Productivity Losses due to Coordination: An Empirical Study · PDF fileProductivity Losses due to Coordination: An Empirical Study of Generalists in a Hospital Lu Wang1, Itai Gurvich1,

Productivity Losses due to Coordination:

An Empirical Study of Generalists in a Hospital

Lu Wang1, Itai Gurvich1, Jan A. Van Mieghem1, Kevin J. O’Leary, MD2

1Kellogg School of Management, Northwestern University2Feinberg School of Medicine, Northwestern University

March 31, 2016

Professional work often involves generalists who act as hubs, discussing and consulting with specialists to

gather information and make decisions. Coordination is critical when tasks, like consultations, require the

simultaneous collaboration of multiple people. Professionals typically have their individual tasks that may

be preempted to switch to collaborative tasks. Task switching can introduce setup times, often mental and

unobservable, when resuming the preempted task and reduce a generalist’s labor productivity.

We analyze the productivity loss from coordination in a field study of hospital medicine general physicians–

“hospitalists”–at Northwestern Memorial Hospital in Chicago. A hospitalist’s patient-care routine includes

visiting patients and consulting with specialized care professionals to guide patient diagnosis and treatment.

We introduce an episodal workflow model that captures the coordination dynamics — each switch and the

episode of work it preempts. A rigorous empirical analysis is presented using a data set assembled from direct

observation of physician activity and pager-log data. We show that more switches between documentation

and communication causally lead to longer documenting time, and that the cause of the switch matters: when

the switch is triggered by the hospitalist the setup impact is smaller. We estimate that a hospitalist incurs a

total setup time of 5 min per patient per day. This is substantial: Caring for 14 patients per day, a hospitalist

spends more than one hour each day on mental setups, which translates to about 20% productivity loss.

In contrast to a static snapshot of collaboration, our episodal workflow model could be deployed in other

professional settings where resource coordination is paramount.

Key words : coordination, multitasking, professional labor, productivity, setup time, empirical.

1. Introduction

Productivity is a key performance metric that captures how efficiently a process makes use of

its available resources. It is expressed as the number of jobs a resource completes per unit of

time and is constrained by the resource capacity. In contrast to capital processing assets (e.g.

machines), human resources use judgment in executing tasks and their choices affect processing

times and hence their productivity. According to the breadth and depth of their tasks, humans

can be categorized on a spectrum between generalist and specialist, with professional work often

involving a mix of both types. We study a setting where generalists act as coordinators (e.g., case

or product managers) who communicate with multiple specialists to gather information and make

decisions. Communications involve both asynchronous interactions (e.g., reaching out via voice

mail, email or text messaging) and simultaneous collaboration which requires the simultaneous

processing by multiple people (e.g., oral communication).

1

Wang, Gurvich, Van Mieghem, O’Leary: Productivity Losses due to Coordination2

Coordination is critical for simultaneous collaboration: professionals typically also have individ-

ual tasks and exercise discretion in prioritizing and switching between collaborative and individual

tasks. Task switching can introduce setup times, often mental and unobservable, when resuming

the preempted task and reduce labor productivity. Froehle and White (2014) capture, using a

discrete simulation model, how an increase in these setup times (“the forgetting rate”) impacts

the processing time and offer process-sequencing remedies. Rosenstein (2002) documents the chal-

lenges in synchronizing nurses and physicians. To alleviate simultaneity requirements, collaborators

may resort to asynchronous communication but that increases distractions, interruptions, and task

switching. Overall, the consulting of multiple specialists, via either simultaneous or asynchronous

communication, can bring about frequent task switching and the associated setup times may reduce

productivity.

We introduce an episodal workflow model that captures the coordination dynamics — each switch

and the episode of work it preempts. We deploy this model using a unique data set to measure how

coordination in practice reduces a generalist’s productivity, through task switching and associated

(unobservable) setup times. Our empirical setting is a field study of hospital medicine physicians

—“hospitalists” — at Northwestern Memorial Hospital (NMH) in Chicago. Hospitalists are general

physicians who are tasked with orchestrating high-quality inpatient care. A hospitalist’s responsi-

bilities include making decisions at each step of a patient care path (surgeries, lab tests, medication

prescriptions, discharge orders, etc.) and coordinating this care with specialists (specialty physi-

cians, lab staff, pharmacists, nurses, etc.). At NMH, a hospitalist is typically assigned 14 patients

per day and must accommodate the schedule of multiple care providers when coordinating each

patient’s care. The hospitalist’s effectiveness in orchestrating this coordinated effort is essential to

the progress of the patient’s treatment.

We conducted a time and motion study by closely observing hospitalists and recording their

activities. Figure 1 distinguishes between individual and collaborative activities. The individual

workflow includes four steps: reviewing the charts of all assigned patients, visiting patients in the

ward (typically between 9 a.m. and 11 a.m.), and then “charting” (or “documenting the chart”)

from around 11 a.m. until the end of the workday.1 Charting involves both repetitive tasks (such

as documenting routine medication or lab orders) and cognitive tasks (such as making diagnoses).

Both types of tasks must be completed in a timely manner in order to proceed with the patient’s

treatment.

1 The hospitalists seldom idle. They are motivated to finish all work as soon as possible and are continuously switchingamong their many tasks.


Figure 1 A hospitalist’s daily workflow involves both individual activities and collaborative activities.

The hospitalist receives a request?

No

Yes The hospitalist reaches out?

•  Collaborative workflow

Send a page

Make a phone call

Face-to-face conversation

Respond Yes

Respond? Receive a page

Receive a phone call

Yes

No

1. Review all patients’ charts

2. Initial bed-side visits to all patients

3. Document progress notes in charts

An interruption?

Finish all charts?

Yes

4. Sign-out

No

•  Individual workflow

7 a.m.

No

Yes

9 a.m.

11 a.m.

2 p.m. to 8 p.m.

This structure of the NMH hospitalists workday was documented before in Tipping et al. (2010)

where it is shown that hospitalists spend most of their time documenting in the Electronic Health

Records (EHR) and that they have frequent communications with specialists during this process.

This charting occupies the lion’s share of the hospitalist work at NMH: It varies from 3 to 9 hours

per day and is therefore a key determinant of hospitalist productivity.

Our observations indicate that care providers prefer simultaneous, verbal communication over

asynchronous email or text-messaging. This inevitably means that the hospitalist must often pre-

empt ongoing documentation (i.e., individual work) to speak to a specialty care provider that is

available at that moment but might not be available shortly thereafter.

Two events can trigger task switching: (i) the hospitalist, finding that she needs some information,

stops her work and reaches out to specialists to collect the required input; or (ii) the hospitalist

switches work in response to a call/message from a specialist. If, for example, the hospitalist needs a

cardiologist’s opinion when making a patient’s diagnoses, she preempts the documentation work for

that patient and reaches out to the cardiologist by phone, text or in person. While waiting for the

cardiologist to respond, the hospitalist may switch to other cases. When available, the cardiologist

will respond to the request by either paging back or making a phone call to the hospitalist. At

that point, the hospitalist may preempt her current individual work (possibly documenting another

case). In this way, the need for information may create multiple task switches.


As the above scenario illustrates, coordination leads to interruptions and switches between indi-

vidual and collaborative tasks. Lab experiments (Eyrolle and Cellier (2000) and Altmann and

Trafton (2004)) show that task interruptions are associated with longer processing times due to

the extra reconfiguration (setup) required upon resuming the task. The aggregate effect may be a

non-negligible reduction in productivity. We asked several hospitalists at Northwestern Memorial

Hospital (NMH): “What are the major hindrances to your productivity?” All hospitalists men-

tioned the delays in discussing cases with other care providers and the constant, distracting task

switching. We quote one hospitalist:

“As a clinician, my work experiences at Emory University Hospital, National Health Service

Lothian in Scotland and Northwestern Memorial Hospital all teach me one thing: interruptions

that come from different conversations can be disruptive to my work. Although the average

length of each progress note is deterministic, frequently interrupted progress notes tend to

take more time to write because I need more thought process engagement to figure out where

I was before being interrupted.”

Humans often incur a mental set up time when resuming a preempted task. We present a detailed

workflow model that allows us to estimate the unobservable mental setup times that accompany

task switching and interruptions. As such, we can analyze the resulting productivity loss that

results from resource coordination. When applied to the Hospital Medicine Division at NMH, our

empirical analysis shows that:

1. The documenting or “charting time” per patient causally increases with the number of

switches the hospitalist experiences while working on this chart.

2. The switch initiatiator matters: “Task switching to reach out” reduces productivity less than

“Task switching to respond.”

3. A substantial setup time accompanies task switching: the estimated setup time is 5min for each

patient’s daily documentation and represents almost 50% of the observed average total charting

time of 11min. Caring for 14 patients per day, a hospitalist thus spends more than one hour each

day on mental setups. Given that documenting time occupies about 40% of a hospitalist total

patient processing time, task switching results in about 20% productivity loss: if hospitalists did

not incur a setup time, they could serve 20% more patients during their shift.

Whereas the magnitude of the productivity loss is specific to this setting, our model and method-

ology are applicable to other organizations and processes where people must coordinate to solve

problems and execute tasks. Conflicting priorities — people may prioritize their own tasks — then

lead to interruptions and switches that compromise productivity. This is relevant to manufactur-

ing and new-product development (Bohn (2000)), software engineering (Perlow (1999)), and the

judiciary (Bray et al. (2015)).


2. Literature Review

The fact that coordination requirements may affect efficiency is underscored, for example, by Brooks

(1975) and Staats et al. (2012) who show how the effectiveness of a team may decrease with its size

due the increased communication needs. Thompson (2000), Girotra et al. (2010), Staats and Upton

(2011) and Gardner et al. (2012) show how team structures that facilitate efficient communication

can improve team performance. Our paper dives into the process mechanisms through which team

coordination introduces inefficiency. From a workflow viewpoint, task switching is a key mechanism

inherent to the coordination of collaborative work.

Evidence of the interruption frequencies to physicians, nurses and other care providers appears

in Flynn et al. (1999), Chisholm et al. (2000), Spencer et al. (2004), Tucker and Spear (2006), Biron

et al. (2009), Halbesleben et al. (2010), Hall et al. (2010), Westbrook et al. (2010), Ballermann et al.

(2011), Ly et al. (2013) and Cole et al. (2015). Operational consequences that are discussed in these

papers include longer patient length of stay, higher hospital costs, cognitive failures and under-

allocation of time to higher acuity tasks. Our paper focuses on productivity as the operational

measure of interest. A detailed workflow model allows us to map coordination-driven interruptions

to cases and capture how they arise endogenously from the decisions of the actors in the process.

In fact, the base processing time (excluding setups), assumed in the literature to be apriori given,

may be itself endogenous in our setting. Our detailed workflow model also allows us to differentiate

between different causes of switches.

The negative effect of switching on productivity is captured via simulation and experiments in

Speier et al. (1999), Schultz et al. (2003), Bendoly et al. (2014), and Bray et al. (2015). Staats and

Gino (2012) empirically study the tradeoff between specialization (less switching) and multitasking

(enhances knowledge). KC (2013) shows that multitasking can negatively affect quality of care.

A rich psychology literature studies the memory retrieval cost associated with task preemptions

and captures how resuming a preempted task after switching out of it is cognitively costly. Pashler

(1994), Rubinstein et al. (2001), Czerwinski et al. (2004) and Salvucci et al. (2009) study how

repetitive task alternations influence participants’ working memory and thus require a memory

retrieval which is time consuming.

Closely related to ours is the work of Froehle and White (2014) who observe physicians in

a radiology department and show, using a discrete-event simulation model, that interruptions

(usually unscheduled requests from other care providers) lead to forgetting and re-work when

resources resume preempted work. Our paper studies hospitalist productivity in the field by taking

each patient on an observed day as the unit of analysis and mapping interruptions to their “trigger”

coordination activity (and hence to the “trigger” patient). This is facilitated by a full episodal model


that breaks the work on a patient into its episodes, which are intervals of continuous work without

interruption. Equipped with the episodal model, we use the field data to empirically quantify the

setup time (or re-work) and translate it into productivity loss. Distinguishing different switching

triggers helps us to suggest how one should alter the workflow to recover the productivity loss.

3. Model and Econometric Specification

3.1. Episodal workflow model

The hospitalist’s productivity, measured by her throughput rate, can be defined as the number of

cases she completes per unit of time. In steady state,

Productivity≡ limt→∞

Number of patient cases processed by the hospitalist during [0, t]

t

=1

The hospitalist’s load per patient case

=1

The average processing time of a patient case

The unit of analysis is case (i, t), defined as all activities related to the care of patient i on day t.

For a case (i, t), the case processing time, PROCESS TIMEi,t, is defined as the total time spent

by the hospitalist on all activities related to the case. It is the sum of the case documenting time,

DOCUMENT TIMEi,t, the case communication time, COMMU TIMEi,t, and other activities

on the case, OTHER TIMEi,t:

PROCESS TIMEi,t =DOCUMENT TIMEi,t +COMMU TIMEi,t +OTHER TIMEi,t

We focus on the impact of task switching on the case documenting time (which, as we shall

show, constitutes 40% of the total patient’s processing time), which we subsequently translate into

the impact on case processing time and productivity.

Communications interrupt the hospitalist’s documentation work and partition the documenting

time of a case into multiple documentation “episodes”. We thus introduce a detailed episodal

workflow model to capture the coordination dynamics. For case (i, t), we let Ni,t denote the total

number of documentation episodes, separated by Ni,t−1 switches. We define episode documenting

time, EPISODE TIMEi,t,j, as the time spent on the jth episode of documenting case (i, t).

The documenting time of case (i, t), DOCUMENT TIMEi,t, equals the sum of all documenting

episodes.

Humans incur a cognitive cost to retrieve their working memory when switching among multiple

tasks. This is supported by vast evidence in both the operations and psychology literature (Staats

and Gino (2012), Pashler (1994), Speier et al. (1999), Rubinstein et al. (2001), Schultz et al. (2003),

Czerwinski et al. (2004), Salvucci et al. (2009), Bendoly et al. (2014)). In the setting of our study,


each time the hospitalist resumes documenting a previously interrupted case, she may incur an

extra setup time. While we cannot observe or directly measure the hospitalist’s memory retrieval

process, we will disentangle the setup time from the observed jth-episode documenting time of case

(i, t), EPISODE TIMEi,t,j, by decomposing it into a set up time, Si,t,j, and a base documenting

time, EPISODE BASE TIMEi,t,j:

EPISODE TIMEi,t,j = Si,t,j +EPISODE BASE TIMEi,t,j

The total documenting time of the case is the sum of the episodes and a case’s base documenting

time is the sum of the episode base documenting times. The base documenting time should be

thought of as a property of case characteristic (e.g., the patient diagnosis and acuity as well as the

multitasking level of the hospitalist in charge on that day), independent of the coordination effects

(such as switches); see Figure 2 below.

The approach to proxy the cognitive (retrieval) delay with a setup time is also taken by KC

(2013) and Froehle and White (2014). Multipe modeling papers also use setup times to capture

these mental costs and to analyze or optimize the performance of the underlying processes; see

e.g. Skinner (1967), Sykes (1970), Eisenberg (1971), Hofri and Ross (1987), Conway et al. (1967),

Dobson et al. (2013). We investigate how much setup time—an additional documenting time due

to task switches and consequent communications—is needed when resuming the documenting of

a preempted case (i.e. starting a new episode). Our empirical setting departs from most modeling

work in that the interruptions and setup times are endogenous, not only in their occurrence (driven

by the decision of actors), but also in their length as evidenced by the distinction between “switching

to reach out” and “switching to respond”.

Communication between individuals is an integral part of information sharing in healthcare

(Uddin et al. (2013)). Based on our observations and interviews with hospitalists, communica-

tions could have disparate effects on the case documenting time. A discussion with a cardiologist

might lead the hospitalist to order additional lab tests for a patient thus increasing this patient’s

documenting time but it might also advance the hospitalist towards a definite diagnosis making

documentation more efficient. It is also possible that the communication between the two does not

change the case documenting time at all. To investigate the impact of communication on the docu-

menting time, our model allows the communication (frequency or duration) to affect documenting

time: communication—either frequency or duration—can adjust the case base documenting time

by bringing in extra information.


Figure 2 Episodal workflow model: Potential variables that affect a hospitalist’s documenting time of a case

Variables

2. Coordination results in communication with collaborators

•  Useful information (communication frequency, collaborator type/number)

Documenting time of patient i’s progress note on day t

or…

3. Coordination switches the hospitalist among tasks

Workflow •  Set-up time (switching type/frequency)

1. Case characteristics

•  Patient information

•  Time of the day •  Multitasking: # of cases that have been started but not yet finished

Workload

Workflow

Workload

or

or

Base documenting time

Adjusted time (due to information

from communication)

Setup time (at each resumed

documentation piece)

+

+

•  Hospitalist’s familiarity with the patient

Let f(COMMU TIMEi,t,j) be the (additive) adjustment to the documentation time from com-

munication about the case between the jth and the (j − 1)th episodes. Aggregating the episodes,

we let DOCUMENT BASE TIMEi,t denote the case base documenting time that equals to∑Ni,t

j=1 EPISODE BASE TIMEi,t,j, Si,t denote the total case setup time incurred by the hospi-

talist that equals to∑Ni,t

j=1 Si,t,j, and g(COMMU TIMEi,t) be the total adjusted documenting

time due to all communications about the case. The total case documenting time is given by

DOCUMENT TIMEi,t =

Ni,t∑j=1

EPISODE TIMEi,t,j

=

Ni,t∑j=1

{EPISODE BASE TIMEi,t,j +Si,t,j + f(COMMU TIMEi,t,j)}

=DOCUMENT BASE TIMEi,t +Si,t + g(COMMU TIMEi,t)

Figure 2 illustrates the episodal workflow model in detail. We consider three groups of variables

that can affect the base documenting time, adjusted load and setup time of a case (i, t). The first

group of variables includes case characteristics such as the patient information and the hospitalist’s

familiarity with the patient. These variables determine the length of the patient’s progress note, or

the case base documenting time. Other case characteristics are associated with workflow features

such as the time of day and the multitasking level (measured by number of cases that have been


started but not yet finished) the hospitalist experiences when starting documenting the case. Such

variables reflect the hospitalist’s prioritization of and time allocated to a case and in turn influence

the case base documenting time. The second variable is the useful information extracted from

communication, which can affect the base documenting time—as discussed above, the adjustment

can be either positive or negative or null. The last group of variables includes the switching-induced

setup times. The case documenting time might be longer than the base documenting time due to

the setup time at each episode introduced by task switching.

The above overview of the theory suggests the following hypothesis:

Hypothesis 1 Si,t,j > 0 and Si,t > 0, where Si,t,j > 0 is the episode setup time of the jth episode of

case (i, t), j ∈ {1,2, ...,Ni,t}, and Si,t > 0 is the case setup time. Consequently, the case documenting

time increases with the number of switches the hospitalist makes while documenting the case.

The setup time required to resume an interrupted documentation might be shorter if the hos-

pitalist can control when she interrupts her work—for example, she can choose to be interrupted

only when reaching a point from which it will be easier to resume later. Our second hypothesis

distinguishes between different triggers of task switching:

Hypothesis 2 The setup time is shorter when a hospitalist resumes a case that is interrupted by

herself to reach out to another person, compared to when she interrupts cases to respond to another

person’s communication request.

As discussed previously, communication has two counterbalancing effects on the case document-

ing time that may cancel each other. We thus include communication time duration in the analysis

to control for the potential benefit and cost of information obtained from communication.

We test the two hypotheses by studying the impact of the variables shown in Figure 2 on the

case documenting time and each episode documenting time shown in Figure 2. We also quantify

the case setup time and episode setup time under different task switching triggers and frequencies.

3.2. Data

We collected the data in the inpatient unit at Northwestern Memorial Hospital (NMH). It is

assembled from three sources: an observational data set (from a time-motion study) and two digital

data sets extracted from the care providers’ pager-logs and the Electronic Health Records (EHR).

During June-July 2014, we closely observed and recorded 4 hospitalists’ activities in the Hospital

Medicine Unit at NMH. Patients hospitalized in this unit are stable but might have multiple

(possibly chronic) health conditions and need to be continuously monitored. Each hospitalist works

for 7 consecutive days and is off work for the next 7 days. On each day of our observational

study we selected one hospitalist for observation based on the staffing schedule (there are about 10

hospitalists scheduled per day at NMH). During our collection period, each hospitalist was assigned


Table 1 The hospitalist’s workflow can be mapped to our collected data.

Case Activity Start time End time Remark

Day Patient

1 A Review chart 6:45:19 6:58:571 B Review chart 6:58:58 6:13:171 · · · ·1 · · · ·1 · · · ·1 A Visit patient 9:26:58 9:31:461 B Visit patient 9:31:47 9:55:411 · · · ·1 · · · ·1 · · · ·1 A Document progress note 11:22:22 11:23:561 F Receive page 11:23:57 11:24:13 Nurse: “Patient F needs NPO?”

1 A Document progress note 11:24:14 11:30:101 F Make phone call 11:30:11 11:32:12 Respond to the nurse

1 F Send page 11:32:13 11:32:30 To the PCP of patient F

1 A Document progress note 11:32:31 11:34:351 A Send page 11:34:36 11:36:20 Reach out to the cardiology team

1 B Document progress note 11:36:21 11:38:001 G Receive phone call 11:38:01 11:39:10 Lab specialist confirms a test schedule

1 G Document progress note 11:39:11 11:39:541 · · · ·1 · · · ·1 · · · ·1 H Document progress note 14:31:04 14:32:131 F Receive phone call 14:32:14 11:36:19 The PCP called to respond the previous request

1 · · · ·1 · · · ·1 · · · ·

12 to 14 patients every day, summing up to 229 patient-day cases over 17 days. Most patients stay

in the hospital for multiple days and we have 113 distinct patients in the data set. We shadowed

the selected hospitalist from 7a.m. to the end of his or her shift on that day (varies from 2p.m. to

8p.m.) and logged all of the hospitalist’ activities, second by second. For each activity, we collected

detailed information regarding the patient to which the activity corresponds, the collaborators

involved if the activity is a communication, and whether the activity is a response to a previous

coordination request sent by either the hospitalist herself or another person.

We also acquired the pager logs (the textual page history) of each shadowed hospitalist and of

all other care providers the hospitalist had page exchanges with. The observational data includes

the time of a page (a coordination request or a response). The pager data complements the obser-

vational data with the sender and receiver IDs, patient identifiers, and the textual content. It helps

us map coordination requests and activities to responses and patients.


The data set assembled from the time-motion observations and the pager logs captures both the

hospitalist’s individual and collaboration workflows (Fig. 1). Table 1 is a sample of this data. After

reviewing all patients’ medical charts and visiting all patients, the hospitalist starts documenting

each patient’s progress notes around 11am. She starts to document patient A’s case at 11:22:23 for

one minute when receiving a page from a nurse who asks a question regarding another patient F.

The hospitalist ignores the request at that moment and continues with patient A’s documentation

for another 6 minutes. She then preempts patient A’s case and switches to responding to the

nurse—a switch to respond. The first documentation episode of case A thus takes 8 minutes (from

11:22:22 to 11:30:10). The conversation with the nurse motivates the hospitalist to touch base

with the Primary Care Physician (PCP) of patient F by sending a text page. She then resumes

documenting the preempted patient A’s case at 11:32:31. This episode of case A lasts for about

2 minutes since she decides to reach out to a cardiologist for a consultation about patient A’s

condition at 11:34:36—a switch to reach out. While waiting for the response of the cardiologist, the

hospitalist starts a new case, patient B. After performing hundreds of other activities during the

day (for simplicity, we do not report the whole activity flow of that day in the table), the hospitalist

receives the response from the PCP of patient F at 14:32:14 when she is working on patient H’s

case. This phone call is to respond to the hospitalist’s page sent out at 11:32:13. This sample

data reflects how the hospitalist’s individual work—activity “document progress note”—may be

interrupted by coordination requests and activities.

The third data set is extracted from NMH’s Electronic Health Records (EHR). The EHR data

contains each patient’s information on each observed day as well as the names and titles of all care

providers involved in the patient’s care. This allows identification of the patient characteristics and

the official title of each person observed communicating with the hospitalist—Physician, Nurse,

Pharmacist and Lab specialist.

Table 2 summarizes the key variables in the assembled data set. We evaluate the hospitalist

productivity with two variables: the case processing time and the case documenting time. We

focus on the impact of different variables on the hospitalist’s case documenting time, which can be

translated into the impact on the case processing time (of which documenting time is one part) and

in turn on her productivity. The average case documenting time is 11.42 minutes, 42% of the 26.26

minutes of average case processing time. After performing a Kolmogorov-Smirnov (KS) simulation

test on the case documenting time, we obtain a p-value of 0.88 and thus establish:

Observation 1: The case documenting time is Weibull distributed.

Observations 1 is further supported by the right skewed distribution of case documenting time

shown in the left panel of Figure 3 and the close fit between the observed and simulated data in

the right panel. The Weibull distribution will be relevant to our later econometric analysis.


Table 2 Summary statistics—casewise

Measure (VARIABLE NAME ) Mean SD

Operational Performance Variables (measured in minutes)

Case processing time (PROCESS TIME ) 26.26 14.96

Case documenting time(DOCUMENT TIME ) 11.42 7.01

Control variables

Case characteristics

Patient-related

Elixhauser Comorbidity Score (ELIX SCORE ) 7.49 9.01

Discharge today? 1=Yes, 0=No (DISCHARGE ) 0.26 0.44

PCP employed by NMH? 1=Yes, 0=No (PCP NMH ) 0.59 0.49

Number of days since the hospitalist has seen the patient (FAMILIARITY ) 1.90 1.00

Workflow-related

Time when starting documenting the case in hours (TIME OF DAY ) 11.66 2.32

Number of other cases the hospitalist has started but not yet finished

when starting documenting the case(MULTITASK ) 3.39 2.41

Task switching characteristics

Number of the hospitalist’s switches from documenting the case to:

reach out to collaborators (SWITCH REACHOUT ) 1.75 1.79

respond to collaborators (SWITCH RESPOND) 1.32 1.53

Communication characteristics

Communication time about the case (COMMU TIME ) 6.16 7.64

Figure 3 The observed documenting time closely follows a Weibull distribution.

0 10 20 30 40

0.075

0.050

0.025

0.000

Case documenting time (minutes)

Distribution of case documenting time

Den

sity

Quintiles for Case documenting time (minutes)

Qui

ntile

s for

Wei

bull

dist

ribut

ion

(min

utes

)

Quintiles for Weibull distribution and case documenting time

0 10 20 30

30

20

10

0

The controls case characteristics are patient and workflow related variables that may affect the

case documenting time and are independent of coordination. The patient-related variables include

(1) the Elixhauser Comorbidity Score, an index that is compiled according to the patient physical

condition and reflects the patient’s disease complexity; (2) a discharge indicator which equals to 1


if the patient is discharged on that day. On the day of discharge the hospitalist has to complete

all paperwork required for discharge and this might increase the documenting time; (3) a PCP

(Primary Care Physician) indicator which equals to 1 if the patient’s PCP is employed by NMH. It

is typically easier for the hospitalist to contact and discuss the patient with a PCP that is employed

by NMH because of the common communication and IT systems; (4) the hospitalist’s familiarity

with the patient on that day measured by the number of days the hospitalist has seen the patient.

We control for the hospitalist familiarity to account for the possibility that documentation speed

increases with familiarity. The workflow-related variables include (1) the time of day; and (2) the

multitasking level (measured by the number of cases that have been started but not yet finished)

when the hospitalist starts documenting the case, which affects the case documenting time through

the hospitalist’s prioritization of the case.

Task switching and communication characteristics summarize the features of hospitalist’s com-

munication activities—the frequencies of switches to reach out or to respond to collaborators and

the communication time duration about the case. We use communication duration to proxy for

the communication information quality—extra or reduced burden to documentation.2

Figure 4 shows the empirical partitioning of the case documenting time into episodes (each

patterned block is an episode). The case documenting time is the sum of these episodes.

Observation 2: Cases with higher task switching frequencies have longer documenting time.

This observation provides a preliminary descriptive confirmation for Hypothesis 1: each time the

hospitalist resumes an interrupted documenting work, she incurs an extra setup time, which adds

to the total documenting time of the case. Arguably, the longer the documentation time of a case,

the higher the chances it will be interrupted —a possible reverse causality that we will rule out

through our episodal analysis and robustness checks in Sections 4.2, and 5.

Table 3 summarizes the episodal variables. The episode documenting time has an average of 2.82

minutes and a large standard deviation of 3.38 minutes. The Kolmogorov-Smirnov simulation test

indicates:

Observation 3: The episode documenting time is log-normally distributed.

The patient-related case characteristics—Elixhauser Comorbidity Score, Discharge today or not,

PCP employed by NMH or not, and the hospitalist’s familiarity with the patient—remain the same

as in Table 2. The episode-specific variables reflect what has happened before a specific episode

and how the hospitalist ends that episode: When the hospitalist starts documenting an episode

2 Our data includes also additional communication characteristics such as the numbers of collaborators with differenttitles (Physician, Nurse, Pharmacist, etc.). However, after performing multiple regression analyses, computing theVariation Inflation Factors (VIFs) of coefficients and comparing the estimated coefficients across regressions, we decideto exclude these to avoid multicollinearity.


Figure 4 Switches partition the case documenting time into episodes and more switches are associated with

longer documenting times.

# of

epi

sode

s res

ultin

g fr

om sw

itche

s

Average case documenting time (minutes) 0 5 10 15

2 1

4 3

6 5

8 7

of a case, we measure the time of the day, the number of started-but-not-yet-finished cases, the

number of not-yet-started cases, and the documented progress of the case from previous episodes.

These reflect the current-episode characteristics and proxy for the base time needed for and the

time the hospitalist intends to allocate to the remaining documentation.

The second set of episode variables captures what has happened since switching from the previous

episode. The trigger of the switch the hospitalist makes from the previous episode of documenting

the same case—to respond or to reach out to another person—is likely to affect the setup time

through whether or not the hospitalist’s mental working memory is prepared for the interruption.

If the initiator is a hospitalist’s decision (to reach out) rather than an interruption (to respond),

the hospitalist might choose to stop at an “efficient” point that would require less setup upon

resumption of the task. In the literature interruptions are typically considered exogenous and

unexpected. Yet ours is a setting where it is important (and indeed statistically significant) to

capture the endogeneity of switches and distinguish between switches based on their cause.

The documenting time in the current episode can also be affected by the elapsed time since

the previous episode and by subsequent events. These, certainly the elapsed time, might affect

the amount of setup required when resuming an interrupted case. We measure the length of this

inter-episode time, the communication frequency and the collaborator types about the case during

this time.


Table 3 Summary statistics—episodewise

Measure (VARIABLE NAME ) Mean SD

Operational Performance Variable (measured in minutes)

Episode documenting time (EPISODE TIME ) 2.82 3.38

Control Variables

Patient-related Case characteristics same as in Table 2

Episode characteristics

Current-episode characteristics

By the time when starting documenting the current episode of the case:

Time of the day in hours(EPISODE TIME OF DAY ) 12.78 2.40

Number of cases the hospitalist has started but not finished(EPISODE MULTITASK ) 4.49 2.64

Number of cases the hospitalist has not yet started (EPISODE NOTSTARTED) 4.96 3.93

The documented progress of the case in percentage (EPISODE PROGRESS) 34% 33%

Switching-from-previous-episode characteristics

The trigger of switching from the previous documenting episode:

0 if no switch, 1 if to respond, 2 if to reach out (PREVIOUS EPISODE SWITCH ) 1.18 0.80

During the time elapsed since the previous episode of documenting the case:

The length of this inter-episode time in minutes(INTER EPISODE TIME) 29.69 70.25

Number of communications about the case(EPISODE COMMU ) 0.44 0.81

Number of collaborator types(EPISODE COLLABORATOR TYPE) 0.51 0.78

Switching-from-current-episode characteristics

The trigger of switching from documenting the current episode:

0 if no switch, 1 if to respond, 2 if to reach out (CURRENT EPISODE SWITCH ) 1.18 0.80

The last set of episode characteristics (switching-from-current-episode) captures how the hospi-

talist ends the current episode. For reasons similar to those discussed above, when the hospitalists

causes a switch she might spend more time on the current episode to have a better starting point

when resuming.

3.3. Econometric Specifications

3.3.1. Case documenting time Recall that patient i on day t, i.e. case (i, t) is the unit of

analysis. We start with running a “naive” regression—Fixed Effect regression (FE)—when fixing

the day effect and including the case characteristics, Xcasei,t , and the coordination (task switching

and communication) characteristics, Xcollabi,t , as the covariates. We consider a log-linear relationship

in the FE regression since DOCUMENT TIMEi,t follows a Weibull distribution:

log(DOCUMENT TIMEi,t) = αfe,t +βcasefe Xcasei,t +βcollabfe Xcollab

i,t +ui,t (1.1)


Survival analysis is a conventional econometric specification to examine how time duration

depends on different factors (Cox (1972), Kalbfleisch and Prentice (2011) and Collett (2015)). We

consider a Weibull model (Carroll (2003)) 34 With σ, θ being the shape and scale parameters of

the Weibull distribution of DOCUMENT TIMEi,t, the hazard rate h(DOCUMENT TIMEi,t)

is then expressed as

h(DOCUMENT TIMEi,t) =σ

θ(DOCUMENT TIMEi,t

θ)σ−1 exp(βcaseweibullX

casei,t +βcollabweibullX

collabi,t )

(1.2)

We “equalize” the impact of case characteristics to account for their confounding effects (such

as patient documentation complexity captured by the patient-related variables and documentation

prioritization captured by the workflow-related variables) that may affect both the case document-

ing time and the need for task switching. Adopting the Propensity Score (PS) Weighting method

(McCaffrey et al. (2004)), we divide all cases into 3 groups according to their task switching

frequencies when being documented: Control group — Group 0 are cases without any switches,

Treatment groups — Group 1 cases have 1 to 3 switches, and Group 2 cases have more than 3

switches. Using the boosted regression (McCaffrey et al. (2004)), we obtain a weight, wi,t for each

case (i, t), that proxies for the case’s probability of having no switch (i.e. belonging to the control

group) in the scenario where no confounding effect exists: switches would happen randomly. We

run a Survey-Weighted Generalized Linear Model (GLM)5:

log(DOCUMENT TIMEi,t) =αps,glm +βcaseps,glmXcasei,t +βcollabps,glmXcollab

i,t +ui,t,

with weight wi,t (1.3)

Lastly, we conduct a survival analysis to ensure the robustness of the statistical impact of each

covariate. We use the Cox Proportional Hazard Model (Cox)6 in which the instantaneous hazard

rate of the documenting time can be expressed as

h(DOCUMENT TIMEi,t) =h0(DOCUMENT TIMEi,t) exp(βcaseps,coxXcasei,t +βcollabps,coxX

collabi,t ),

with weight wi,t (1.4)

As discussed in Cox (1972), h0(DOCUMENT TIMEi,t) is the baseline hazard function that does

not need to be specified, and the hazard rate of each variable (βcaseps,cox and βps,collabcox ) can be estimated

using partial maximum likelihood.

3 (the survreg function in the statistical software R)

4 All survival analyses in this paper specify day, t, as a cluster.

5 Using svyglm4 function in R.

6 The weights obtained from PS weighting method are incorporated in the svycoxph4 function in R.


3.3.2. Episode documenting time Our Hypothesis 1 stipulates that task switching impacts

the case documenting time through a setup time that is added to each documentation episode.

To analyze this dynamic, we run a fixed effect regression (fixing the case effect) and a survival

analysis. Recall that the episode documenting time follows a log-normal distribution. Thus, for

the jth documenting episode of patient i on day t, we take the log(EPISODE TIMEi,t,j) as the

dependent variable and express the fixed effect regression equation as

log(EPISODE TIMEi,t,j) = γfe,i,t + δcasefe Xcasei,t,j + δepisodefe Xepisode

i,t,j +ui,t,j (2.1)

where Xcase and Xepisode are summarized in Table 2 and Table 3, respectively. The hazard rate

function used for the survival analysis (Cox (1972)) is

h(EPISODE TIMEi,t,j) = h0(EPISODE TIMEi,t,j) exp(δcasecox Xcasei,t,j + δepisodecox Xepisode

i,t,j ) (2.2)

To deal with the confounding effect, we can also assign a weight to each episode using the

Propensity Score Weighting method. Since each episode can only incur at most one switch, we

now group episodes according to the switching trigger instead of switching frequency: an episode

belongs to Group 0 if it is not interrupted (i.e. the last episode of a case), Group 1 if it ends

because the hospitalist switches to respond to a collaborator, and Group 2 if the hospitalist switches

to reach out. Such grouping addresses the concerns that potential confounding factors such as

Switching-from-last-episode characteristics (events prior to the current episode) or Current-episode

characteristics might affect how the hospitalist end the current episode and the episode length

simultaneously. After equalizing the impact of case and episode characteristics with the Propensity

Score and obtaining a weight, vi,t,j for each episode (i, t, j), we conduct both a GLM4 and a survival

analysis4 to ensure the robustness of each covariate:

log(EPISODE TIMEi,t,j) =γps,glm + δcaseps,glmXcasei,t,j + δepisodeps,glm Xepisode

i,t,j +ui,t,j

with weight vi,t,j (2.3)

h(EPISODE TIMEi,t,j) =h0(EPISODE TIMEi,t,j) exp(δcaseps,coxXcasei,t,j + δepisodeps,cox Xepisode

i,t,j )

with weight vi,t,j (2.4)

The regression results are reported in section 4—Table 4 and Table 5, respectively. Further

validation and robustness checks appear in section 5.


Table 4 Regression results—casewise

Before PS weighting After PS weighting

FE (1.1) Weibull (1.2) GLM (1.3) Cox (1.4)Variable Est.Coef Est.exp(Coef) Est.Coef Est.exp(Coef)

(SE) (SE) (SE) (SE)

INTERCEPT 0.08∗∗∗ 3.30∗∗∗

(0.20) (0.57)Case characteristics

ELIX SCORE 0.00 0.99 0.00 0.99(0.00) (0.00) (0.01) (0.01)

DISCHARGE 0.11 0.85∗ 0.17 0.80(0.10) (0.08) (0.16) (0.18)

PCP NMH 0.10 1.01 0.09 1.02(0.08) (0.07) (0.17) (0.15)

FAMILIARITY −0.01 1.08∗ −0.06 1.06(0.05) (0.03) (0.08) (0.06)

TIME OF DAY −0.06∗ 1.04∗ −0.13∗∗ 1.19∗∗∗

(0.02) (0.02) (0.04) (0.03)MULTITASK 0.03 0.98 −0.03 0.99

(0.02) (0.02) (0.03) (0.04)Task switching

SWITCH REACHOUT 0.09∗∗ 0.93∗∗∗ 0.14∗∗ 0.88∗

(0.03) (0.02) (0.05) (0.05)SWITCH RESPOND 0.15∗∗∗ 0.91∗∗∗ 0.26∗∗∗ 0.72∗∗∗

(0.03) (0.03) (0.07) (0.08)Communication

Log(COMMU+1) 0.11∗ 0.91∗ 0.04 0.84(0.05) (0.04) (0.10) (0.09)

Log(scale) 2.06∗∗∗

(0.05)Regression statistics

Log-likelihood (Pr>χ2) < 0.001 < 0.001

Adjusted R2 0.33 0.33

∗,∗∗ ,∗∗∗ denote significance at the 5%,1%, and 0.5% levels, respectively.

4. Results and Discussion

4.1. The impact of task switching on documenting time: case-wise

The estimation results for Equations 1.1 to 1.4 are reported in Table 4. Among the case character-

istics, only TIME OF DAY is significant across the four models. Its estimated coefficients in (1.1)

and (1.3) being negative and its estimated exponential coefficients in (1.2) and (1.4) being larger

than 1 indicate the tendency to speed up documentation towards the end of the workday. Other

case characteristics do not display significant impact on the case documenting time, suggesting

that the base documenting time does not vary much across cases. They do not show statistical

significance in GLM (1.3) and Cox (1.4). This can be attributed to the relative stability of patients


in the internal medicine division and/or to the nature of the hospitalist documentation work: the

standardized formats of diagnosis report, treatment plan, medical orders and etc. With everything

else being controlled for via control variables or through the Propensity Score weighting, most

patient case characteristics do not significantly contribute to the documentation content of time.

The effects of task switching are evident in Table 4. The estimated coefficients of

SWITCH REACHOUT and SWITCH RESPOND are statistically significant in all four

columns. In FE (1.1) and GLM (1.3), the coefficients are positive, representing a marginal increase

in log(DOCUMENT TIME) due to a switch. The coefficients in Weibull (1.2) and Cox (1.4),

interpreted as the instantaneous hazard rate of the case being finished, are less than 1 and indicate

a slower documenting speed due to an additional switch. Formally, we establish the first result (the

causal relationship will be validated in later sections):

Result 1: A greater number of switches is associated with longer documenting time.

The estimated coefficients of SWITCH REACHOUT in (1.1) and (1.3) are smaller than

those of SWITCH RESPOND, suggesting that a switch to reach out to another person is

less penalizing than a switch to respond. Furthermore, the estimated exponential coefficients of

SWITCH REACHOUT in (1.2) and (1.4) are larger than those of SWITCH RESPOND,

which implies that the hospitalist can document a case faster if she experiences a switch to reach

out than if she switches to respond. To empirically test these coefficient comparisons, we performed

1000 bootstrap regressions for each model: each time, we sample 1000 data points from the original

data set with replacement—this alleviates the limitation of small sample size. By performing one-

sided t-tests on the 1000 pairs of coefficients (SWITCH REACHOUT ,SWITCH RESPOND)

for each model, we confirm the comparisons discussed above. These comparisons imply:

Result 2: A switch triggered by the hospitalist to reach out introduces a smaller penalty to the

documenting time of the preempted case than a switch triggered by responding to other collaborators.

These two results are further captured in our counterfactual predictions of the tail probabilities of

case documenting time in Figure 5, where tail probability is defined as Pr(Case documenting time≥

t), for t ≥ 0. The left panel shows that the observed tail probability of case documenting time

diminishes to zero when t approaches 20 minutes. If no case experienced any switches, our model

would predict the tail probability using Equation 1.4; notice that it falls faster than the observed

tail probability. It would go to 0 as t approaches around 15 minutes. This means that the hospitalist

would document faster if she would never switch to other activities while documenting a case. A

slightly smaller improvement in documentation speed is predicted if all cases would belong to the

low-switching-frequency group—the hospitalist makes 1 to 3 switches per case. If the hospitalist

makes more than 3 switches per case, the tail probability shifts to the right, which suggests that

she would document slower. The right panel retains the observed switching frequency of each case


Figure 5 Predicting case documenting time with the Cox Proportional Hazards Model

0 10 20 30

0.2

0.4

0.6

0.8

1.0

If all cases haveObserved interruptionsNo interruptionsOnly Low−frequency interruptionsOnly High−frequency interruptions

Tail probability of case documenting time under different switching−frequency scenarios

t (minutes)

Pr (

case

doc

umen

ting

time

grea

ter t

han

t)

0 10 20 30

0.2

0.4

0.6

0.8

1.0

If all cases' interruptions' types areas observedfrom collaboratorsby hospitalists

Tail probability of case documenting time under different switching−type scenarios

t (minutes)

Pr (c

ase

docu

men

ting

time

grea

ter t

han

t)

Tail probability of case documenting time: under different switching-frequency scenarios

Tail probability of case documenting time: under different switching-type scenarios

No case has any switch

All cases have 1 to 3 switches

All cases have switches as observed in the data

All cases have more than 3 switches

All cases’ switches are conducted by the hospitalist to reach out

All cases have switches as

observed in the data

All cases’ switches are conducted to respond to other people

0 10 20 30 0 10 20 30

1.0 0.8

0.4 0.2

0.6

1.0 0.8

0.4 0.2

0.6

but performs a counterfactual on different switching triggers: the tail probability curve shifts to

the left if all observed switches would have been initiated by the hospitalist herself to reach out,

suggesting a faster documentation. The curve largely shifts to the right if all switches would have

been made in response to collaborators’ requests, indicating a slower documentation.

The estimated coefficients of communication time are not statistically significant. This is possibly

because that the two directions of effects cancel each other out: on one hand, longer communications

might generate additional information to document; on the other hand, some communications

might provide information that will help the hospitalist conclude her diagnoses more efficiently

and reduce the documenting time. This leads to:

Result 3: Communication does not significantly affect the case documenting time.

Letting R and TPROCESS denote the hospitalist productivity (i.e. throughput rate) and average

case processing time, respectively, we derive the counterfactual change in the hospitalist produc-

tivity if the task switching impact could be removed (indicated by .̂ ) as:

4RR

=“R−RR

=

1

T̂PROCESS− 1

TPROCESS

1TPROCESS

=−4TPROCESST̂PROCESS

To fill in the numbers, we first predict the case documenting time using Equation 1.3 by setting

both SWITCH REACHOUT and SWITCH RESPOND to zero. With the 1000 predicted

documenting times obtained from previous 1000 bootstrap regression results, we calculate that the

average predicted documenting time, T̂DOCUMENT is 6.5min with a standard deviation of 0.8 ≈1min. In other words, out of the observed average documenting time of 11.4min, approximately

11.4− 6.5 = 4.9≈ 5min (with a small variation: 1min of standard deviation) is due to setup times.

Formally, we establish:


Result 4: Task switching introduces 5± 1 minutes of setup time to each case.

The predicted no-switch case processing time is obtained by substituting the observed document-

ing time with the predicted one, keeping the time spent on other activities fixed. From the 1000

bootstrap regression results, the average predicted case processing time, “TPROCESS, is therefore

21.4min. Recalling that the average observed processing time TPROCESS is 26.3min, the relative

change in productivity if the setup times were zero is

4RR

=−4TPROCESST̂PROCESS

=−1

26.3− 1

21.41

21.4

= 19%

The standard deviation is obtained as 3%, still calculated with the 1000 predictions from the

bootstrap regressions. Formally, we have

Result 5: In the absence of task switching, hospitalist productivity would improve by 19%±3%,

corresponding to 3 more patients per day compared to the current 14-patient assignment.

4.2. The impact of task switching on documenting time: episode-wise

We turn our attention to the detailed analysis of documenting episodes. Table 5 reports the

results for regression equations 2.1 to 2.4 in . The estimated coefficients of switching-from-previous-

episode and switching-from-current-episode characteristics help validate and explain the mecha-

nisms behind Results 1 to 4.

First, the patient-related and the current-episode characteristics (the time of day when the

episode starts, the multitasking level, the number of remaining cases that have not been started

and the documented progress ratio of the case) affect the remaining documentation work for the

case. The negative coefficients of EPISODE NOTSTARTED in (2.1) and (2.3) suggest that the

hospitalist allocates less time to the current episode if she still has many not-yet-started cases.

The negative coefficients of EPISODE PROGRESS in (2.1) and (2.3) show that the more doc-

umentation work that has been completed, the less time needed in the current episode. This might

seem intuitively expected (the more we do, the less we have remaining) but one must recall that

communication (through information added) can lengthen the documenting time. This shows that

the communication effect is not sufficiently strong to break this intuition.

The two dummy variables PREV IOUS EPISODE SWITCH = 1 and

PREV IOUS EPISODE SWITCH = 2 reflect how the hospitalist ends the previous episode—to

respond to an interruption or to reach out to a specialist. These two variables both equal 0

if the current episode is the first one of the case; i.e., there is no switching from the previous

episode. The first episode of a case does not require any setup time from the hospitalist since

there is no prior interruption of working memory. Therefore, keeping all other variables being

controlled for, the coefficients of these two dummy variables measure the incremental effects


Table 5 Regression results—episode-wise, fixing the effect of Case

Before PS weighting After PS weighting

FE (2.1) Cox (2.2) GLM (2.3) Cox (2.4)Variable Est.Coef Est.exp(Coef) Est.Coef Est.exp(Coef)

(SE) (SE) (SE) (SE)

INTERCEPT 1.98∗∗∗

(0.40)

Patient-related case characteristics

ELIX SCORE 1.00 −0.00 1.00(0.00) (0.00) (0.00)

DISCHARGE 0.92 0.06 0.87(0.08) (0.09) (0.08)

PCP NMH 0.89 0.20∗ 0.84∗

(0.07) (0.08) (0.07)

FAMILIARITY 1.06 −0.04 1.08(0.03) (0.04) (0.03)

Current-episode characteristics

EPISODE TIME OF DAY 0.10 1.01 −0.03 1.01(0.06) (0.02) (0.02) (0.02)

EPISODE MULTITASK −0.01 1.01 −0.03 1.01(0.03) (0.01) (0.01) (0.01)

EPISODE NOTSTARTED −0.07∗ 1.03∗ −0.03 1.03(0.03) (0.01) (0.02) (0.01)

EPISODE PROGRESS −2.31∗∗∗ 5.71∗∗∗ −2.26∗∗∗ 20.26∗∗∗

(0.19) (0.16) (0.16) (0.18)

Switching-from-previous-episode characteristics

PREVIOUS EPISODE SWITCH= 1 (respond) 0.82∗∗∗ 0.55∗∗∗ 0.86∗∗ 0.35∗∗∗

(0.14) (0.11) (0.14) (0.14)

PREVIOUS EPISODE SWITCH= 2 (reach out) 0.74∗∗∗ 0.57∗∗∗ 0.84∗ 0.36∗∗∗

(0.14) (0.11) (0.15) (0.14)

EPISODE INTER EPISODE TIME −0.00∗∗∗ 1.00∗∗∗ −0.00∗∗ 1.00∗∗

(0.00) (0.00) (0.00) (0.00)

EPISODE COMMU 0.10 0.89∗ 0.15∗ 0.86(0.08) (0.07) (0.06) (0.07)

EPISODE COLLABORATOR TYPE −0.00 1.04 −0.07 1.05(0.09) (0.08) (0.08) (0.08)

Switching-from-current-episode characteristics

CURRENT EPISODE SWITCH= 1 (respond) −0.79∗∗∗ 2.03∗∗∗ −0.90∗∗∗ 2.27∗∗∗

(0.13) (0.10) (0.12) (0.09)

CURRENT EPISODE SWITCH= 2 (reach out) −0.89∗∗∗ 2.10∗∗∗ −1.04∗∗∗ 2.40∗∗∗

(0.13) (0.09) (0.10) (0.08)

Regression statistics

Log-likelihood (Pr>χ2) < 0.001 < 0.001

Adjusted R2 0.15 0.36


due to resumption, i.e., setup time for reconfiguring the interrupted working memory from

previous episode. The estimates are positive and significant in FE (2.1) and GLM (2.3), and the

estimated exponential coefficients in Cox (2.2) and (2.4) are smaller than 1. These suggest a

positive incremental effect from resumption—a switch out from a case induces a setup penalty


Table 6 Estimated setup time per episode

Episode index1 2 3 4 5 6 7 8 ...

Estimated setup time (minutes) 1.61 1.84 2.46 1.79 2.12 1.57 1.39 ...Standard deviation (minutes) 0.29 0.28 0.33 0.33 0.47 0.33 0.31 ...1We do not estimate the setup time for the first episode since we assume there is no setup time needed.

when resuming the case. The coefficient of PREV IOUS EPISODE SWITCH = 1 being larger

than that of PREV IOUS EPISODE SWITCH = 2 in FE (2.1) and GLM (2.3) imply that

the magnitude of this setup time is larger if the previous switch is initiated to respond to an

interruption compared to reaching out. This confirms Hypothesis 1 and further validates Result 1

and is consistent with the descriptive Figure 4.

Table 5 also shows that the communication frequency and the types of collaborators that inter-

acted with the hospitalist since the previous episode do not significantly contribute to the current

episode’s documenting time. This further validates Result 3.

The switching-from-current-episode effects—CURRENT EPISODE SWITCH = 1 and

CURRENT EPISODE SWITCH = 2—are statistically significant and negative. In FE (2.1)

and GLM (2.3), the coefficients of CURRENT EPISODE SWITCH = 1 are −0.79 and −0.90

respectively and those of CURRENT EPISODE SWITCH = 2 are −0.89 and −1.04. The neg-

ativity, common to all these variables, shows that a documentation episode that does not end with

a switch (CURRENT EPISODE SWITCH would equal to 0) is longer than episodes that are

interrupted and end with a switch. Moreover, the hospitalist ends the current episode earlier when

the switch is made to reach out compared to when it is made to respond. For example in GLM

(2.3), the coefficient of CURRENT EPISODE SWITCH = 1 is −1.04 and is smaller than that

of CURRENT EPISODE SWITCH = 2, −0.90. Same conclusions are drawn from FE (2.1),

Cox (2.2) and (2.4). Following similar bootstrap procedures, we validate such coefficient compar-

isons with one-sided t-tests on estimated coefficients from each bootstrap regression results. This

confirms Hypothesis 2 and validates Result 2. It is possible because when the hospitalist controls

the switching timing (as in the case of reaching out), she will stop the documentation at a point

from which it will be easy to resume in a later episode. Consequently, she incurs a shorter setup

time relative to the case where the switch is driven by a response.

As mentioned earlier, one may be concerned with the possibility of reverse causality, namely that

it is not only that setups increase documenting time but that cases that have longer documenting

time are inevitably more likely to be interrupted and hence require more setups. We will address this

possibility of endogeneity via standard econometric methods in Section 5 yet the episodal model

here, while not ruling out the possibility that longer documenting times lead to more switches,

supports the existence of direct causality: switches increase documenting times.


Table 7 The robustness-checking models

Model feature Model a Model b Model c Model d

PS weighting the data by grouping the data according to:

switching frequency X Xswitching trigger X X

In the coordination characteristics, include:

the task switching frequency group Xthe task switching trigger group X

the number of switches due to each trigger X XEstimated setup time (in minutes) 5 6 6 5

The relative change in productivity 19% 23% 23% 19%Standard deviation 3% 3% 3% 3%

Within our episodal model one can circumvent the possible reverse effect of the documenting

time on the number of switches by keeping the latter (switching frequency) constant and only

nulling the effect of the switch itself. This is achieved by setting the switching-from-previous-

episode variables equal to zero. Specifically, within each bootstrap regression of Equation 2.3,

we estimate the setup time needed for each episode by following similar procedures as in sec-

tion 4.1: we first predict the documenting time of each episode by setting the dummy variables

CURRENT EPISODE SWITCH = 1 and CURRENT EPISODE SWITCH = 2 equal to

zeros. The gap between the predicted (counterfactual) and the observed documenting time is,

purely, the effect of case resumption and is around 2min as shown in Table 6. In other words, we

find a 2min setup time for each of the second and later episodes of a case. Aggregated over all

episodes of each case, the total setup time for a case is 5.6 minutes on average and falls in the 95%

confidence interval of the estimated case setup time of Result 4 in section 4.1: 4.9± (1.96× 0.8)

minutes. In this way the detailed episodal model provides a robustness check to the result 4 (for

the simpler case level model) that 5 minutes of the documentation time are added through task

switches and their associated setup times.

5. Robustness5.1. Model validation and robustness

We first check the robustness of our propensity score weighting method. We take Equation 1.3 as

the baseline model, Model a, and further include three models. The models are summarized in

Table 7. Model b is a GLM conducted after the same PS weighting as Model a but includes the

task switching frequency group dummy variable instead of the number of switches due to each

trigger. Model c and Model d are GLMs conducted after PS weighting the data according to the

switching trigger group: Group 0 if no switch, Group 1 if the case’s switches are all to respond to

other people, Group 2 if the case’s switches are all to reach out to other people, and Group 3 if the

case’s switches include both reaching out and responding. We consider the total number of switches

of each case and the number of switches due to each trigger in the two models, respectively.


Figure 6 The residuals of four robustness-checking models are i.i.d. and normally distributed.

Model dModel c Model dModel c

Model a Model b Model a Model b

Normal-QQ plots for the residuals Residuals are independent o predictions

Res

idua

ls

Sam

ple

Qua

ntile

s

Model predictionsTheoretic Quantiles

We report the estimated coefficients in an on-line appendix. The resulting changes in document-

ing time and in productivity when setting switch equal to zero are consistently around 20% in the

four models. The use of GLM is validated by checking the i.i.d. assumption of residuals. As shown

in Figure 6, the residuals of the four models all are normally distributed and are independent of

predictions. Similar residual patterns are observed for Equation 2.3 as well (which is not reported

in this paper for simplicity). This further validates Result 5.

5.2. Confounding factors

We further validate the above test by showing that there does not exist a confounding factor that

can affect both the hospitalist task switching frequency and her EHR documenting time. Since task

switching is initiated to perform communication about a patient, potential confounding factors

should relate to both communication needs or difficulty (more switches are needed to communicate

with a collaborator that is hard to reach at) and the time needed for the hospitalist documenting

the EHR. Therefore, we assume that the major potential confounding factors are: patient condition

complexity, the hospitalist familiarity with the patient, her work experience and the collaborator

availability. The first two factors relate to the EHR content length (relates to the documenting

time) and communication needs for the patient, and both are already controlled for in the regres-

sion analysis. The third factor, hospitalist work experience, determines how likely the hospitalist

communicates with other people for consultations or discussions, and how proficient she makes

diagnosis and documents progresses in the EHR. It is excluded from the analysis since we fixed the

effect of day; this is equivalent to fixing the effect of hospitalist experience since only one hospitalist

is observed on each day in our analysis. The last factor, collaborator availability, potentially causes


a confounding effect by indirectly impacting the hospitalist documenting time through affecting the

hospitalist switching frequency. A collaborator is any specialist the hospitalist communicates with

about the patient, and whose availability thus determines the communication difficulty and conse-

quently affects the hospitalist task switching needs. To rule out this fourth potential confounding

factor, we first assume the existence of such endogeneity caused by collaborator availability, then

construct instrumental variables (IV) that proxy for collaborator availability. We finally rule out

this assumption with the Durbin-Wu-Hausman test: if the test does not reject the null hypothe-

sis that task switching is exogenous given the IVs in use, we are able to claim that collaborator

availability has no confounding effect.

To validate the choice of IVs and to perform the test, a Two-Stage-Least-Squares regression is

needed. For patient i on day t, we establish:

SWITCHi,t = π0 +π1Zi,t +π2Xi,t + ε3.1i,t (3.1)

By replacing SWITCHi,t by ̂SWITCHi,t obtained from Equation 3.1, we further have:

log(DOCUMENT TIMEi,t) = φ0 +φ1̂SWITCH i,t +φ2Xi,t + ε3.2i,t (3.2)

In the above equations, SWITCHi,t represents the number of switches (both to reach out and

to respond) made by the hospitalist while documenting patient i’s EHR on day t, Xi,t includes the

control variables (excluding switches) summarized in Table 2. Zi,t represents a valid IV. We select 2

IV candidates that satisfy the two criteria for a valid IV: a) Exclusion — the IV, Z, is uncorrelated

with the error term in Equation 3.2, i.e. Cov(Z, ε3.2) = 0; and b) Inclusion: Cov(Z,SWITCH) 6=

0. In other words, a valid IV should affect the hospitalist documenting time only through the

hospitalist task switching frequency.

We select two IV candidates that measure collaborator workload to proxy their availability, based

on the assumption that busier collaborators are more difficult to reach at and thus less available.

For all patients (not limited to the observed patients) hospitalized in the inpatient unit during our

observational period, the EHR records all medical activities (medication orders, lab tests, surgeries,

consultations and etc.) and care providers who conduct those activities. We thus construct the two

IV candidates by measuring: 1) average number of patients a collaborator see on that day; and 2)

average number of activities a collaborator performs on that day.

These two IVs satisfy the Exclusion criterion because collaborator workloads do not directly

affect the hospitalist’s documenting time of any case. This is due to the nature of the hospital-

ist documentation work: it is an individual mind-focused work and thus its time depends on the

length of content the hospitalist intends to input in the EHR and the hospitalist’s thinking process.


Table 8 Validation of Instrumental Variables and Specification Test Results

Instrumental variable Correlation with SWITCH

Average # of:patients each collaborator1 see on that day -0.14***2

activities each collaborator1 performs on that day -0.18***2

Specification tests p-value

Sargan test 0.22Durbin-Wu-Hausman test 0.39

1For each patient on a day, a collaborator is a person the hospitalist communicates with about the patient on that day.2 ∗,∗∗ ,∗∗∗ denote significance at the 5%,1%, and 0.5% levels, respectively.

Therefore, the workloads—how many patients seen or activities performed—of any collaborator

other than the hospitalist should not directly affect the hospitalist documentation work. We empir-

ically test the Exclusion criterion by performing a Sargan test. As shown in the bottom part of

Table 8, the large p-value 0.22 suggests that we do not reject the null hypothesis that both IVs are

uncorrelated with ε3.2 in Equation 3.2.

The two candidates satisfy the Inclusion criterion since collaborator workloads determine their

availabilities and frequencies to communicate with the hospitalist, and inevitably relates to the

hospitalist switching frequency. To empirically validate, we calculate the correlations between the

two IVs and SWITCH. The second column in the top part of Table 8 indicates that the correlations

are statistically significant and not equal to zeros.

The last row of Table 8 is the key in ruling out the endogeneity concern due to collaborator

availability. The null hypothesis of Durbin-Wu-Hausman test is that SWITCH is exogenous. The

large p-value 0.39 suggests that we cannot reject the null hypothesis and conclude that collaborator

availability does not cause a confounding effect.

6. Concluding remarks

The setting in this paper has the following fundamental characteristics: there is a set of human

resources that each have their own individual work (the hospitalist and her collaborators all have

their own individual tasks) but also collaborative activities. These characteristics are likely to be

found in a variety of information-intensive professional services (e.g. manufacturing, new-product

development, software engineering and the judiciary).

In this setting, we examine the impact of coordination on the productivity of physicians using

data collected at Northwestern Memorial Hospital. The detailed observational data, together with

an episodal empirical model, enable us to explore the effect of coordination on hospitalist pro-

ductivity through task switching. Our model estimates the unobservable setup time from task

switching to be about 5min, accounting for almost 50% of the observed case documenting time,

which translates into a 20% productivity loss.


Innovative policies and technologies have been developed and implemented in various hospitals,

aiming at improving the coordination efficiency among physicians and nurses; e.g., see Zwarenstein

and Bryant (2000), Lingard et al. (2008), and Wayne et al. (2008). The experience with Structured

Interdisciplinary Rounds (SIDR) currently being implemented at Northwestern Memorial Hospital

(O’Leary et al. (2010) and O’Leary et al. (2011)) can shed light on avoiding task preemptions

that are not productive. SIDR refers to a regular short meeting held at 10 a.m. on every weekday

when hospitalists and nurses exchange information regarding their shared patients. It improves the

communication efficiency by batching the hospitalist’s coordination activities with nurses within a

certain time frame. It is desirable to design coordination protocols that reduce the cost of infor-

mation collection by better coordinating hospitalists with the providers with whom they interact.

Our results show that the benefit can be significant.

The focus of this paper is not on offering implementable remedies. Yet, from our results one can

draw conclusions about what the general direction should be:

(1) Postpone and consolidate information outreach of each case, and perhaps even per batch of

cases. Instead of reaching out to collaborators while documenting, it may be better from a produc-

tivity perspective to complete documentation task to the extent possible and then reach out to the

(possibly several) collaborators at once. This then minimizes the possibility of being interrupted by

a collaborator response. Our observation in the hospital suggests that some hospitalists do follow

such batching heuristics and write a batch of cases before initiating any coordination requests and

before responding to any interruptions. Batching, however, comes at the possible cost of missing

opportunities of communicating with other people when they are available which leads to the sec-

ond point. Ideally, if collaborators could agree to synchronize when they do their individual work,

then they could all be available simultaneously for the coordination work. This would streamline

team communication and minimize delays and task switching.

(2) Consolidate information sharing among collaborator pairs. Recall that, regardless of how

well resources are synchronized, each communication inflicts task switching which increases the

documenting time. Therefore, to the extent possible, it is preferred to consolidate all information

sharing between the same two people in one communication. (In contrast to piecemeal information

sharing via multiple interactions, which each would inflict a task switch.)

(3) Standardize work to reduce the cognitive cost from task switches: The setup time due to task

switching can be alleviated through better standardization and design of the content and format

of medical charts, e.g. a checklist for documented work.


Coordination is necessary yet challenging: it may result in better patient care but it introduces

task switching and productivity losses. Our study of the impact of coordination on productivity

begs further work on the impact of coordination on quality of care. While we can proxy some

aspect of quality with communication time the hospital does not track the needed quality data,

which would require another study. Yet, the measurement of productivity is a first step towards

improvement. As the chief of hospital medicine told us: “Of course patient care quality is crucial,

but we hospitalists also want to simply improve our productivity.”

Finally, our study also highlights a trade-off between flexible and dedicated resources, which is

fundamental in operations management. Hospitalists, viewed as generalists, are flexible resources

who provide patient care to patients with a large variety of conditions and endow the hospital with

some economies of scale. It is this flexibility, however, that necessitates some of the coordination

that, in turn, affects the productivity. A patient with a cardio-vascular condition might require

fewer of these communications but such dedicated allocation compromises economies of scale. Thus,

a better understanding of the effects of coordination feeds into fundamental operations strategy

questions. We hope that this study may serve as a stepping-stone to further research of such settings

and potential solutions to the challenges they offer.

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Appendix. Model validation and robustness

Table 9 Robustness checking results of four models described in Table 7

PS weight according to theswitching frequency group switching trigger group

Model a Model b Model c Model dVariable Est.Coef Est.Coef Est.Coef Est.Coef

(SE) (SE) (SE) (SE)

INTERCEPT 3.30∗∗∗ 3.14∗∗∗ 2.28∗∗∗ 2.41∗∗∗

(0.57) (0.58) (0.39) (0.38)Case characteristics

ELIX SCORE 0.00 0.00 0.00 0.00(0.01) (0.01) (0.01) (0.01)

DISCHARGE 0.17 0.15 0.19 0.20.(0.16) (0.15) (0.11) (0.12)

PCP NMH 0.09 0.13 0.06 0.06(0.17) (0.18) (0.12) (0.12)

FAMILIARITY −0.06 −0.07 −0.04 −0.05(0.08) (0.07) (0.06) (0.07)

TIME OF DAY −0.13∗∗ −0.12∗∗ −0.06∗ −0.06∗

(0.04) (0.04) (0.03) (0.03)MULTITASK −0.03 −0.04 −0.01 0.00

(0.03) (0.03) (0.02) (0.02)Task switching

TASK SWITCHING FREQUENCY GROUP DUMMYGroup 1: 1 to 3 switches 0.62∗∗∗

(0.14)Group 2: 4 or more switches 1.05∗∗∗

(0.21)TASK SWITCHING TRIGGER GROUP DUMMYGroup a: all switches are reaching out 0.51∗∗

(0.16)Group b: all switches are responding 0.42∗

(0.18)Group c: switches include both reading out and responding 0.93∗∗∗

(0.17)SWITCH REACHOUT 0.14∗∗ 0.16∗∗∗

(0.05) (0.04)SWITCH RESPOND 0.26∗∗∗ 0.19∗∗∗

(0.07) (0.04)Communication

Log(COMMU+1) 0.04 0.05 0.06 0.02(0.10) (0.09) (0.07) (0.08)


Table 9 reports the baseline model (Equation 1.3) and the 3 other models described in Table 7. Regardless

of the model the coefficients of task switching characteristics are consistently significant. Also consistent is

the fact that the effect of switching to reach our is smaller than that of switching to respond.

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Productivity Losses due to Coordination: An Empirical Study · PDF fileProductivity Losses due to Coordination: An Empirical Study of Generalists in a Hospital Lu Wang1, Itai Gurvich1,