Erdil, Emerson
MODELING THE DYNAMICS OF ELECTRONIC HEALTH
RECORDS ADOPTION IN THE U.S. HEALTHCARE SYSTEM
NADIYE O. ERDIL
Ph.D. Candidate
Department of Systems Science and Industrial Engineering
State University of New York (SUNY) at Binghamton
PO Box 6000 Binghamton, NY 13902
607-768 2809
nadiye @ binghamton.edu
C. ROBERT EMERSON
Professor
Department of Systems Science and Industrial Engineering
State University of New York (SUNY) at Binghamton
PO Box 6000 Binghamton, NY 13902
607-777 7663
remerson @binghamton.edu
ABSTRACT
The adoption of Electronic Health Records (EHRs) moves slowly despite a near consensus in the
healthcare industry that their use could be a critical factor in addressing quality and cost issues.
Barriers and benefits of EHRs, the adoption process, and potential remedies to speed up the
process are subject to numerous studies. In this study, a casual loop diagram of the EHR
adoption process is developed and discussed. Through this model, factors influencing the
process and the relationships between them are examined. The model is intended to be the
backbone of future stock-flow models which will provide a test bed to explore an understanding
of the EHR adoption process and to evaluate various policy options.
Keywords: Electronic Health Records, System Dynamics, Simulation, Casual-loop Diagram
INTRODUCTION
Rapidly rising healthcare costs, its burden on the U.S. economy, its effects on peoples’ lives,
population health, and furthermore, the concerns of inferior healthcare quality are compelling
forces behind this nation’s efforts to improve the delivery of healthcare. To guide these efforts,
better integration and effective utilization of Health Information Technology (HIT) with an
emphasis on Electronic Health Records (EHRs) is one of the propositions that is brought up
often [1, 2, 3, 4,5]. Nonetheless, EHR adoption moves slowly regardless of a near consensus that
their use could be a critical factor in addressing the issues listed above [6, 7, 8, 9, 10]. Many
proponents, including bipartisan supporters in Congress, have not yet been successful in
speeding up the adoption pace. There is a general search for explanations.
Erdil, Emerson
Healthcare systems are complex systems. The highly fragmented structure of the United
States healthcare system along with its financial flow challenges the understanding of healthcare
system-problems. With little understanding of the healthcare system’s behavior and the problems
associated with it, evaluating the response of the system to interventions becomes a daunting
task. Nevertheless, it is not unsolvable. This study takes up a System Dynamics (SD) approach to
tackle this problem. The SD methodology provides tools to study complex problems in system
behavior; and more importantly, it allows exploration of policy options, through simulation. The
main objective of this study is to uncover the dynamics of the Electronic Health Record adoption
process in the United States healthcare system, and then to evaluate the impacts of various policy
decisions that might accelerate this adoption.
BACKGROUND
Several studies have been conducted which analyze the EHR adoption process in the United
States healthcare system. Commonly listed benefits of EHRs are classified under: (1) improved
patient safety and quality of care; and (2) reduced costs. These benefits result from capturing
more accurate and more complete health information on a patient’s entire health history and
structuring data more efficiently for easier and quicker access. Often stated benefits include
avoided duplicate tests, improved coordination and management of chronic conditions and
preventive services, increased efficiency in scheduling and communication, improved billing and
claims processing, improved reporting for public health and clinical research, reduced medical
mistakes, improved workflow, and so on [2, 3, 11, 12, 13]. On the other hand, commonly cited
barriers to the adoption process, which are identified as the causes of the slow adoption pace, are
high costs, delayed return on investments, misaligned financial benefits, third party payer
system, fragmented system, first mover disadvantage, lack of standards on terminology and
technology, security and privacy issues, the political process, and so on [4, 6, 7, 10, 14, 15, 16, 17,
18].
Studies exploring EHR adoption provide insights to the issues in the adoption and the
implementation process. They reveal that the EHR adoption process in the U.S. healthcare
system is a healthcare system structure and policy issue [6, 7, 14, 19]. A common approach in these
studies is to analyze the system in a piecemeal fashion. This divide-and-conquer approach
deconstructs the problem so that factors can be studied in isolation. A systems approach would
complement the existing research. Such an approach would allow for analysis of the system as a
whole. The goal would be understanding of the underlying factors in the adoption process that
determine the behavior of the overall complex system. In this effort, this research uses a System
Dynamics model, allowing for the study of various policy decisions.
METHODS
The System Dynamics methodology is used in the analysis of complex systems. Complex
systems are defined by large number of variables, multiple interacting feedback loops, nonlinear
relationships, and a dynamic nature. Analysis of causes and effects in complex systems does not
follow simple if-then statements. For example, closing of the chains of causes and effects may
spread through time or the causes may not be found in the immediate vicinity of the effects [20].
The SD methodology is built on the supposition that it is the system structure and policies that
are usually the home to causes [20]. The methodology was introduced by Jay W. Forrester in the
early 1960s to study complex systems such as the urban dynamics problem. Today, it has a wide
Erdil, Emerson
range of applications including healthcare. The attributes of the U.S. healthcare system, matching
with the characteristics of a complex system, fortify the use of the SD methodology.
Casual loop diagrams and stock-flow diagrams are two of the SD modeling techniques. A
casual loop diagram is a pictorial representation of the major factors and feedback loops. It
captures the underlying structure of the dynamics of the system which arise from the interaction
of two types of feedback loops. A reinforcing (positive) loop is a snowball effect where a change
in a state produces a result which pushes the system to create more of the same change. A
balancing (negative) loop, on the other hand, creates forces to reverse a change. The second
modeling technique, a stock-flow diagram, is an augmentation of casual loop diagrams. Stock-
flow diagrams consist of stocks that are accumulations of resources, and flows that are rates of
changes that fill and drain these resources. These models can be simulated using SD simulation
software.
As mentioned before, the main objective of this study is to develop an understanding of the
EHR adoption process and to evaluate various policy options. This will be accomplished through
developing a stock-flow model of the system which will provide a test-bed for simulation. To
build such a model, the study started with a casual loop model which is the focus of this paper.
The model, which is discussed in the following sections, captures the cause and effect
relationships (feedback loops) among factors influencing the adoption process.
RESULTS
The Casual Loop Model
The causal loop model, discussed in this study, captures the major variables that have been
identified through a literature review. Topics that frequently turn up in the literature, and are
stressed by experts, constitute the foundation of the model shown in Figure 1. Although stocks
and flows are not commonly used in causal loop diagrams, this model contains two stocks and a
flow to emphasize the focus of the model. The stocks Adopted_Population and
Not_Yet_Adopted_Population represent the number of providers that have and do not have EHR
systems in use. The flow adopting indicates the number of providers adopting per time.
Some of the variables in the model are aggregated over many individual factors such as
cultural_barrier which represents the organizational cultural issues including change
management, resistance to new technology, commitment, etc. Cultural issues are barriers that
prevent providers from adopting EHR systems. On the other hand, industry_pressure stands for
the forces in the healthcare industry that might accelerate the adoption of EHR systems. Provider
organizations using EHRs, insurer/payer organizations, and the regulation sector tend to be the
sources of these forces. While market_maturity reflects the maturity level of the EHR products in
the market, EHR_usage_performance indicates the productivity gains from an EHR system
implementation.
The plus/minus signs on the arrows, in Figure 1, indicate how one variable changes as a
result of a change in the other. The plus sign represents change in the same direction, while the
minus sign corresponds to the opposite direction. The reinforcing and the balancing loops are
shown with letters R and B respectively.
Erdil, Emerson
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(= adoption Gussie a
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\ -4 in ga a ie ‘mature products \ \
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\ barriers issues affecting xp \ Gap maintenance Eu
\ AL attractiveness i \ & EHR Usage costs implementation
NS Performance costs
SS. ans \ omoting EHR Z
rate of adoption fraction of insua SEHR Faoption due te i +4
of population adopted SP aeaie standards inerensing @s /
ware apie 4 * /
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regulation insurer/payer RR SSS EY
@ sca provider Sy mae! market f a
iekel \ market BP
- _
k Sieg maturity
Not Yet Adopted FS Adopted a
Population adopting | Population
Sho ae?
Figure 1 EHR adoption process in the U.S, Healthcare System - the Causal Loop Model
The Feedback Loops
The overall goal of this study is to obtain a model, such as Figure 1, that encloses the factors
influencing the EHR adoption process and the use of that model to test policy changes. This is
accomplished through the feedback loops captured by the casual loop model. A starter model,
shown in Figure 1, exhibits the feedback loops identified thus far in this study. The following
section outlines each of these loops. It should be noted that although the loops are presented
separately, they are not disconnected. On the contrary, the work of their interactions is what
determines the dynamics of the system.
The causal loop diagram in Figure 1 is built around the provider population which is divided
into two as adopted and non-adopted population. The model assumes that once adopted a
provider does not abandon an EHR system. Therefore, there is only one direction to the flow
which is from the non-adopted population to the adopted population. All of the loops originate
from the adopted population.
Considering which stakeholder in the healthcare system is affected by the feedback loops
shown in Figure 1, the model can also be divided into two: the provider sector and the
insurer\payer sector.
Provider Sector:
Provider sector can be further broken into two parts: financial effects and behavioral effects.
Financial effects would capture loops such as EHR system maintenance costs; and behavioral
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effects would cover loops such as the influence of system-interoperability on the providers’
actions. The loops that capture behavioral effects on providers’ actions are R1, R2, R5, B3, R6,
BS and R8; and the loops that show financial effects are B1, B2 and R3.
Loop R1 ‘more adopters attract non-adopters’ (Figure 2)
As the adopted population grows, the increasing presence of EHR systems would attract the
non adopted population. This, in turn, increases the rate of adoption.
Loop R2 ‘increasing number of adopters break barriers’ (Figure 3)
As the number of EHR users increases, EHR systems would become the norm. In response,
the forces that prevent the spread of EHRs, cultural barriers in this case, start to diminish.
Consequently, the negative affect of cultural barriers on the attractiveness of EHR products
decreases; therefore, the rate of adoption increases which results in more providers adopting.
Adopted Adopted
Population 7 Popalation’
adopting
adopting finction of
R secon ee. population adopted
population adopted rate of adoption
rate of
adoption fog Stltual bairs
attlactiveness for
attractiveness for potential adopters
potential adopters
Figure’2 sop RI snore adoptets attraction adopters? Figure 3 Loop R2 ‘increasing number of adopters break
barriers’
Loop R5 ‘resolving open issues increases the adoption rate’ (Figure 4)
An increasing presence of EHR systems would create a force over the healthcare industry to
address EHR related issues particularly since more people would be affected. Increased pressure,
however, does not always develop solutions to issues. But, the model assumes that it does, to
reduce the complexity. Therefore, in this loop, the a
negative influence of security & privacy issues on EHR 7 Population
system’s attractiveness is lessened as the pressure to ae freton of population
address these issues increases. In response, the rate of
adoption increases and produces more system adoptions. rate of adoption R ‘adisthy pressure:
While the mechanics indicate that Loop R5 is a
reinforcing loop, it should be noted that pressure from ear ad peau andra nos
the adopter population only would not be enough to abfpeine atirachyenss
force the industry to take actions. Therefore, this loop soi bean
would not be dominant until the other players such as Figure 4 Loop RS
the regulation market and the insurer/payer market get ‘resolving open issues increases the adoption rate’
involved.
Loops B3 and R6 ‘effects of interoperability’ (Figure 5 & 6)
Interoperability is assumed to be an incentive to potential EHR system adopters. Therefore,
increasing levels of interoperability would attract more users. The system behavior reflecting
interoperability is dependent on EHR standards. In the model, there are two factors influencing
EHR standards: market_maturity and pressure_to_address_issues_affecting_attractiveness. As a
result, there are two loops, B3 and R6, involving interoperability.
Erdil, Emerson
Common to most emerging technology markets, the EHR product market started with no
uniform standards. Therefore, there are numerous products on the market that are not compatible
with each other. As the market grows with no uniform standards (since the U.S. healthcare
system does not have a uniform standard set - even now) [10, 21] vendors’ increasing proprietary
interests keep building systems which complicate the achievement of standards. This interaction
is captured in Figure 5 by the arrow from market_maturity to EHR_standards. Loop B3 indicates
that as market_maturity increases the possibility of achieving a uniform standards set decreases;
this, in turn, lengthens the time to achieve system interoperability. Since interoperability is
considered as an attractive factor, without it, EHR systems’ attractiveness would decrease, and
thus, the number adopting would decrease. Nevertheless, the healthcare industry can push for
EHR standards which would then be captured by a second loop. Loop R6 shows that the
healthcare industry’s pressure accelerates the standardization process, and thus, the reaching of
the system interoperability. This situation would then step up the adoption process creating a
reinforcing loop working against Loop B3.
Adopted Adopted
gm Population 7 Popalation Ne
ssa adopting fiaction of population
ne fraction sf population adopted
adopter
rate of adoption \dustry 88
sate of adoption te of adopt R industry pressure
B market maturity
attractiveness for pressure to address
attractiveness for potential adopters Fararenied
potential adopters EHR standards is india
interoperability —
interoperability — standards
Figure 5 Loop B3 ‘barrier to interoperability’ Figure 6 Loop R6 ‘encouraging interoperability’
Loops B5 and R8 ‘risk of purchasing products obsolete in future’ (Figures 7 & 8)
Loops BS and R8 are similar to Loops B3 and R6. In this case, the factor affected by
EHR_standards is the risk of purchasing a product that might become obsolete. Loop B5 shows
the effect of evolving market, and Loop R8 captures the influence of the industry pressure. With
the market prolonging the achievement of uniform EHR standards, the risk of purchasing a
product obsolete in future increases. But, then again, the pressure built up to address EHR issues
would accelerate the process of agreement on EHR standards generating a reinforcing loop
working against Loop B5.
Considering the general view of the system reflected by the model and compared to other
factors represented in the model, risk_of_purchasing_a_product_obsolete_in_future is a weak
factor in terms of its influence on the adoption process. However, the purpose of this research is
to develop a model that can be used to test policies and as a backbone for future larger models.
Although, this factor has a negligible affect on the current model, including this factor shows
how the base model can be enlarged.
Adopted Adopted
7” Population 6 ebegie Population
adopting Stier ot pain fraction of popaton
atop
rate of option
rate of adoption B market maturity R industry pressure
attractiveness for
sNensag potential adoptere pressure to adem neues
Soonees EHR standards affecting etactivenee
risk of purchasing a
risk of purchasing a product obsolote in EHR
product obsolote in ture standards:
future . ———, a,
Figure 7 Loop BS ‘increasing risk’ Figure 8 Loop R8 ‘decreasing risk’
Erdil, Emerson
Loop B1 “increasing implementation costs with mature products’ (Figure 9)
An increasing number of EHR users would attract investors and vendors into the EHR product
market; and the competition in the market would increase. As can be seen in new technology
markets, the progress will accelerate, and new and more mature products will evolve. In this
model, all these responses are accumulated under the variable market_maturity. With increased
competition, ideally, a decline in implementation costs should be observed. However,
considering how fast the digital technology advances, and thus new and improved structures are
needed to support the advancements, implementation costs of EHR products would increase as
more enhanced products are released. Increased implementation costs would then diminish EHR
systems’ attractiveness. Diminished attractiveness then lowers the rate of adoption which implies
less providers adopting per time.
Adopted
Population
adopting fraction of population.
adopted
rate of adoption B
market maturity
attractiveness for
‘potential adopters BHR implementation
costs
Figure 9 Loop BI ‘increasing implementation costs with mature products”
Loop B2 “increasing maintenance costs with mature products’ (Figure 10)
Similar to implementation costs, software maintenance costs increase with enhanced
products. Therefore, a balancing effect is seen in Loop B2. Increasing EHR maintenance costs
reduce the provider revenue, which in turn cause the attractiveness of EHR products to decline.
Declining attractiveness would negatively affect the rate of adoption, causing fewer providers to
acquire EHR systems.
Adopted
Population
fiaetion of population
adopting ‘ulopted
rate of adoption
B sarkel maturity
attractiveness for
potential adopters EHR maintenance
costs
wrovider
revente
Figure 10 Loop B2 ‘increasing maintenance costs with mature products”
Loop R3 “increasing provider revenues with mature products’ (Figure 11)
Similar to Loops B1 and B2, Loop R3 shows the effects of market maturity on EHR
products. The assertion is that a maturing market produces enhanced products. In this case, the
characteristic reflected is the performance of the EHR products. ‘Performance’ indicates the
improvements realized at a provider’s facility as a result of the EHR system employment. As
more sophisticated and intelligent EHR products are released, greater improvements would be
observed, particularly when compared to previous periods where the EHR products were still
considered an emerging technology. Increased return on investments resulting in increased
provider revenues attracts more potential users. Therefore, the adoption rate starts to accelerate.
Erdil, Emerson
Adopted
Population
fiaction of population
adopting ‘adopted
rate of adoption
, R svat ey
attractiveness for
‘potential adopters FER Uae
rovider
revenue
Figure 11 Loop R3 ‘increasing provider revenues with mature products’
Insurer\Payer Sector:
The loops that capture financial effects on insurer\payers’ actions are B4, R7 and R4.
Loops B4 and R7 ‘providing incentives for adoption’ (Figures 12 & 13)
In Loops B3 and R6, a provider’s response to changing levels of interoperability is discussed. In
this section, the response of insurers/payers is modeled. Loop B4 captures the industry’s efforts
that lead to increasing interoperability. Loop R7, on the other hand, brings in the market’s
resistance that slows down this process. Similar to Loops B3 and R6, there are two loops
working against each other. However, for the insurer/payer, the path from the market creates a
reinforcing loop, while the path from the industry generates a balancing one. In the case of
providers’ response (Loops B3 and R6), it is the opposite. This is because the high level of
interoperability is in the providers’ best interest. The interoperability works the opposite way for
the insurer/payer because it provides the infrastructure to share (and exchange) not only the
clinical information, but also the financial information. While the insurer/payer would benefit
from clinical data sharing, the outcome of financial data sharing would outweigh this benefit [14].
Loop B4, then, captures the influence of increasing interoperability on the insurer/payer
behavior, while Loop R7, working against Loop B4, shows the effects of decreasing levels.
Adopted Adopted.
yf Population x Pa Population Yq
fincton of population faction of population
sions adopiet adopting doped
sate of adoption B tse pa ate of adoption z smarket maturity
attractiveness for ? )
pressure to addvess attractiveness for
potential adopters issues affecting potential adopters eR Mandenle
atiactiveness
providing incentives providing incentives
for adoption EHR standacds for adoption interoperability
ia insureripayer
intwerpayer interoperability revenue
Figure 13 Loop R7 ‘insurer\payer’s increasing interest in
Figure 12 Loop B4 ‘insurer\payers’ decreasing interest in providing incentives?
providing incentives’
Loop R4 ‘promoting EHR adoption due to increasing revenues’ (Figure 14)
Similar to the case in Loop R3, increasing EHR usage performance also increases the
insurer\payer revenue. As more providers use EHR systems, the greater benefits are realized by
the insurer\payer. To encourage EHR usage then, the insurer\payer starts developing programs
such as higher reimbursement rates for EHR users. These incentives attract non-adopters, and
thus the rate of adoptions increases generating more adopters per time.
Erdil, Emerson
Adopted
Population
adopting faction of poplation
rate of adoption
R market maturity
altractiveness for
potential adopters EHR Usage
Performance
providing incentives
for adoption insurer\payer
tevene
Figure 14 Loop R4 ‘promoting EHR adoption due to increasing revenues’
DISCUSSION
In this paper, an SD model was presented to study the EHR adoption process in the United
States healthcare system which is a complex process. Factors included in the model were drawn
from a literature review through which issues were brought up and captured. With the issues and
the underlying factors identified, a casual loop diagram was developed to grasp the dynamics of
the system.
This causal loop method reflects the anticipated behavior of the overall system, as given in
the literature. Taking a Systems Dynamics view brings a new approach to the study of the
adoption process. Feedback loops discussed in the previous section, reveal how the factors
influencing the process interact and how these interactions affect the behavior of the system.
Since implementation and maintenance costs, security and privacy issues, and misaligned
financial benefits are the most commonly listed issues in the adoption process, loops B1, B2, R5,
B4, R7, R3 and partially R4 are expected to be the significant feedback loops of this model. R4
is considered partially significant because the model, in its early stages, represents only a part of
the picture involving financial benefits.
The overview of the model does not show a particular dominant loop that could force the
system to go in a particular direction. This, in fact, could be the explanation for the current
adoption patterns. More work remains to be done to draw a firm conclusion. A stock-flow
diagram spawned from this causal loop model is needed for testing such assertions. In order to
have a sound stock-flow model, working with experts is needed to finalize the factors included in
the model in addition to the literature review. The next step, then, is to gather quantitative data
for the simulation.
This study has several limitations that are identified as future work for the extension of this
starter model. The model reflects only the provider and the insurer/payer organizations interests
in the adoption process. Other stakeholders include patients, the regulation sector, high-tech
industry, general public, etc. These stakeholders also have interests and influences on the
process; and should be included in future work in order to improve the representation of the
system.
Several assumptions of the model that could be altered include (1) there is no withdrawal
once a provider organization adopts an EHR system, (2) increasing pressure leads to the
generation of solutions, and (3) increasing levels of interoperability hurts insurer/payer revenues.
The latter assumption is based on a view articulated in the literature [14, 15], but there is no
quantitative data that supports this assertion.
The objective of this research is to bring a System Dynamics approach to the analysis of the
EHR adoption process as a test vehicle for policy and regulatory decisions. The proposed
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approach is valuable in several ways. First, the model can be free of bias in terms of portraying
the system; therefore, it provides a common study ground for interested parties. Second, an
overall view of the system is presented by the model; as a result, factors and their interactions
can be examined without losing the systems perspective on the issue. Third, the casual loop
modeling is capable of capturing feedback loops in the system; thus, it provides a helpful
structure for understanding the system behavior. Finally, a simulation test-bed that evaluates
policies and strategies can be obtained by expanding the causal loop model, which is the ultimate
goal of this research; consequently, developing a tool for policy makers. This paper presents the
initial stage of this research where SD modeling is applied to obtain a preliminary casual-loop
model.
CONCLUSION
This study brings a systems perspective to the analysis of EHR adoption process in the
United States healthcare industry by utilizing the System Dynamics methodology. The casual
loop model offers insights to understanding the major factors influencing the adoption process
and their interactions, as well as the feedback loops that operate in the system. The study, in its
early stages, is limited in terms of factors included. Nonetheless, it provides a foundation for
development of larger causal loop models and stock-flow models.
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About the Authors
Nadiye O. Erdil, (B.S., Computer Science — M.S., Industrial Engineering) is a Ph.D. candidate in
System Science at Binghamton University. Her research interests include health systems and
health information systems.
Cc.
Robert Emerson is professor of Industrial and Systems Engineering at Binghamton
University. His research interests include finding solutions to policy questions within complex
systems. The applications range from manufacturing to healthcare.
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