This is a discussion. Please make sure it is understandable when read

The Role of the RN/APRN in Policy-Making

Word cloud generators have become popular tools for meetings and team-building events. Groups or teams are asked to use these applications to input words they feel best describe their team or their role. A “word cloud” is generated by the application that makes prominent the most-used terms, offering an image of the common thinking among participants of that role.

What types of words would you use to build a nursing word cloud? Empathetic, organized, hard-working, or advocate would all certainly apply. Would you add policy-maker to your list? Do you think it would be a very prominent component of the word cloud?

Nursing has become one of the largest professions in the world, and as such, nurses have the potential to influence policy and politics on a global scale. When nurses influence the politics that improve the delivery of healthcare, they are ultimately advocating for their patients. Hence, policy-making has become an increasingly popular term among nurses as they recognize a moral and professional obligation to be engaged in healthcare legislation.

Revisit the Congress.gov website provided in the Resources and consider the role of RNs and APRNs in policy-making.

Resources:

Advocacy

https://www.nursingworld.org/practice-policy/advocacy/

Evaluating Policy Implementation

https://www.cdc.gov/injury/pdfs/policy/Brief%204-a.pdf

Congress

https://www.congress.gov/

Reflect on potential opportunities that may exist for RNs and APRNs to participate in the policy-making process.

By Day 3 of Week 8

Post an explanation of at least two opportunities that exist for RNs and APRNs to actively participate in policy-making. Explain some of the challenges that these opportunities may present and describe how you might overcome these challenges. Finally, recommend two strategies you might make to better advocate for or communicate the existence of these opportunities to participate in policy-making. Be specific and provide examples.

300-350 words. in text citations. 3 references within last 5 years

POLICY
IMPLEMENTATION,
STREET-LEVEL
BUREAUCRACY, AND
THE IMPORTANCE
OF DISCRETION

Lars Tummers and Victor Bekkers

Lars Tummers
Department of Public Administration
Erasmus University Rotterdam
P.O. Box 1738, NL-3000 DR Rotterdam
The Netherlands
E-mail: [email protected]

Victor Bekkers
Department of Public Administration
Erasmus University Rotterdam
P.O. Box 1738, NL-3000 DR Rotterdam
The Netherlands
E-mail: [email protected]

Abstract

Street-level bureaucrats implementing public
policies have a certain degree of autonomy –
or discretion – in their work. Following
Lipsky, discretion has received wide atten-
tion in the policy implementation literature.
However, scholars have not developed theo-
retical frameworks regarding the effects of
discretion, which were then tested using
large samples. This study therefore develops
a theoretical framework regarding two main
effects of discretion: client meaningfulness
and willingness to implement. The relation-
ships are tested using a survey among 1,300
health care professionals implementing a
new policy. The results underscore the
importance of discretion. Implications of the
findings and a future research agenda is
shown.

Key words
Discretion, public policy, policy implementa-
tion, street-level bureaucracy, quantitative
analysis

© 2013 Taylor & Francis

Public Management Review, 2014

Vol. 16, No. 4, 527–547, http://dx.doi.org/10.1080/14719037.2013.841978

INTRODUCTION

In his book Street-level bureaucracy: Dilemmas of the individual in public services, Michael
Lipsky (1980) analysed the behaviour of front-line staff in policy delivery agencies.
Lipsky refers to these front-line workers as ‘street-level bureaucrats’. These are public
employees who interact directly with citizens and have substantial discretion in the
execution of their work (1980, p. 3). Examples are teachers, police officers, general
practitioners, and social workers.
These street-level bureaucrats implement public policies. However, street-level

bureaucrats have to respond to citizens with only a limited amount of information or
time to make a decision. Moreover, very often the rules the street-level bureaucrats
have to follow do not correspond to the specific situation of the involved citizen. In
response, street-level bureaucrats develop coping mechanisms. They can do that
because they have a certain degree of discretion – or autonomy – in their work
(Lipsky 1980, p. 14). Following the work of Lipsky, the concept of discretion has
received wide attention in the policy implementation literature (Brodkin 1997; Buffat
2011; Hill and Hupe 2009;

P E R S O N A L V I E W

No big data without small data: learning health care
systems begin and end with the individual patient
José A. Sacristán MD PhD1 and Tatiana Dilla PharmD2

1Medical Director, 2Head of Health Outcomes Research, Medical Department, Lilly, Madrid, Spain

Introduction
We live in the era of big data. Data volume doubles every 2 years
and it has been estimated that every 2 days, more data are gener-
ated than were produced in human history up to 2003. The devel-
opment of technology and new analytical capabilities, which allow
the handling of large data volumes from different sources, have
generated high expectations regarding the potential of big data for
understanding the world and aiding in decision making [1]. Tech-
nological development is so rapid that it is difficult to imagine all
the applications that may result from the analyses of the data that
are generated globally every day.

A broad consensus exists concerning the vast possibilities of big
data in research and in the optimization of medical care, improving
their quality and reducing their cost [2,3]. However, big data
applications in the health sector lag behind those of other areas of
knowledge, such as the physical sciences, economics, businesses
or social networks, where data mining techniques are giving rise to
unprecedented qualitative changes [4].

Variability is the essence of biomedical sciences. In medicine,
the heterogeneity of individuals calls for personalized decisions to
benefit individual patients. Theoretically, the potential of big data
in the field of health may be limited by increasingly personalized
medicine. This paper analyses the potential barriers that may
impede the development of big data in medicine and research and
proposes ways of moving forward to generate a ‘learning health
care system’ that aims to improve health outcomes for current and
future patients in an efficient manner.

Barriers that may slow the
development of big data in research
and medicine
The main limitations of big data in clinical research and in medical
care are well known and are related to methodological, technologic
and legal factors. Among the methodological barriers, the low
quality of data (incomplete data, lack of standardization) and the
existence of an analytical methodology that remains insufficiently
developed are most prominent [5,6]. The biases inherent to the
analyses conducted on databases (often used for administrative and
billing purposes) have been widely described [7]. Obvious technical
and analytical difficulties exist in managing a very large volume of
data that is constantly changing and that resides in different reposi-
tories, along with frequent linkage and interoperability issues. A
significant part of the data is ‘noise’, which presents challenges
when the noise grows faster than the signal. Different databases
with different degrees of quality and comple

C Academy ot Managernent Review
1996, Vol. 21. No. 4, 1055-lDBO,

^ THE CHALLENGE OF
INNOVATION IMPLEMENTATION

KATHERINE I. KLEIN
JOANN SPEER SORRA

University of Maryland at College Park

Implementation is the process of gaining targeted organizational
members’ appropriate and committed use of an innovation. Our model
suggests that implementation eiiectiveness—the consistency and
quality of targeted organizational members’ use oi an innovation—is
a function oi (a) the strength oi an organization’s climate ior the imple-
mentation oi that innovation and (b) the fit of that innovation to targeted
users’ values. The model speciiies a range of implementation outcomes
(including resietance, avoidance, compliance, and commitment): high-
lights the equifinality of an organization’s climate ior implementation;
describes within- and between-organizational diiferences in innova-
tion-values fit; and suggests new topics and strategies for implementa-
tion research.

Innovation implementation within an organization is the process of
gaining targeted employees’ appropriate and committed use of an innova-
tion. Innovation implementation presupposes innovation adoption, that
is, a decision, typically made by senior organizational managers, that
employees within the organization will use the innovation in their work.
Implementation failure occurs when, despite this decision, employees use
the innovation less frequently, less consistently, or less assiduously than
required for the potential benefits of the innovation to be realized.

An organization’s failure to achieve the intended benefits of an innova-
tion it has adopted may thus reflect either a failure of implementation or
a failure of the innovation itself. Increasingly, organizational analysts
identify implementation failure, not innovation failure, a s the cause of
many organizations’ inability to achieve the intended benefits of the inno-
vations they adopt. Quality circles, total quality management, statistical
process control, and computerized technologies often yield little or no
benefit to adopting organizations, not because the innovations are ineffec-
tive, analysts suggest, but because their implementation is unsuccessful

We are very grateful to Lori Berman. Amy Buhl, Dov Eden. Marlene Fiol, John Gomperts,
Susan Jackson. Steve Kozlowski, Judy Olian. Michelle Paul, Ben Schneider, and the anony-
mous reviewers for their extremely helpful comments on earlier versions oi this article. We
also thank Beth Benjamin, Pamela Carter. Elizabeth Clemmer. and Scott Rails for their help
in collecting and analyzing the interview data ior the Buildco and Wireco case studies.

1055

1056 Academy of Management Review October

(e.g., Bushe, 1988; Hackman & Wageman, 1995; Klein & Rails, 1995; Reger,
Gustafson, DeMarie, & Mullane, 1994).

Innovation scholar