Science discussion questions

There are two discussions here I need you to treat separately in 2 separate word documents. The 2 havedifferent topics. You will prepare 2 separate word documents solutions with 1 page each and single line
spacing.
The text for the discussions are taken from the book: Transforming Health Care Management:
Integrating Technology Strategies
Ivan J. Barrick
You may also use the other resources attached. I copied the chapter 1 and did not
complete chapter 2. If you have the book please read it further because I need you to
cite from the book. Please look below for the copies from chapter 1 and two.
Instructions :
Discussion 1 Topic:Digital Trends in Health Care
It is often said that health care is digitally transforming. Consider, some of the
current and future trends in the digitalization of health care. For this discussion,
list and explain at least three digital trends in health care that play a role in health
care data analysis and then discuss what leadership and team approaches may
be needed.
Discussiion 2 Topic: Strategic Approaches to
Analyzing Data in Healthcare
Now that you have researched current and future trends, discuss some of the
strategic approaches to analyzing data in health care. Consider team
approaches, leadership models, organizational strategies, and performance
management.
CHAPTER 1
Digitally Transforming Health Care: An Introduction
This chapter introduces strategies for digital transformation of health care, justifies the need for this
book, and, most importantly, describes an approach that drives effective change into a patient-centric
seamlessly integrated network that delivers high-quality, efficient, and effective health care.
INTRODUCTION
A heightened level of awareness and special interest by payers, employers, and healthcare processionals
has been driven by the exponential rise in healthcare delivery costs during the last decade. Society has
for some time recognized that this unsustainable trend reflects an urgent need for a dramatic increase
in quality and competitiveness. More recently, this recognition has been coupled with an amplified
awareness of much more consumer-oriented patients, employers, and payers who have now driven
health care to the very top of America’s political
DIGIT ALL Y
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agenda. A number of states have legislated mandatory healthcare insurance cov- erage for residents and
more states are in the process of creating similar legisla- ture. The days of uncontrolled and unlimited
healthcare resource consumption are history. Increasingly competitive pressure has forced leaders to
recognize that radical processes redesign to rapidly streamline operations is long overdue, and without
focused effective action providers will quickly become uncompetitive and risk organizational irrelevance.
All stakeholders in the healthcare industry are experiencing these demands for reform, forcing
fundamental organizational change, a new technologic philosophy, and an urgency for significantly
improved performance.
This digital transformation began with federal regulations. Now all providers must be deliberately
focused to simultaneously ensure patient safety, significantly increase quality, and substantially reduce
costs as mandated rather than as just desirable objectives. This transformation cannot be based solely
on a definition of quality from the Joint Commission on Accreditation of Healthcare Organizations
(JCAHO). This pervasive patient-centric transformation must be driven by cus- tomer’s needs, wants, and
expectations.
What is urgently needed is a visible affirmation of leadership’s commitment to pursue quality as a vital
mission of the organization. Quality is not just a socially provocative idea. Quality and cost are two key
criteria by which employer, payers, and patients now evaluate healthcare purchase decisions. Quality
has become a key strategic element of healthcare management in response to these very well-informed
consumers. Any healthcare organization that is not focusing in a patient-centric man- ner on internal
and external customers is writing a prescription for failure. A dramatic paradigm shift has occurred as
patients have become more knowledgeable and now behave as assertive customers with demanding
expectations. These behaviors must be effectively integrated into clinical processes and care delivery
service designs. This paradigm shift represents a fundamental “sea change” from traditional provider
behaviors that were guided by a notion that providers know best.
In response to these demanding challenges, providers now recognize that an effective transformational
response must be driven by aggressive business strategy. Such an approach relies on digital
transformation as leverage to consistently deliver safer higher quality patient care with increasing
operational effectiveness and improved productivity. Such a savvy strategy requires a substantial IT
investment that results in comprehensively redesigned “mission critical” processes. Today, progressive
digitally transformed provider organizations have already gone beyond advanced clinical systems with
the early adoption of seamlessly integrated infor- mation and medical technologies, requiring
substantial process redesign in all care delivery venues throughout every organization.
Traditional healthcare organizations have long been criticized for extended retention of outdated legacy
systems, resisting adoption of decidedly disruptive
and contemporary transformational technologies that require rigorous process redesign. By continuing
this reactive posture and deferring inevitable acquisition and adoption of more effective patient-centric
transformational technologies to improve care delivery safety, quality, and efficiency, these
organizations are lag- ging further and further behind more digitally adaptive organizations. Competition is already leveraging transformational technologies. These “disruptive technologies” are pervasive
and seamlessly integrate communications, imaging, and information among and between diagnostic
centers, laboratories, patient care venues, pharmacies, surgical suites, nurse call and communications
systems, light- ing systems, pagers, cellular and mobile phones, personal digital assistants (PDAs), and
heating, ventilating, and air conditioning (HVAC) systems.
HISTORICAL PERSPECTIVES
Digitally transformed pioneers have simplified, streamlined, and redesigned processes to increase and
expedite throughput with compressed delivery cycle times to adapt to increasingly demanding market
forces. These pace-setting provider organizations produce and consume real-time information to
expedite and improve care delivery with updated clinical data as demanded by both quality- driven
physicians and assertive patients empowered with information. Addition- ally, these pioneers have an
improved capability to generate quality data to meet payer’s increasing demands and to prepare for
inevitable reimbursement changes as they occur.
Transformational information technologies digitize current technology and instruments on seamlessly
integrated data networks to enable real-time clinician access into each patient’s longitudinal electronic
medical record (EMR), anytime, anywhere. Digitally advanced hospitals have demonstrated reduced
lengths of stay, increased care quality, and increased reimbursement that generates generous mar- gins.
Quantitative benefits are incremental and sometimes challenging to realize and even more difficult to
credibly demonstrate measurable material return on sig- nificant upfront investments. Pioneering
providers who made these investments in transformational technologies (i.e., at least 5% or more of
operating budgets) have achieved and can enjoy these digitized capabilities.
Early initiatives in digital transformation with computing in health care can be traced back more than 50
years when only mainframe computers were available and only in major hospitals with sufficient
resources (i.e., funding, time, and effort) to afford to acquire, install, house, maintain, and use these
machines. Examples of these early pioneers are
•
El Camino Hospital, Mountain View, California • Monmouth Medical Center, Long Branch, New
Jersey
DIGIT ALL Y
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INTRODUCTION
••••
Veteran’s Administration, Washington, DC Danderyd Hospital, Stockholm, Sweden London Hospital,
London, England Hanover Hospital, Hanover, Germany
Hardware and software vendors and system integrators, all with reputations for effective design, project
management, and implementation of complex systems in other service industries, joined with these
hospitals to collaborate, develop, and support very early versions of patient information systems.
Examples of vendors who demonstrated visionary insight for market opportunities are
•
Burroughs Corporation •
Control Data Corporation •
General Electric •
Honeywell, Inc. •
International Business Machines (IBM) •
National Cash Register
Company (NCR) •
Lockheed Information Systems Division • McDonnell-Douglas • Technicon
Corporation
No single vendor, system, or application can be considered representative of all others because of
different needs, goals, and anticipated impacts in each provider organization. For example, unique
requirements for design and infra- structure, functions and features, and variable technical and
technology investment capabilities had to be customized in each development site. That said, for historical perspective a case study is presented on the now legendary experiences at El Camino Hospital in
Mountain View, California.
CASE STUDY
This facility has been selected for this case study because of its operational experience with a broad
range of IT and evolutionary care delivery changes that have occurred over time. This organization’s
experiences are reflective of early healthcare information system (HIS) development initiatives and system capabilities that were representative HIS prototypes, characteristics, and rich functions and features
for more than 50 years.
System development at El Camino Hospital began as a successful pio- neering prototype that was widely
recognized as an effective model for health- care digital transformation for ongoing development of
patient information
management systems throughout North America and Europe. Lessons learned during this project
focused on user information needs and require- ments to change skeptical physician and nurse user
attitudes. Similar expe- riences could be described for other provider organizations and a variety of
commercially available healthcare IT applications with similar rich histories.
When this prototype system was installed, El Camino was a 450-bed com- munity general hospital with a
medical staff of 340 physicians, serving patients under the care of personal physicians (i.e., a
nonteaching hospital without any internship or residency program). At that time the hospital provided
emer- gency room care and diagnostic procedures for patients referred by staff physicians. Various
versions and upgrades to hardware and software sup- ported the operation at El Camino Hospital since
1972. Three years of system development at the hospital preceded initial prototype implementation.
Instal- lation of this initial system version throughout the hospital took 9 months, after which the
National Center for Health Services Research awarded funds to El Camino Hospital for evaluation of the
project. System hardware, a large main- frame computer, was located at a regional computer center
several miles from El Camino Hospital, with a second computer available for backup support. Data were
maintained at this central processing facility using magnetic disks and reel-to-reel tapes for temporary
and archival storage.
El Camino Hospital’s 58 video terminals and 31 printers were linked to this computer center via
dedicated high-speed telephone lines. Most soft- ware was written in assembly language, using COBOL
for financial reports. This prototype system was a true hospital-wide system designed to store patient
and financial data and to aggregate and communicate appropriate data and information, either
automatically or upon request.
Hospital objectives for this first-generation patient care information sys- tem included provision of
•
More efficient healthcare delivery •
Improved patient care by facilitating and enhancing
nursing and ancillary
department activities • Reduction and/or containment of operational costs
Physicians, nurses, ancillary service personnel, and admitting staff entered data through video terminals,
which consisted of a light video sensitive screen, keyboard, and light pen for rapid selection of
information presented on the screen. Direct physician use, which has historically distinguished this
system from most other hospital-wide systems then and now, requires nursing or other support
personnel to enter data for physicians. Terminals were located at each nursing station and in ancillary
service departments. Authorized personnel
accessed the system by typing a unique user identification code on the key- board. This security
procedure ensured that hospital personnel could only enter, change, view, and print information
relevant to their job.
As a terminal screen displayed a list of items (e.g., laboratory tests that a physician desired to order), a
specific item was selected and entered into the computer system by pointing a light pen at the desired
phrase and pressing a switch on the barrel of the pen. Using this light pen, a physician could enter a full
set of medical orders (e.g., laboratory work, medications, x-rays, diet, activity, etc.) for a specific patient.
These displays reminded physicians to make complete orders; for exam- ple, when a medication was
ordered, the display noted the need to specify scheduling and method of administration (i.e., oral,
intravenous, or intra- muscular) in addition to dosage. The keyboard was used to enter any infor- mation
that was not displayed on the video display. A physician or nurse at the nursing station printed copies of
orders for verification and then these verified orders were automatically routed and printed in each
appropriate ancillary department for diagnostic or therapeutic service.
This successful demonstration project led to other large-scale data pro- cessing applications in medicine
and health record systems as computer growth accelerated in other industries. Hospitals began to justify
additional system investment and then demonstrate continuing productivity gains and early evidence of
increased process efficiency.
These early successes were achieved at very high costs, emphasizing the need for senior executive
leadership to take responsibility for organiza- tional transformation (e.g., anticipate and prepare for
resistance from some staff). Approximately 10% of user personnel were adaptive and embraced these
operational changes rather quickly. Another 10 to 15% demonstrated passive resistance to changes and
eventually left the organization because of their inability to effectively adapt to transformation-related
process changes throughout the organization. Others eventually adapted as required by emu- lating
behavior changes of earlier adapters.
El Camino Hospital continues to use this industry-leading technical plat- form to support an effective,
efficient, and rich portfolio of state-of-the-art functions and features that have facilitated and sustained
digital transforma- tion for more than 50 years. Over time, this initially mainframe-based system has
been upgraded and is now integrating unique intelligence gathering via an Internet protocol (IP)-based
network infrastructure with knowledge-driven clinical, financial, and management information software
and services throughout the enterprise. As recently as 2005, El Camino Hospital has con- tinued to be
repeatedly named to the Honor Roll of the U.S. News & World Report annual “America’s Best Hospitals
List.”
A number of these Honor Roll hospitals use this system vendor’s clinical applications, including the
perennially top-ranked facility, The Johns Hopkins Hospital. Most of these “digitally transformed”
providers use integrated clini- cal decision support, informatics, and computerized physician order entry
(CPOE) applications to enable managers to effectively provide safe, efficient, and accurate patient care
as well as to analyze and manage operations.
Keystone Systems, the healthcare industry’s recognized information sys- tem benchmarking report,
indicates that this vendor’s CPOE solutions are in use by more inpatient organizations and by more
physicians than CPOE solu- tions from all other vendors. Keystone Systems is an independent research
and consulting firm that specializes in monitoring and reporting performance of healthcare IT vendors
and healthcare professional services firms.
This vendor has been repeatedly recognized as a leading provider of solu- tions based on physician
usage in the Keystone Systems’ CPOE Digest 2005, which reported that when ease-of-use and rich
functional capabilities are key decision-making criterion, healthcare organizations selected this system
more often than other vendor. Interoperability of this system’s applications repre- sents an important
digital transformation achievement by El Camino Hospi- tal and other provider organization users as
required to achieve the U.S. federal government’s goal of a nationwide EMR for most Americans by
2014.
Having been recognized worldwide as an industry leader in remote healthcare information processing
and having met rigorous standards and requirements of the ISO 9001:2000 certification program, this
systems inte- grator has been registered as an ISO 9001:2000-compliant company. Micro- soft
Corporation and this vendor recently announced a multiple year strategic alliance.
With a single clinical platform spanning an entire healthcare enterprise and linking these applications to
the community, this system received InfoWorld’s Top 100 Award for 2005 as one of the most innovative
uses of enterprise technology in health care. This award was given for an interoper- ative wireless
companion to all advanced clinical solutions, enabling wireless access to real-time clinical data anytime
from anywhere.
MODERN DEVELOPMENTS
Today, a digital hospital relies on technology as an integral and fundamental part of business strategy.
Technology is applied to every facet of clinical and business operations, such as integrating patients,
personnel, process, technology, and cul- tural elements. IT is now defined more broadly as healthcare
providers go beyond
dvanced clinical systems to include fundamental patient-centric integration and digital transformation of
strategic processes associated with information and med- ical technologies. Industry standards and
legislation, such as the Health Insurance Portability and Accountability Act (HIPAA) and the Clinical
Context Object Workgroup (CCOW), are leveraging seamless connectivity to enhance more effec- tive
communication among stakeholders and business partners in the community.
There are an increasing number of real-world examples that innovative pro- viders recognize and
embrace requirements that are best achieved through IT- driven digital transformation. Organizations
described as digital hospitals tend to be stand-alone, newly built, specialty facilities designed as
advanced highly auto- mated facilities. Digital value was embraced in initial designs of these facilities.
Fundamental principles driving digital transformation within any healthcare provider organization have
been emulated from successful automation experiences in healthcare and other service industries.
As of this writing, specific goals associated with digital transformation have yet to be clearly articulated
because a comprehensive vision of future health and healthcare delivery has not yet coalesced. Most
community hospitals have at least some automation of patient registration, scheduling, and billing as
well as within laboratories, imaging, and pharmacy departments.
Governmental leadership at the federal, state, and regional level is being coor- dinated by an increasing
number of collaborative initiatives that focus on specific components of a truly standardized national
infrastructure required to achieve a functional EMR in all delivery venues a reality. Examples of these
collaborations are as follows:
•
Workgroup for Electronic Data Interchange-Strategic National Implemen- tation Process (WEDISNIP) is a nonprofit organization dedicated to im- proving health care through support of national and
regional electronic commerce initiatives.
•
Typical programs, conferences, policy, and advisory white papers have included
••••
HIPAA Strategic National Implementation Process National Provider Identifier Outreach Initiative WEDI
Regional Affiliates Academic Medical Centers Events Groups Clinical and Electronic Health Record
Initiatives
•
WEDI members and other healthcare stakeholders have been informed and educated about
benefits and strategies to improve information management and exchange.
•
As a leading advisory group to the Centers for Medicare and Medicare (CMS) in the Department
of Health and Human Services (DHHS), WEDI- SNIP facilitated national and regional collaboration,
providing policy guidance and leadership to the healthcare industry on how to use and leverage the industry’s collective
knowledge, expertise, and information resources to improve the quality, affordability, and availability of
health care. For exam- ple, WEDI-SNIP was the leading policy advisory organization to CMS
administrators during regulatory development and implementation phases of HIPAA Title II, a massive
conversion of healthcare electric data exchange privacy and security regulations.
•
The College of Healthcare Information Management Executives (CHIME) provides professional
development programs and services exclusively for CIOs in health care. CHIME advocates more effective
use of information management within health care. CHIME events and activities were designed to
reflect these objectives (e.g., CIO-oriented surveys, benchmarking data on staffing ratios, sample
documents, job descriptions, research, education forums, focus groups, and peer networking activities).
Recent advances in information systems, networks, and telecommunication technologies enable global
real-time collaboration and consultation among and between physicians and other members of the
healthcare delivery team. As an example, therapeutic remote monitoring and robotic surgical
procedures are increasingly common. These interactions are conducted via secure networks that
provide sufficient bandwidth to support simultaneous voice, data, images, and text.
Healthcare professionals are challenged to embrace each technological advance, thereby requiring
lifelong learning. Digital transformationalists must continually gain and sustain state-of-the-art
knowledge, acquire necessary skills, and develop and demonstrate expertise and technical competency.
Providers in all venues are continually challenged to deliver higher quality lower cost patient care while
using fewer and less-expensive resources. Organizational viability of current providers can only be
sustained by achieving higher levels of productivity at all levels. Within the last decade medical
technology has advanced more than in the last 100 years. As clinical information accumulates with
instantaneous access and at an ever-increasing rate, healthcare professionals are challenged to process
and use information about new discoveries, medications, procedures, and technology even before these
products and services are commercially available.
As technology is changing lives, ethical considerations and choices associ- ated with breakthrough
applications of this technology are having varied and far- reaching implications. Currently, IT
professionals are discovering, designing, implementing, and using these applications and will be
introducing, experiencing, and assessing benefits of these new and continually emerging technologies
early in their careers. Today’s aspiring healthcare professionals need to be aware of this awesome
responsibility and the multitude of difficult choices awaiting them.
A broad definition of computer and systems literacy is necessary to begin to understand how to rapidly
acquire and aggregate these data and then instantly pre- sent accurate and meaningful information to
authorized personnel. Hence, some working definitions are presented:
•
A computer is an electronic device capable of rapidly and reliably enabling data input and
performing manipulation, storage, calculation, communica- tion, informational transformation,
reporting, and presentation.
•
Information systems are a group of interoperable components, applications, and associated
technologies working together to complete a set of specified tasks.
This book is an examination of applications supporting a variety of clinicians and many other healthcare
professionals with varying degrees of computer knowl- edge and proficiency. Hospital and other
provider organizations are beginning and will have to then sustain a digital transformation that becomes
increasingly auto- mated with ever-more sophisticated systems and telecommunication networks. As
this technical evolution advances, providers are increasingly dependent on IT to manage workflow and
perform work by an increasing computer-literate workforce.
IT professionals, systems and operations analysts, and management engineers must be more
knowledgeable about computers and related terminology to be capa- ble of explaining their use and
competently designing, developing, acquiring, implementing, maintaining, and refining systems,
applications, and associated processes and tasks. IT literate professionals must be aware of social and
ethical questions concerning computers and information systems and associated transfor- mational
applications, processes, and tasks. Although IT provides many benefits to our society, misuse and abuse
of computer technology can and does occur.
CHAPTER 2
Multi-disciplinary Concepts: A Fundamental Foundation
This chapter introduces data collection topics, analytic techniques, and team dynamics tools. These
essential tools and techniques are used to collect, aggregate, and analyze data. When used in various
combinations, these techniques generate team discussions that lead to effective decision-making by
team consensus. Exam- ples of these techniques and concepts are
The initial focus is on common tools used by process improvement teams, business process
reengineering task forces, and IT professionals of all disciplines. All IT professionals must be proficient to
effectively lead digital transformation initiatives in health care. As digital transformationalists, these
multidisciplinary individuals lead process redesign and automation-driven projects using a variety of ITs.
FLOWCHART
A flowchart is a fundamental tool used to explain, communicate, and facilitate a collaborative
understanding of a process, including logic and essential relationships among and between various
process or system components. Flowcharts document how systems, applications, functions, and
processes operate. This graphic tool is sometimes considered to be an “imagineering” tool that
visualizes and facilitates analyses to identify problems, bottlenecks, delays, and opportunities for
potential process improvements. Analysts can visualize each unique component, task, deci- sion, and
event in a process to understand their relationships to design, reengineer, or imagine an “ideal” process.
Visual images are easier to understand and explain than painstaking descriptions of the same process
documented in lengthy para- graphs or narrative pages.
A flowchart is a graphic representation of a process enabling teams and team members to better
visualize and understand key operational sequences, decisions, delays, movements, and storage
activities within a process or system.
Truly, a picture is worth a thousand words. Team members from different dis- ciplines can quickly
understand key functions, decisions, and relationships in a process. A flowchart has defined start and
end points to establish boundaries and to frame each perspective for analysis. Typical events such as
answering a tele- phone call, taking a patient’s vital signs, and printing orders are each functional
components that constitute a process and as such are depicted in a process flow- chart. Each event is
represented by a symbol that has a distinct meaning and is connected by arrows indicating both
direction and logic of process flow with a distinct end point. Process flowchart construction requires
discussion and collab- oration of knowledgeable team members who are stakeholders of each portion
for the entire process and who are recognized as “subject matter experts” regarding each key process
component.
Flowchart construction requires a team to
•
Define start and end points and identify key workflow components, deci- sions points, and
related flowchart steps
•
Collaborate and document each process step and thereby enable all team members and
executive sponsors to review and understand process begin- ning and ending points
•
Define and document assumptions to avoid any potential confusion • Sequence each process,
task, or even
sign a symbol, such as those in Figure 2.1 •
Validate this clearly and concisely developed process
flow with personnel
involved in the process and most knowledgeable about each relevant process
component • Identify and correct any gaps to eliminate any misunderstandings
Process automation technologists and digital transformationalists use variations of these simple generic
flowchart symbols to depict unique activities or actions. Many variations and additional symbols are
used. Symbols are occasionally cus- tomized as necessary to show unique characteristics that are
dependent on each process, or system, as well as to explain specialized activities.
Historically, systems analysts and computer programmers used flowcharts to graphically document
logical and detailed steps encoded in computer programs. Operations analysts, management engineers,
and process improvement, reengi- neering, and Six Sigma teams commonly use flowcharts to document
current states and depict future states of processes or systems under study. This aspect of flow- charting
and process documentation is vital to ensure that all team members under- stand complete processes or
workflows, even though individual team members may have expert knowledge of only a limited portion
of an entire process.
Figure 2.2 illustrates a simple process flowchart depicting outpatient clinic visit process activities from
beginning to end. Figure 2.3 is an example of a “deployment”
Arrow:
Circle:
Diamond:
Oval:
Rectangle:
Denotes Operations or Activity Flow
Depicts Process Movement, Transport, Direction or To/From Page Connection
Highlights Decisions, Inspections or Evaluation
Defines Process Start and Finish
Concisely Describes Key Operations
FIGURE 2.1
Essential flowcharting or imagineering symbols.
or “horizontal” flowchart showing process actions by different individuals or departments. This variation
is frequently used to show complex process “hand- offs” from individual to individual or from
department to department. These hand-off transactions are very often opportunities for process
improvement. Many commercial, open source, and software programs include capabilities to quickly
and accurately develop flowcharts of many types and to edit with corrections and changes over time.
PARETO CHARTING AND ANALYSIS
Pareto charts are the most widely and creatively used process improvement tools. Pareto analyses
facilitate data organization and presentation to isolate and show those “vital few causes” of a problem
from the “trivial many.” This technique is best used when problems must be identified to focus team
members on a relevant issue and further isolate and eliminate others from consideration and analysis.
Team efforts can then effectively focus on those problems, offering the most poten- tial for substantial
process or system improvement. A Pareto diagram displays the relative frequency or size in either an
ascending or descending bar graph. Ideally suited to focus team attention on key problems, this tool is
especially useful to identify opportunities of a highest value. Quality improvement, process reengineering, or a Six Sigma team can effectively allocate resources by using the Pareto technique to do the
following:
•
Understand relative importance of problems in a simple, quickly interpreted, visual format
•
Reduce the probability of shifting a problem because a proposed solution removes some causes
but worsens others (i.e., the result of unintended con- sequences; there is no net organizational benefit
by simply moving a prob- lem from one department to another)
•
Measure progress using a visible and easy to understand format
As depicted in Figure 2.4, a Pareto diagram construction involves
•
Identifying problems to be investigated •
Collecting appropriate data and using Pareto
analysis techniques to analyze
zero-based data types (e.g., a number of complaints with an expectation that
an ideal situation will reduce complaints to zero) •
Classifying data by categories such as type of
complaints, nursing units or
departments, shifts, diagnosis-related groups (DRGs), and patients •
Arranging error categories by
type, quantity, and percentage in either ascend-
ing or descending order •
Stratifying data as required (i.e., weekly data grouped into weekdays
and
weekends and then classified by day or by shift as subgroups based on
unique characteristics or useful categories) • Creating Pareto diagrams as specialized bar graphs (e.g.,
displaying types of
patient complaints, their respective totals, percentage of overall total, cumulative totals, and cumulative percentages) •
Drawing a cumulative Pareto curve with cumulative
percentages above interval (e.g., each complaint connected with a solid line) • Verifying that each graph displays an obvious
Pareto pattern (i.e., categories
with similar percentages may require an alternative stratification of data so that distinct problem
categories are isolated)
For example, as depicted in Figure 2.4, patient complaints should be catego- rized, with infrequent
complaints included as an “all others” category. If an “all
FIGURE 2.4
Pareto analysis: patient perceptions of emergency staff attitudes.
others” bar reflects more than 50% of the largest individual bar in the graph, the “all others” category
should be separated and then displayed individually.
Pareto analysis and interpretation focuses on the tallest bars first because those categories usually
represent major contributing causes to the problem under study. Focusing on these problem categories
first results in more expeditious problem- solving and deploys scarce resources most effectively.
Pareto graph construction, display, and analysis must include perceptions of patients, staff, and other
key stakeholders in any process being studied. As with most tools, there are a number of specialized
variations. For example, variations of Pareto diagrams include “cause” breakdowns as tallest bars are
broken into sub-causes in subsequently linked Pareto diagrams. This limited overview highlights the
typical presentation that is most useful in multidisciplinary team initiatives.
ISHIKAWA (CAUSE AND EFFECT) DIAGRAMMING
Ishikawa diagramming is a common technique used for initial problem identifica- tion and dispersion
analysis. A team’s learning curve through these incremental steps is necessary to discover each critical
problem in a process and to understand each problem’s root cause(s).
By using Ishikawa diagramming techniques, digital transformation teams focus on problems, not on
various symptoms or other distractions, differing interests, or unique and sometimes important
disciplinary bias. This analysis encourages all team members to gain a consistent collective knowledge
about a problem and to develop team consensus and support for further analyses of potential solutions.
A classic Ishikawa diagram resembles a fish skeleton with causes presented as ver- tebrae branching
from a central spine that leads to an observable effect. As depicted in Figure 2.5, initial versions of an
Ishikawa diagram usually take a generic form of a fish skeleton with branches (vertebrae) shown as core
policies, personnel, processes, and procedures or people, methods, material, instruments, and equipment. Additional team consideration, discussion, and brainstorming yield increas- ingly detailed
breakdowns of these causes. Ultimately, a root cause or causes are presented as vertebrae, frequently
requiring further data collection and validation using other tool
RUN CHARTS
Run charts show trends as observations over time. They are used to visualize process characteristics
such as errors and increased or decreased activity that changes over time. Meaningful trends can be
identified as a team monitors unusual events in a process that affects average process performance.
This basic tool enables team members to visualize individual data points and to monitor a process to see
if a “trend” is developing or changing.
Run charts are simple to construct and easy to use and interpret. As with other charting techniques,
team members should focus only on critical changes, being cautious not to consider every variation as
significant. Teams use run charts to monitor performance and identify meaningful trends or shifts in
average process performances.
In clinical laboratories, known control specimens are included in each “run” of glucose analyses to
ensure that all reagents and instrument components are per- forming within expected specifications.
Figure 2.6 is a run chart showing control values for a month. Because control chart construction begins
with an overlay of a run chart with statistical control protocols, this data set is also used in control chart
examples:
•
Track useful information. •
Predict potential variation as data are collected and plotted. •
Focus attention on vital changes to detect meaningful trends.
ONTROL CHARTS
Control charts are graphs that display process characteristics and compare process performance with
statistically derived control limits that measure process variation. Understanding and mastering run
chart concepts, construction, and use to identify process variation is necessary before moving on with
this natural transition to even more fundamental statistical process principles. These concepts and
traditional industrial engineering principles have evolved from their manufacturing origins.
Deming developed these principles to understand and measure process variation over time. By
constructing and using control charts with control limits, IT profes- sionals can incorporate IT to monitor
and manage any healthcare delivery process. Deming concluded that quality is best achieved by simply
controlling and man- aging process variation. Similarly, radical improvement of care delivery processe
s more effectively attained by applying robust IT applications to drive digital transformation of mission
critical processes.
All processes exhibit some inherent variation because variation is simply unavoidable. For example, an
expected proportion of laboratory analytic or billing errors varies from one day to another. By
recognizing this information as funda- mental fact, a conscientious effort is made to control and reduce
variation. Com- mon cause variations are due to variations that are inherent in a process. Special cause
variations are not inherent in a process but are controllable by management:
•
Poor workplace lighting or other aspects of a worker’s environment • Ill-equipped tools,
excessive background noise, and poor ventilation •
Instrumentation or other equipment
malfunction • Poorly designed technical tools, software interfaces, or information systems •
Management failure to provide feedback to each worker
Although complex statistical and comprehensive quantitative analyses are sometimes difficult to
understand and master, they are fundamental concepts and metrics commonly used to manage
healthcare delivery process performance by decreasing process variance from average performance:
•
A control limit is a line (or lines) on a control chart used to assess process variation significance.
•
Variation is the difference of output values of a process from the process mean.
•
V ariation
reflects both common
causes and
special causes. V ariation
beyond a control limit reflects special causes affecting a process.
•
Graphic problem-solving techniques (e.g., control charts) display these variations as important
“current state” problems to be solved and show whether “future state” interventions have been
effective enough to demonstrate a sustainable and controlled process.
•
Statistical process control uses statistical tools and techniques (e.g., control charts) to analyze
process capability, outputs, or outcomes. Appropriate man- agement action or other necessary
countermeasures are required to achieve and sustain improved process capability.
•
Data universe is a population under study from which a sample is drawn. •
Prevention is a
future-oriented strategy that improves quality by directing analysis and action to correct processes, not
ad-hoc problems. Prevention
s more effectively attained by applying robust IT applications to drive digital transformation of mission
critical processes.
All processes exhibit some inherent variation because variation is simply unavoidable. For example, an
expected proportion of laboratory analytic or billing errors varies from one day to another. By
recognizing this information as funda- mental fact, a conscientious effort is made to control and reduce
variation. Com- mon cause variations are due to variations that are inherent in a process. Special cause
variations are not inherent in a process but are controllable by management:
•
Poor workplace lighting or other aspects of a worker’s environment • Ill-equipped tools,
excessive background noise, and poor ventilation •
Instrumentation or other equipment
malfunction • Poorly designed technical tools, software interfaces, or information systems •
Management failure to provide feedback to each worker
Although complex statistical and comprehensive quantitative analyses are sometimes difficult to
understand and master, they are fundamental concepts and metrics commonly used to manage
healthcare delivery process performance by decreasing process variance from average performance:
•
A control limit is a line (or lines) on a control chart used to assess process variation significance.
•
Variation is the difference of output values of a process from the process mean.
•
V ariation
reflects both common
causes and
special causes. V ariation
beyond a control limit reflects special causes affecting a process.
•
Graphic problem-solving techniques (e.g., control charts) display these variations as important
“current state” problems to be solved and show whether “future state” interventions have been
effective enough to demonstrate a sustainable and controlled process.
•
Statistical process control uses statistical tools and techniques (e.g., control charts) to analyze
process capability, outputs, or outcomes. Appropriate man- agement action or other necessary
countermeasures are required to achieve and sustain improved process capability.
•
Data universe is a population under study from which a sample is drawn. •
Prevention is a
future-oriented strategy that improves quality by directing analysis and action to correct processes, not
ad-hoc problems. Prevention
s more effectively attained by applying robust IT applications to drive digital transformation of mission
critical processes.
All processes exhibit some inherent variation because variation is simply unavoidable. For example, an
expected proportion of laboratory analytic or billing errors varies from one day to another. By
recognizing this information as funda- mental fact, a conscientious effort is made to control and reduce
variation. Com- mon cause variations are due to variations that are inherent in a process. Special cause
variations are not inherent in a process but are controllable by management:
•
Poor workplace lighting or other aspects of a worker’s environment • Ill-equipped tools,
excessive background noise, and poor ventilation •
Instrumentation or other equipment
malfunction • Poorly designed technical tools, software interfaces, or information systems •
Management failure to provide feedback to each worker
Although complex statistical and comprehensive quantitative analyses are sometimes difficult to
understand and master, they are fundamental concepts and metrics commonly used to manage
healthcare delivery process performance by decreasing process variance from average performance:
•
A control limit is a line (or lines) on a control chart used to assess process variation significance.
•
Variation is the difference of output values of a process from the process mean.
•
V ariation
reflects both common
causes and
special causes. V ariation
beyond a control limit reflects special causes affecting a process.
•
Graphic problem-solving techniques (e.g., control charts) display these variations as important
“current state” problems to be solved and show whether “future state” interventions have been
effective enough to demonstrate a sustainable and controlled process.
•
Statistical process control uses statistical tools and techniques (e.g., control charts) to analyze
process capability, outputs, or outcomes. Appropriate man- agement action or other necessary
countermeasures are required to achieve and sustain improved process capability.
•
Data universe is a population under study from which a sample is drawn. •
Prevention is a
future-oriented strategy that improves quality by directing analysis and action to correct processes, not
ad-hoc problems. Prevention
s more effectively attained by applying robust IT applications to drive digital transformation of mission
critical processes.
All processes exhibit some inherent variation because variation is simply unavoidable. For example, an
expected proportion of laboratory analytic or billing errors varies from one day to another. By
recognizing this information as funda- mental fact, a conscientious effort is made to control and reduce
variation. Com- mon cause variations are due to variations that are inherent in a process. Special cause
variations are not inherent in a process but are controllable by management:
•
Poor workplace lighting or other aspects of a worker’s environment • Ill-equipped tools,
excessive background noise, and poor ventilation •
Instrumentation or other equipment
malfunction • Poorly designed technical tools, software interfaces, or information systems •
Management failure to provide feedback to each worker
Although complex statistical and comprehensive quantitative analyses are sometimes difficult to
understand and master, they are fundamental concepts and metrics commonly used to manage
healthcare delivery process performance by decreasing process variance from average performance:
•
A control limit is a line (or lines) on a control chart used to assess process variation significance.
•
Variation is the difference of output values of a process from the process mean.
•
V ariation
reflects both common
causes and
special causes. V ariation
beyond a control limit reflects special causes affecting a process.
•
Graphic problem-solving techniques (e.g., control charts) display these variations as important
“current state” problems to be solved and show whether “future state” interventions have been
effective enough to demonstrate a sustainable and controlled process.
•
Statistical process control uses statistical tools and techniques (e.g., control charts) to analyze
process capability, outputs, or outcomes. Appropriate man- agement action or other necessary
countermeasures are required to achieve and sustain improved process capability.
•
Data universe is a population under study from which a sample is drawn. •
Prevention is a
future-oriented strategy that improves quality by directing analysis and action to correct processes, not
ad-hoc problems. Prevention
M DYNAMICS FACILITATION TECHNIQUES
Remaining tools and techniques in this chapter are less quantitative and more qualitative in nature. Each
tool capitalizes on and uses these facilitation techniques to create ideas by harnessing the power of
team dynamics and dialogue. This facil- itation focuses team members on problem identification,
analysis, and solution development.
These tools are used during and after initial brainstorming sessions or other group dynamics techniques
from previous analyses (e.g., NGTs, cause and effect analysis, force field analyses, etc.). Some experts
consider these techniques alter- ative or specialized forms of structured brainstorming.
Figure 2.13 shows a conceptual application of these group dynamics using a real-world example drawn
from the design and development of the National Health Information Network (NHIN). This set of
problem identification and solution development techniques is especially useful as each team member
gains an under- standing of potential issues that identify driving forces, priorities, and desired outcomes.
These tools and techniques facilitate systematic identification, analy- ses, and classification and
prioritization of potential influential cause and effect
Med Health Care and Philos (2015) 18:13–22
DOI 10.1007/s11019-014-9568-7
SCIENTIFIC CONTRIBUTION
Health-care needs and shared decision-making in priority-setting
Erik Gustavsson • Lars Sandman
Published online: 8 May 2014
 Springer Science+Business Media Dordrecht 2014
Abstract In this paper we explore the relation between
health-care needs and patients’ desires within shared
decision-making (SDM) in a context of priority setting in
health care. We begin by outlining some general characteristics of the concept of health-care need as well as the
notions of SDM and desire. Secondly we will discuss how
to distinguish between needs and desires for health care.
Thirdly we present three cases which all aim to bring out
and discuss a number of queries which seem to arise due to
the double focus on a patient’s need and what that patient
desires. These queries regard the following themes: the
objectivity and moral force of needs, the prediction about
what kind of patients which will appear on a micro level,
implications for ranking in priority setting, difficulties
regarding assessing and comparing benefits, and implications for evidence-based medicine.
Keywords Needs
Health-care needs
Shared decisionmaking
Desires
Priority setting
Rationing
E. Gustavsson (&)
Division of Health and Society, Department of Medical and
Health Sciences, Linköping University, 581 83 Linköping,
Sweden
e-mail: erik.gustavsson@liu.se
E. Gustavsson
L. Sandman
The National Center for Priority Setting in Health Care,
Department of Medical and Health Sciences, Linköping
University, Linköping, Sweden
L. Sandman
School of Health Sciences, University of Borås, 510 90 Borås,
Sweden
Introduction
The idea that health care should be distributed according to
need is widely endorsed in the discussion of bioethics as
well as in official guidelines for resource allocation (Crisp
2002; Hasman et al. 2006; Lindsay and Reidar 2008; Hope
et al. 2010; Juth 2013; Swedish Health Care Act 1982: 763,
2 §; The NHS Constitution 2013). The challenges which a
plausible principle of need has to meet have recently been
subject to discussion (Crisp 2002; Hasman et al. 2006;
Hope et al. 2010; Juth 2013), but more work is needed in
order to construct such a principle. In the present paper we
focus on a particular problem concerning the principle of
need which arises because of the increasing emphasis on
involving patients in decisions about their care (The NHS
Constitution 2013; Swedish Health Care Act 1982: 763;
Mead and Bower 2000; Da Silva 2012). To ascribe weight
to both the idea of distributing health care according to
need, and the idea of allowing the patient’s desires1 to
matter in shared decision-making (SDM) within health care
give rise to a possible tension. This paper aims to capture
this tension and to discuss the implied queries.
The structure of the paper is as follows. In the sections
‘‘Health-care needs’’, ‘‘The goal(s) of health care’’ and
‘‘Shared decision-making (SDM) and desires’’ we outline
some general characteristics of health-care needs, SDM and
desires. In ‘‘Needs and desires’’ section we discuss how to
plausibly distinguish between needs and desires for health
care. In ‘‘Three cases: the piano player, the chess player and
the opera singer’’ section we present three cases which aim to
1
In this paper we employ, following Parfit (2011), the notion of
desire in order to denote what a person wants. Hence desires and
wants are used interchangeably. There is a current debate about how
desires are related to other volitional attitudes such as preferences.
See e.g. Schroeder (2009) for a discussion of this issue.
123
14
bring out queries which arise due to this double focus and we
discuss two ways in which this tension may be conceptualized. Drawing on these cases, ‘‘Tension between SDM and
need-based priority setting’’ section aims to bring out and
discuss the following points. First, the more room given to a
patient’s desires in a decision-making process, the less room is
left for the notion of need to do the normative work it was
designed to do in the first place. Second, it implies certain
difficulties with regard to the accessibility of needs. Third,
there are practical implications concerning how to rank
patients with regard to needs when it comes to priority-setting.
Fourth, SDM opens up for further difficulties for priority
setting related to how to understand benefits and implications
for evidence-based medicine. In ‘‘Summing up and conclusions’’ section we sum up and conclude.
Health-care needs
The distribution of health care according to need is often
understood in the following way: the greater one’s need, the
higher the priority one should be accorded.2 The conceptual
structure of needs can be understood in the following way: a
subject x can be benefited by some object y in order to achieve
some goal z (henceforth referred to as the xyz model) (Gustavsson 2014). In ordinary language and general guidelines
for priority-setting in health care the subject x is traditionally
understood, implicitly or explicitly, as an individual or a
representative group of individuals.3
The object y is the thing needed. Since this discussion is
concerned with need for health care we consider an appropriate term for the object y to be ‘‘health-care intervention’’
(intervention for short). Hence the intervention refers to the
object which x needs in order to achieve the goal z. Some need
theorists take the proposition that ‘‘x needs y in order to
achieve z’’ to mean that y is a necessary condition for x in
order to achieve z. As EG has argued elsewhere, we prefer
another interpretation: y can benefit x in order to achieve z.4
E. Gustavsson, L. Sandman
Whatever goal z one sets for a need in the xyz model it
is with a reference to this goal that a need arises. There has
to be some difference between a person’s actual state of z
and the valued state of z (Liss 1993). For example, a patient
may have a low cardiac function which may be associated
with a risk for premature death and sometimes a reduced
quality of life (QoL). Hence there is a gap between the
patient’s actual state of having a low cardiac function and
the valued state of having a higher cardiac function. The
patient needs a certain drug or other intervention in order to
close (fully or partly) the gap between low cardiac function
and higher cardiac function.
From the xyz analysis it seems clear that when x needs
y, x always needs y in order to achieve z, which is to say
that all needs are instrumental.5 To say that needs are
instrumental is to say that whenever x needs y, x will
always need y for the purpose of something else, in order to
achieve some goal. Thus, as health-care needs are instrumental they have to be complemented with some theory of
human good as its goal z in order to have normative
implications.6 Our starting-point here is that whatever such
theory one ends up with it will be closely linked to the goal
of health care.7 In other words, the most reasonable way to
understand z in the xyz model is closely linked to the most
reasonable way to understand the goal of health care.8
Furthermore an intervention may carry benefits which go
beyond the goal(s) of health care. But what should the
goal(s) of health care be? Below we outline some general
views on this matter.
The goal(s) of health care
Well-being as a goal of health care
Some writers argue that the most reasonable position here
would be some general theory of well-being (Crisp 2002;
and to some extent Juth 2013). A common way to distinguish between theories of well-being is to classify them as
2
In this paper we discuss the reasons needs provide for a certain
allocation of resources. There may of course be other relevant aspects
(cost-effectiveness, human dignity etc.).
3
In this paper we shall assume that this is the most reasonable
approach; however, it may be argued that a collective (such as a
couple, a family or even a population) may need a certain intervention
as a collective (and not simply as individuals) as well. If one accepts
such a possibility the issue at stake in this paper would be far more
complex than our discussion suggests. To fully account for such a
complicating factor is beyond the scope of this paper.
4
See (Gustavsson 2014), where two versions of ‘‘can’’ are discussed,
one strong interpretation where a need can be satisfied in a particular
situation s and one weaker interpretation where the ability to satisfy a
need may be taught. The former interpretation will depend on what
interventions and competences are available in s. When we discuss
options for the patient in the following we have this interpretation in
mind.
123
5
Even though some writers disagree (Thomson 1987, 2005), see
(Crisp 2002; Juth 2013; Gustavsson 2014) for arguments that all
needs are instrumental.
6
A complicating factor here is how life-length is to be taken to relate
to well-being. As important as this discussion is we shall only partly
discuss this issue below.
7
It may be argued that the goals of medicine have changed over time
and it may be difficult to distinguish between the goals of different
stakeholders (individual professionals, hospital boards, professional
medical organizations etc.) (see Fleischhauer and Hermerén 2006).
When we refer to the goal(s) of health care we are concerned with the
normative question of what the goal(s) of health care should be, not
what it is/they are or has/have been.
8
See Liss (2003) who argues that one should adhere to this position
for rational reasons.
Decision-making in priority-setting
either hedonistic theories, preferentist theories or ‘‘objective list theories’’ (Parfit 1984; Sumner 1996; Brülde 1998;
Feldman 2004).
1.
2.
3.
Hedonism A key concept in hedonistic theory is
pleasure or happiness. A person is living a good life
if that person is living a life which contains more
pleasure than pain. Hence hedonists often focus on a
person’s experiences.
Preferentism According to prefentist theory a person is
living a good life if that person gets what he or she
desires. The important aspect here is thus that a
person’s life has a greater fulfillment of positive
desires than frustrated desires, or fulfillment of negative desires or aversions.
Objective list theories Adherents of these theories
argue that to what extent a person’s life goes well is
dependent on a number of values. These values are
good for a person independently of his or her attitude
towards them. Thus a life goes well, according to these
theories, to the extent the values on the list are present
in that life.9
Health as a goal of health care
Others have assumed that the goal of health care should be
understood in terms of some theory of health (Liss 1993;
Daniels 1995). For present purposes it is enough to roughly
distinguish between two perspectives on health: the biomedical and the holistic.10 An influential example of the
former is the bio-statistical account of health (BST)
according to which a person is healthy if his or her organs
and tissues are functioning normally given a statistically
normal environment, i.e. making a statistically normal
contribution to the person’s survival. This type of theory
takes health to be the absence of disease, and whether a
person is healthy or not is independent of the person’s
attitudes or evaluation (Boorse 1977).11
A dominant holistic theory of health claims that a person
is healthy if he or she has the ability to achieve his or her
vital goals. The full analysis goes: ‘‘A is healthy if, and
only if, A has the ability, given standard circumstances, to
realize his vital goals, i.e. the set of goals which are necessary and jointly sufficient for his minimal happiness’’
(Nordenfelt 1995, p. 90).12 In contrast to BST, to what
9
On top of this we might have a number of more or less complex
combinations of these three basic theories (see e.g. Brülde 1998).
10
Sometimes other terms are used to mark this distinction: naturalist
versus normativist, or analytic versus holistic.
11
See (Nordenfelt 1995, pp. 23–34) for criticism of this view.
12
This, however, should not be interpreted as the goals which a
person actually has (Nordenfelt 1995, p. 96). Rather there is some
objective relation between a person’s vital goals and minimal
15
extent a person is healthy according to HTH is partly
dependent on that person’s evaluations since one’s minimal
happiness will be partly up to one’s own assessment.13 In
the next section we shall discuss whether there is reason to
employ one of these types of theory rather than the other,
given an idea of SDM.
The conceptual room for the patient’s desires given
different goals of health care
Does a belief in the importance of SDM in medical decision-making provide a reason for preferring one of these
types of theories over another? For example, one may
argue that HTH leaves more room than BST for the
patient’s desires in that it takes into account certain individual aspects, via the notion of vital goals and their
relation to minimal happiness. As true as this is, it is only
true with regard to what room one leaves for individualizing a patient’s need within the notion of health. An
adherent of BST may plausibly add one or more goals to
health and thereby make room for the patient’s desires to
play a part. Simplicity is a virtue when it comes to matters
of this kind but it is difficult to see that keeping the
‘‘individualizing component’’ within the notion of health
would have any practical implications, given that adherents
of BST are pluralists regarding the goal of health care.
Hence this is an argument for HTH to the extent that one
wishes to maintain a monist position regarding this goal.
Furthermore it should be mentioned that none of these
theories can fully account for a patient’s desires since one
may desire ‘‘less than optimal care’’ according to both
theories. We therefore conclude that a preference for this or
that theory does not matter for our argument in this paper.14
Shared decision-making (SDM) and desires
SDM is often considered to be a preferred approach to
medical decision-making in that it takes the patient’s perspective into account but does not make the professional’s
Footnote 12 continued
happiness. In later publications Nordenfelt rather refers to ‘‘…state of
affairs which are necessary…’’ (Our italics. Nordenfelt 2007, p. 93).
Either way the plausibility of HTH seems partly to depend on whether
one can plausibly (in practice) distinguish between vital goals (or
state of affairs) and goals actually set by an individual.
13
See (Brülde 2000a) for criticism of this view. See (Nordenfelt
2000) for a response to (Brülde 2000a), and (Brülde 2000b) for a
response to (Nordenfelt 2000).
14
It is only if one takes the position that the goal of health care can
be based on BST alone no room is left for the patient’s desires.
However, this does not seem as a plausible position—nor does it (to
the best of our knowledge) have any adherents. Even a prominent
adherent of BST as Boorse (1977) does not adhere to this position.
123
16
judgment of how the patient is best benefited superfluous,15
and it has been a trend in recent decades (Da Silva 2012).
Sandman and Munthe (2009) argue that there are nine
models of SDM where the patient’s desires can influence
the decision to different degrees. Furthermore they argue
that some models leave room for the patient to exercise
autonomy while some do not. The models move ‘‘from
classical (Hippocratic) paternalism at one end of the scale…and from a pure form of patient choice at the other
end’’ (Sandman and Munthe 2009, p. 291). In classical
paternalism the professional decides with a minimal
influence from the patient, in pure patient choice the patient
decides with a minimal influence from the professional.
Thus there are two sides to consider with regard to the
decision. First there is the professional’s view of what
treatment would be in the patient’s best interest, second
there is the patient’s own view of this. It is far from clear
how ‘‘patient’s best interest’’ is best understood. The way
in which we put it here leaves room for at least two possibilities: (1) that the patient may be wrong about what is in
his or her best interest, and (2) that the professional may be
wrong about what is in the patient’s best interest.
Da Silva (2012) states that ‘‘[s]hared decision making is
feasible wherever there is no one best evidence-based course
of action’’ (Da Silva 2012, p. 8). However, this seems like a
too narrow role for SDM, since even in cases where there is a
best evidence-based course of action, the patient and professional might still disagree on the whether the outcome of this
course of action is valuable to achieve. Whether SDM should
be used or not is therefore primarily a question of values and
not about evidence (or lack of evidence). In this paper we do
not take a stand concerning which model of SDM is the most
reasonable one—it is enough, for present purposes, to say that
we are interested in models where the patient’s desire is
allowed to influence the outcome (cf. Sandman and Munthe
2009, pp. 291–292), for example that the patient’s desire
results in a modification of the treatment. Hence our interpretation of SDM is different from Da Silva’s since it leaves
room for the possibility that the treatment with the best evidence base is not necessarily in the patient’s best interest.
The important contribution which the patient makes to
SDM is his or her desire about his or her situation. SDM is a
method designed partly to bring out this desire. To desire an
object is, roughly, to have a positive attitude towards that
object.16 One may distinguish between telic and instrumental
desires. Parfit (2011) suggests that a desire is telic when ‘‘we
want some event as an end, or for its own sake’’ (Parfit 2011,
p. 44). Such a desire takes the following form: some subject x
15
In this paper we present no argument as to why it is important to
include the patient’s desires in the decision-making process (see
(Nordin 2000; Sandman et al. 2012) for such arguments).
16
See again Schroeder (2009) for a discussion.
123
E. Gustavsson, L. Sandman
wants some object y (period).17 On the other hand a desire is
instrumental, on this view, when ‘‘we want some event as a
means, because this event would or might cause some other
event that we want’’ (Parfit 2011, p. 44). It follows, then, that a
desire can have the same structure as a need: x wants y in
order to achieve z. This is simply to say that x may want y for
the sake of something else. In the end there will be, it seems
reasonable to say, a z which is desired for its own sake, i.e. the
object of a telic desire.
One may need y and desire y simultaneously. But it does
not necessarily follow from that one needs y that one desire
y, nor vice versa. The relation between needs and desires
will be further discussed in the next section.
Needs and desires
It is often claimed that one may desire what one does not
need and that one may need what one does not desire.
Meanwhile needs are often considered to have a close
relation to, and indeed are sometimes confused with,
desires (or wants). For example, in ordinary language needs
and desires are sometimes used interchangeably. So, when
we say that we badly need a drink, we may be referring to
the desire for a drink. Perhaps instrumental desires are the
ones most often confused with needs since they take the
same formal structure.18 In this section we shall attempt to
characterize the suggested difference between the two
notions.
Frankfurt (1984) suggests that satisfying a need has a
moral weight that satisfying a desire does not have. He
further claims that this Principle of Precedence is appropriate with regard to the same object. For instance, if Jack
needs y and Jill wants y, Jack’s needing y seems to override
that Jill wants it. However, as Frankfurt recognizes: ‘‘The
moral importance of meeting or of not meeting a need must
… be wholly derivative from the importance of the end
which gives rise to it’’ (Frankfurt 1984, p. 2). This remark
is well in line with the instrumental interpretation of needs
presented above. Since we understand needs as being
instrumental the normative force will, in our terminology,
generally depend on z.19
17
For this idea see further (Parfit 1984; Rabinowicz and Österberg
1996; Brülde 1998).
18
Statements about needs and instrumental desires are often
elliptical. That is, they often implicitly presuppose the goal
component.
19
There are cases when y will decide the normativity of the question.
For example, if there is generally some normative problem with using
y, this might influence the normative issue. However, if there are
absolute moral rules against using a certain y (for example if y is
actively and intentionally killing another person)—this might settle
the matter regardless of z. Here we deem these situations to be rare.
Decision-making in priority-setting
One way to understand the difference between desire
and need is in terms of objectivity.20 A need may be
objective in at least two senses: (1) the relation R between
the object y and the goal component z is independent of the
person’s beliefs about R, (2) the goal component z is
something valuable for a person independent of his or her
attitudes towards a given z.
We take objectivity of the relation R to be a defining
characteristic for something to qualify as a need. This
seems to be what Wiggins (1998, p. 6) has in mind when he
writes: ‘‘…if one wants something because it is F, one
believes or suspects that it is F. But if one needs something
because it is F, it must really be F, whether or not one
believes that it is.’’ Griffin (1986, p. 41) expresses this
thought in a similar way: ‘‘…while ‘desire’ is, ‘need’ is not
tied to a subject’s perception of the object…’’ As we
understand Wiggins and Griffin this is to say that needs are
objective in the sense that x may need y independent of
what x believes about y and/or the relation between y and a
particular z.21
From this sense of objectivity it follows at least two
characteristics of needs which make them different from
desires. First, one may fail to want what one needs given a
certain z. For example, Jill may suffer from pneumonia for
which there is an effective intervention I, however, she
may lack information about I and therefore she does not
want I. Perhaps she does not know that there is an intervention like I at all or she knows that there is such an
intervention but not that I has the properties which it
actually has. Still it makes perfect sense to say that Jill
needs I. Second, one may be mistaken about what one
needs. For example, Jill may want a drug D1 because she
believes (incorrectly) that D1 will cure her pneumonia but
D1 does not have the relevant properties to cure it.
Therefore it makes no sense to say that she needs D1,
rather she needs D2 which does in fact have the relevant
properties for curing her pneumonia. Accordingly whether
x needs y or not is a matter of how a given y actually is
constituted and how it therefore would affect a given z at a
given time.22
17
Secondly we may consider the objectivity of the goal for
which one need something, i.e. the z. Firstly, let us consider a telic desire—something one wants for its own sake.
For example, one may have a telic desire to live a long and
happy life. Usually we do not think of such an end as
valuable because it leads us to something else which we
want. It follows from the instrumentality of needs that
needs cannot be telic in this sense. We do not need to live a
long and happy life in order to achieve something else,
rather we may need certain other things in order to live a
long and happy life. Secondly, let us consider an instrumental desire—something one wants because it may lead
to something else one wants. For example, one may want y
(instrumentally) because one believes that y will lead to z
for which one has a telic desire. As noted above an
instrumental desire has the same formal structure as a need,
however, the end-point of an instrumental desire is necessarily something which one wants in some sense. This is
different from a need since it makes perfect sense to say
that one may need a given intervention in order to achieve,
for example, optimal health in a BST sense independently
of whether one wants to have optimal health in the BST
sense or not. However, needs are not necessarily objective
with regard to z. In accordance with Frankfurt (1984) one
may distinguish between volitional needs and non-volitional needs. The former would be a need that to some
extent is depending on one’s desires. In our terminology:
the goal component z is (at least partly) constituted by a
desire. A non-volitional need would rather be when one
needs a particular y in order to achieve a given z which is
good for a person independent of his or her attitudes
towards that z.
This section suggests that needs are objective in at least
one sense which desires are not: objectivity of the relation
between y and z. However, the question is still whether
they should be understood as objective in the sense that z is
valuable for x independent of x’s attitude towards z. As we
shall see when we consider three examples, this difference
may pull us in different directions; and this may pose
difficult questions, some of which we shall discuss below.
20
For further discussion of the relation between desires and needs
see e.g. Wiggins 1998; Thomson 1987; Griffin 1986.
21
These are all theoretical points. Whether we ‘‘know’’ that x needs
y or not within the sphere of health care will depend on whether there
is good reason to believe that y can benefit x in order to achieve z.
Hence in practice the crucial question will not be whether some y
really is F but whether we have good reasons to believe that this y is
F.
22
One may object here that given that one has a rational or fully
informed desire one would not desire D1 but D2. Hence needs may be
fully accounted for in terms of e.g. rational desires. One way to
approach such an objection would be to say that a person who has
rational desires will know what he needs and therefore desire what he
needs. As also noted by Wiggins (1998, p. 6): ‘‘There must of course
Three cases: the piano player, the chess player
and the opera singer
Often when patients’ desires are discussed in the context of
priority setting they are discussed in relation to
Footnote 22 continued
be many other ways of arriving at rational wants than via needs; but
insofar as rationality comes into the matter at all—i.e. rationality as
conceived independently of given actual motivations—the idea of
need surely has to be at least coeval with the idea of want, and should
be accorded its own semantic identity.’’
123
18
E. Gustavsson, L. Sandman
interventions in the following way. Before the professional
meets the patient there has been a ranking of different
interventions at a patient group level (for example based on
need) that could be relevant to use in relation to the
patient’s condition. The professional then offers (and
sometimes also recommends) the patient one or a set of
alternative interventions. Based on the patient’s desires he
or she accepts or declines the offer. If a patient declines a
given intervention, his or her decision ought to be
respected, on the basis of the principle of autonomy.23
However, desires may play a more complex role in this
context. We shall now discuss three cases designed to
illustrate this.24
1.
2.
The Pianist Consider first the case of Mary, a
professional pianist whose hand is injured in a car
accident. She wants to have her hand’s function back
to the level it had prior to the accident. Since she is a
professional pianist, this implies a level of functioning
beyond what most people have and make use of.
Things may be different in the case of, say, Margaret, a
professor who does not depend as much on her hand’s
functioning in daily life and will do with normal
functioning.
The chess player Consider next the case of a chess
player, Jesper, who suffers an injury to the anterior
cruciate ligament (ACL) of his knee.25 One option for
him is to have a new ACL constructed, which would
restore the knee to its former level—he will once again
have the ability to run, for example. The problem with
this option is that the rehabilitation will take a lot of
time and effort. He will have to see a physiotherapist
every week and do exercises every day. Jesper does not
23
It may be argued that when people decline a certain intervention, it
is because they have some problem with the intervention per se.
Consider for instance John, a Jehovah’s Witness who refuses a blood
transfusion because of his religious beliefs. Though it may seem in
such a case that the patient has a desire concerning the intervention y,
another way to understand this situation is that he has a desire directed
towards the goal component. His desire is not primarily to have
optimal health or well-being in this life but to live some other life
after this life—or rather, the latter desire overrides the former desire
(since he may still desire a life with optimal health and well-being
provided that this does not conflict with life on the other side of
death). The reason he declines y (the blood transfusion), is because he
believes y will frustrate this goal. Thus such cases can often be
understood in terms of the desire’s being directed, in the first place,
not towards y but towards z.
24
In these examples people desire to modify their treatment for
different reasons. One may consider these reasons either more or less
appropriate. The question of what reasons it is appropriate to take into
account in such situations is a difficult normative one which needs
more thorough analysis. It is worth noting here that the notion of
‘‘window of compromise’’ (discussed below) may offer some sort of
answer.
25
The human knee has four major ligaments. The anterior cruciate
ligament is one of them.
123
3.
want that. He does not desire to be able to run again, it
does not matter to him. Jesper’s other option is to
simply have the ACL removed. Then the knee will
work fine in everyday life but he will not be able to do
more demanding things such as running. He does not
want to undergo the treatment where they construct a
new ACL, for him it is enough to just get the knee to a
state where he can sit, stand and walk without pain.
Things may be different for, say, a person who likes to
run the marathon but now has the same knee trouble.
The opera singer Consider William, a patient who is
taking a diuretic as part of the treatment for certain
heart complications but who wants to reduce his intake
in order to avoid constant visits to the toilet and is
willing to accept the increased risk involved in such a
reduction. Assume that the standard dosage for this
kind of patient is 10 pills a day, which gives a 90 %
reduction in the risk of future complications. The pills
will still have some effect if he takes 5 a day, but 4 or
fewer are likely to have no effect. Taking 5 will reduce
the risk of future complications by 45 %, not 90.
However, taking 5 will also reduce the number of visits
to the toilet. William knows that one of the side effects
of this diuretic (especially if the dosage is as much as
10 pills) is that one constantly has to go to the toilet.
Now, this is unfortunate for him in view of his
profession. He is an opera singer and his performances
often last up to 4 h. Therefore he strongly desires to
avoid constant visits to the toilet, even if the price to
pay is increased risk of future complications.26
Unlike his brother who works in an office and has the
same sort of heart trouble, William would rather have
increased risk of future complications than suffer the side
effects of the more potent intervention.27
Two ways to conceptualize these cases: volitional needs
and non-volitional needs
It may be a difficult task how one should understand these
cases in terms of needs and desires since there may be
intuitions pulling in different directions. In this section we
shall, following Frankfurt’s distinction between non-volitional needs and volitional needs, conceptualize what is
going on in the three cases presented above.
26
To provide a patient with 5 rather than 10 pills here may in certain
contexts be referred to as ‘‘providing less than optimal care’’ (Lantos
et al. 2011). However, such a position presupposes that one does not
regard the particular patient’s desires as having anything to do with
what is optimal care for him or her. We discuss this further below.
27
The case where x should be given a milder intervention may also
derive from strictly medical considerations. For example, x may have
other complications than heart failure (kidney trouble, for example)
and therefore ought not to take 10 pills.
Decision-making in priority-setting
(A)
(B)
Non-volitional needs Let us start with the piano
player. Does her desire to have her hand’s function
back to the level it had prior to the accident have any
implications on her need? One way to go about this
question would be to try to maintain a sharp
distinction between needs and desires and accordingly argue that the piano player and the professor
have identical non-volitional needs. The difference
between them is that they happen to desire different
ways of life. In this vein one would most reasonably
arrive at the same conclusion regarding the chess
player case, Jesper and the marathon runner; they
have identical non-volitional needs but different
desires. It seems also to follow from this line of
reasoning that the opera singer William and his
brother have identical non-volitional needs—both of
them need 10 pills—but they have different desires.
In this way it is possible to maintain a clear
distinction between the need and desire of the
patients.
Volitional needs Does it make sense to say that the
opera singer needs 10 pills while this would make
him achieve a state that he strongly desires not to
achieve? One may find it more reasonable to say that
he does not need (given the nature of his life) a 90 %
reduction in the risk of future complications in
combination with having to constantly go to the
toilet. He rather needs, given the interventions
available in this situation, the intervention that does
not make him visit the toilet constantly but at the
same time have the (lower but accepted) effect of
only resulting in a 45 % reduction in the risk of
future complications.28 Hence he has a different goal
z with reference to which we may understand his
need for health care. If one argues along these lines
the goal of a patient’s need z may be partly or even
substantially constituted by the patient’s desire.
In the same vein one may argue that it may make better
sense to say that a professional pianist has a greater need
than a professor would have with the same hand injury.
Similarly it would seem that the chess player has a smaller
need than the marathon runner who has suffered the
identical type of injury.
Note that one may arrive at the same normative conclusion about what one should do in a specific situation
according to (A) and (B). According to the former it
matters what a patient needs and what he or she wants.
19
These are two independent factors which both should affect
a decision. According to (B) a patient’s desire affects the
patient’s need in a way that may be relevant for what ought
to be done.
The issue at stake is whether it makes sense to say that
the z component of a need may be partly constituted by a
desire. We shall explore some arguments to this effect,
however, our main objective in the next section is to
highlight some important aspects which need to be discussed in any health care system which wants to practice
priority setting according to need and practice SDM. These
aspects seem to arise independent of whether one is leaning
towards (A) or (B).29
Tension between SDM and need-based priority setting
In this section we will analyze more in detail different
problematic aspects or queries arising from the tension
between desires within SDM and a need-based priority
setting.
The objectivity and moral force of needs
As mentioned above needs may be objective in at least two
ways: (1) objectivity of the relation R, (2) objectivity of the
goal component z. According to our interpretation of
Frankfurt non-volitional needs are objective in both these
senses and falls under the principle of precedence. Volitional needs are also objective, however, only in the former
sense and they do not fall under this principle (cf. Frankfurt
1984, pp. 4–6). The idea lurking here seems to be that
needs to a great extent gain their moral force from this
objective characteristic of the goal.
To adhere to position (A) may seem to have the
advantage of maintaining the distinction between needs
and desires. But why would it be important to maintain this
distinction? One answer may be that needs have a normative force which desires do not have and to adhere to
(A) would help to maintain this normative force of needs.
But that cannot be it since the (A) adherent would, by
letting desires matter for an outcome (in line with SDM)
have to diminish the weight given to needs in priority
setting. It follows then that also desires seem to have some
kind of normative force which has to be accounted for.
In addition one could argue that there may be some
needs for which such an analysis cannot account satisfactorily. For example, it may strike some as counter intuitive
28
One may suggest that what the opera singer really needs is an
intervention that gives him 100 % reduction in the risk of future
complications and no increased urge to go to the toilet. But this is
something which health care cannot offer in the present state of
affairs. See further footnote 4.
29
Above we have assumed that the goal component z of a need will
be closely linked to the goal(s) of health care. It follows from the
(A) interpretation, however, that the latter will be wider than the
former.
123
20
to say that the opera singer needs 10 pills. One reason for
that may be the following: many people would agree that
the intervention appropriate for William is 5 pills therefore
he ought to be given 5 pills. To say that the appropriate
intervention would be 10 pills creates some kind of gap
between what a person needs and what he or she ought to
be given.
Given that one rather understands the relation according
to (B) the idea that needs are objective in this sense collapses, (B) may account for the patient’s needs in a more
reasonable way meanwhile distorting the distinction
between needs and desires. But it may also be noted that
some needs do not even seem to arise without a desire
partly constituting the goal component. For example, the
need for assisted reproductive technologies does not seem
to arise independent of some desire to become pregnant.
Independent of whether one adheres to (A) or (B) it
follows that the more a patient’s desire is allowed to
influence the decision, the more one moves away from
distributing health care according to need and the closer
one comes to distributing it according to wants. But again
this is not due to one’s choice of (A) or (B) per se, but this
is an implication of introducing patient’s desires within
SDM in a need-based priority setting. Such an argument
suggests again that the idea of distributing health care
according to need has some objective characteristic of the
goal z lurking in the background. But our discussion suggests that it may be easier to uphold this objective characteristic on a group level (henceforth referred to as the
macro level) than on an individual level (henceforth
referred to as the micro level).30 This will be developed
further in the following.
The inaccessibility of needs
There is a classical tension between the macro and micro
level since patients at the latter level may deviate from
assumptions made on the former. This tension arises
independently of whether one practices SDM or not,
however, it seems that SDM pushes this problem one step
further since a patient may now introduce aspects which
did not even enter into the equation on a macro level. For
example, SDM opens the door to the possibility that the
best evidence-based course of action may no longer be the
30
It may be objected that decision-makers are already able to assess
people’s needs and their desires on a macro level. QoL (or Health)
instruments are used to assess how people are affected by health-care
problems and interventions to handle these problems, and the data
acquired are then used in priority setting. But the evaluation of the
need component of the problem (the effect on the person with no
intervention) is less frequently used than how certain interventions
affect these factors. Second, these instruments are based on only a
rough picture of what kind of desires people usually have.
123
E. Gustavsson, L. Sandman
course of action that is in the patient’s best interest. A
patient may desire to have ‘‘less and more than optimal
care’’. Exactly how difficult these problems will be to
handle will depend on the extent to which individual
patients will diverge from the ‘‘normal’’ patient.
Implications for needs in priority setting
When a principle of need is used in priority setting it is
generally firstly used at the macro level. By assessing the
general need of the group of patients (together with other
aspects that should guide the priority setting) we arrive at a
certain ranking of a given intervention related to this need.
In this assessment of the need of the patient group, we
assume a certain objectivity or inter-subjectivity of these
needs, i.e. that we can roughly determine the degree of
need for a certain patient group’s condition. But when we
turn to the individual level there may be several individual
deviations. The difficult normative question here is whether, and if so to what extent, the patient’s desire in certain
cases should determine his or her ranking on the micro
level. Intuitively one may say that it seems reasonable to
answer in the affirmative in, for example, the pianist case.
According to the (B) interpretation one might add that
the reason for why we should prioritize the pianist in favor
of the professor is that the pianist has a greater need than
the professor. In this vein one could argue that it is given
the goal (or, more precisely, given that there are different
goals), that the pianist’s need is greater than the professor’s. This line of reasoning suggests that it is worse to be
in the pianist’s situation than to be in the professor’s situation—and, similarly, that it is worse to be in the marathon runner’s situation than in the chess player’s situation.
According to (A) the size of x’s need may be determined
independent of what x wants. One may say according to
(A) that what determines how we should prioritize x
depends on how badly off one is and what that person
wants. But the intuition that we should prioritize the pianist
in favor of the professor seems dependent on that it is
worse to be in the pianist’s shoes than in the professor’s.
That is, her desire to play the piano does not seem to be
distinguishable (in any easy way) from how badly off she
is. The (B) interpretation captures this intuition.
Moving on to the opera singer case, how should we
understand the shift from 10 to 5 pills? Here it is difficult to
reason in terms of the above-mentioned one dimensional
gap when determining the size of the need of the opera
singer. This, since we are here dealing with two different
dimensions, where William, the opera singer, in a sense has
higher demands when it comes to QoL (he does not accept
the lowered QoL that would result from 10 pills), but
accepts a lower level when it comes to reducing the risk to
his longevity. A tentative idea would be to introduce a two
Decision-making in priority-setting
dimensional assessment of the need in terms of a weighing
together of QoL and life-length, in line with the qualityadjusted life years (QALY)-measurement. SDM then
strongly suggests that it should be up to the patient to
decide which one matters more for him or her. Hence, in
the opera singer case it will be more complex to assess
whether William has a greater need than his brother. To
arrive at such a conclusion requires more work on the
relationship between QoL and life-length.
Further implications for priority setting
We have argued that the double focus of needs and desires
within SDM poses problems regarding how to understand
how badly off a patient is. In this section we shall briefly
discuss two further implications for priority setting.
First, it may be argued that a plausible interpretation of a
need principle should imply that there are stronger reasons
to give priority to the office worker rather than the opera
singer since the treatment is of greater benefit to him. But
this approach only helps if we have a clear-cut definition of
what benefitting these people (individually) consists in.
According to the (A) interpretation the office worker seems
to have been benefited more than the opera singer since he
has a risk reduction of 90 % while the opera singer has a
risk reduction of 45 %. Accordingly they have identical
needs, the only difference is that the opera singer is satisfied with ‘‘less than optimal care’’ for private reasons. But
saying that 5 pills is ‘‘less than optimal care’’ for the opera
singer presupposes that one does not regard his desire as
partly determining what optimal care is for him. Hence it
does not seem to capture all that matters for an outcome.
Such a conclusion seems to be at odds with the key idea of
SDM as well as counter intuitive.
In line with the (B) interpretation it may seem that both
the opera singer and the office worker have been benefited
in a way which make it reasonable to say that their needs
are satisfied—but through a dosage of 5 in the case of the
first, and 10 in the case of the second. The desires presented
within SDM are in different ways and to a differing degree
connected to various dimensions of a good life. These
dimensions are not easily transferable or easily compared
with each other. Hence we no longer have any simple idea
about how to understand how a patient is best benefited and
especially how to compare a benefit to one with benefit to
the other.
Second, the idea of benefit has a close relation to the
notion of evidence-based medicine for which SDM has
implications. These implications have to do with the fact
that there will not always be sufficient data connected to all
the options patients may desire. In the opera singer case we
assume that we know something about what effect different
amounts of pills have on a particular heart condition. We
21
know that 10 pills will reduce the risk of future complications by 90 % while 5 pills will reduce it by 45 %, and
we know that 4 pills have no effect (and so on). But for
practical reasons the requisite knowledge will seldom be
available to professionals and patients.31
In situations where there are rich data, however, we may
introduce some room for negotiation, a ‘‘window of compromise’’ (Sandman et al. 2012; Sandman and Munthe
2009).32 For example, in the opera singer case it seems that
5–10 pills would constitute this window of compromise.
This would partly answer the question to what extent a
patient’s desire should be allowed to influence a medical
decision. A well-worked-out idea about the reasonable
boundaries for such a window of compromise may strike
the appropriate balance between the patient’s and the
professional’s opinion.33
Summing up and conclusions
In this paper we have explored the relation between a
patient’s desires within SDM and the idea of distribution of
health care according to need. The discussion throughout
this paper suggests that there is a tension between these two
ideas which raises the following issues.
Firstly, normatively relevant needs are traditionally
assumed to be objective. To let a patient’s desire matter for
his or her ranking in priority setting is to diminish the role
of this traditional idea. It seems, however, easier to uphold
this approach to needs on a macro level than on a micro
level.
Secondly, to move from a macro level to a micro level
involves a difficulty to predict what patients one may
expect on the latter. To introduce SDM pushes this difficulty one step further.
Thirdly, a difficult normative issue arising from this
double focus is whether, and if so to what extent, a
patient’s desires should have implications for a patient’s
ranking in priority setting on a micro level. Furthermore
the introduction of SDM may make a patient’s desire not
only determine his or her QoL (and/or health) but also how
to weigh QoL (and/or health) against life-length.
31
A different, though related, question is how to relate to situations
where there is only weak evidence for a given intervention.
32
It is worth noting here, as is also mentioned in (Sandman et al.
2012; Sandman and Munthe 2009), that the size of a reasonable
window of compromise is not determined solely by the degree of
evidence for a given intervention but also by such factors as access to
resources and ethical boundaries…