Solved by verified expert:There are hundreds of systems used throughout a hospital. Some of these systems are administrative, some are for patient treatment and diagnosis, and others are used for storing and distributing information. Describe three types of systems that are used throughout a hospital. What external forces impact their use? Who are the key stakeholders involved with managing these systems?For all discussion posts, back up your writing with terms and concepts from class readings. Remember, these same terms and concepts can be used in your Critical Thinking assignments and in the Portfolio Project, so use the discussion area as a place to practice applying terms and concepts to the situations being addressed.Your initial posting should be 250-500 words and must be submitted by Thursday, midnight, of this week.
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Information Systems Research
Vol. 22, No. 3, September 2011, pp. 419–428
issn 1047-7047 eissn 1526-5536 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,
fichman@bc.edu
Rajiv Kohli
Mason School of Business, College of William & Mary, Williamsburg, Virginia 23187,
rajiv.kohli@mason.wm.edu
Ranjani Krishnan
Broad College of Business, Michigan State University, East Lansing, Michigan 48824,
krishnan@bus.msu.edu
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
Research anchored in the healthcare context must
begin by reflecting on what is distinctive about healthcare 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 disciplines (e.g., doctors, nurses, administrators, and
insurers), treatment options, healthcare delivery processes, 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 economics, public health, business, epidemiology, sociology, 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.
The importance of healthcare to individuals and governments 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 technology (IT) in clinical and diagnostics equipment, IS
are uniquely positioned to capture, store, process, and
communicate timely information to decision makers for better coordination of healthcare at both the
individual and population levels. For example, data
mining and decision support capabilities can identify potential adverse events for an individual patient
while also contributing to the population’s health by
providing insights into the causes of disease complications. 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.
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The Stakes Are Life and Death
Healthcare influences the quality of our lives and
how we function within the society. Healthcare mistakes 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 individuals 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 control infectious diseases can cause serious public health
issues. Therefore, healthcare quality is diligently pursued 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 association between IS and medical errors in three primary
healthcare processes—sensing, controlling, and monitoring. 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 complementarities 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 experience 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 consistent with conventional beliefs that automation complements 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 decision 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 influences 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
Fichman, Kohli, and Krishnan: Editorial Overview
Information Systems Research 22(3), pp. 419–428, © 2011 INFORMS
(SARS). In such cases, IS plays an essential role at both
patient and population levels. Mobilizing and coordinating 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 coordination of public health IS. Drawing on the loose
coupling framework, they find that increased involvement of public health agencies is not always helpful, especially when there is clinical uncertainty about
effective treatment mechanisms. They conclude that
coordination should be such that public health policy 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, coupling 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 centrality 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 network, 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 information could fall into the wrong hands. Although electronic 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 further 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 individuals’ privacy concerns and trust in the electronic storage 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 information is requested (patient care, research, or marketing),
Fichman, Kohli, and Krishnan: Editorial Overview
Information Systems Research 22(3), pp. 419–428, © 2011 INFORMS
and the type of stakeholder requesting the information (doctors/hospitals, the government, or pharmaceutical companies). In addition, the authors explore
the link between an individual’s emotions regarding
his/her current medical state and willingness to provide access to PHI. As a framework for their analyses, the authors use privacy boundary theory and the
risk-as-feelings perspective. Analyses using a nationally representative sample of 1,089 individuals indicate 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’ willingness 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 relationship between privacy concerns and willingness to
disclose is particularly acute when the information
request comes from government/public health agencies (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 benefits. 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 information, 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 electronic health records (EHR) by providers can increase
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administrative efficiency, reduce healthcare costs by
eliminating unnecessary duplication of medical tests,
and most importantly, reduce medical errors. However, 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 personal health records (PHR). The PHR draws health
information from multiple sources, including the
physician or hospital’s EHR, and provides the individual with the flexibility to manage his or her
own PHR. While such platforms are mainly intended
to serve patients, they may also hold the potential to improve the incentives for providers to share
EHR data. In this context, Bandyopadhyay et al.
use an analytical game-theoretic model to investigate three research questions: Do providers resist
EHR sharing, even when it increases social surplus? Which providers stand to gain most from EHR
sharing? What role can a Web-based PHR platform
play in solving incentive problems and encouraging providers to share EHRs? The authors analytically 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 participation the PHR platform provider will have to selectively 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 sharing (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 Anderson 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 adoption is that powerful actors in care delivery often
resist technology. Part of this arises from professional
norms: physicians are primarily concerned with treating the patient to the best of their ability and regard
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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 administrative personnel to explore the drivers of system use
and the system’s effect on patient satisfaction. Kane
and Labianca explore the association between preference for IS avoidance and three outcomes: efficiency 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 complementary) contributions.
Social network theory suggests that an individual’s network position influences behavior and performance. 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 acculturation 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 overall, 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 technology 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 consequences (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 postadoption resistance where individuals avoid working with an information system despite the need and
opportunity to do so. Using archival data they examine the efficiency and quality effects of IS avoidance at
three levels: the individual user level (physician), the
shared group level (healthcare team, including paraprofessionals and administrators), and the configural
Fichman, Kohli, and Krishnan: Editorial Overview
Information Systems Research 22(3), pp. 419–428, © 2011 INFORMS
group level (which accounts for the positions of individuals 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 association with outcomes. The qualitative analyses provide
insights into this pattern of results. At the individual level, users who avoided the system were able
to compensate by using brokering relationships, i.e.,
assigning a representative to interact with the system on their behalf. At the shared group level, clusters 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 information. These results also provide insights into why IS
avoidance at the configural group level was associated 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 individuals had a central position in the social network.
Health Care Is Multidisciplinary
The findings reported in the previous sections indicate that there are multiple barriers to the adoption
and use of IT in healthcare organizations, despite
robust findings …
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