The Role of Info Sys in Healthcare

Information Systems Research
Vol. 22, No. 3, September 2011, pp. 419–428
issn1047-7047 eissn1526-5536 11 2203 0419 http://dx.doi.org/10.1287/isre.1110.0382
©2011 INFORMS
Editorial Overview
The Role of Information Systems in Healthcare:
Current Research and Future Trends
Guest Senior Editors
Robert G. Fichman
Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467,
fi[email protected]
Rajiv Kohli
Mason School of Business, College of William & Mary, Williamsburg, Virginia 23187,
[email protected]
Ranjani Krishnan
Broad College of Business, Michigan State University, East Lansing, Michigan 48824,
[email protected]
I
nformation systems have great potential to reduce healthcare costs and improve outcomes. The purpose of
this special issue is to offer a forum for theory-driven research that explores the role of IS in the delivery of
healthcare in its diverse organizational and regulatory settings. We identify six theoretically distinctive elements
of the healthcare context and discuss how these elements increase the motivation for, and the salience of, the
research results reported in the nine papers comprising this special issue. We also provide recommendations
for future IS research focusing on the implications of technology-driven advances in three areas: social media,
evidence-based medicine, and personalized medicine.
Key words: healthcare information systems; special issue
Introduction
The importance of healthcare to individuals and gov-
ernments and its growing costs to the economy have
contributed to the emergence of healthcare as an
important area of research for scholars in business
and other disciplines. Information systems (IS) have
much to offer in managing healthcare costs and in
improving the quality of care (Kolodner et al. 2008).
In addition to the embedded role of information tech-
nology (IT) in clinical and diagnostics equipment, IS
are uniquely positioned to capture, store, process, and
communicate timely information to decision mak-
ers for better coordination of healthcare at both the
individual and population levels. For example, data
mining and decision support capabilities can iden-
tify potential adverse events for an individual patient
while also contributing to the population’s health by
providing insights into the causes of disease compli-
cations. Despite its importance, the healthcare domain
has been underrepresented in leading IS journals.
However, interest is increasing, as demonstrated by
the proliferation of healthcare tracks in IS conferences,
special interest groups, and announcements of special
issues among leading journals.
Research anchored in the healthcare context must
begin by reflecting on what is distinctive about health-
care and on how such distinctions could or should
inform our theorizing. Distinctiveness of the context
drives us toward new theory or theoretical extensions
that hold greater promise to explain IS phenomenon
(e.g., adoption and impacts). At the most general
level, a striking feature of the healthcare industry is
the level of diversity that characterizes patients (e.g.,
physical traits, and medical history), professional dis-
ciplines (e.g., doctors, nurses, administrators, and
insurers), treatment options, healthcare delivery pro-
cesses, and interests of various stakeholder groups
(patients, providers, payers, and regulators).
Because of this diversity, research in healthcare is
eclectic and spans many disciplines, including eco-
nomics, public health, business, epidemiology, sociol-
ogy, and strategy. This is reflected in the diversity of
papers comprising this special issue, not only in terms
of the theoretical frameworks but also in the unit of
analysis employed. In the remainder of this section
we identify six theoretically distinctive elements of
the healthcare context that tie together the research
results reported in the nine papers comprising this
special issue.
419
Fichman, Kohli, and Krishnan: Editorial Overview
420 Information Systems Research 22(3), pp. 419–428, ©2011 INFORMS
The Stakes Are Life and Death
Healthcare influences the quality of our lives and
how we function within the society. Healthcare mis-
takes have serious consequences that can affect our
ability to carry out social and productive endeavors.
Recent reports highlight the gravity of adverse events
in hospitals and the dangers such events pose to indi-
viduals and the public (Piontek et al. 2010). More
generally, medical errors (a leading cause of adverse
events and other ills) are expensive, increase patient
hospital length of stay, and cost human lives (Classen
et al. 1997). At the population level, the failure to con-
trol infectious diseases can cause serious public health
issues. Therefore, healthcare quality is diligently pur-
sued and vigilantly executed, and IS can facilitate
such pursuit by highlighting and monitoring errors at
various stages along the continuum of care.
In this issue, Aron et al. examine the associa-
tion between IS and medical errors in three primary
healthcare processes—sensing, controlling, and moni-
toring. They focus on two types of errors—procedural
and interpretive. Using an agency framework, the
authors explore the relationship between hospital
management and clinicians and the complementari-
ties between training and automation systems. After
all, humans are the primary response agents when
the technology detects a potential error. The tension
between innate and often subjective human experi-
ence and dispassionate automation poses challenges,
especially in the presence of conflicting situational
signals that demand an urgent response.
The findings emerging from Aron et al. are consis-
tent with conventional beliefs that automation com-
plements professional training and that in particular,
improved training enables professionals to exploit
automation. However, their findings in the domain of
error detection are counter to contemporary thinking
that the role of IS in enforcing quality is most effective
when promoting compliance with procedures and
other routine work. They find that training, combined
with automation, overcomes interpretive errors in deci-
sion making but not procedural errors. This points to
the importance of IT complementarities and provides
instances where technology may, in fact, increase the
incidence of errors (Fernandopulle and Patel 2010).
Finally, Aron et al. find that automation indeed influ-
ences agents’ behavior by serving as a record for their
actions, thus encouraging agents to act in the interests
of the principal. Previous IS literature has proposed a
panoptic role for IS in enforcing agency relationships
(Sia et al. 2002).
When potential healthcare risks extend to the larger
population, the demand for resources increases, as do
the consequences of improper resource deployment.
Many lives are at risk during outbreaks of infectious
diseases, such as severe acute respiratory syndrome
(SARS). In such cases, IS plays an essential role at both
patient and population levels. Mobilizing and coordi-
nating hospital and public health resources becomes
a race against time, because controlling the spread of
the disease is just as important as treating it. Also
in this issue, Chen et al. examine the SARS outbreak
in Taiwan and develop seven guidelines for coor-
dination of public health IS. Drawing on the loose
coupling framework, they find that increased involve-
ment of public health agencies is not always help-
ful, especially when there is clinical uncertainty about
effective treatment mechanisms. They conclude that
coordination should be such that public health pol-
icy makers and healthcare providers can engage and
disengage as warranted during an outbreak. These
findings led Chen et al. to identify situations where
a decoupling between public health authorities and
the healthcare providers can enable parties to conduct
a more independent examination. Subsequently, cou-
pling can be reactivated for public policy formulation
and communication.
These findings have significant implications for
the design and development of IS to support public
health policy. Combined with the findings of Kane
and Labianca (in this issue) regarding network cen-
trality and influence, the loose coupling approach can
facilitate the development of new theory regarding
how influential actors in a loosely coupled network
can supplement or enhance a formally coupled net-
work, for example, in a disease outbreak. This poses
another opportunity for theory development to find
the optimal balance of the actor and the technology
and their ability to decouple on an ”as needed” basis.
Healthcare Information Is Highly Personal
Another hallmark of healthcare information is that
it is highly personal. As a result, any transfer of
information between parties via technology involves
risks—both actual and perceived—that the informa-
tion could fall into the wrong hands. Although elec-
tronic information can be made as secure as paper
records, electronic storage may be perceived as having
a higher likelihood of leakage, and such fears get fur-
ther compounded by media attention. Thus, patients’
perceived probability of compromised privacy is often
higher than the actual probability.
Variations in individuals’ willingness to disclose
personal health information (PHI) is the focus of
Anderson and Agarwal (in this issue). Consistent
with prior research, the authors posit that individu-
als’ privacy concerns and trust in the electronic stor-
age of PHI will affect willingness to disclose. Going a
step further, the authors explore how these effects are
moderated by three sets of contextual variables: the
type of information requested (general health, mental
health, or genetic), the purpose for which the informa-
tion is requested (patient care, research, or marketing),
Fichman, Kohli, and Krishnan: Editorial Overview
Information Systems Research 22(3), pp. 419–428, ©2011 INFORMS 421
and the type of stakeholder requesting the informa-
tion (doctors/hospitals, the government, or pharma-
ceutical companies). In addition, the authors explore
the link between an individual’s emotions regarding
his/her current medical state and willingness to pro-
vide access to PHI. As a framework for their analy-
ses, the authors use privacy boundary theory and the
risk-as-feelings perspective. Analyses using a nation-
ally representative sample of 1,089 individuals indi-
cate that the type of requesting stakeholder and the
purpose for which the information is being requested
are important moderators of the relationship between
concern and trust and willingness to provide access
to PHI.
An in-depth understanding of individuals’ willing-
ness to disclose personal information is critical not
only because it has implications for effectiveness of
treatment protocols but also because of its impact on
public policy in dealing with epidemic outbreaks such
as SARS (Chen et al.). Anderson and Agarwal add
important insights to the literature on individuals’
disclosure decisions and offer guidance for healthcare
policy. For example, they find that the negative rela-
tionship between privacy concerns and willingness to
disclose is particularly acute when the information
request comes from government/public health agen-
cies (versus hospitals or pharmaceutical companies).
Another interesting result is that individuals trust
nonprofit hospitals with electronic health systems to
a much higher extent than they trust government and
for-profit organizations, which might give advocates
of government-sponsored single-payer systems some
pause. Their results also suggest that individuals who
feel sad, angry, or anxious about their current health
status are more willing to provide access to their PHI
and that such individuals may more easily fall victim
to misuse of health information.
Digitization of health information has several bene-
fits. However, the research of Anderson and Agarwal
underscores the need to understand the situational
factors that drive individuals’ comfort with sharing
healthcare information in an electronic format. One
implication of this research for policy makers is to
explore more stringent regulation of medical informa-
tion, for example, to require that stakeholders clearly
identify who they are, for what purpose they will use
the data, and even to set limits on the amount of time
that the stakeholder will have access.
Healthcare Is Highly Influenced by Regulation and
Competition
While the paper by Anderson and Agarwal examines
the factors driving the propensity of patients to share
personal health information, Bandyopadhyay et al.
(in this issue) analyze the propensity of healthcare
providers to share patients’ records. Sharing of elec-
tronic health records (EHR) by providers can increase
administrative efficiency, reduce healthcare costs by
eliminating unnecessary duplication of medical tests,
and most importantly, reduce medical errors. How-
ever, such sharing is much lower in the United States
relative to many other countries.
Recently, for-profit companies, notably, Google and
Microsoft, have made forays into the market for per-
sonal health records (PHR). The PHR draws health
information from multiple sources, including the
physician or hospital’s EHR, and provides the indi-
vidual with the flexibility to manage his or her
own PHR. While such platforms are mainly intended
to serve patients, they may also hold the poten-
tial to improve the incentives for providers to share
EHR data. In this context, Bandyopadhyay et al.
use an analytical game-theoretic model to investi-
gate three research questions: Do providers resist
EHR sharing, even when it increases social sur-
plus? Which providers stand to gain most from EHR
sharing? What role can a Web-based PHR platform
play in solving incentive problems and encourag-
ing providers to share EHRs? The authors analyti-
cally demonstrate that a downside of EHR sharing is
that customers will find it easier to switch providers,
resulting in loss of provider revenue. To ensure partic-
ipation the PHR platform provider will have to selec-
tively subsidize healthcare providers. The likelihood
of subsidization increases in the heterogeneity of the
value provided by healthcare providers to consumers.
The findings of Bandyopadhyay et al. contribute to
the literature on information sharing and switching
costs. Their results also provide insights into why the
United States lags behind Europe in sharing PHRs.
Most European countries have a single (public) payer
that has the ability to subsidize, as well as to exert
pressure, if required, for providers to share. Moreover,
the risk of sensitive health information leaking out
and being misused is reduced when there is less need
to transmit data across providers and platforms.
However, whether a public platform for EHR shar-
ing (like the European countries) or a for-profit option
(like the focus of Bandyopadhyay et al.) is feasible in
the U.S. environment is complicated by issues related
to privacy and trust. Given the findings of Ander-
son and Agarwal (that patients are less likely to trust
either the government or for-profit organizations),
progress toward a public system in the United States
may face additional challenges.
Healthcare Is Professionally Driven and
Hierarchical
One of the barriers to healthcare technology adop-
tion is that powerful actors in care delivery often
resist technology. Part of this arises from professional
norms: physicians are primarily concerned with treat-
ing the patient to the best of their ability and regard
Fichman, Kohli, and Krishnan: Editorial Overview
422 Information Systems Research 22(3), pp. 419–428, ©2011 INFORMS
other activities as administrative irritants. Given the
hierarchical nature of healthcare, technology aversion
by an influential physician or nurse is likely to affect
other caregivers.
Two papers in this issue—Kane and Labianca and
Venkatesh et al.—use network theory to examine the
factors driving physician resistance to IS and the
effects of such resistance on outcomes. Venkatesh
et al. develop a model that encompasses physicians,
paraprofessionals (such as nurses), and administra-
tive personnel to explore the drivers of system use
and the system’s effect on patient satisfaction. Kane
and Labianca explore the association between pref-
erence for IS avoidance and three outcomes: effi-
ciency of care, patient satisfaction, and quality of care.
Although the fundamental questions are similar, the
two papers differ in the methods used and outcomes
studied, and have produced different (albeit comple-
mentary) contributions.
Social network theory suggests that an individ-
ual’s network position influences behavior and per-
formance. Venkatesh et al. argue that variations in
healthcare technology use arise from network ties
within and across professional domains. Specifically,
they posit that more connected doctors are less likely
to use technology, owing to their greater accultura-
tion and commitment to traditional medical practices.
They find that while the E-healthcare system in their
study has a positive effect on quality of care over-
all, in-group ties among doctors and out-group ties
to doctors has a negative effect on system use for
all groups, indicating that doctors likely hamper the
spread of technology. Physicians’ rejection of technol-
ogy is a serious problem that can lead to poor quality
of care, medical errors, and low patient satisfaction.
When we add mistrusting patients (Anderson and
Agarwal) and nonsharing providers (Bandyopadhyay
et al., in this issue) to the problem of doctors who
not only make inadequate use of technology but also
adversely influence others’ usage of technology, the
situation is compounded and likely results in errors
(Aron et al.) and potentially serious public health con-
sequences (Chen et al.).
Although Venkatesh et al. is a longitudinal study,
its focus is primarily on the initial implementation
of healthcare technology. Kane and Labianca build
on this topic by examining postadoption resistance.
They use the term IS avoidance to denote passive post-
adoption resistance where individuals avoid work-
ing with an information system despite the need and
opportunity to do so. Using archival data they exam-
ine the efficiency and quality effects of IS avoidance at
three levels: the individual user level (physician), the
shared group level (healthcare team, including para-
professionals and administrators), and the configural
group level (which accounts for the positions of indi-
viduals in the team). They supplement their findings
with qualitative data.
Quantitative analysis reported in the paper reveals
that IS avoidance is negatively associated with patient
outcomes only at the configural group level; at the
individual and shared group level there is no associa-
tion with outcomes. The qualitative analyses provide
insights into this pattern of results. At the individ-
ual level, users who avoided the system were able
to compensate by using brokering relationships, i.e.,
assigning a representative to interact with the sys-
tem on their behalf. At the shared group level, clus-
ters of usage were observed, whereby individuals
who used or avoided the system tended to work
with other users with similar usage patterns. Thus,
these clusters could use a different mechanism (such
as Post-it notes or paper flags) and ensure that the
entire shared group had the same level of informa-
tion. These results also provide insights into why IS
avoidance at the configural group level was associ-
ated with negative patient care outcomes. That is, the
network structures that evolved to compensate for IS
avoidance were less effective in compensating for the
adverse effects of avoidance when the avoiding indi-
viduals had a central position in the social network.
Health Care Is Multidisciplinary
The findings reported in the previous sections indi-
cate that there are multiple barriers to the adoption
and use of IT in healthcare organizations, despite
robust findings that IT can improve patient outcomes.
Oborn et al. (in this issue) make a further contribu-
tion in this regard by studying whether, despite a
diversity of use of IT across different groups (which
could include avoidance), an overall unity in use can
emerge because of the multidisciplinary nature of
healthcare.
Most healthcare is provided in interdisciplinary
teams. For example, surgery requires a team con-
sisting of the surgeon, the physician, anesthesiolo-
gists, diagnostic staff such as radiologists and pathol-
ogists, and nurses. These specialists may either be
housed within the same organization, or they may be
collaborating from different organizations. Regardless
of the organizational form, speedy access to reliable
health information is essential to ensure good patient
outcomes.
Oborn et al. conduct a field study of electronic
patient record use across multidisciplinary teams
using a practice theory lens (Orlikowski 2000) to
examine how healthcare IS applications become
objects that are embedded within embodied practices
and how individuals coordinate and align their uses
of technology with others across diverse practices. By
such a process of coordination and alignment, connec-
tions are established between specialist groups. Such
Fichman, Kohli, and Krishnan: Editorial Overview
Information Systems Research 22(3), pp. 419–428, ©2011 INFORMS 423
connections are effective even if they are partial in
the sense that the elements of one specialist practice
do not get subsumed into that of another but are
translated by the other practice in a different man-
ner. Oborn et al. refer to this process of coordination
and alignment of healthcare information technologies
in use as unity in diversity. They examine this process
using a year-long interpretive field study of a regional
breast care center in England that had recently intro-
duced a Web-based clinical information system that
interfaced with the hospital’s administrative system
and used tablet computers to record information.
The breast cancer treatment was coordinated in a
team consisting of oncologist, radiologist, pathologist,
surgeon, and specialist nurses in surgery and oncol-
ogy. Results reveal a dynamic interplay between unity
and diversity. Regarding diversity, different special-
ties differed in the type and the extent of use of the
new technology. For example, surgeons, who have
to perform physical exams, found it cumbersome
to carry around tablet computers and instead relied
on nurses to provide more extensive documentation.
This finding is similar to that of the study of Kane
and Labianca, which found evidence of brokering
relationships among different professionals. By con-
trast, radiologists were especially comfortable with
the use of technology in their assessments (because of
the high-tech nature of their profession) and actually
used the technology to obtain more influence in the
group. Oncologists found the technology bothersome
in emotionally charged patient encounters. However,
they used the technology in the patients’ absence to
support oncology research via easier access to infor-
mation from other disciplines. Pathologists exhibited
idiosyncratic use, such as including narratives rather
than just tick boxes, perhaps owing to their academic
orientation. Despite this diversity in use, all parties
to care managed to coordinate their use of the tech-
nology to facilitate the multidisciplinary teamwork
essential for the success of the care.
Several implications for implementation and adop-
tion of healthcare IS arise from Oborn et al. For
example, usage patterns are complex and entangled;
therefore, it is highly simplistic to classify usage
as use and nonuse (or rejection). Most individuals
involved in patient care have a variety of relationships
with others involved in the care of the same set of
patients, and these relationships vary across practices
and individuals. For example, nurses perceived tech-
nology as depersonalizing the patient-nurse relation-
ship and, hence, continued to use their paper records,
which provided added flexibility to record impor-
tant personal information. Nurses’ nonuse was not
driven either by their rejection of the technology or
by their lack of familiarity with it; nurses understood
the technology, supported surgeons in their patient
encounters, and assisted with data entry during the
multidisciplinary meetings. Taken together, the stud-
ies of Oborn et al., Venkatesh et al., and Kane and
Labianca provide a nuanced understanding of the fac-
tors driving variations in the type and extent of use of
healthcare information technology by different types
of professionals involved in the treatment process.
Healthcare IS Implementation Is Complex with
Important Implications for Learning and
Adaptation
The healthcare delivery setting is characterized by a
tension between the need for orderly routines and the
need for sensitivity to variation in local conditions.
The need for routines arises from the importance of
high reliability in what are often life-and-death situ-
ations involving very personal matters. The need for
sensitivity to variation arises from the diversity of (a)
healthcare providers (with differing professional roles,
training, and experience), (b) patients (with differing
personal characteristics, conditions, and medications)
and (c) medical procedures and treatments—all of
which converge during healthcare delivery.
This tension between the routine and the variable
magnifies both the complexity and importance of
effective learning and adaptation surrounding health-
care IS implementation and use. Systems and imple-
mentation techniques that work well in one setting
may fail in another. In many ways, learning and adap-
tation are two sides of the same coin when it comes
to new IS. Learning is required to determine the best
way to adapt both technology and organization to
achieve a good fit between the capabilities the tech-
nology affords and the desired patterns of actual use.
Once the needed adaptations have been identified, a
different kind of learning is required to incorporate
these adaptations into organizational routines and to
ensure continuous improvement going forward.
Two papers from this special issue explicitly
address different facets of learning and adaptation
surrounding IS implementation and use. Goh et al.
focus on the implementation of a new clinical doc-
umentation system to develop a model of how
to achieve effective routinization of new IT, while
Mukhopadhyay et al. look beyond initial implemen-
tation to identify—for users of an IT-enabled physi-
cian referral system that has already been thoroughly
routinized—the factors affecting the rate of produc-
tivity improvement from learning-by-doing.
Goh et al. examine the mechanisms underlying
successful healthcare IS deployments from the per-
spective, of organizational routines—relatively stable
“action repertoires” executed by actors to accomplish
organizational work (Feldman and Pentland 2003).
The routines perspective, together with observations
from a longitudinal field study of a clinical docu-
mentation system (CDS) implementation, allows the
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424 Information Systems Research 22(3), pp. 419–428, ©2011 INFORMS
authors to unpack the “black box” of adaptation and
learning surrounding new IT. The CDS provides criti-
cal support to daily operations by serving as a shared
information repository and facilitating communica-
tion among various care providers (nurses, physi-
cians, fellows, medical residents, and others). The
quality and efficiency of healthcare delivery is heav-
ily dependent on the efficacy of the daily routines for
creating, accessing, modifying, and using these docu-
ments, and so the shift from paper-based to system-
based charts is a high-stakes endeavor.
Goh et al. use key concepts from narrative networks
(Pentland and Feldman 2007) and adaptive structura-
tion (DeSanctis and Poole 1994; Markus 2010) to con-
ceptualize healthcare IS as an intervention that alters
the flow of events in a narrative network. Specifically,
they propose a dynamic, process model of adaptive
routinization of healthcare IS that delineates the major
channels through which IS and routines interact, iden-
tifies the different stages in the dynamic coevolution
process, and isolates the pivotal role of two forms of
agency (leadership and personal innovativeness) in
enabling the virtuous cycle of coevolution. They find
that the key to successful implementation is to man-
age the coevolution process between routines and IS
and to actively orchestrate a virtuous cycle through
agent action.
Mukhopadhyay et al. look beyond implementa-
tion to address the ongoing processes of learning-
by-doing that occur after IS has become thoroughly
routinized. They use the distinctive contextual fea-
tures of IT-enabled physician referral systems (IT-PRS)
as an occasion to extend learning curve models of
organizational improvement. A physician referral is
the transfer of care from one physician to another, an
act that has major financial implications and requires
extensive coordination to ensure quality and continu-
ity of care. The success of this coordination process
depends on how effectively human agents use the
multitude of capabilities provided by the IT-PRS. The
IT-PRS provides the “glue” that ensures an accurate
and timely transfer of patients.
Learning curve scholars seek to model precisely
how the rate of learning in performing a routinized
task relates to cumulative experience with the task
and to individual and contextual factors. Prior work
has focused mainly on single types of workers per-
forming single types of tasks. However, the diversity
of the healthcare context means that, as in the case of
IT-PRS, the same basic task can be performed by dif-
ferent kinds of actors with different initial skill sets,
which provides an interesting context to examine how
human agents learn. The authors develop learning
curve modeling extensions that account for multiple
agent skill types, multiple referral task types, and the
possibility of learning spillovers across task types.
Findings reveal that skill and task type do influence
learning rates. Specifically, medical domain experts
are initially more productive but learn more slowly
on the medically intense referrals (i.e., emergencies)
compared with nondomain experts. In contrast, sys-
tems experts are initially more productive but learn
more slowly on the most procedurally intense refer-
rals (out-of-network nonemergencies). Across all skill
types, the rates of learning increase with overall task
complexity, and significant learning spillovers occur.
The studies by Goh et al. and Mukhopadhyay
et al. both provide excellent examples of researchers
who have used the distinctive characteristics of the
healthcare delivery setting to anchor their theoreti-
cal framing and contribution. Although they utilized
frameworks developed outside of systems—narrative
networks in the former case and learning curve mod-
els in the latter case—they have utilized the contex-
tual nuances to extend and enrich these frameworks
in ways that are bound to be recognized and appreci-
ated by organizational researchers beyond IS.
Future Directions for Healthcare
IS Research
The nine the papers in this issue are representa-
tive of the primary streams of healthcare IS work
to date (Agarwal et al. 2010), and we believe much
can be done to move forward in these areas. How-
ever, we also see some notable gaps. In particular, we
see three areas where major technological advances
are opening new vistas of IT-driven healthcare prac-
tice that have not yet received much attention
from IS researchers: (1) social media in healthcare,
(2) evidence-based medicine, and (3) personalized
medicine.
Social Media in Healthcare
The intersection of healthcare and social media
represents a promising space for future IS research.
Social media communities have been particularly
active in the healthcare domain (Kane et al. 2009),
and this is no accident: the primary driver of value
in these communities—commons-based peer produc-
tion (Benkler 2002)—appears especially well suited to
healthcare.
Peer production is a mode of production in which
individuals (usually unpaid) collaborate on a large
scale to produce work products without hierarchi-
cal control (firms) or market exchanges (prices, con-
tracts) to guide them. Increasingly there is a trend
for individuals, often amateurs, to self-select and self-
organize to edit medical articles on Wikipedia or to
share detailed information about their own medical
conditions and treatments in online communities like
Braintalk.com or Inspire.com. Although some special-
ists have found impressive generation and sharing of
Fichman, Kohli, and Krishnan: Editorial Overview
Information Systems Research 22(3), pp. 419–428, ©2011 INFORMS 425
medical knowledge in these communities (e.g., Hoch
and Ferguson 2005), such a process challenges the sta-
tus quo in the medical discipline, which is character-
ized by rigid hierarchies and strong norms about who
should be providing medical information.
Benkler (2002, 2006) notes several conditions that
increase value of peer production relative to markets
and hierarchies as applied to the production of knowl-
edge products, and these conditions certainly hold in
the case of healthcare. First, in healthcare there seem
to be especially strong appropriation mechanisms (such
as a desire to make a social contribution or to increase
one’s social standing) to substitute for monetary com-
pensation in motivating participation. As shown by
Anderson and Agarwal (in this issue), individuals are
more willing to share personal healthcare informa-
tion when they think it could help others. Second, a
large pool of potential contributors is available that is
diverse in knowledge and motivation. Finally, there
exists a wide range of granularity of potential con-
tributions (to ensure that people with varying levels
of knowledge and motivation will each have suitably
sized contribution opportunities) and mechanisms to
effectively aggregate these diverse contributions. This
is where emerging social media platforms come into
play, as these platforms provide for effective aggre-
gation through the mechanisms of information fil-
tering and knowledge synthesis (Kane et al. 2009)
We see a number of worthwhile questions for future
research at the intersection of healthcare practice and
social media:
• What conditions lead to the formation and vital-
ity of health-oriented social-media communities?
• What are the most effective design rules for the
platforms supporting these communities? Greater or
fewer restrictions on who can participate? Anony-
mous or nonanonymous?
• What posture should large providers be taking
with regard to these platforms? Should they actively
encourage participation by their professional staff and
patients? How?
• Should data from social media communities be
used in medical research (as was recently done in a
study of off-label lithium use among ALS suffers on
PatientsLikeMe.com (Wicks et al. 2011))?
Evidence-Based Medicine
Evidence-based medicine (EBM)—“the conscientious,
explicit, and judicious use of current best evidence
in making decisions about the care of individual
patients” (Sackett et al. 1996)—is an idea that goes
back decades but has been gaining increased atten-
tion among researchers and in the popular press
(Carey 2006) as a tool to address concerns about
healthcare costs and quality. EBM stands in contrast
to anchoring decisions on personal habits, tangible
and intangible incentives unrelated to care, or medical
traditions that have little or no empirical validation.
The marked variation across geographic locations in
how clinical interventions get prescribed for the same
conditions shows that factors other than evidence
influence medical decision making (Timmermans and
Mauck 2005).
The barriers to widespread adoption of EBM are
substantial; however, IS can play an important coun-
teracting role. We highlight three barriers and poten-
tial IS contributions. The first is the dearth of
knowledge about the actual efficacy of many com-
mon treatments. The rise in digital storage of personal
medical information gives researchers opportunities
to discover knowledge about the link between treat-
ments and outcomes on a scale that was not possible
previously. Another emerging avenue for knowledge
discovery arises from using digital technology to
enable new kinds of mathematical healthcare model-
ing and simulations (Lumpkin 2007). This implies that
implementation and use of healthcare analytics tools
and how they should be integrated with electronic
health records warrants future research attention.
A second barrier is the difficulty of getting newly
discovered knowledge into the hands of practitioner’s
in a way that actually influences practice. It often
takes a decade for medical research results to be
translated into clinical guidelines and an additional
decade for the guidelines to be widely diffused (Green
et al. 2009, L’Enfant 2003). A move to Internet-
enabled directed searches when confronted with spe-
cific cases (rather than generalized journal reading)
can enable practitioners to more efficiently utilize
their scarce reading time (Sackett et al. 1996). IS
researchers could investigate the antecedents and
consequences of directed searches, including which
Internet resources (e.g., search engines, medical infor-
mation portals, and social media communities) are
most effective and which contextual factors (e.g., cul-
ture norms, incentives, routines) might enhance their
effectiveness.
A third barrier arises from practitioner resistance to
adoption of EBM, which is often connected to aver-
sion to the standardized clinical guidelines that form
the basis of much EBM. Such resistance arises from
physicians’ desire for autonomy, incentive conflicts,
and fear of litigation. Potential solutions include treat-
ing guidelines as a rallying point for a comprehensive
program of change and involving end users (i.e., care-
givers) in the design of guidelines (Timmermans and
Mauck 2005). It is interesting to note that these recom-
mendations parallel longstanding IS implementation
wisdom. IS can also be used to promote education of
other stakeholders (e.g., patients, payers) on the effi-
cacy of diagnostic and treatment options so that they
can hold caregivers more accountable.
Fichman, Kohli, and Krishnan: Editorial Overview
426 Information Systems Research 22(3), pp. 419–428, ©2011 INFORMS
Personalized Medicine
Personalized medicine means using knowledge about
an individual’s unique physiological makeup and
medical history to tailor medical care most appropri-
ately to that individual. It promises to allow earlier
and more precise diagnoses, cheaper and more effec-
tive treatments, and minimization of treatment side
effects (Glaser et al. 2008).
Some of the best-known success stories come from
genetics-driven personalization, such as Herceptin,
a monoclonal antibody treatment that can be quite
effective—but just for women with a particularly
aggressive form of breast cancer. Another example is
warfarin, a coagulant that can now be more precisely
dosed based on certain gene variations that affect
individuals’ drug metabolization, thereby avoiding
thousands of cases per year of serious bleeding and
strokes (Aspinall and Hamermesh 2007).
When integrated with electronic medical records
(EMR) systems, IS tools can help practitioners use this
rich profile data to identify the best candidates for
particular interventions in much the same way that
marketers use consumer profile data to identify the
best prospects for a particular product. For example,
Duke University used EMR data to identify patients
who had risk factors predisposing them to complica-
tions from the H1N1 virus, thus allowing caregivers
to provide focused outreach efforts (McGee 2009).
The Cancer Biomedical Informatics Grid, launched in
2004, provides researchers with a shared repository of
medical data, together with analytics tools that can
help to identify the best candidates for clinical tri-
als. Success in sequencing the human genome in a
cost-effective manner may usher in a future where a
person’s genome is a standard part of his or her elec-
tronic health record (Singer 2010).
We see four broad implications for future IS
research related to personalized medicine. The first
relates to infrastructure design. What architectures
will be needed to provide the processing cycles and
storage required to analyze detailed medical profile
data (including possibly entire genomes) on a large
scale? The second relates to analytics. What context-
specific factors will affect the adoption and use of
analytics to support personalized medicine in a clin-
ical setting? The third relates to decision support.
Could the move to personalized medicine trigger
increased attention to rule-based systems and other
forms of advanced clinical decision support? Finally,
we see potentially dramatic implications for research
on privacy and security. Although genetic discrimi-
nation has been outlawed in the United States, for
many people fears persist that knowledge of their
genetic predispositions could fall into the wrong
hands and be used against them in decisions about
insurance and employment. Here again, IS solutions
can ensure anonymity of personal data, whether it
is about genetic predispositions or treatments. IS can
also enforce authorization controls and usage tracking
to ensure that all access is recorded.
Special Issue Process
In February 2008, an ISR announcement on ISWorld
and other outlets invited scholars from around the
world to submit papers for a special issue entitled
“The Role of Information Systems in Healthcare Orga-
nizations: Synergies froman Interdisciplinary Perspec-
tive.” In light of the diversity of healthcare delivery
systems across nations, the call encouraged submis-
sions addressing all segments of healthcare, includ-
ing providers (such as hospitals, physicians), payers
(such as government, insurers, and employers), and
consumers (patients). The special issue also encour-
aged submission of papers encompassing a variety of
theoretical and methodological perspectives.
Submissions were due in February 2009. A total
of 53 manuscripts was received. As a first pass, all
three senior editors evaluated each of the submis-
sions to assess its fit with the special issue’s focus,
theoretical and methodological strengths and weak-
nesses, and novelty of contribution to understanding
of the role of IS in healthcare. After this assessment,
26 manuscripts were selected for further peer review,
each by two reviewers. At the conclusion of this peer
review, a total of 10 manuscripts were chosen for fur-
ther revision.
Authors receiving a first-round revision decision
were invited to present their papers at the Special
Issue Workshop at Boston College in September 2009.
The goal was to provide feedback at the midpoint in
the revision cycle when there was still time to adjust
the revision strategy. Prior to the workshop, authors
were asked to submit a revision strategy and conduct
any revised analysis if requested by the reviewers.
All of the editorial review board (ERB) members,
reviewers, ISR SEs, and selected others were invited
to attend the workshop. Following the workshop,
authors documented the key points of guidance that
they received, and how they intended to incorpo-
rate this guidance. The handling SE worked with the
authors to resolve any conflicts between the origi-
nal review package and the workshop feedback. The
authors’ report and the SE’s response were shared
with the reviewers, including those who were not in
attendance at the workshop, so that they could con-
sider these issues when handling the revised paper.
The authors resubmitted their papers by January
2010, including a consolidated response document
covering both the original review package and advice
from the workshop. Eventually, nine papers were
accepted for publication in the special issue.
Fichman, Kohli, and Krishnan: Editorial Overview
Information Systems Research 22(3), pp. 419–428, ©2011 INFORMS 427
Acknowledgments
Many people have generously shared their time and effort
in keeping the process moving and bringing the special
issue into fruition. Jerry Kane served as the associate edi-
tor of the special issue and, in this role, was instrumental
in framing the special issue and in organizing and hosting
the Special Issue Workshop. In addition, Jerry stepped in,
on short notice, to provide much needed help as an ad hoc
reviewer. The guest editors express our sincere thanks to all
the members of the editorial review board and the review-
ers for their time and effort and their willingness to work
under the tight deadlines, as well as to the attendees to
the Special Issue Workshop. The guest editors offer special
thanks to V. Sambamurthy, who as then EIC, supported our
vision of the salience of healthcare in IS research and to the
ISR editorial office in bringing this special issue to print.
Editorial Review Board Special Issue
Corey Angst, University of Notre Dame
Ramji Balakrishnan, University of Iowa
Indranil Bardhan, University of Texas, Dallas
*Michael Barrett, University of Cambridge, UK
Anandhi Bharadwaj, Emory University
Anol Bhattacherjee, University of South Florida
Carol Brown, Stevens Institute of Technology
*Brian Butler, University of Pittsburgh
*Mike Chiasson, University of Lancaster, UK
*Elizabeth Davidson, University of Hawaii
Sarv Devaraj, University of Notre Dame
Leslie Eldenburg, University of Arizona
John H. Evans, University of Pittsburgh
Samer Faraj, McGill University, Canada
Tina Blegind Jensen, Copenhagen Business School,
Denmark
Mark Keil, Georgia State University
Bill Kettinger, University of Memphis
Joe Labianca, University of Kentucky
Karen Locke, College of William & Mary
Lars Mathiassen, Georgia State University
Nirup Menon, George Mason University
*Carsten Osterlund, Syracuse University
Rema Padman, Carnegie Mellon University
Ajay Vinze, Arizona State University
Jim Warren, University of Auckland, New Zealand
*Molly Wasko, University of Alabama at Birmingham
*Attended Special Issue Workshop
Ad Hoc Reviewers
*Andrew Burton-Jones, University of British
Columbia, Canada
*Gordon Gao, University of Maryland
*Jerry Kane, Boston College
Liette Lapointe, McGill University, Canada
Eric Overby, Georgia Institute of Technology
Guy Pare, HEC Montréal, Canada
Brian Pentland, Michigan State University
Ray Reagans, Massachusetts Institute of Technology
Sandeep Sahay, University of Oslo, Norway
Matthew Shum, California Institute of Technology
Jeff Smith, Miami University of Ohio
Eli Snir, Washington University in St. Louis
Jonathan Wareham, ESADE, Spain
Sean Xu, Hong Kong University of Science and
Technology, Hong Kong
Kai Zheng, University of Michigan
Feng Zhu, University of Southern California
*Attended Special Issue Workshop
Other Special Issue Workshop Attendees
Rob Fichman, Boston College (workshop leader)
Rajiv Kohli, College of William & Mary (workshop
leader)
Ranjani Krishnan, Michigan State University
(workshop leader)
Jerry Kane, Boston College (workshop coordinator)
Ritu Agarwal, University of Maryland
Ravi Aron, The Johns Hopkins University
Shubho Bandyopadhyay, University of Florida
Yi-da Chen, University of Arizona
Ramkumar Janakiraman, Texas A&M University
Helen Kelley, University of Lethbridge, UK
Jie Mein Goh, IE Business School, Spain
Tridas Mukhopadhyay, Carnegie Mellon University
Eivor Oborn, Royal Holloway University of London,
UK
Zafer Ozdemir, Miami University of Ohio
Sam Ransbotham, Boston College
Param Vir Singh, Carnegie Mellon University
Tracy Ann Sykes, University of Arkansas
Viswanath (Venki) Venkatesh, University of Arkansas
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