray) and dashed (pink) lines represent the averages of the unfamiliar
and familiar respective points. Focusing on the gray circles—visits with unfamiliar patients around decadal birthdays, there is
a significant 4.5 percentage points increase in the likelihood to use basic diagnostic tests. None of the differences around other
birthdays show a similar increase and they are all statistically insignificant. Turning to visits with familiar patients, the pink x's,
the change in the use of basic diagnostics around all birthdays, including decadal birthdays is quite small and statistically insig-
nificant (except around birthdays ending with 8). Overall, this figure demonstrates the main result of this paper. In visits with
unfamiliar patients, the use of diagnostic tests after decadal birthdays increases and this increase is unique—it is not apparent
around other birthdays and in visits with familiar patients.
SHURTZ
Unfamiliar
Familiar
Mean
SD
Mean
SD
(1)
(2)
(3)
(4)
Mean age 46.30 15.75 52.68 16.64
Share women 0.59 0.49 0.58 0.49
Share born in Israel 0.69 0.46 0.69 0.46
Share hypertension 0.21 0.41 0.32 0.47
Share smokers 0.37 0.48 0.38 0.49
Share hyperlipidemia 0.35 0.48 0.47 0.50
Share asthma 0.06 0.24 0.07 0.25
Share overweight 0.22 0.41 0.28 0.45
Share diabetes 0.12 0.32 0.18 0.38
Share referral to X-ray 0.04 0.19 0.05 0.21
Share referral to blood test 0.07 0.26 0.16 0.37
Share referral to urine test 0.02 0.13 0.04 0.19
Share referral to basic diagnostic test 0.12 0.33 0.23 0.42
Share upper respiratory infection acute 0.04 0.20 0.02 0.15
Share upper respiratory tract infection 0.03 0.18 0.01 0.12
Share viral infection unspecified 0.02 0.15 0.01 0.12
Share visit in prior 360days 0.22 0.42 0.78 0.42
Share visit any physician in prior 90days 0.33 0.47 0.47 0.50
Number of patients 5532 12,290
Number of physicians 96 77
Observations 7098 25,695
Note: The table includes office visits in the clinics used in this study in the period 2011–2014 by visit type (see text).
TABLE 1 Summary statistics
1717

3.2 | Visits with familiar and unfamiliar patients
In this section, we report our findings regarding the effect of patients' decadal birthdays on utilization of basic diagnostic tests
for the two visit types we describe above—visits with familiar and unfamiliar patients. 14
Panels (a) and (b) of Figure3 illustrate the effect visually. They plot the likelihood of using basic diagnostic tests during
office visits of unfamiliar and familiar patients, respectively, against the running variable—days elapsed relative to a patient's
nearest decadal birthday, 180days before and after the decadal birthday, in 6-day bins. We fit two quadratic regression models
to the data separately, one below the decadal birthday threshold and one above it. 15 As panel (a) of the figure illustrates, the
decadal birthday threshold shows a 4.5 pp increase in the likelihood to use basic diagnostics in unfamiliar patients' visits. By
contrast, there appears to be a very small effect of about 1 pp in the familiar patients' visits even though the outcome's baseline
level is almost twice as large in that group.
To quantify this effect numerically, we estimate the model in Equation(3) using a local linear regression analysis with a
triangular kernel and mean-squared error (MSE) optimal bandwidth [see Imbens and Kalyanaraman(2012)]. Since there is
no reason to expect a slope in the outcome variable around the cutoff, we use a zero-order polynomial to avoid overfitting the
data. 16 Columns (1) and (2) of Table2 report the corresponding estimates [β0 in Equation(3)] for visits with unfamiliar and
familiar patients, respectively. We add a graphical representation of the point estimation to Figure3 where a polynomial of
SHURTZ
FIGURE 1 Share of diagnostic tests by age, all visit types. The figure plots the share of visits with any basic diagnostic tests, by age in
quarters [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 2 Δ utilization of basic
diagnostic tests past birthdays. The figure
plots the difference in the likelihood of
using any basic diagnostic test between the
periods of 29days after a patient's birthday
and 29days before it. The horizontal solid
gray line and the dashed pink line are
the average of the respective points for
unfamiliar and familiar patients. Vertical
spiked lines represent the 95% confidence
intervals [Colour figure can be viewed at
wileyonlinelibrary.com]
1718

order zero is fit within the optimal bandwidth (marked by the vertical dashed lines). Consistent with the visual impression, the
table shows a significant 4.5 pp estimate in visits with unfamiliar patient. Visits with familiar patients show an insignificant 1
pp increase.
To examine the sensitivity of these results to the choice of bandwidth, we run the regressions using any bandwidth between
10 and 100days around a decadal birthday. Figure4 reports the results. As panel (a) of the figure shows, around the optimal
bandwidth of 39days, the unfamiliar patients results appear to be quite stable with statistically significant estimates of about
4pp.As we increase the bandwidth toward a hundred days, the estimates decrease to about 2 pp, yet they remain statistically
significant. The familiar patients results are small and statistically insignificant for any bandwidth. 17 Given that basic diagnos-
tics are used in about 12% of visits with unfamiliar patients, the results for visits with unfamiliar patients reflect an increase in
the likelihood of using basic diagnostic tests in the range 37%-16%, depending on the bandwidth used. Thus, while the results
SHURTZ
FIGURE 3 Utilization of basic
diagnostic tests around decadal birthdays.
Panels (a) and (b) of the figure plot the
likelihood of using any basic diagnostic
test, by days elapsed relative to a patient's
nearest decadal birthday, 180days before and
180days after the decadal birthday, in 6days
bins, for visits with unfamiliar and familiar
patients, respectively. The vertical solid line
represents the decadal birthday threshold.
A zero-order polynomial (gray solid line) is
fit within the optimal bandwidth (vertical
dashed lines) [Colour figure can be viewed at
wileyonlinelibrary.com]
(a)
(b)
Unfamiliar Familiar
(1)
(2)
RDD estimate 0.045** 0.009
(0.016) (0.011)
Bandwidth 39 39
Effective observations 1559 5516
Observations 7098 25,695
Note
: This table provides the RDD estimates of the likelihood to use basic diagnostic tests as per Equation(3
).
One or two asterisks indicate significance at 5% or 1%, respectively.
TABLE 2 The effect of decadal
birthdays on utilization of basic diagnostic
tests
1719

are qualitatively similar and statistically significant for any bandwidth, the exact magnitude of the point estimates is sensitive
to the choice of bandwidth. 18
3.3 | Selection checks
Our identification assumption is based on the premise that the timing of office visits around decadal birthdays is as good as
random. One threat to identification is that patients may “manipulate the threshold”, namely, time their arrival at the clinic
systematically around their decadal birthday. Such selection may arise if, for example, patients exercise left digit heuristics. If
patients' perception of their age is described by Equation(1), then upon reaching a decadal birthday, they may tend to go to see
a physician more (or less) frequently. We examine this issue by testing whether the number of observations (office visits) below
the threshold is different from the number of observations above it. If the number of visits changes abruptly around the decadal
birthday threshold, this would indicate that patients “manipulate the threshold,” and our identification assumption would lose
credibility. To formally test this issue, we follow Cattaneo etal.(2019) and McCrary(2008).
Panels (a) and (b) of Figure5 provide a graphical representation of the test of the continuity in the density around the decadal
birthday threshold for visits with unfamiliar and familiar patients respectively. They display the “raw” histogram of the data in a
radius of 50days around the threshold and the visit density estimates using local polynomial density estimations and their 95%
confidence intervals. As the figure shows, in both panels, the raw data appear to trend smoothly around the threshold, and the
density estimates from both sides of the threshold are very close to each other, and the confidence intervals overlap.Consistent
with this impression, the formal tests of the null hypothesis that the density of the running variable is continuous at the decadal
birthday threshold can not be rejected for the two visit types.
SHURTZ
FIGURE 4 Utilization of basic
diagnostic tests around decadal birthdays,
varying bandwidths. Panel (a) and (b) plot
the RDD estimates of Equation(3) by
bandwidth, for visits with unfamiliar and
familiar patients, respectively. The light blue
area represents the 90% confidence intervals
around the estimates [Colour figure can be
viewed at wileyonlinelibrary.com]
(a)
(b)
1720

We further explore this issue by examining systematic differences in observable predetermined characteristics around the
decadal age threshold in the two visit types. If patients self-select to either side of the decadal age threshold, this may be mani-
fested in a discontinuity in one or more of the observable patient characteristics. We, therefore, examine the six most common
chronic patient conditions coded in the data to test for discontinuities in their prevalence around the decadal birthday threshold.
Additionally, to test for patient selection which is not captured by clinical characteristics, we add a seventh variable: the number
of visits to the clinic in the prior 90days. This variable may identify selection in the dimension of healthcare consumption
habits. We report the results of this analysis, derived using the same methodology as the main results, in Table3. The corre-
sponding figures are shown in the appendix (Appendix Figures A5 and A6 in the Supporting InformationS1). The table and
figures show no indication of a sharp change in observable patient characteristics in either visit type.
SHURTZ
FIGURE 5 Visit density around decadal birthdays. Panels (a) and (b) of the figure plot the density tests for visits with unfamiliar and familiar
patients, respectively. The figure on the right shows number of visits by days relative to a patient's nearest decadal birthday, 50days before and
50days after the decadal birthday. The vertical solid line represents the decadal birthday threshold. The figure on the left plots the estimated visit
density 180days before and 180days after the decadal birthday [Colour figure can be viewed at wileyonlinelibrary.com]
(a)
(b)
1721

3.4 | Difference-in-discontinuities analysis
We want to assess if the effect we find in visits with unfamiliar patients is larger than that of visits with familiar patients in a
statistically meaningful sense. To this end, we use the following difference-in-discontinuities model:
=0+1
⋅
 +0
⋅
+1
⋅

⋅
 +𝜖
(4)
The coefficient β1 captures the difference in the effect of decadal birthdays across the two visit types. We report these estimates
in Table4. The 3.5 percentage points difference (s.e. 0.023) is not highly significant, but it allows rejecting the one-sided null
that the difference between the groups is zero with a p-value of 6.2%. 19 Despite the power limitation, this result shows that the
difference between the two groups is statistically valid.
We interpret our result as showing that when physicians encounter unfamiliar patients, they use left digit bias. However,
an alternative interpretation would be that the difference arises from cross-sectional differences in physician response to the
threshold. Namely, some physicians tend more to meet unfamiliar patients, and these physicians are also more likely to exhibit
heuristic thinking. We want to examine this issue. If cross-sectional differences between physicians drive the difference between
the two visit types, then introducing physician fixed effects to the DD-RD analysis should absorb this result. Column (2) reports
the results of the DD-RD analysis with physician (and year and month) fixed effects, holding the bandwidth fixed. The estimate
is 3 pp, and the p value of the one-sided null increases to 10%. While the results are smaller and less significant, they remain
SHURTZ
Hypertension
Smoking
Asthma
Hyperlipidemia
Obesity
Diabetes
Visit history
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(a) Unfamiliar
RDD estimate 0.014 0.004 0.009 −0.017 −0.004 −0.005 0.043
(0.023) (0.029) (0.014) (0.026) (0.028) (0.026) (0.053)
Bandwidth 32 29 29 33 24 17 30
Effective observations 1277 1149 1149 1314 938 664 1189
Observations 7098 7098 7098 7098 7098 7098 7098
(b) Familiar
RDD estimate −0.003 −0.018 0.009 −0.007 −0.012 0.016 0.032
(0.014) (0.015) (0.008) (0.015) (0.015) (0.016) (0.034)
Bandwidth 32 29 29 33 24 17 30
Effective observations 4540 4106 4106 4665 3396 2365 4252
Observations 25,695 25,695 25,695 25,695 25,695 25,695 25,695
Note: This table provides the RDD estimates for the analysis of selection on observables. One or two asterisks indicate significance at 5% or 1%, respectively.
TABLE 3 Selection on observables
(1)
(2)
(3)
DD-RD estimate 0.035 0.030 0.029
(0.023) (0.023) (0.023)
Physician FEs No Ye s Yes
Time FEs No No Yes
Patient characteristics No No Yes
Bandwidth 38 38 38
Effective observations 6884 6884 6884
Observations 32,793 32,793 32,793
Note
: This table provides the DD—RDD estimates of the likelihood to use basic diagnostic tests as per
Equation(
3). Time fixed effects include year and month-of-year fixed effects. The patient characteristics
which are included are: cubic polynomial of age, gender, and the following chronic conditions: hypertension,
smoking, asthma, hyperlipidemia, obesity and diabetes. One or two asterisks indicate significance at 5% or
1%, respectively.
TABLE 4 The effect of decadal
birthdays on utilization of basic diagnostic
tests, difference-in-discontinuities
1722

similar, suggesting that they are not driven by differences across physicians but by the type of patients they meet. As expected,
controlling for patient characteristics has a very small effect on the results (column (3)). 20
An additional concern that arises in our setting is that patients may behave differently exactly on their birthday, for example,
they prefer not to visit the clinic on their decadal birthday because they have special plans for that day that change their normal
schedule. This issue may imply that patients who arrive at the clinic exactly on their birthday are different from those who do
not, which may influence the results. To examine this concern, we perform a “donut hole” analysis. 21 Namely, we examine the
sensitivity of the results to the elimination of observations very close to the decadal birthday threshold. If our results are driven
by visits that occur exactly on the birthday or very close to it, we might worry that they reflect a mere “birthday effect”. Table5
reports the estimation results that omit observations in radii 2 and 4days around a decadal birthday, respectively. As the results
indicate, the estimates appear to be slightly more pronounced than the baseline estimates in Table2. The effect of omitting 2
and 4days around a decadal birthday in the unfamiliar patients' visits is 5.3 and 5.6 pp, respectively. The effect remains small
and insignificant in the familiar patients' visits, and the DD-RD estimates are 4.1 and 4.8 pp.
3.5 | Age-based guidelines
Another issue to keep in mind is that PCPs sometimes work with age-based guidelines, mostly for preventive care purposes.
Such guidelines introduce simplifying rules of thumb that may induce “coarseness” to physicians' decision-making. For exam-
ple, it is recommended for patients above age 50 to perform an occult blood test annually. Therefore, one may expect a sharp
increase in the utilization of that test just above age 50, even though colon cancer risk rises smoothly with age with no discon-
tinuous “jump” in risk upon turning 50.
The use of age-based guidelines should be more relevant for physicians that meet with their regular patients and provide
preventive care. It is less likely to arise in the context of unfamiliar patients, where patients seek immediate care by a physician
they are not enrolled with. Nevertheless, we now take a closer look at this issue for both visit types and assess if our results have
to do with such guidelines.
While we are not aware of guidelines involving our basic diagnostic tools that explicitly coincide with decadal birthdays,
one recommendation that might induce such interaction for blood tests is to measure cholesterol periodically. It is recommended
to measure blood cholesterol levels every five years, starting at age 35 for men and 40 for women. Hence, it could be the case
that physicians tend to prescribe this test when they meet patients just after decadal birthdays.
We cannot observe blood cholesterol tests separately. However, the data allow us to distinguish between two types of blood
tests. The first, blood biochemistry, measures certain chemicals in the blood. It includes the cholesterol test and also blood sugar
level, electrolytes, creatinine, and uric acid. These tests provide information about general health and on the function of organs
like the liver and kidneys. The second, blood hematology, includes blood count, a common blood test to detect infections and
other disorders, which is often prescribed in acute conditions. To take a closer look at this issue, we run the analysis again,
excluding blood biochemistry. These two types of blood tests are often taken together. Hence, this breakdown of the outcome
variable is a bit coarse. However, if cholesterol tests, as opposed to diagnoses of acute conditions, are driving our baseline
results, this exercise should generate weaker results. Panel (a) of Table6 shows the results of this regression. The estimates are
very similar to the results reported in Table2.
Next, we analyze the outcome variable element by element. Panels (b)-(d) report the results for blood tests, X-rays, and
urine tests, respectively. While the estimates are smaller and lose their statistical significance, the unfamiliar patients' results
SHURTZ
Unfamiliar
Familiar
Diff
Radius of omitted days
Two
Four
Two
Four
Two
Four
Around birthday
(1)
(2)
(3)
(4)
(5)
(6)
RDD estimate 0.053** 0.056** 0.012 0.008 0.041 0.048*
(0.017) (0.017) (0.012) (0.012) (0.023) (0.024)
Bandwidth 39 39 39 39 39 39
Effective observations 1497 1413 5232 4950 6729 6363
Observations 7098 7098 25,695 25,695 32,793 32,793
Note: This table shows RDD estimates for the analysis of the “donut hole” analysis. One or two asterisks indicate significance at 5% or 1%, respectively.
TABLE 5 The effect of decadal birthdays, donut hole estimates
1723

are all positive and larger than those of familiar patients, consistent with the gist of the baseline analysis. 22 Finally, we break
down blood tests outcome to blood biochemistry and blood hematology [panels (e) and (f)]. The estimates again show that our
findings are not driven by blood biochemistry tests, indicating that our results are associated with acute conditions.
3.6 | Consequences of increased diagnostic tests utilization
Does the increased use of diagnostic tests in visits around decadal birthdays have consequences for subsequent treatment? The
answer to this question may shed some light on the normative interpretation of the results. If the increase in the use of basic
SHURTZ
Unfamiliar
Familiar
DD-RD
(1)
(2)
(3)
(4)
(a) All no bio-chem
RDD estimate 0.048** 0.009 0.039 0.029
(0.018) (0.012) (0.025) (0.026)
Bandwidth 29 29 29 29
Effective observations 1149 4106 5255 5255
(b) Blood test
RDD estimate 0.035* 0.003 0.032 0.026
(0.017) (0.013) (0.027) (0.028)
Bandwidth 24 24 24 24
Effective observations 938 3396 4334 4334
(c) X-ray
RDD estimate 0.017 0.004 0.013 0.013
(0.009) (0.005) (0.011) (0.011)
Bandwidth 44 44 44 44
Effective observations 1776 6204 7980 7980
(d) Urine test
RDD estimate 0.006 −0.003 0.009 0.010
(0.007) (0.005) (0.009) (0.009)
Bandwidth 47 47 47 47
Effective observations 1886 6609 8495 8495
(e) Blood bio-chem
RDD estimate 0.025 −0.001 0.026 0.021
(0.015) (0.013) (0.025) (0.026)
Bandwidth 25 25 25 25
Effective observations 971 3550 4521 4521
(f) Blood no bio-chem
RDD estimate 0.037* −0.000 0.037 0.029
(0.017) (0.013) (0.027) (0.027)
Bandwidth 21 21 21 21
Effective observations 825 2951 3776 3776
Controls No Yes No Yes
Observations 7098 25,695 32,793 32,793
Note
: This table provides the RDD estimates of the number of diagnoses as per Equation(3). Controls
include physician fixed effects; time fixed effects, which include year and month-of-year fixed effects;
patient characteristics: cubic polynomial of age, gender, and the following chronic conditions: hypertension,
smoking, asthma, hyperlipidemia, obesity and diabetes. One or two asterisks indicate significance at 5% or
1%, respectively.
TABLE 6 The effect of decadal
birthdays by diagnostic tool
1724

diagnostics in visits with unfamiliar patients is associated with changes in subsequent treatment and ultimately with improved
patient outcomes, these additional tests are beneficial. On the other hand, if these tests are not associated with any change in the
course of treatment and patient outcomes, they probably represent a waste of resources. We assess this question by examining
whether visiting an unfamiliar physician just after a decadal birthday affected the likelihood of the following four outcomes: a
subsequent visit to the clinic, a prescription for antibiotics, a referral to a specialist, and a referral to the ED (all within 60days
of the visit). 23 This set of outcomes covers the relevant potential physician's responses to informative diagnostic tests results. If
the increase in diagnostic tests affected treatment, it should show up in these subsequent treatment outcomes.
We report the results in Figure6 and Table7. The impression that the four panels of Figure6 create is that all four outcomes
trend quite smoothly around the decadal age threshold. The estimates in Table7 support this impression. None of the four
outcomes significantly change around the decadal birthday threshold. These results suggest that the increase in the utilization
of diagnostic tests around decadal birthdays does not induce changes in the course of treatment of affected patients.
SHURTZ
FIGURE 6 Subsequent outcomes around decadal birthdays, unfamiliar patients. Panels (a), (b), (c) and (d) of the figure plot the number of
subsequent visits within 60days, and the likelihood of receiving a prescription for antibiotics, a referral to a specialist or a referral to the ED by
days elapsed relative to a patient's nearest decadal birthday, 180days before and 180days after the decadal birthday, in 6days bins. The vertical
solid line represents the decadal birthday threshold [Colour figure can be viewed at wileyonlinelibrary.com]
1725

3.7 | Non decadal birthdays
If our results arise from a left digit bias, visits with unfamiliar patients around birthdays that do not end with zero (“non decadal
birthdays”) should show smaller effects. If, on the other hand, the results are an artifact of some other “birthday effect”, we
may see a similar pattern around the birthdays that do not end with zero. We examine this by analyzing the utilization of basic
diagnostic tests in all visits around non decadal birthdays using the same RDD approach we have used so far.
We show these results graphically in Figure7. The utilization of basic diagnostic tests trends smoothly around non decadal
birthdays. Consistent with this impression, the corresponding estimates, reported in Table8, show no change in the utilization
of basic diagnostic tests around non decadal birthdays. In columns (1)–(9) of Table9, we perform the analysis separately for
each of the non decadal birthdays ending in digits 1–9, respectively. We do not find statistically significant results for any of
these birthdays. 24
This analysis shows that visits with unfamiliar patients around non decadal birthdays do not result in a significant increase
in the utilization of basic diagnostic tests, indicating that the results we find for visits around decadal birthdays are unique.
These results support the view that the increase in the utilization of basic diagnostic tests around decadal birthdays indeed arises
from physicians' left digit bias with respect to the patient's age.
4 | CONCLUSION
Do PCPs use simplifying heuristics in their clinical decision-making? Concretely, this paper examines whether PCPs exercise
the so-called left digit bias with respect to patients' age. We rely on comprehensive administrative visit level data from a large
Israeli HMO to examine the utilization of basic diagnostic tests in visits that take place around a decadal birthday within a
regression discontinuity framework. In essence, we examine if there is evidence that visits that occur just after a decadal birth-
day are associated with more utilization of basic diagnostic tests.
This study provides new evidence on the nature of heuristic thinking by PCPs. It shows that in settings with longitudinal
physician-patient relationships and clear patient information, there is no evidence that PCPs exhibit left digit bias. However,
SHURTZ
Subsequent visit
Antibiotics
Referral to a
specialist
Referral
to the ED
(1)
(2)
(3)
(4)
RDD estimate −0.018 −0.023 0.012 0.019
(0.075) (0.029) (0.024) (0.020)
Bandwidth 35 28 34 42
Effective observations 1386 1103 1350 1683
Observations 7098 7098 7098 7098
Note
: This table provides the RDD estimates for the analysis of subsequent outcomes of decadal birthdays.
One or two asterisks indicate significance at 5% or 1%, respectively.
TABLE 7 Outcomes of decadal
birthdays, visits with unfamiliar patients
FIGURE 7 Utilization of basic
diagnostic tests around non decadal birthdays,
unfamiliar patients. The figure plots the
likelihood of using any basic diagnostic test,
by days elapsed relative to a patient's nearest
non decadal birthday, 180days before and
180days after the decadal birthday, in 6days
bins. The vertical solid line represents the
decadal birthday threshold [Colour figure can
be viewed at wileyonlinelibrary.com]
1726

when PCPs meet sporadically unfamiliar patients seeking immediate care, they exercise left digit bias with respect to the
patient's age. Hence, the evidence suggests that heuristic thinking tends to emerge in relatively confusing and opaque circum-
stances with scarce background information. This result sheds light on the impact of decision-making settings on expert choices
in medicine and beyond.
A priori, it is not clear if the increase in diagnostic tests is efficient. The decadal age threshold may operate as a naturally
occurring reminder about the patient's age inducing the physician to order the “age-appropriate” amount of diagnostic tests.
While we cannot determine conclusively whether the additional diagnostic tests indicate “waste”, we find no evidence of impact
on subsequent treatment. If one is willing to take our results at face value, then from a health care policy perspective, our find-
ings can be viewed as evidence in favor of developing physician-patient relationships and care continuity in the primary care
setting. The longitudinal physician-patient relationship appears to alleviate some cognitive burden and reduce the tendency to
use heuristics.
CONFLICT OF INTEREST
The author has no conflict of interest to declare.
DATA AVAILABILITY STATEMENT
Data available on request due to privacy/ethical restrictions. The data used in this research are proprietary. This means that
should the data be requested by a reader, we will be able to make it available provided the owners of the data agree to this.
ORCID
Ity Shurtz https://orcid.org/0000-0002-9608-0767
ENDNOTES
1 See for example, Chan Jr etal.(2019), Currie and MacLeod (2020), Chandra and Staiger (2020), Abaluck etal.(2016) and Mullainathan and
Obermeyer(2019).
SHURTZ
(1)
(2)
(3)
RDD estimate 0.008 0.009 0.009
(0.007) (0.007) (0.007)
Time FEs No Yes Ye s
Physician FEs No Ye s Yes
Patient characteristics No No Yes
Bandwidth 31 31 31
Effective observations 9049 9049 9049
Observations 52,841 52,841 52,841
Note
: This table provides the RDD estimates of the likelihood to use basic diagnostic tests as per Equation(3
),
for non decadal birthdays. Time fixed effects include year and month-of-year fixed effects. The patient
characteristics which are included are: cubic polynomial of age, gender, and the following chronic conditions:
hypertension, smoking, asthma, hyperlipidemia, obesity and diabetes. One or two asterisks indicate
significance at 5% or 1%, respectively.
TABLE 8 The effect of non decadal
birthdays, visits with unfamiliar patients
Age ending with
One
Two
Three
Four
Five
Six
Seven
Eight
Nine
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
RDD estimate 0.025 −0.018 0.004 0.013 −0.012 0.004 −0.020 0.027 0.044
(0.019) (0.022) (0.021) (0.022) (0.016) (0.020) (0.027) (0.022) (0.029)
Bandwidth 26 23 29 26 44 34 21 32 23
Effective observations 940 827 1011 878 1455 1079 622 893 634
Observations 6827 6579 6395 6072 6060 5752 5275 5045 4836
Note
: Columns (1)-(9) of this Table9 provide the RDD estimates of the likelihood to use basic diagnostic tests as per Equation(3), separately nine times of each of the
non-decadal birthdays ending in digits 1-9, respectively. One or two asterisks indicate significance at 5% or 1%, respectively.
TABLE 9 The effect of non decadal birthdays, by the last digit
1727

2 Gabaix(2017) provides a recent review of this topic.
3 See also https://stats.oecd.org/. More than 40% of physician workforce in a number of high-income countries is comprised by primary care physi-
cians (Papanicolas etal.,2018).
4 The data include the physician identity as well as the patient's regular physician identity allowing us to distinguish between visits with a patient's
regular physician and visits with another physician.
5 These results are derived using a zero-order local polynomial regression with MSE optimal bandwidth, but we find that they are not sensitive to
the bandwidth, degree of the polynomial function, and the kernel function we use. We also test the continuity of the density of visits around the
decadal birthday threshold and find no evidence of such a pattern nor for “selection on observables”.
6 see Chandra etal.(2011) and Saposnik etal.(2016) for reviews of existing literature on behavioral aspects of physician decision making.
7 This study also contributes to the existing literature about the so-called left digit bias in other contexts. For example, Lacetera etal.(2012), Repetto
and Solís(2017), Ater and Gerlitz(2017) and Shlain(2018) (see also the literature review on this issue in that paper).
8 The header includes some basic patient information such as date of birth, gender, address, and phone number.
9 To reiterate, in our context, this is not a decimal: 11 stands for 11 months and not 0.11 of a year.
10 Every Israeli resident may freely choose an HMO. The Israeli health care system is largely publicly funded, with much of the HMOs' budget being
derived from health taxes.
11 The number of physicians seeing unfamiliar patients is larger because, by definition, a temporary physician would only appear in that group, while
permanent physicians should appear in both groups. However, these 19 physicians account for only 313 observations. Excluding them from the
sample does not change the results.
12 Visits with unfamiliar patients include encounters with physicians who substitute the regular physician for an extended time, for example, if the
regular physician is on maternity leave. Thus, in some cases, patients do have prior contact with that physician.
13 Appendix Figure A1 in the Supporting InformationS1 shows this by visit type.
14 The analysis of the joint sample—unfamiliar and familiar patients appears in Appendix Figure A2 and Appendix Tables A1 and A2 in the Support-
ing InformationS1.
15 In Appendix Figure A4 in the Supporting InformationS1, we present a full “scatter plot” of the data, the likelihood of using basic diagnostic tests
in each day relative to the decadal birthday. Figure A3 in the Supporting InformationS1 provides another version of the figure with the “optimal”
quantile-spaced bins using Integrated Mean Squared Error (IMSE) method (Calonico etal.,2015).
16 Table A3 in the Supporting InformationS1 shows the results for using a first-order polynomial, which are slightly larger.
17 The results are not sensitive to the choice of the kernel function we apply. Epanechnikov and uniform kernels provide very similar results.
18 This is often the case in RDD designs Cattaneo etal.(2017).
19 We set the bandwidth in all the regressions according to the optimal bandwidth in the unfamiliar group—39days around a decadal birthday.
20 Time fixed effects include year and month-of-year fixed effects. The patient characteristics included are cubic polynomial of age, gender, and the
following chronic conditions: hypertension, smoking, asthma, hyperlipidemia, obesity, and diabetes—the same patient-level variables we use in
the selection-on-observables analysis.
21 See for example, Barreca etal.(2016).
22 Appendix Figures A7 and A8 in the Supporting InformationS1 display the RDD figures that correspond to the analysis of each of the outcome
variables we report in Table6.
23 Ideally, one would want to include long term outcomes, but those are not available to us.
24 Birthdays that end with nine exhibit a relatively large point estimate. However, the result is insignificant and it tends to zero as the bandwidth
increases.
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SUPPORTING INFORMATION
Additional supporting information can be found online in the Supporting Information section at the end of this article.
How to cite this article: Shurtz, I. (2022). Heuristic thinking in the workplace: Evidence from primary care. Health
Economics, 31(8), 1713–1729. https://doi.org/10.1002/hec.4534
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