Final Report 

Industrial and Systems Engineering and Health Care: 

Critical Areas of Research 

Prepared for: 

Agency for Healthcare Research and Quality 

U.S. Department of Health and Human Services 

540 Gaither Road 

Rockville, MD 20850 

www.ahrq.gov 

Contract No. 290-09-00027U 

Prepared by: 

Rupa Sheth Valdez, Edmond Ramly, and Patricia Flatley Brennan 

University of Wisconsin-Madison, Madison, WI 

Authors: 

Rupa Sheth Valdez 

Edmond Ramly 

Patricia Flatley Brennan 

AHRQ Publication No. 10-0079

May 2010 

HEALTH IT 
This document is in the public domain and may be used and reprinted with permission except 

those copyrighted materials that are clearly noted in the document. Further reproduction of those 

copyrighted materials is prohibited without the specific permission of copyright holders. 

Suggested Citation: 

Valdez RS, Ramly E, Brennan PF. Industrial and Systems Engineering and Health Care: Critical 

Areas of Research--Final Report. (Prepared by Professional and Scientific Associates under 

Contract No. 290-09-00027U.) AHRQ Publication No. 10-0079. Rockville, MD: Agency for 

Healthcare Research and Quality. May 2010. 

None of the investigators has any affiliations or financial involvement that conflicts 

with the material presented in this report. 

ii 
Acknowledgments 

We would like to recognize the sustained help of Teresa Zayas-Cabán, the AHRQ Project 

Officer. We would also like to thank all reviewers who provided feedback on earlier drafts of 

this report. In particular, we would like to thank Brian Denton, Assistant Professor, North 

Carolina State University; Michael M.E. Johns, University Chancellor, Emory University; 

Michelle L. Rogers, Assistant Professor, Drexel University; William B. Rouse, Professor, 

Georgia Institute of Technology; James M. Walker, Chief Health Information Officer, Geisinger 

Health System; José Zayas-Castro, Professor, University of South Florida for their thoughtful 

feedback. 

iii 
 

Contents 

Executive Summary ................................................................................................................... 1 

Background.......................................................................................................................... 1 

Objectives ............................................................................................................................ 1 

Methodology........................................................................................................................ 1 

Vision of an Ideal Health Care Delivery System................................................................. 2 

Barriers and Facilitators to Achieving Breakthrough Change with ISyE Knowledge ........ 2 

Research Agenda ................................................................................................................. 2 

 Knowledge Innovation ................................................................... ......... 3 

 Knowledge Transfer..................................................................... ........... 6 

Meta-Knowledge Integration..................................................................... 6 

Action Agenda........................................................................... ................. 6 

Collaboration....................................................................................... 6 

Education and Training........................................................................... 7 

Funding........................................................................................ ..... 7

 Dissemination.................................................................................. ... 7 

 Administration.............................................................................. ....... 7 

Chapter 1: Introduction............................................................................ .......... 9 

Chapter 2: Methodology........................................................................... .......... 13 

Background Report .............................................................................. ..... 13 

Workshop............................................................................................................................. 14 

Workshop Scope............................................................................. ...... 14 

Workshop Format .......................................................................... ....... 14 

Synthesis...................................................................................... ...... 16 

Chapter 3: Vision of an Ideal Health Care Delivery System.............................. ............. 17 

A New System ..................................................................................................................... 17 

Patient-Centered System...................................................................... ......... 17 

An Engineered System.......................................................................... ....... 18 

Chapter 4: Barriers and Facilitators to Achieving Breakthrough Change with ISyE ................ 21 

Knowledge ........................................................................................................................... 21 

Barriers................................................................................................................................. 21 

Facilitators............................................................................................... 25 

v 
 

Chapter 5: ISyE and Health Care Research Agenda.................................................................. 27 

Purpose and Scope of the Research Agenda........................................................ 27 

Challenges Encountered in Developing a Research Agenda Focused on New IsyE 

Methods.............................................................................................................................. 28 

Presentation of the Research Agenda .................................................................................. 29 

Research Agenda: Knowledge Innovation ..................................................................... 30 

Research Agenda: Knowledge Transfer ........................................................................ 40 

Research Agenda: Meta-Knowledge Integration ........................................................... 41 

Summary of Research Agenda............................................................. ............ 42 

Discussion of Research Agenda...................................................................... 44 

Chapter 6: ISyE and Health Care Action Agenda........................................... ............ 45 

Purpose and Scope of the Action Agenda........................................................... 45 

Presentation of the Action Agenda............................................................ ...... 45 

Collaboration....................................................................................... 45 

Education and Training........................................................................... 47 

Funding....................................................................................... ...... 47 

Dissemination................................................................................ ...... 48 

Administration.............................................................................. ....... 49 

Chapter 7: Conclusion................................................................................................................ 51 

References......................................................................................... ............ 53 

Tables 

Table 1: Mapping Between Barriers Identified and Recommendations Proposed .................... 24 

Table 2: Research Agenda Items That Support Breakthrough........................ .................. 42 

Table 3: Research Agenda Items That Support Sustainability........................ .................. 43 

Table 4: Research Agenda Items That Support Capacity Building..................... ............... 44 

Appendixes 

Appendix A: Background Report .............................................................. ........... A-1 

Appendix B: Workshop Participants........................................................... ........... B-1 

Appendix C: Workshop Schedule.............................................................. ............ C-1 

Appendix D: ISyE Small Group Assignments........................................................................... D-1 

Appendix E: Health Care Challenge Area Small Group Assignments ...................................... E-1 

vi 
Executive Summary 

Background 

Industrial and systems engineering (ISyE) has the potential to address many of the challenges 

faced by the health care delivery system. Mindful of potential synergies between health care and 

ISyE, the Agency for Healthcare Research and Quality (AHRQ) and the National Science 

Foundation (NSF) convened a workshop in which experts in both fields were asked to explore 

the critical areas of research at the intersection of ISyE and health care, with a special emphasis 

on the supportive role of health information technology (IT). 

Objectives 

The objectives of the project were to (1) articulate a vision for an ideal health care delivery 

system, (2) determine why current efforts to apply ISyE knowledge to health care have not 

resulted in meaningful change, and (3) propose a research and action agenda that should be 

pursued to enable the field of ISyE to substantially contribute to the realization of an ideal health 

care delivery system. 

Methodology 

Background materials included thirteen seminal reports generated in the last decade by 

various national bodies (National Academy of Sciences, Institute of Medicine, National 

Academy of Engineering, National Science Foundation), and a background report reviewing and 

critiquing them. Analysis of background materials led to the formulation of purpose statements 

for the workshop, guided participant invitations, and helped shape the activities undertaken as 

part of the workshop and afterwards. The background materials were circulated to all participants 

prior to the workshop. Presentations and keynote addresses during the workshop contributed to 

providing a backdrop for discussions. 

Small group discussions were used to probe deeply into how specific ISyE specialties could 

address health care challenges. Five ISyE specialty groups and six health care challenge area 

groups were constructed. Each participant was assigned to one ISyE group and one crosscutting 

health care challenge area group. Large group discussions served to identify points of consensus 

and tension, and to report out conclusions reached by the small groups. Discussions were 

captured by professional note takers, and small group discussions were professionally facilitated. 

This final report synthesizes and discusses the outcomes of the workshop within the context 

of the knowledge gleaned from the background materials. We emphasize throughout the report 

that the project objectives call for system-wide breakthrough change, and propose a research 

agenda and an action agenda. 

1 
Vision of an Ideal Health Care Delivery System
 

Presentations by the workshop chair, two keynote speakers, and six domain specific experts 

informed the creation of a vision that departs from current realities and explores characteristics 

of an ideal system. The emphasis is on a system that is new, patient-centered, and engineered: 

(1) The new, redesigned system is integrated, ubiquitous, distributed, responsive, expansive, 

flexible, and resilient. (2) Delivery of health care is personalized, facilitated by secure 

information flow, and mindful of patient privacy. Transparency and open access enable people to 

make informed choices about their health, with a focus on prevention and health promotion. (3) 

The delivery system is information-optimized and runs smoothly, efficiently, and safely. All 

stakeholders leverage ISyE and information and communication technologies to drive both 

subsystem and system-wide changes. Incentives are aligned to enhance quality of life for all, at 

the individual and population levels. Evidence-based analytics and mathematical modeling 

inform standard care processes and biomedical knowledge discovery. 

Barriers and Facilitators to Achieving Breakthrough Change 

with ISyE Knowledge 

Nine barriers and four facilitators to achieving breakthrough change to the health care delivery 

system were identified. 

Barriers: (1) Lack of widespread use of ISyE tools, (2) inadequate current ISyE knowledge, (3) 

insufficient health IT infrastructure, (4) structural and cultural traditions in the health care 

delivery system emphasizing only subsystem improvement and short-term outcomes and 

rewards, (5) insufficient pathways which promote use of ISyE knowledge in health care, (6) lack 

of efficient ways to spread knowledge between ISyE professionals working in health care, (7) no 

clear funding structures to support development of new ISyE knowledge, (8) current policies 

constraining level of change possible, and (9) lack of professionals with an adequate 

understanding of both ISyE and health care. 

Facilitators: (1) Increasing recognition of the potential of ISyE to improve health care, (2) 

progress in both recognition of the need for and the development of new ISyE tools, (3) progress 

in the use of IT to disseminate ISyE knowledge, and (4) current political climate of health care 

reform. 

Research Agenda 

Two main challenges were faced in developing a research agenda focused on new ISyE 

knowledge: (1) lack of clarity about the scope of ISyE and(2) belief in the adequacy of current 

ISyE knowledge leading to difficulty thinking “out of the box.” Despite these challenges, 

research directions were identified and divided into topics. The research agenda is divided into 

three themes: 

•	 Stimulate innovation in ISyE methods better aligned with the complex, distributed, 

and stochastic nature of health care (Knowledge Innovation); 

2 
 

•	 Accelerate knowledge transfer of ISyE methods to solve currently recognized health 

care challenges (Knowledge Transfer); and 

•	 Integrate over-arching meta-knowledge lessons gleaned from purposefully- targeted 

research projects (Meta-Knowledge Integration). 

To aid agencies and researchers in utilizing the research agenda effectively, a secondary, 

complementary categorization of key themes of the research agenda items is offered. 

“Breakthrough” includes items that are essential to realizing the vision of the new health care 

delivery system. “Sustainability” includes items that are likely to have benefit and improve the 

health care delivery system, but will not lead to breakthrough changes. “Capacity building” 

includes items that are necessary to expand the breadth and depth of ISyE knowledge relevant to 

health care. These categories are conceptualized as complementary, and are all likely to be 

necessary to achieving the vision of an ideal health care delivery system. 

Knowledge Innovation 

The majority of the research agenda focused on stimulating ISyE knowledge to address 

unresolved, unanswered questions posed by the challenges in contemporary health care delivery. 

The knowledge innovation directions are presented along the lines of system monitoring, system 

modeling, and system modification. System monitoring research agenda items are required to 

improve assessment of the system and communication of these assessments to relevant 

stakeholders. System modeling research agenda items are required to improve understanding of 

system components and interactions between system components. Finally, system modification 

research agenda items are required to ensure efficient and effective system transformation. 

System monitoring 

The agenda items concerning system monitoring are divided into four subtopics: data 

collection, integration, characterization, and presentation. 

Data Collection 

(1) (Breakthrough) Consumer-facing health IT solutions that allow patients to self-report 

their observations, that track and report on trends, and that interact with providers’ annotations; 

(2) (Sustainability) Efficient and pervasive methods of data capture; (3) (Sustainability) New 

automatic data collection technologies to capture observations from patients and their 

environments (e.g., sun exposure and food intake); (4) (Sustainability) Theories and methods 

beyond natural language processing for the translation of lay person language into structured 

computable data. 

3 
 

Integration 

(1) (Breakthrough)Technologies which enable data to flow quickly and securely through the 

whole health care delivery system and be available in real-time when and where needed; (2) 

(Sustainability) Efficient methods for integrating large amounts of data from disparate sources; 

(3) (Sustainability) Adequate integration of data collection into workflows in manners that 

ensure data validity while minimizing interference with clinical workflows; (4) (Sustainability) 

Efficient means of integrating information generated from different perspectives (e.g., different 

providers, patients, administrators). 

Characterization 

(1) (Breakthrough) Methods to operationalize contextual knowledge to understand 

generalizability of data; (2) (Sustainability) Methods to characterize how the outcomes relate to 

the processes; (3) (Capacity building) Methods to characterize processes, inputs, and outcomes. 

Presentation 

(1) (Breakthrough) Methods to effectively collect and share data in real-time to foster 

situational awareness of all individuals involved in patient care; (2) (Capacity building) Methods 

to collect and present information that is valuable to diverse stakeholders such as patients, 

nurses, primary care and specialty physicians, pharmacists, and social workers; (3) (Capacity 

building) Theories and methods for the translation of numerical, analytical, and computational 

results into understandable and actionable information that multiple stakeholders (e.g., nurses, 

primary care and specialty care physicians, or pharmacists) and lay people can seamlessly 

retrieve to ensure the human monitoring of the system. 

System Modeling 

The agenda items concerning system modeling are divided into four subtopics, covering 

descriptive, predictive, and prescriptive models, as well as inconvenient realities that models 

need to be able to handle. 

Descriptive models 

(1) (Breakthrough) Frameworks that explore the integration of many care sources in the 

production and delivery of care services, and the coordination among these sources (e.g., at end 

of life care); (2) (Breakthrough) Methods to model systems as sets of flows and processes, not 

just sets of components; (3) (Breakthrough) Models that explore the effective use and allocation 

of different vehicles of health care delivery (e.g., “focused factories” versus integration, such as 

Mayo Clinics and Kaiser Permanente); (4) (Sustainability) Models of trust between patients, 

providers, and technology. 

4 
 

 

Predictive models 

(1) (Breakthrough) Models to evaluate entire systems and large-scale system changes before 

they are implemented; (2) (Capacity building) Models to mitigate uncertainties about the future. 

Prescriptive models 

(1) (Breakthrough) Models of collaboration and competition among health care stakeholders; 

(2) (Breakthrough) Models that consider how health IT can be integrated into decision making 

processes, how evidence-based knowledge can be integrated into practice; (3) (Sustainability) 

Models that appropriately consider the conflicting objectives of multiple stakeholders and make 

optimal recommendations for the system overall; (4) (Sustainability) Models that provide 

guidance about when either standardization or customization is necessary; (5) (Capacity 

building) Models to explore the role and consequences of automation, and provide guidance 

about what can be fully or partially automated; (6) (Breakthrough) Mathematical programming 

models. 

Models that can handle inconvenient realities

 (1) (Breakthrough) Methods to build models from incomplete, inaccurate, and unreliable 

data; (2) (Breakthrough) Methods to build models from inconsistent data coming from disparate 

sources; (3) (Breakthrough) Methods to model unstable systems; (4) (Breakthrough) Methods to 

model large-scale, distributed systems where loose coupling occurs; (5) (Breakthrough) Models 

that can integrate qualitative and contextual knowledge (e.g., culture, ethics, law, psychology, 

social networks, and politics) and be responsive to changes these qualitative and contextual 

factors; (6) (Sustainability) Methods to model the dynamics between micro-changes (at the 

patient and provider level) and macro-changes (at the population, market and policy levels); (7) 

(Capacity building) Models that incorporate errors and interaction of events. 

System Modification 

The agenda items concerning system modification are divided into three subtopics: researchpractice integration, top-down decomposition, and bottom-up integration. 

Research-practice integration 

(1) (Sustainability) Iterative knowledge development and transfer between research and 

practice; (2) (Sustainability) Improving translation from mathematical and technical languages 

into lay person terminology; (3) (Capacity building) Improving lay people’s understanding of 

analytical results by developing enhanced data visualization techniques. 

Top-down decomposition 

(1) (Breakthrough) Determining ways to modify public and private incentives to influence 

patients to stay healthy, providers to work in the best interest of their patients, and organizations 

to be efficient, without unintended negative consequences; (2) (Breakthrough) Exploring 

payment structures that accommodate technologically mediated interactions between providers 

5 
 

and patients (e.g., text messaging, email, visits by teleconference, etc.); (3) (Sustainability) 

Testing of change and implementation theories, and exploration of the tension between pushing 

for the application of existing knowledge and trying to develop more usable new knowledge; (4) 

(Capacity building) Determining the benefits, limitations, and appropriate use of national, 

regional, and institutional forcing functions within the health care setting. 

Bottom-up integration 

(1) (Breakthrough) Determining appropriate approaches to stimulating system-wide change, 

exploring ways to coordinate between bottom-up integration and top-down decomposition; (2) 

(Sustainability) Exploring how social network theories can be used to trigger and facilitate 

culture change; (3) (Capacity building) Determining the role of culture as a necessary element of 

health care improvement, including the national political conversation and at the level of the 

patient and provider. 

Knowledge Transfer 

It is timely to accelerate knowledge transfer of ISyE methods to solve currently recognized 

health care challenges. Two areas of research for knowledge transfer were identified:(1) 

(Capacity building) Identification of best practices for dissemination and adoption of ISyE 

knowledge; (2) (Capacity building) Identification of best practices for spreading new ISyE 

knowledge between research and industry and within industry. 

Meta-Knowledge Integration 

There is a need to integrate over-arching meta-knowledge lessons gleaned from purposefullytargeted research projects. Five areas of research for the development of meta-knowledge were 

identified:(1) (Sustainability) Characterizing health care challenges; (2) (Capacity building) 

Mapping the usefulness of ISyE knowledge to different health care contexts; (3) (Breakthrough) 

Exploiting synergies within ISyE knowledge derived from different subdisciplines; (4) 

(Sustainability) Identification of best practices for use of ISyE knowledge; (5) (Capacity 

building) Characterizing research frontiers and directions at the intersection of ISyE and health 

care. 

Action Agenda 

Action items in five domains were identified to push the research agenda forward and ensure 

a timely realization of the vision of an ideal health care delivery system. These domains include 

collaboration, education and training, funding, dissemination, and administration. 

6 
 

Collaboration 

1) Creating consortia of all stakeholders including government, providers, payers, consumers, 

the insurance industry, and vendors; (2) Promoting multi-stakeholder conversations through 

interdisciplinary projects; (3) Fostering partnerships among and between agencies, organizations, 

associations, academia, and industry; (4) Identifying or forming a professional home at the 

intersection of ISyE and health care. 

Education and Training 

(1) Creating and enhancing interdisciplinary higher education programs; (2) Expanding 

professional development and cross-training; (3) Initiating early and mid-career fellowships in 

health care for ISyE professionals; (4) Compiling a library of case studies. 

Funding 

(1) Investing in high potential research focusing on the knowledge innovation directions 

presented in the research agenda; (2) Supporting community-based, low-tech, low cost research; 

(3) Providing capacity building start-up funding for organizations; (4) Creating centers of 

excellence; (5) Requiring multidisciplinary grantee teams. 

Dissemination 

(1) Facilitating publication; (2) Fostering networking; (3) Launching and supporting 

demonstration projects within real health care organizations. 

Administration 

(1) Joint solicitations and collaborative funding to drive the research agenda proposed in this 

report; (2) Accelerating the proposal cycle for grants relevant to the research agenda in this 

report. 

7 
 

Chapter 1: Introduction 

The idea that the United States’ health care delivery system must be changed has reached a 

tipping point. Daily, we are bombarded with health care news at the levels of government, 

organizations, and individuals. We read about Federal and State efforts to introduce 

comprehensive health care reform. We listen to local organizations discuss the steps they are 

taking to try to improve the quality of care while reducing costs. We watch reports about 

individuals who have poor or no access to health care services. The momentum for change is 

clearly present. 

For over a decade there has been wide-spread recognition of the potential value of applying 

engineering knowledge to improving health care. Numerous reports document deliberations from 

consensus conferences and evidence-based practice workshops focused on this topic. However, 

there has been little uptake of the innovations and improvement strategies advocated in these 

reports. A specific and actionable research agenda on how to achieve change using engineering 

knowledge is needed by current and potential funding organizations concerned with improving 

the health of the country. While many engineering disciplines could contribute to the 

improvement of the health care delivery system, it is the intent of this project to provide research 

innovation guidance related to the application of industrial and systems engineering (ISyE) 

solutions to realizing the vision of an ideal health care delivery system. 

The field of industrial and systems engineering (ISyE) has a long history of improving the 

structure, processes, and outcomes of complex systems in many industries. ISyE leverages 

information, information technology, information systems and human resources to enable system 

wide improvement. Although ISyE was originally focused on manufacturing environments, over 

time, the purview of industrial and systems engineers has expanded to include numerous services 

(e.g., transportation, hospitality, energy, and finance). Over the last several decades, there has 

been particular interest in applying the tools and techniques of ISyE to improve the health care 

delivery system. Organizations such as the Institute of Medicine and the National Academy of 

Engineering have recently made explicit calls for increased application of ISyE tools to improve 

the health care delivery system.8 

A particular challenge of such an endeavor lies in the complexity of the health care delivery 

system. This complexity is evidenced in multiple ways. 

1.	 Multiple stakeholders, including providers, insurers, payers, regulators, funding 

authorities, vendors, and consumers each have their own set of objectives, some of which 

overlap and others of which are in conflict. 

2.	 The health care delivery system is a multi-level system requiring numerous levels of 

analysis such as those outlined in Building a Better Delivery System8

: 

a.	 The individual patient; 

b.	 The care team, which includes professional care providers (e.g., clinicians, 

pharmacists, and others), the patient, and family members; 

c.	 The organization (e.g., hospital, clinic, nursing home, etc.) that supports the 

development and work of care teams by providing infrastructure and 

complementary resources; 

9 
d.	 The political and economic environment (e.g., regulatory, financial, payment 

regimes, and markets), the conditions under which organizations, care teams, 

individual patients, and individual care providers operate. 

3. Each level consists of multiple interacting components (e.g., multiple clinicians within a 

clinic, multiple clinics within a hospital). 

4. The components across and within levels may have very loose organizational ties. 

5. The boundaries between components and levels are dynamic and uncertain. 

6. The components are not contained within a hierarchical and circumscribed 

 system. 

Current approaches to applying ISyE knowledge in health care have been limited to isolated 

efforts directed towards improving subsystems of the health care delivery system such as a 

practice, a unit, a clinic, or at the most macro-level, an institution. In addition to focusing on 

subsystems instead of the system as a whole, such efforts have emphasized incremental as 

opposed to breakthrough change. The rationale for this focus on incremental change at the 

subsystem level is two-fold. First, ISyE knowledge was originally developed for use within a 

clearly bounded system that has a hierarchical structure. Second, the culture and incentive 

structure of the health care delivery system emphasizes improvement from a provider or 

organizational perspective (within components) instead of from a patient or societal perspective 

(across components). Such isolated efforts to incrementally improve subsystems separately, 

however, have not, and are unlikely to, scale to breakthrough improvement at the level of the 

entire health care delivery system. Thus, although current knowledge has been essential in 

bringing about incremental change at the subsystem level, extending the range of application of 

knowledge will not scale to enable breakthrough change at the system-wide level. 

Mindful of potential synergies, the Agency for Healthcare Research and Quality (AHRQ) 

and the National Science Foundation (NSF) partnered on a project designed to accomplish three 

objectives: 

1.	 Articulate a vision for an ideal health care delivery system; 

2.	 Determine why current efforts to apply ISyE knowledge to health care have not 

resulted in meaningful change; and 

3.	 Propose a research and action agenda that should be pursued to enable the field of 

ISyE to substantially contribute to the realization of an ideal health care delivery 

system. 

At the core of this project is the realization that straightforward application of existing ISyE 

techniques is insufficient to realize the vision of an ideal health care delivery system. What is 

needed is a paradigm shift targeted toward determining how to best stimulate breakthrough 

change and system-wide improvement through innovations in ISyE knowledge, and the ways 

these can be applied to health care. Special attention was given to developments in health IT 

necessary to facilitate the ISyE advances. The study focus areas recommended in the research 

agenda and the initiatives and programs recommended in the action agenda aim to support the 

realization of the proposed vision for the ideal health care delivery system. Realizing that such a 

change may also realistically require complementary efforts, however, the research and action 

agendas also support the development of initiatives to promote knowledge transfer and the 

10
 
integration of ISyE meta-knowledge that will be necessary to sustain the quality of the health 

care delivery system and to the build capacity ISyE knowledge applicable to health care. 

Within the larger ISyE community the terms “model,” “optimize,” and “improvement” are 

sometimes ambiguous. For example the term “model” may be used to mean a conceptual 

framework such as Donabedian’s structure-process-outcome framework or to mean an operations 

research tool such as a simulation or an influence diagram. The term “optimal” is similarly used 

in multiple ways within the field of ISyE and may refer to a theoretical construct such as the idea 

that a system functions best when the social and technical subsystems are jointly optimized or to 

a mathematical construct in which the relevant maximum or minimum is sought. Finally, the 

term “improvement” is used at different levels of magnitude to mean quality improvement or 

continuous improvement that occurs in small steps or large-scale initiatives that result in 

sweeping change. In this report the terms “model” and “optimize” carry, as appropriate, each of 

these meanings and should be interpreted in context. The term “breakthough improvement” is 

used to mean the entire process of analysis, design, implementation, and evaluation required to 

achieve a result fundamentally better than the current state and the term “incremental 

improvement” is used to mean the entire process of analysis, design, implementation, and 

evaluation required to achieve a result that is marginally better than the current state. 

As detailed in Chapter 2, this project was comprised of three elements:(1) a background 

report,(2) a workshop, and(3) a final report. Each element of the project was informed by the 

preceding elements (e.g., the final report was informed by the background report and the 

workshop). This final report attempts to accurately reflect the deliberations of the workshop 

participants while simultaneously staying faithful to the knowledge gleaned during the writing of 

the background report. The outcome of this project will be widely disseminated to both ISyE and 

health care audiences at meetings of professional organizations. It will also be more widely 

disseminated to these and other relevant audiences through the project website and social 

networks. 

11
 
Chapter 2: Methodology 

The objectives of this project were realized by conducting a review of the salient literature 

and by engaging experts in the fields of ISyE and health care in a critical discussion. A 

background report containing a summary and critical review of 13 seminal reports and 

workshops related to the subject matter of this project was produced and disseminated among 

identified experts in the two fields. During a 2-day workshop held on September 21-22, 2009, in 

Washington, DC, these experts engaged in intense reflection and discussion about(1) a vision of 

an ideal health care delivery system,(2) why change using current ISyE methods remains 

intractable, and (3) the research and action agenda needed to enable ISyE to meaningfully 

contribute to the realization of an ideal health care delivery system. Upon conclusion of the 

workshop, participants were asked to provide feedback to help set priorities for the research and 

action agenda. 

Background Report 

A background report analyzing selected materials1-13 appraising the status of health care 

delivery and opportunities for improvement generated by the National Academy of Science was 

generated in preparation of the workshop. The purpose of the background report was to provide 

workshop participants with an overview of the current discourse related to this subject and to 

stimulate discussion among them during the workshop. As such, the background report contained 

both a presentation and critique of the progress that has been made to date in achieving the three 

objectives of this project. The literature included in this report was not intended to be 

exhaustive; rather, literature was chosen that had made significant contributions to discourse 

located at the intersection of ISyE and health care and that contained material likely to stimulate 

discussion among workshop participants. 

Three major conclusions emerged from the review of these materials. The first conclusion 

was that current visions of the health care delivery system suffer from a lack of prescription 

regarding how the ideal system should respond to the expanding needs for health care in 

contemporary society. The conclusion was that both the tools of ISyE and the culture of health 

care are focused on improving the current system through subsystem improvement and not 

necessarily through fundamental change to the entire system. The third conclusion was that 

although preliminary steps have been taken toward detailing new ISyE methods that could 

stimulate real change, the proposed methods remain grounded in today’s health care delivery 

system. A copy of the background report, with pointers to the source reports employed in its 

development, may be found in Appendix A. 

Analysis of background materials led to the formulation of purpose statements for the 

workshop, guided participant invitations, and helped shape the activities undertaken as part of 

the workshop and afterwards. The background report and source materials were circulated to all 

workshop participants and a 2-day workshop was held to further: 

13
 
1.	 Explicate a vision of an ideal health care delivery system, 

2.	 Explore why change in the health care delivery system using current ISyE methods 

remains unachievable and intractable, and 

3.	 Establish the research and action agenda needed to enable ISyE to meaningfully 

contribute to the realization of an ideal health care delivery system. 

Workshop 

Workshop Scope 

The purpose of the 2-day workshop was to engage a group of experts in the fields of ISyE 

and health care in addressing the three objectives of this project. Care was taken to ensure that 

workshop participants included individuals from both academia and industry and individuals at 

varying stages of the career trajectory. A mix of individuals possessing skills in industrial and 

systems engineering and/or deep knowledge of the processes and systems of health care was 

sought. Furthermore, a strong emphasis was placed on inviting individuals who were both 

embedded in and new to the discourse of improving the health care delivery system. See 

Appendix B for a full list of workshop participants. 

Workshop Format 

The goal of the workshop was accomplished through a combination of formal presentations 

and both large and small group discussions. During a total of nine presentations, the workshop 

chair, Dr. Patricia Flatley Brennan, and eight invited speakers proposed a vision for an ideal 

health care delivery system. The two keynote speakers, Dr. Maulik Joshi (President, Health 

Research & Educational Trust and Senior Vice President of Research, American Hospital 

Association) and Mr. Aneesh Chopra (United States Chief Technology Officer) provided broad 

visions for the ideal health care delivery system of the future. The remaining six speakers 

provided visions that were grounded in their area of industrial and systems engineering expertise 

as shown below: 

1. Finance and quantitative decisionmaking 

Brian Denton, PhD 

Assistant Professor, Edward P. Fitts Department of Industrial and Systems Engineering 

North Carolina State University 

2. Information technology 

James M. Walker, PhD, FACP 

Chief Health Information Officer 

Geisinger Health System
 

3. Systems analysis, change, and implementation theory 

José L. Zayas-Castro, PhD 

Professor and Chair, Department of Industrial and Management Systems Engineering 

University of South Florida 

14
 
                                                       

4. Materials management and production processes 

Eugene Schneller, PhD 

Professor, School of Health Management & Policy, W. P. Carey School of Business 

Arizona State University 

5. Human factors and sociotechnical systems 

Stephanie Guerlain, PhD 

Associate Professor, Department of Systems & Information Engineering 

University of Virginia 

6. Quality engineering 

James Benneyan, PhD 

Associate Professor, Mechanical, Industrial, and Manufacturing Engineering Department 

Northeastern University 

Executive Director, New England VA Healthcare Engineering Partnership 

These six speakers also provided insight into why current efforts to improve the health care 

delivery system using ISyE knowledge have failed to result in significant change. 

Large group discussions were primarily used for two purposes: (1) to reflect, as a group, on 

the information presented by a speaker, and (2) to report out conclusions reached during the 

small group discussions. Both forms of large group discussion allowed workshop participants to 

question, clarify, or add to the material presented and to remain engaged in the entire scope of 

the workshop discussion. The large group discussions were also instrumental in identifying 

points of consensus and tension between workshop participants. 

Small group discussions were used to probe deeply into how specific ISyE specialties could 

address health care challenges. The field of ISyE is broad in scope; in this report we use the term 

ISyE liberally to include diverse subdisciplines that may be rooted in applied mathematics, 

computer science, and psychology. Five ISyE groups and six health care area groups were 

constructed as shown below. 

Each participant was assigned to one ISyE group and one cross-cutting health care challenge 

area group. 

ISyE groups 

1. Information technology, finance, and quantitative decisionmaking1 

2. Systems change, analysis, and implementation theory 

3. Materials management and production processes 

4. Human factors and sociotechnical systems 

5. Quality engineering 

1 The information technology, finance and quantitative decisionmaking groups were originally conceptualized as two separate 

groups; however because some participants’ schedules required late arrival, these groups were combined into one to ensure a 

lively and productive discussion. 

15
 
Health care challenge area groups 

1. Managing acute illness and disease 

2. Creating effective models of health promotion and disease prevention 

3. Insuring chronic disease management 

4. Enhancing end-of-life experience 

5. Facilitating public health 

6. Accelerating discovery 

During the ISyE small group discussions, participants were asked to consider how the 

specialty could contribute to the realization of an ideal health care delivery system. Participants 

were also asked to further discuss challenges to achieving the ideal health care delivery system 

and ways in which these challenges could be surmounted. Although asked to spend a little time 

determining what knowledge is currently available to address these challenges, participants were 

primarily tasked with identifying new ISyE knowledge that must be gained through new research 

directions within the specialty in order to stimulate breakthrough change at the system level, not 

incremental change only at the subsystem level. Participants were also asked to determine what 

programs and initiatives must be put in place for such research to be conducted, disseminated, 

and sustained. 

During the health care challenge area discussions, small group participants were tasked with 

identifying the difficulties presented by the specific health care challenge areas to stimulate the 

conceptualization of new ISyE knowledge that should be created in order to address these 

challenges. Participants were assigned to work with a different set of individuals than those with 

whom they were matched for the ISyE themed groups to further stimulate innovative ideas. 

Multiple methods were used to ensure that both small and large group interactions were 

productive and captured for later use. All large group discussions were captured using a 

recording device and by a professional note taker. All small group discussions were 

professionally facilitated and captured by a professional note taker. Upon completion of the 

workshop, the lead note taker created a summary of the entire workshop based upon the notes 

generated from both the large and small group discussions. A copy of the workshop schedule, 

ISyE small group assignments, and health care challenge area small group assignments are in 

Appendixes C, D, and E, respectively. 

Synthesis 

The authors listed on this report created a first draft by synthesizing the background report, the 

workshop discussions, and the workshop participant feedback. This report was then circulated to 

all workshop participants and outside reviewers (experts in the fields of ISyE and health care 

who were not present at the workshop). Based upon the comments received, the final report was 

revised and submitted to AHRQ and NSF for review and publication. 

16
 
Chapter 3: Vision of an Ideal Health Care Delivery 

System 

This section synthesizes the vision of the ideal health care delivery system of the future. 

Some of the points come directly from specific speakers, and those are noted in parentheses. 

Other ideas that are not attributed to particular individuals were mentioned by participants or 

came up in the discussions. 

A New System 

The workshop chair, all six session chairs, and the two keynote speakers articulated the 

characteristics of the ideal health care delivery system of the future in their vision statements, 

without drawing the details of its structure. The new system is not merely an extension of the 

existing system but is fundamentally different. 

1.	 The ideal health care delivery system is an integrated system that has improved linkages 

across multiple access points (Zayas-Castro, Brennan), unlike the current fragmented 

health care “system.” 

2.	 The health care of the future is delivered to a global, multi-cultural, growing, and aging 

population, and the ideal delivery system needs to be designed accordingly, not improved 

incrementally from the current one (Brennan). 

3.	 It is ubiquitous, distributed, responsive, expansive, flexible, resilient, and it 

incorporates systems engineering concepts (Joshi). 

A Patient-Centered System 

At the center of the system are the patient and their family (Joshi). 

1.	 Care is personalized for them, with consistency throughout the lifespan, and memory of 

their preferences and particularities. 

2.	 Information about their health is secure, digitized, and readily available online at their 

fingertips for their review and annotation (Chopra). All information, including selfreported observations, sensor data, and provider diagnoses and prescriptions is integrated 

in separate but interlinked layers. 

3.	 Technologies and policies are in place to ensure their privacy is never compromised 

(Chopra), and configuration options give them the flexibility to personalize how much of 

what they share with whom and until when, should they choose to (Joshi). 

17
 
4.	 Decisions about their health are made collaboratively with their interdisciplinary care 

team of which they are core. When they need to make a decision, their care team ensures 

they have the guidance and autonomy they need to make informed choices. 

5.	 Consumer health IT gives them the data they need when they need it; with the 

visualization options they prefer (Joshi, Chopra, Guerlain). 

6.	 Their health care is not limited to a few episodes of acute care (Joshi), and emphasizes 

prevention and continuity of care, with a focus on chronic care management. Care is 

integrated beyond institutional silos and into the community (Brennan). 

7.	 Handoffs and transitions of care are understood to be error-prone areas and procedures 

are in place to prevent or mitigate problems (Joshi). 

8.	 Care teams manage conditions together (e.g., heart disease, depression, diabetes) rather 

than in silos (Walker). Patients have open access to care (Joshi). Resources are wrapped 

around them when they need them, and retracted seamlessly when they are ready to move 

on with their life. 

9.	 Transparency in the system enhances quality and patient safety, and where appropriate, 

mandatory public reporting gives patients the power to choose their provider and 

treatment (Brennan, Joshi) 

An Engineered System 

Despite the demanding and unpredictable nature of providing personalized care to a large 

number of patients throughout their life, the delivery system runs smoothly and efficiently. 

1.	 Industrial and systems engineering tools that have produced cost savings, increased 

responsiveness, and improved quality in other industries (Denton, Benneyan) are 

leveraged by all stakeholders through both bottom-up and top-down approaches (ZayasCastro). In addition to driving subsystem improvements, ISyE helps guide system-wide 

policies (Denton). 

2.	 The conflicting objectives of the different players (Zayas-Castro) are coordinated to 

deliver quality care efficiently and safely to everyone. The system provides incentives 

to clinicians to keep their patients healthy, to patients to stay healthy, and to providers to 

be efficient (Denton). 

3.	 Care for common conditions is streamlined with high quality information technology, 

and care for uncommon conditions is supported by state-of-the-art communication 

technologies (Walker). 

4.	 There is a national concept of standard care processes identified with expert-informed 

evidence-based analytics (Walker, Benneyan) and mathematical modeling (Denton). Best 

practices are the default practice (Joshi). 

18
 
5.	 The system is information-optimized. Process outcomes are routinely fed back into the 

processes for continuous improvement. All parts of the system share generalizable 

knowledge to learn from each other rather that separately invent the wheel (Walker). 

6.	 The costs of biomedical knowledge discovery are reduced by the use of simulation and 

modeling-assisted randomized controlled trials where operations research techniques are 

used to narrow the field down to a few good solutions worth trying (Denton). 

7.	 Engineers and clinicians work hand in hand to continuously improve the system 

(Walker), and all stakeholders are involved in the conversation, including clinicians 

(Zayas-Castro) and patients. Quality improvement is concentrated on the quality 

planning phase of the Juran trilogy (Benneyan) to maintain high system reliability 

(Joshi). 

8.	 The health care delivery system treats materials as assets, as they are in other industries, 

and makes effective use of materials through dynamic supply chains and product 

identification standards (Schneller). 

19
 
Chapter 4: Barriers and Facilitators to Achieving 

Breakthrough Change with ISyE Knowledge 

This section presents nine barriers that were identified as having prevented current efforts at 

using ISyE knowledge from resulting in an ideal health care delivery system. As a complement, 

this section also presents four facilitators that articulate why now is the right time to address 

these barriers. These facilitators are likely to accelerate the ability of the field of ISyE to make 

meaningful contributions to the realization of an ideal health care delivery system in the future. 

Barriers 

Over the course of this project, nine primary barriers were identified as to why current efforts 

at using ISyE knowledge in health care have not resulted in breakthrough change. Potential 

means of addressing the barriers outlined below are integrated into the research and action 

agendas. 

1. Lack of wide-spread use of ISyE tools 

Perhaps the most common rationale provided for the failure of ISyE knowledge to create 

breakthrough change to the health care delivery system is that the tools, techniques, and methods 

of ISyE simply have not been used in a pervasive manner. Lack of resources, awareness, and 

motivation are all cited as reasons for which current ISyE tools are not prevalent in health care. 

Multiple publications included in the background report maintain that current ISyE tools have 

the power to accomplish fundamental change in health care and that dissemination of current 

knowledge is the key to achieving better results. Workshop participants agreed that lack of use of 

current ISyE knowledge is one reason why breakthrough change has not been achieved, but 

cautioned against claiming it was the primary reason. 

2. Inadequate ISyE knowledge 

ISyE knowledge targets knowable, well-circumscribed elements of a system. Given that ISyE 

knowledge has traditionally been used primarily in manufacturing settings, ISyE tools, 

techniques, and methods were often developed for improvement of a small-scale, strictly 

bounded system such as a production line or a manufacturing company, within which the goal is 

clearly defined. ISyE knowledge was not purposefully developed for large-scale systems such as 

a nation’s health care delivery system where the boundaries are fuzzy and the goals are 

constantly in flux. 

Consequently, there is a mismatch between the scale for which ISyE knowledge was 

developed and the scale for which ISyE knowledge is needed to realize a vision of an ideal 

health care delivery system. If current ISyE knowledge is used at the level for which it was 

designed, it may result in improvement of subsystems of the health care delivery system (at the 

level of a physician’s practice, a clinic, or perhaps even an organization). This level of 

improvement is necessary but not sufficient. Furthermore it is unreasonable to conclude that 

improvement of subcomponents of the system will translate to overall, integrated improvement 

21
 
of the entire system. This is particularly true given that the field of ISyE lacks a sophisticated 

system of models in which models at the subsystem level integrate with models at a system-wide 

level.

 In addition, much current ISyE knowledge is unlikely to scale to use in a larger system. For 

example, tools often used for quality improvement at a subsystem level such as rapid cycle 

testing or value stream mapping are likely to be difficult to apply at the system level of a national 

health care delivery system. Similarly, current mathematical modeling techniques that rely 

heavily on robust data and are based on optimizing from a single stakeholder perspective are 

unlikely to be as useful in a problem space that contains incomplete data from disparate sources 

and that requires joint optimization of numerous stakeholder perspectives. 

Finally, there is a lack of meta-knowledge within ISyE. Specifically there is little 

understanding of what tools, techniques, and methods are most appropriate for a given problem 

area and under what circumstances they are most effective. Without such knowledge, ISyE 

knowledge is likely to be used in a haphazard manner; the choice of tools, techniques, or 

methods is often informed by the familiarity or preferences of an individual or organization or on 

the latest trend instead of on evidence of appropriateness and effectiveness. 

3.	 Insufficient health IT infrastructure 

The current health IT infrastructure in place to support the effective application of ISyE 

knowledge within health care is suboptimal. Existing ISyE tools, techniques, and methods rely 

on the availability of accurate and complete data. At the local level, health IT within health care 

organizations often does not adequately facilitate output of useable data. There are multiple 

reasons for this. First, these systems were not created with industrial and systems engineers or 

other researchers or administrators involved with improving the health care delivery system as 

the customer of the data generated. Consequently, these IT systems do not always facilitate 

capture of process data that is salient for ISyE improvement efforts. Second, data from these 

systems is often provided as a “data dump,” with little, if any, meaningful interpretation. At the 

regional and national level, health IT infrastructure does not facilitate the generation of large, 

integrated datasets, which are a prerequisite for using ISyE tools on a larger scale. 

4.	 Structural and cultural traditions in the health care delivery system emphasizing only 

subsystem improvement and short-term outcomes and rewards 

At all levels, the structure of the health care delivery system promotes the use of ISyE 

knowledge that leads to subsystem improvement. Each provider has his/her own panel of 

patients, each specialty has its own specialized knowledge, and each health care organization has 

its own market share. There is an incentive to conduct improvement at the level of the subsystem 

to maintain current competitive advantage. The benefits of coordinated improvement across 

providers, specialties, and organizations in order to create an ideal health care delivery system 

are oftentimes less apparent from these perspectives. Existing approaches that reinforce 

subsystem improvement afford excellent managerial control. However, attending to either 

patient-specific or aggregate social perspectives requires moving beyond subsystem 

improvement toward ISyE approaches that guide improved overall system performance. 

22
 
5.	 Insufficient pathways which promote use of ISyE knowledge in health care 

Organizations such as the Institute for Healthcare Improvement (IHI) and the Leapfrog 

Group have strongly supported the use of current ISyE knowledge to improve subsystems within 

the health care delivery system. Their efforts have been instrumental in spreading awareness of 

current ISyE knowledge; however, they have primarily promoted the use of such knowledge at 

the level of a practice, clinic, or health care organization. These organizations, which serve as a 

bridge between ISyE and health care, have assumed that the overall health care delivery system 

will remain fairly constant and that it is the prerogative of each organization to improve locally. 

Little attention has been focused on promoting the development of new ISyE knowledge that 

may be used to improve the health care delivery system as a whole. 

6.	 Lack of efficient ways to spread knowledge between ISyE professionals working in 

health care 

The community of ISyE professionals working in health care is fragmented, precluding 

effective and efficient diffusion of best practices within the community. A growing number of 

professional organizations within the field of ISyE – for example the Institute for Operations 

Research and the Management Sciences (INFORMS), the Human Factors and Ergonomics 

Society (HFES), and the Institute of Industrial Engineers (IIE) – have created a health section. 

Additionally, organizations such as the American Medical Informatics Association (AMIA) and 

the Health Information and Management Systems Society (HIMSS) serve as professional homes 

for ISyE professionals working at the intersection of IT and health care. As a result ISyE 

professionals often only maintain a cursory awareness and understanding of the new ISyE 

knowledge that has been generated and presented to another institution, for example at an annual 

conference of one society. 

7.	 No clear funding structures to support development of new ISyE knowledge 

Research conducted at the intersection of ISyE and health care does not have a funding 

home. Traditionally, basic engineering research is funded through the National Science 

Foundation and health services research is funded through the Agency for Healthcare Research 

and Quality and private foundations such as the Robert Wood Johnson Foundation and the 

California Healthcare Foundation. This can cause funding sources to determine that a proposed 

project is within the purview of another organization and can cause researchers to remain 

uncertain about what funding source to pursue. Thus, research at the intersection of ISyE and 

health care can be left unfunded because it does not appear to align directly with the priorities of 

any one particular funder, not because of lack of meritorious ideas and proposals. 

8.	 Current policies constraining level of change possible 

Existing policies do not always facilitate realization of an ideal health care delivery system. 

For example, the realization of a responsive health care delivery system is difficult because of 

policies that preclude the development of efficient supply chains in areas such as 

pharmaceuticals. Similarly, development of a completely patient-centered health care delivery 

system would likely require a realignment of incentives but, unfortunately, such change is slow 

23
 
 

to come by and unlikely to occur within the next 5 to 7 years. Furthermore, although adoption of 

health IT is often envisioned as facilitating the development of an ideal health care delivery 

system, current policies pushing the rapid uptake of meaningful use could result in undesirable 

unintended consequences. For example, pressure for rapid adoption may inhibit or prohibit the 

use of appropriate design and implementation strategies, resulting in suboptimal technologies 

and poor integration into practice. Furthermore, the high cost of health IT systems is likely to 

prevent institutions from continually investing in new systems, potentially resulting in stagnation 

of health IT innovation. 

9. Lack of professionals with an adequate understanding of both ISyE and health care 

This barrier is evidenced in two ways: (1) the lack of health care personnel with sufficient 

understanding of how to use ISyE solutions, and (2) the lack of ISyE professionals with a good 

working knowledge of health care. Without an adequate number of professionals with an 

understanding of both domains, the challenge of achieving an ideal health care delivery system 

will be insurmountable. Both current and future ISyE tools that can facilitate the realization of an 

ideal health care delivery system will remain within the domain of only the largest health care 

organizations. This may result in the improvement of a health care organization, but will impede 

the realization of a national ideal health care delivery system. 

There was a disagreement among workshop participants about whether the low levels of 

ISyE penetration in health care are due to a low supply of qualified professionals, or if the 

market demand for them is still too low. Some believe that demand will follow the supply as an 

increasing number of ISyE professionals in health care demonstrate the benefits of ISyE. Others 

reminded the group that there has been a growing supply of ISyEs trained to work in health care 

over the last three decades and that demand has not kept pace. 

Table 1 presents a mapping between the barriers identified and the recommendations 

proposed in this report. 

Table 1. Mapping between barriers identified and recommendations proposed 

Theme Barrier Recommendation Reference 

Adoption Lack of widespread use of ISyE tools Knowledge Transfer Research 

Agenda and Education and 

Training and Dissemination 

Action Agenda 

Knowledge quality Inadequate current ISyE knowledge Knowledge Innovation and MetaKnowledge Integration Research 

Agenda 

IT infrastructure Insufficient health IT infrastructure Collaboration and Dissemination 

Action Agenda 

Units of analysis Structural and cultural traditions in the 

health care delivery system 

emphasizing only subsystem 

improvement and short-term 

outcomes and rewards 

Knowledge Innovation Research 

Agenda 

Private and nongovernmental 

organizational support 

structures 

Insufficient pathways which promote 

use of ISyE knowledge in health care 

Collaboration Action Agenda 

24
 
Table 1. Mapping between barriers identified and recommendations proposed (continued) 

Theme Barrier Recommendation Reference 

Fragmentation of ISyE 

and health care 

communities 

Lack of efficient ways to spread 

knowledge between ISyE 

professionals working in health care 

Collaboration Action Agenda 

Government structures No clear government structures to 

support development of new ISyE 

knowledge 

Collaboration, Funding, and 

Administration Action Agenda 

Policy constraints Current policies constraining level of 

change possible 

N/A 

Multidisciplinary 

knowledge 

Lack of professionals with an 

adequate understanding of both ISyE 

and health care 

Education and Training Action 

Agenda 

Facilitators 

In contrast to the barriers above, four facilitators likely to promote the use of new ISyE 

knowledge in realizing the vision of an ideal health care delivery system were identified. A 

characterization of each facilitator is provided below. 

1. Increasing recognition of the potential of ISyE to improve health care 

Multiple stakeholders have acknowledged that ISyE knowledge has the potential to make 

meaningful contributions to realizing the vision of an ideal health care delivery system. This 

opinion was pervasive throughout the publications reviewed in preparation of the background 

report and was shared by both ISyE and non-ISyE professionals (e.g., social scientists, clinicians, 

or administrators) at the workshop. Additionally, the presence of multiple individuals from both 

AHRQ and NSF at the workshop suggests an acknowledgement of the need to invest in 

development of ISyE tools, techniques, and methods that can be used to achieve the vision of the 

ideal health care delivery system. An increasing number of health care institutions advertising 

ISyE positions and an increasing number of academic programs at the intersection of these two 

fields provide further evidence of this increased recognition. 

2. Progress in both recognition of the need for and the development of new ISyE tools 

Both a review of the literature and, to a larger extent, the workshop discussions provided 

evidence that there has been progress in both recognizing the need for and the development of 

new ISyE tools to confront the challenges of health care. One publication reviewed in the 

background report – Building a Better Delivery System – recognized the need for new ISyE 

knowledge to realize a vision of an ideal health care delivery system8

 and suggested a handful of 

avenues of further research. Workshop participants suggested many more avenues of new ISyE 

research to facilitate health care transformation. The research agenda presented below reports on 

avenues of inquiry suggested in previous reports and those generated by workshop participants. 

Some progress has been made in developing new ISyE tools, techniques, and methods that 

may prove useful in creating breakthrough change. For example, new methods of modeling have 

been developed that account for multiple conflicting objectives and uncertainties. Game theorybased models have also been developed which can account for multiple players whose decisions 

are contingent upon the actions of others. The creation of this new ISyE knowledge serves as a 

25
 
foundation upon which additional ISyE knowledge may be developed, leading to an increasing 

sophistication of ISyE solutions for health care. 

3. Progress in the use of IT to disseminate ISyE knowledge 

Both the Internet and organizational intranets are being used to disseminate ISyE knowledge, 

increasing awareness of these tools, techniques, and methods within the health care community. 

Websites of organizations such as IHI and AHRQ now contain ISyE resources that health care 

professionals may use to facilitate improvement. Intranets of health care organizations have also 

begun including such tools for use by employees. Such IT infrastructure will be beneficial in 

spreading new ISyE knowledge that is gained through execution of the proposed research 

agenda. 

4. Current political climate of health care reform 

The current political climate has drawn health care reform to a prominent position on the 

national stage. The emerging political will may lead to strategies that better address some of the 

barriers identified above. For example, existing policies which impede realization of an ideal 

health care delivery system may be revisited and new initiatives such as the promotion of a 

patient-centered health care delivery system or the enhanced development, deployment and 

acceptance of professionals in science, technology, engineering and math (STEM) may bolster 

the long-term supply of individuals with an interest in and understanding of both ISyE and health 

care. 

26
 
Chapter 5: ISyE and Health Care Research Agenda 

This section presents a research agenda that will facilitate realization of the vision of an ideal 

health care delivery system through the transfer and development of existing and new ISyE 

knowledge. A discussion of the purpose and scope of the research agenda and the challenges of 

creating the research agenda is also contained within this section. 

Purpose and Scope of the Research Agenda 

The primary purpose of the research agenda is to provide guidance related to the type of 

research that should be prioritized at the intersection of ISyE and health care to realize the vision 

of an ideal health care delivery system. Completion of this research agenda should lead to 

improvement in the health of society by improving the performance of the health care sector. 

Furthermore, completion of this research agenda should lead to creation of new ISyE knowledge 

that may be used to improve not only the health care industry, but also other manufacturing and 

service industries. Finally, it is intended that completion of this research agenda will lead to 

clearer understanding of the health IT resources required to achieve the vision of an ideal health 

care delivery system. 

The scope of this research agenda may be characterized in terms of timeframe and content. 

This research agenda is intended to be completed in the next 5 to 7 years to yield change in the 

next 10 to 15 years. At the start of the project, the research agenda was envisioned as only 

containing content related to the creation of new ISyE knowledge. This initial scope followed 

from an assumption that current ISyE knowledge, even if adopted across the health care industry, 

would not lead to breakthrough change. However, during the workshop, it became evident that 

many participants strongly felt that content related to knowledge transfer should also be 

contained within the research agenda. These participants opined that current ISyE knowledge has 

the potential of producing meaningful change when used pervasively and in conjunction with 

new knowledge. Consequently, the research agenda presented here is intended to provide 

guidance on the types of investigation required to (1) discover and develop new ISyE knowledge 

particularly germane to achieving the vision of an ideal health care delivery system, (2) achieve 

effective knowledge transfer of existing ISyE knowledge within health care, and (3) integrate 

meta-knowledge about the use of current and new ISyE knowledge. 

The original intent was to present the content of the research agenda related to new ISyE 

knowledge as a grid, with the five ISyE disciplines and the six health care challenge areas used 

as the basis for the small group discussions as the axes. However, by the end of the workshop, it 

was clear that such a model would be inappropriate given that the problems identified are 

relevant across the health care challenge areas and that the contributions of multiple perspectives 

will be required to develop the necessary ISyE knowledge. Furthermore, it was determined that 

any attempt to divide the research agenda using this framework was contradictory to the 

multidisciplinary approach envisioned by workshop participants. As a result, the research agenda 

for the development of new ISyE knowledge is structured around the monitoring, modeling, and 

modification of the health care delivery system. 

27
 
Challenges Encountered in Developing a Research Agenda 

Focused on New ISyE Methods 

Two primary challenges were encountered during the process of articulating the research 

agenda focused on new ISyE methods. These challenges are identified and characterized below. 

1.	 Lack of clarity about the scope of ISyE 

Perhaps the most significant challenge encountered was defining the boundaries of the ISyE 

discipline. By its very nature, ISyE is multidisciplinary, drawing on traditions as diverse as 

psychology and organizational behavior (human factors) and mathematics and computer science 

(operations research). As the application areas of ISyE have expanded, so too have the traditions 

on which ISyE knowledge and solutions have been based. 

During the workshop, the content proposed for the ISyE research agenda often overlapped 

with content that would be more appropriate for a public policy, business, law, medicine, 

computer science, psychology research, public health or urban planning research agenda. For 

example, participants offered research agenda content related to: 

a.	 Creating incentive structures and tax policies (overlap with public policy and 

business), 

b.	 Implementing an oversight committee analogous to the Institutional Review Board to 

mitigate unintended consequences of health IT implementation (overlap with law), 

c.	 Prioritizing clinical information for both providers and patients (overlap with 

medicine), 

d.	 Improving of IT interoperability (overlap with computer science), 

e.	 Increasing patient motivation for behavioral change (overlap with psychology), 

f.	 Designing better nutrition and screening programs (overlap with public health), and 

g.	 Determining appropriate location of clinics, bike paths, community gardens (overlap 

with urban planning). 

Although it was determined that the discipline of ISyE could make research contributions to the 

issues noted above, content that was determined to be more strongly within the purview of 

another discipline was excluded from the research agenda. 

2.	 Belief in the adequacy of current ISyE knowledge leading to difficulty thinking “out of 

the box” 

The initial intent of this project was to set the stage for the realization of an ideal health care 

delivery system either through creation of new ISyE knowledge. This intent was accompanied by 

the understanding that current ISyE knowledge suffers from limitations that prevent its 

application from resulting in breakthrough change. The reasons why current ISyE knowledge is 

limited were detailed in the background report that was disseminated to all workshop participants 

and was emphasized by the workshop chair, Dr. Patricia Brennan, throughout the 2-day event. 

However, despite this attempt to push thinking forward and focus on expanding ISyE 

knowledge, many participants remained focused on the perceived value of current ISyE 

knowledge, particularly ISyE knowledge related to their specific research or industrial interests. 

28
 
Although new research directions were generated, significant portions of the discussion focused 

on issues of knowledge transfer and bridging the gap between knowledge and action. The reason 

for this focus is unclear. It may have been that participants did not have an opportunity to read 

the background report, and, therefore, did not realize the focus of the workshop until later. It may 

have been that participants wanted to push their own agendas forward. Or it may have been that 

participants believed in the potential of current ISyE tools to create real change. 

Existing ISyE knowledge has seen many successes in manufacturing, aviation, and banking, 

including improvement of production lines and transportation networks, reduction of waiting 

times and queue lengths, and achievement of Six Sigma quality in factories. These successes, 

however, have primarily occurred at the level of a site or organization, not at the level of the 

entire enterprise or sector. Thus, ISyE knowledge has not succeeded in improving entire 

industries, as is evidenced by the recent financial crisis and the problems currently faced by 

airlines and manufacturers. 

Rationalization for focusing in the research agenda on transferring existing knowledge stems 

from a belief among workshop participants that current ISyE knowledge has the power to create 

breakthrough change and the reasoning that if current methods have not taken hold, the 

likelihood that new methods will be adopted is low. However, previous discussion in this report 

demonstrates that even if barriers to adoption were overcome, current ISyE knowledge would 

result in incremental change at best. 

As discussed in the background report and the vision statement, it is unreasonable to expect 

the current health care delivery system to become an ideal health care delivery system by 

incremental improvement alone. It is also unreasonable to expect the application of current ISyE 

knowledge alone to create the needed breakthrough change. In fact, attempts to use ISyE to 

improve the health care delivery system have been made for the past three decades without 

significant large-scale impact. 

This challenge was tackled by: (1) proposing ways to expand the meaningful adoption of 

existing and new methods, (2) recognizing that existing methods are insufficient and that even 

without the barriers to adoption, there is a need for new knowledge to realize an ideal vision of 

the health care delivery system, (3) determining that meta-knowledge is needed to guide the 

appropriate and effective use of ISyE tools, techniques, and methods. 

Presentation of the Research Agenda 

The research agenda is divided along the lines of knowledge innovation, knowledge transfer, and 

meta-knowledge integration. The knowledge innovation directions are presented under three 

topic areas: system monitoring, system modeling, and system modification. Whereas research 

within the domain of knowledge transfer will expedite the use of effective existing ISyE 

knowledge, research within the domain of knowledge innovation will lead to the creation of new 

tools, techniques and methods that may be used to improve the health care delivery system. The 

integration of meta-knowledge is complementary to both these efforts. In this research agenda, 

greatest emphasis is placed upon knowledge innovation, because it is believed that better longterm value will be gained by investing in innovation rather than by spreading existing 

knowledge. 

To aid agencies and researchers in utilizing the research agenda effectively, an additional 

categorization of the agenda items is offered: 

29
 
 

Breakthrough: These items are essential to realizing the vision of the new health care 

delivery system 

Sustainability: These items are likely to have benefit and improve the health care 

delivery system, but will not lead to breakthrough changes 

Capacity building: These items are necessary to expand the breadth and depth of ISyE 

knowledge relevant to health care 

These categories are conceptualized as complementary, and all likely to be necessary to achieve 

the vision of an ideal health care delivery system. 

Research Agenda: Knowledge Innovation 

The majority of the research agenda focused on stimulating ISyE knowledge to address 

unresolved, unanswered questions posed by the challenges in contemporary health care delivery, 

as well as anticipated future challenges. New knowledge can result from stimulating innovation 

in ISyE methods better aligned with the complex, distributed, and stochastic nature of health 

care. Beyond research that seeks to enhance the use of current ISyE tools, new directions and 

new knowledge are needed. This section of the research agenda discusses new areas of research 

at the intersection of ISyE and health care, with a specific focus on how health IT can facilitate 

the resolution of health care challenges with new ISyE knowledge. The discoveries made 

through research in these areas of new ISyE knowledge, if successful in health care, are likely to 

be valuable and applicable to other industries. 

The goal of the knowledge innovation directions listed below is to build the ideal health care 

delivery system described in the vision. Therefore, this section describes the new research 

directions from a systems perspective, along three lines: system monitoring, system modeling, 

and system modification. System monitoring research agenda items are required to improve 

assessment of the system and communication of these assessments to all relevant stakeholders. 

System modeling research agenda items are required to improve understanding of system 

components and interactions between system components. Finally, system modification research 

agenda items are required to ensure efficient and effective system transformation. Since the term 

“modeling” carries different meanings in different communities and subdisciplines, it should be 

interpreted in context. 

System monitoring 

A reoccurring theme throughout the workshop was the absence of clean data for industrial 

and systems engineers to use. Existing ISyE techniques are data-dependent and current automatic 

data collection methods in health care are inadequate and lag behind other industries like 

manufacturing and aviation. They are (1) poorly automated, (2) slow, (3) insensitive to human 

factors engineering principles, and (4) often deliver non-computable data. 

In order to properly leverage existing and future ISyE methods, research is needed to address 

system-monitoring issues. New methods should have adequate levels of (1) automation, (2) 

pervasiveness, (3) integration, and (4) usability, and properly address issues of (a) privacy, (b) 

efficiency, and (c) human factors. Theories and frameworks are needed to characterize and 

operationalize the tradeoffs between the features listed above. The research agenda should 

include the development and evaluation of the following. 

30
 
 

Data Collection 

1.	 Consumer-facing health IT solutions that allow patients to self-report their 

observations, that track and report on trends, and that interact with providers’ 

annotations 

Motivation: The aviation and banking industries have leveraged IT to push many tasks to the 

customer, like e-ticketing, boarding pass printing, and online banking. The customer is even 

a more integral part of the production process in health care, and involving them fosters 

patient engagement and fits the spirit of patient-centered care, leading to possible benefits 

like shared decisionmaking and increased adherence. 

Category: Breakthrough 

2.	 Efficient and pervasive methods of data capture 

Motivation: Much of the ISyE toolkit relies heavily on data and industrial and systems 

engineers often spend considerable time collecting data (e.g., time studies, run charts, and 

check sheets) that often could easily be captured by IT systems. 

Category: Sustainability 

3.	 New automatic data collection technologies to capture observations from patients and 

their environments (e.g., sun exposure and food intake) 

Motivation: Sensor technologies and others can assist providers in efficiently monitoring 

changes in patient condition and ensuring patient safety. 

Category: Sustainability 

4.	 Theories and methods beyond natural language processing for the translation of lay 

person language into structured computable data 

Motivation: Many data sources in health care are humans, and much of the ISyE techniques 

rely on computers for large-scale projects. ISyE tools could be more efficiently implemented 

in the health care setting if those human inputs could be easily made analyzable by 

computers. 

Category: Sustainability 

Integration 

1.	 Technologies which enable data to flow quickly and securely through the whole health 

care delivery system and be available in real-time when and where needed 

Motivation: Interoperability is a recognized requirement of ideal health IT, and though it 

does depend on institutional, State, and Federal regulations, it also presents technical barriers 

that have yet to be overcome. 

Category: Breakthrough 

2.	 Efficient methods for integrating large amounts of data from disparate sources 

Motivation: Unless policy changes mandating integration of all health information occur 

within the time horizon of this research agenda, it can be expected that health care 

organizations will have to sift through large amounts of data from different health plans and 

31
 
 

individuals, with different formats and resolutions. Similar assumptions can be made about 

individuals and health plans having to deal with information from multiple providers. 

Category: Sustainability 

3.	 Adequate integration of data collection into workflows in manners that ensure data 

validity while minimizing interference with clinical workflows 

Motivation: Collection of data related to system characteristics is a required means to the end 

of system improvement. However data collection requiring action on the part of providers 

may conflict with efficiency and patient safety if it interferes too heavily with clinical 

workloads and workflows. 

Category: Sustainability 

4.	 Efficient means of integrating information generated from different perspectives (e.g., 

different providers, patients, and administrators) 

Motivation: It is important to integrate all perspectives to enable coordination and fullyinformed decisions. Beyond interoperability and integration across electronic health records 

(EHR), there will soon be a need to integrate EHR data with personal health records (PHR) 

data while keeping them separable, to accommodate integration of patient and provider data. 

There will also be a need to integrate within EHRs information among providers and with 

other stakeholders. 

Category: Sustainability 

Characterization 

1.	 Methods to operationalize contextual knowledge to understand generalizability of data 

Motivation: Competition between health care organizations across the country is less relevant 

than that between manufacturing companies competing for the global market. Therefore, it is 

conceivable that organizations could make returns on their investments in ISyE by selling 

their improvement findings and methodologies to non-competing provider organizations in 

different states. One ingredient to such practice would be the ability to identify generalizable 

knowledge. This can also provide guidance about the balance between mass customization 

and standardization based on the context. 

Category: Breakthrough 

2.	 Methods to characterize how the outcomes relate to the processes 

Motivation: Little is known about the relation between processes and outcomes in health 

care, and it is not surprising that there is often little relation between cost and quality. ISyE is 

intuitively well suited to characterize such production functions, but new methods and 

theories will likely be necessary given the complexity and relative unpredictability of the 

health care. 

Category: Sustainability 

3.	 Methods to characterize processes, inputs, and outcomes 

Motivation: A pitfall of computer-based analysis is the reliance on data that is available or 

can be readily measured, often quantitatively, and made computable. One can see how this 

32
 
 

can bias analyses by overlooking less tangible factors. Leveraging analytical methods that 

require operationalizable data requires the ability to somehow operationalize qualitative data. 

Category: Capacity building 

Presentation 

1.	 Methods to effectively collect and share data in real-time to foster situational awareness 

of all individuals involved in patient care 

Motivation: Problems of coordination and synchronization at points of transition of care from 

one provider to another constitute patient safety risks, and can lead to redundant or 

conflicting work (e.g., running an expensive and potentially harmful test that the patient has 

already had done at another hospital). 

Category: Breakthrough 

2.	 Methods to collect and present information that is valuable to diverse stakeholders such 

as patients, nurses, primary care and specialty physicians, pharmacists, and social 

workers 

Motivation: Current data collection approaches are often single-sided and do not generate 

information that is usable by all stakeholders without overloading them. 

Category: Capacity building 

3.	 Theories and methods for the translation of numerical, analytical, and computational 

results into understandable and actionable information that multiple stakeholders (e.g., 

patients, nurses, primary care and specialty care physicians, or pharmacists) and lay 

people can seamlessly retrieve to ensure the human monitoring of the system 

Motivation: Monitoring the system requires the collected data to be accessible and analyzable 

both by computers and by health care professionals. Those busy humans need timely and 

concise information to be able to make sure all processes are running as they should, and to 

handle unanticipated events. 

Category: Capacity building 

System Modeling 

Modeling the health care delivery system could have many aims, including (1) developing a 

better understanding of the system and the interactions within it, (2) predicting the future 

behavior of parts of the system or the status of improvement efforts, and (3) recommending 

actions to be taken by the system. The first three categories below address those aims. The last 

addresses inconvenient realities that models need to be able to handle. 

Descriptive models 

1.	 Frameworks that explore the integration of many care sources in the production and 

delivery of care services, and the coordination among these sources (e.g., at end of life 

care) 

Motivation: The health care delivery system of the future will cater to increasingly mobile 

individuals who frequently change employment and residence and seek care in different 

33
 
states and even globally. Care services are also diversifying and becoming more complex, 

with more and more sub-specializations and levels of care. 

Category: Breakthrough
 

2.	 Methods to model systems as set of flows and processes, not just sets of components 

Motivation: Current ISyE tools were created assuming knowable, stable, well-defined 

systems that are collections of components. Fluctuating complex systems like health care 

need a different characterization of systems, as sets of processes where information, people, 

and materials flow between changing and overlapping subsystems. 

Category: Breakthrough 

3.	 Models that explore the effective use and allocation of different vehicles of health care 

delivery (e.g., “focused factories” versus integration, such as Mayo Clinics and Kaiser 

Permanente) 

Motivation: As the health care industry is becoming more complex, delivery vehicles are 

diversifying and need to be studied to understand how organizations decide on volume and 

specialization tradeoffs, and to conduct comparative-effectiveness analyses of the results. 

Category: Breakthrough 

4.	 Models of trust between patients, providers, and technology 

Motivation: Interpersonal trust plays a larger role in health care than in manufacturing for 

which ISyE was initially developed, partly because patients are both an actor in and the 

recipients of the care processes. With increasing use of technology in health care, especially 

health IT that interfaces with patients and providers, trust in the technology and in the system 

as a whole is paramount, and should be better understood. 

Category: Sustainability 

Predictive models 

1.	 Models to evaluate entire systems and large-scale system changes before they are 

implemented 

Motivation: Efficient large-scale simulation could save testing costs, prevent safety hazards 

due to immature designs, and accelerate the system design and implementation process. 

Category: Breakthrough 

2.	 Models to mitigate uncertainties about the future 

Motivation: Short-term uncertainties include the demand for beds in a given week, and longterm uncertainties include the type of health conditions that are likely to become prevalent in 

the future. Examples include models to forecast the demand for health care services, and 

models to anticipate new diseases (e.g., H1N1, other pandemics). 

Category: Capacity building 

34
 
Prescriptive models 

1.	 Models of collaboration and competition among health care stakeholders 

Motivation: If organizations allowed patient data to flow freely between organizations and 

shared generalizable knowledge, the patient experience would be improved and the 

organizations would save costs by not reinventing the wheel. The system as a whole would 

be more Pareto efficient. Understanding why health care stakeholders do not collaborate and 

the circumstances in which they do could help modify policies and incentive structures to 

foster collaboration for the betterment of the system as a whole. 

Category: Breakthrough 

2.	 Models that consider how health IT can be integrated into decisionmaking processes, 

how evidence-based knowledge can be integrated into practice 

Motivation: Health IT is being introduced into practice as a way to ensure that 

decisionmaking is grounded in evidence. However, the push might lead to unintended 

consequences if health IT solutions are implemented prematurely. Research about how to 

better integrate health IT in decisionmaking processes is needed. 

Category: Breakthrough 

3.	 Models that appropriately consider the conflicting objectives of multiple stakeholders 

and make optimal recommendations for the system overall 

Motivation: Different stakeholders in the health care delivery system seek to optimize 

different objectives. All the perspectives need to be systematically taken into account to 

determine which combination of tradeoffs is best for the overall system and leads to best 

joint optimization of the multiple objectives. 

Category: Sustainability 

4.	 Models that provide guidance about when either standardization or customization is 

necessary 

Motivation: A vision of a personalized patient-centered system requires customization and a 

vision of an integrated efficient system requires standardization. The vision of the ideal 

system described in this report makes this a non-trivial tradeoff and a challenging research 

topic. How can mass customization be achieved while retaining the benefits of 

standardization? 

Category: Sustainability 

5.	 Models to explore the role and consequences of automation, and provide guidance 

about what can be fully or partially automated 

Models to explore the role and consequences of automation, and provide guidance about 

what can be fully or partially automated 

Motivation: Too much automation might make the system unsafe and impersonal, too little 

might make it inefficient and unsafe on a different level. Research to model the benefits and 

pitfalls of automation in different contexts would help address this delicate tradeoff. 

Category: Capacity building 

35
 
6.	 Mathematical programming models 

Motivation: Decades of research have laid the foundation for quantitative models (e.g., 

hazard rate models) that measure the association between risk factors, treatments, and health 

outcomes. However, mathematical programming models that use these inputs to determine 

optimal decisions for individual patients, or treatment policies for populations are in their 

infancy (e.g., maximizing quality adjusted life span for a patient, minimizing cost to a health 

system). There are significant opportunities to build optimization models to aid all 

stakeholders in making complex tradeoffs between the benefit and burden of treatment. 

Category: Breakthrough 

Models that can handle inconvenient realities 

1.	 Methods to build models from incomplete, inaccurate, and unreliable data Motivation: 

ISyE tools typically rely on complete and accurate data. The current health care delivery 

system produces data that is incomplete and often inaccurate. Until it is determined how to 

capture reliable data, an ideal health care delivery system will require tools that can generate 

value despite unreliable data while informing users about the level of confidence they can 

have in each of the data elements. 

Category: Breakthrough 

2.	 Methods to build models from inconsistent data coming from disparate sources 

Motivation: ISyE tools typically rely on consistent and integrated data. The current health 

care delivery system produces data that is coming more and more from disparate sources and 

lacks consistency and integrity. Until it is determined how to fully integrate data from 

disparate sources, or until a single data standard is adopted, an ideal (optimal) health care 

delivery system will require methods to models huge distributed complex systems like the 

US health care industry. Such knowledge would evidently be useful for other industries as 

well. 

Category: Breakthrough 

3.	 Methods to model unstable systems 

Motivation: ISyE tools typically assume knowable, stable, and well-defined systems. The 

current health care delivery system is none of the above. Until it is determined how to build 

such a system, an ideal health care delivery system will require models that are either robust 

enough or nimble enough to handle the perturbations in the system. 

Category: Breakthrough 

4.	 Methods to model large-scale, distributed systems where loose coupling occurs 

Motivation: ISyE tools were developed for optimization of well-defined, circumscribed, 

bounded, knowable systems (e.g., a production line, a factory, a waiting line at a bank) and 

not to optimize entire industries or sectors. In fact, improving the health care sector is 

particularly challenging because of its complexity, politicization, fragmentation, and loose 

coupling. ISyE successes in manufacturing and aviation did not lead to optimal industries. It 

is unreasonable to believe that current ISyE knowledge is adequate in optimizing the health 

care sector, or that optimizing microsystems will lead to the optimization of the entire 

system. An ideal (optimal) health care delivery system will require methods to model large36
 
scale distributed complex systems. Such knowledge would evidently be useful for other 

industries as well. 

Category: Breakthrough 

5.	 Models that can integrate qualitative and contextual knowledge (e.g., culture, ethics, 

law, psychology, social networks, and politics) and be responsive to changes in these 

qualitative and contextual factors 

Motivation: The development of ISyE tools that focus on unbounded rather than bounded 

systems necessitates consideration of contextual factors. Examples of contextual factors that 

must be considered include an increasingly multicultural population, persistent health care 

disparities, and potential unintended consequences of health IT policies and definitions of 

meaningful use. 

Category: Breakthrough 

6.	 Methods to model the dynamics between micro-changes (at the patient and provider 

levels) and macro-changes (at the population, market, and policy levels) 

Motivation: ISyE approaches to problem solving generally select a single level of analysis 

when modeling a problem as opposed to an approach that is multi-level. A single level 

approach works well in highly-bounded, circumscribed systems, but it may not when applied 

to health care delivery. Market level and policy level changes have traditionally been more 

within the purview of economics and policy analysis than that of ISyE and systems-thinking. 

However, if the goal is to optimize the health care industry provider-level optimization 

cannot suffice, and new ISyE tools are needed that can model the interdependences between 

the system-wide picture and the microsystems view. 

Category: Sustainability 

7.	 Models that incorporate errors and interaction of events 

Motivation: ISyE tools were developed for manufacturing, where computer-controlled 

machines can largely replace human error, and where interactions of events can be 

anticipated based on the programming of the machines. Until it is determined how to build an 

entirely automated health care delivery system, and whether that would be advisable, an ideal 

health care delivery system will require models that can simulate and help prevent errors and 

interactions of events. 

Category: Capacity building 

System Modification 

Monitoring and modeling the system are only useful if there are efficient ways to modify it. 

Three system modification angles are discussed: translating research into practice, top-down 

decomposition, and bottom-up integration. The first addresses research about ways to make 

knowledge developed by this agenda useful. The second addresses research aimed at guiding 

administrators and policymakers initiating and sustaining change to the health care delivery 

system. The third addresses research in organic ways to initiate grassroots change to the health 

care delivery system. 

37
 
Research-practice coordination 

1.	 Iterative knowledge development and transfer between research and practice 

Motivation: Recent grants have recognized the need for translational research from basic 

knowledge into practice, however, there is a need to further this process into bilateral and 

continuous development and refinement between the two domains. 

Category: Sustainability 

2.	 Improving translation from mathematical and technical languages into lay person 

terminology 

Motivation: One main system change challenge in health care is that humans are not 

reprogrammed, but rather trained, and need understandable actionable information consistent 

with their terminology and context. 

Category: Sustainability 

3.	 Improving lay people’s understanding of analytical results by developing enhanced 

data visualization techniques 

Motivation: One barrier to understanding numerical and analytical information is often data 

visualization, and although there is a considerable body of knowledge in that field, it needs to 

grow to keep up with changing technology. 

Category: Capacity building 

Top-down decomposition 

1.	 Determining ways to modify public and private incentives to influence patients to stay 

healthy, providers to work in the best interest of their patients, and organizations to be 

efficient, without unintended negative consequences 

Motivation: A reoccurring theme among the participants was that the main challenge of the 

health care industry was the perverse incentives. For example, participants noted that patients 

are given few incentives to stay healthy and that it is providers who may be penalized for 

having patients who are unwilling to care for themselves. Furthermore, providers are 

provided greater monetary incentives for services that may not be necessary or have 

incremental value for patients than they are for preventive services. 

Category: Breakthrough 

2.	 Exploring payment structures that accommodate technologically-mediated interactions 

between providers and patients (e.g., text messaging, email, or visits by teleconference) 

Motivation: Providing personalized care to an increasingly growing population will strain the 

already understaffed system, and technology can help increase the throughput and allow 

providers to help patients with common conditions faster than in conventional visits. It is 

important to determine how to pay providers for time spent delivering technologically 

mediated care, when current reimbursement structures do not accommodate for it. 

Category: Breakthrough 

38
 
3.	 Testing of change and implementation theories, and exploration of the tension between 

pushing for the application of existing knowledge and trying to develop more usable 

new knowledge 

Motivation: A primary challenge in this project was the belief within the group that new 

knowledge should not be pursued while there are no effective channels to facilitate the 

implementation of even existing knowledge. However, it is possible that low implementation 

of existing knowledge is due in larger part to its inadequacy than to the low demand for it or 

the absence of channels for it. 

Category: Sustainability 

4.	 Determining the benefits, limitations, and appropriate use of national, regional, and 

institutional forcing functions within the health care setting 

Motivation: Health care “problem owners” may be needed to coordinate improvement 

efforts, but it is not clear whether regulation is necessary or useful at all levels, or if it might 

lead to unnecessary bureaucracy. 

Category: Capacity building 

Bottom-up integration 

1.	 Determining appropriate approaches to stimulating system-wide change, exploring 

ways to coordinate between bottom-up integration and top-down decomposition 

Motivation: Different solutions can be reached when approached from the top-down (e.g., 

policy driven) or from the bottom-up (e.g., provider, patient driven). Research is needed to 

determine how to integrate these perspectives to achieve what is best for overall system 

performance. 

Category: Breakthrough 

2.	 Exploring how social network theories can be used to trigger and facilitate culture 

change 

Motivation: Social networks may be modeled by complementing existing and new ISyE 

related knowledge like graph theory with social sciences and human factors. 

Category: Sustainability 

3.	 Determining the role of culture as a necessary element of health care improvement, 

including the national political conversation and at the level of the patient and provider 

Motivation: New sociotechnical systems theories of culture change could be integrated with 

existing social science theories to provide guidance on how culture change can be 

systematically initiated and managed. 

Category: Capacity building 

Research Agenda: Knowledge Transfer 

It is timely to accelerate knowledge transfer of ISyE methods to solve currently recognized 

health care challenges. Both the documents reviewed for the background report and the 

workshop discussion primarily focused on action agenda items required to effectively 

disseminate existing ISyE knowledge. For example, recommendations related to knowledge 

39
 
transfer included demonstration projects, training in ISyE knowledge for health care 

professionals, and centers of excellence and resource centers. In addition to identifying means of 

effective knowledge transfer, however, two new research directions within the domain of 

knowledge transfer were also identified: 

1.	 Identification of best practices for dissemination and adoption of ISyE knowledge 

Motivation: There is currently a gap between awareness and use of ISyE knowledge, 

including use of health IT. Research is needed to determine barriers and facilitators to
 

adoption and sustained use of ISyE knowledge at the levels of individual practices, clinics, 

organizations, and at regional and national levels. 

Category: Capacity building
 

2.	 Identification of best practices for spreading new ISyE knowledge between research 

and industry and within industry 

Motivation: Development of new ISyE knowledge is only the first step. Effective and 

efficient pathways must be developed to spread such knowledge between organizations. 

Category: Capacity building 

Research Agenda: Meta-Knowledge Integration 

Research and knowledge building in ISyE has evolved in a viral fashion, serendipitously 

through projects that address a broad range of health care problems. It is timely to characterize 

the research space, set priorities strategically, and devise ways to efficiently glean systemic 

knowledge across disparate resources. Continuous development and refining of the research 

agenda for ISyE and health care requires meta-research, or research aimed at creating priorities 

and understanding of best practices within the research agenda. 

1.	 Characterizing health care challenges 

Motivation: A systematically derived understanding of health care is needed to identify the 

most severe problems and the problems that are most easily amenable to change. This is 

necessary to facilitate a targeted approach to ISyE knowledge creation and use. 

Category: Sustainability 

2.	 Mapping the usefulness of ISyE knowledge to different health care contexts 

Motivation: ISyE lacks the meta-knowledge needed to map which tools, techniques, and 

methods are appropriate and effective to address specific health care problems and 

challenges. 

Category: Capacity Building 

3.	 Exploiting synergies within ISyE knowledge derived from different subdisciplines 

Motivation: ISyE is composed of multiple methodological subdisciplines, and it is essential 

to have a plurality of methods to address the complex problems of health care. Investment in 

one methodological area (e.g., human factors) needs to be complemented with investment 

other area (e.g., operations research). Meta-knowledge is necessary to identify potential 

40
 
synergies between and within ISyE subdisciplines, and to tackle specific topical areas with a 

“systems approach.” 

Category: Breakthrough 

4.	 Identification of best practices for use of ISyE knowledge 

Motivation: Currently there is no understanding of what ISyE tools, techniques, or methods 

are more effective than others in creating change in the health care delivery system. All ISyE 

knowledge is often disseminated as being equally valuable. Studies comparing the 

comparative value of diverse types of ISyE knowledge in different problem settings are 

needed to ensure effective and efficient realization of change. 

Category: Sustainability 

5.	 Characterizing research frontiers and directions at the intersection of ISyE and health 

care 

Motivation: A systems understanding of ISyE knowledge and its frontiers is needed to guide 

new research efforts and continuously evaluate and redefine current priorities. Such efforts 

will lead to the continuous refinement of the research agenda at the intersection of ISyE and 

health care. 

Category: Capacity building 

41
 
 

Summary of Research Agenda 

Tables 2-4 list the research agenda items by category, under breakthrough, sustainability, and 

capacity building. 

Table 2. Research Agenda Items That Support Breakthrough 

System Monitoring 

(Knowledge 

Innovation) 

• Consumer-facing health IT solutions that allow patients to self-support 

their observations, that track and report on trends, and that interact with 

providers' annotations 

• Technologies which enable data to flow quickly and securely through the 

whole health care delivery system and be available in real-time when 

and where needed 

• Methods to operationalize contextual knowledge 

• Methods to effectively collect and share data in real-time to foster 

situational awareness of all individuals involved in patient care 

System Modeling 

(Knowledge 

Innovation) 

• Frameworks that explore the integration of many care sources in the 

production and delivery of care services, and the coordination among 

these sources ( e.g., end of life care) 

• Methods to model systems as set of flows and processes not just sets of 

components 

• Models that explore the effective use and allocation of different vehicles 

of health care delivery (e.g., “focused factories” versus integration, such 

as Mayo Clinics and Kaiser Permanente) 

• Models to evaluate entire systems and large-scale system changes 

before they are implemented 

• Models of collaboration and competition among health care stakeholders 

• Models that consider how health IT can be integrated into 

decisionmaking processes, how evidence-based knowledge can be 

integrated into practice 

• Models of collaboration and competition among health care stakeholders 

• Models that consider how health IT can be integrated into 

decisionmaking processes, how evidence-based knowledge can be 

integrated into practice 

• Methods to build models from incomplete, inaccurate, and unreliable 

data 

• Methods to build models from inconsistent data coming from disparate 

sources 

• Methods to model unstable systems 

• Methods to model large-scale distributed systems 

• Models that can integrate qualitative and contextual knowledge ( e.g., 

culture, ethics, law, psychology, social networks, and politics) and be 

responsive to its changes 

System 

Modification 

(Knowledge 

Innovation) 

• Determining ways to modify public and private incentives to influence 

patients to stay healthy, providers to work in the best interest of their 

patients, and organizations to be efficient, without unintended negative 

consequences 

• Exploring payment structures that accommodate technologicallymediated interactions between providers and patients ( e.g., text 

messaging, email, or visits by teleconference) 

• Determining appropriate approaches to stimulating system-wide change, 

exploring ways to coordinate between bottom-up integration and topdown decomposition 

Meta-Knowledge 

Integration 

• Exploiting synergies within ISyE knowledge derived from different 

subdisciplines 

42
 
Table 3. Research Agenda Items That Support Sustainability 

System Monitoring 

(Knowledge 

Innovation) 

• Identification of best practices for use of ISyE knowledge 

• Efficient and pervasive methods of data capture 

• New automatic data collection technologies to capture observations from 

patients and their environment ( e.g., sun exposure and food intake) 

• Theories and methods beyond natural language processing for the 

translation of lay person language into structured computable data 

• Efficient methods for integrating large amounts of data from disparate 

sources 

• Adequate integration of data collection into workflows in manners which 

ensure data validity while minimizing interference with clinical workflows 

• Efficient means of integrating information generated from different 

perspectives ( e.g., different providers, patients, administrators) 

• Methods to characterize how the outcomes relate to the processes 

System Modeling 

(Knowledge 

Innovation) 

• Models of trust between patients, providers, and technology 

• Models that provide guidance about when standardization or 

customization is necessary 

• Models that appropriately consider the conflicting objectives of multiple 

stakeholders and make system-optimal recommendations 

• Methods to model the dynamics between micro-changes (at the provider 

level) and macro-changes (at the market and policy levels) 

System 

Modification 

(Knowledge 

Innovation) 

• Iterative knowledge development and transfer between research and 

practice. 

• Improving translation from mathematical and technical languages into 

lay person terminology 

• Testing of change and implementation theories, and exploration of the 

tension between pushing for the application of existing knowledge and 

trying to develop more usable new knowledge 

• Exploring how social network theories can be used to trigger and 

facilitate culture change 

Meta-Knowledge 

Integration 

• Characterizing health care challenges 

• Identification of best practices for use of ISyE knowledge 

43
 
Table 4. Research Agenda Items That Support Capacity Building 

System Monitoring 

(Knowledge 

Innovation) 

• Identification of best practices for dissemination and adoption of ISyE 

knowledge 

• Identification of best practices for spreading new ISyE knowledge 

between research and industry institutions and among industry 

institutions 

• Methods to characterize processes, inputs, and outcomes 

• Methods to collect and present information that is valuable to diverse 

stakeholders such as patients, nurses, primary care and specialty 

physicians, pharmacists, and social workers 

• Theories and methods for the translation of numerical, analytical, and 

computational results into understandable and actionable information 

that multiple stakeholders (nurses, primary and specialty care 

physicians, pharmacists) and lay people can seamlessly retrieve to 

ensure the human monitoring of the system 

System Modeling 

(Knowledge 

Innovation) 

• Models to mitigate uncertainties about the future 

• Models to explore the role and consequences of automation, and 

providing guidance about what can be fully, partially, or not at all 

automated 

• Optimization models 

• Models that incorporate errors and interaction of events 

System 

Modification 

(Knowledge 

Innovation) 

• Improving lay people's understanding of analytical results by 

developing enhanced data visualization techniques 

• Determining the benefits, limitations, and appropriate use of national, 

regional, and institutional forcing functions within the health care 

setting 

• Determining the role of culture as a necessary element of health care 

improvement, including the national political conversation and at the 

level of the patient and provider 

Knowledge 

Transfer 

• Identification of best practices for dissemination and adoption of ISyE 

knowledge 

• Identification of best practices for spreading new ISyE knowledge 

between research and industry and within industry 

Meta-Knowledge 

Integration 

• Mapping the usefulness of ISyE knowledge to different health care 

contexts 

• Characterizing research frontiers and directions at the intersection of 

ISyE and health care 

Discussion of Research Agenda 

The proposed research agenda primarily emphasizes knowledge innovation, and discusses the 

transfer of existing knowledge, as well as the meta-knowledge necessary to improve the 

effectiveness of ISyE knowledge at the intersection with health care. However, two main 

obstacles face the success of the research agenda in driving the vision of an ideal health care 

delivery system.(1) Incentives are currently short-run oriented, and misaligned. As a result, there 

is no desire for mid-long term analyses to optimize large complex systems.(2) The tenure and 

promotion processes have emphasized numbers (e.g., papers, publications, and research dollars) 

without necessarily looking at the potential long-term “real engineering and research impact.” 

The following action agenda presents recommendations to overcome these and other obstacles 

identified in Chapter 4. 

44
 
Chapter 6: ISyE and Health Care Action Agenda 

This section presents an action agenda that should be pursued by relevant funding agencies 

and organizations to accelerate realization of the research agenda. A discussion of the purpose 

and scope of the action agenda and the challenges of creating the research agenda is also 

contained within this section. 

Purpose and scope of action agenda 

Identification of a research agenda at the intersection of ISyE and health care is only the first step 

to realizing the vision of an ideal health care delivery system. Much support is needed from 

funders in order to ensure that the research areas identified are pursued in a timely and effective 

manner and the results of such research disseminated appropriately. The purpose of the action 

agenda is to identify how such funders can support accomplishment of the research agenda. The 

scope of the action agenda, therefore, includes programs and initiatives that are directly within 

the purview of these agencies. As a result, although participants mentioned large-scale policy 

changes as an important part of an overall action agenda, these are not included in the action 

agenda. Examples of such policy changes included releasing all public data sets relevant to this 

research agenda and realigning incentives for provider, patients, and health care institutions. 

Presentation of the Action Agenda 

The action agenda proposed below is divided into five sections: (1) collaboration, (2) education 

and training, (3) research, (4) dissemination, and (5) administration. We recommend programs 

and initiatives in each of these areas in order for the research agenda to be appropriately 

accomplished and disseminated. Examples of initiatives and programs that should be supported 

are indentified and characterized within each section of the action agenda. 

Collaboration 

We recommend support for programs, centers, and initiatives that bring together multiple 

stakeholders within the health care delivery system to jointly address problems. 

1.	 Consortia of all stakeholders: We recommend support for the creation of consortia that 

include providers, payers, consumers, the insurance industry, and vendors. Inclusion of 

vendors is particularly important since they are often absent from the conversation but an 

integral part of any comprehensive solution. Creation of such consortia will stimulate 

conversations between stakeholders who often have conflicting motivations, likely resulting 

in more innovative and acceptable solutions to the problems of the health care delivery 

system. 

45
 
2.	 Multi-stakeholder conversations: We recommend support for the execution of 

interdisciplinary projects that bring together all relevant stakeholders mentioned above. 

Examples include seminars, workshops, and challenge-specific meetings that bring together 

multidisciplinary groups to address a specific disease process or other bounded problem. 

Additionally, workshops that focus on the “capacity-building” items identified in the research 

agenda would likely accelerate the development of new ISyE knowledge. 

3.	 Partnerships: We recommend support for the creation and leveraging of working 

relationships among Federal agencies (e.g., AHRQ, National Institutes of Health (NIH), 

NSF, Veterans Affairs (VA)) and between Federal agencies and other organizations working 

on improving the health care delivery system. Partnerships should be pursued with volunteer, 

governmental, and non-governmental organizations such as those identified below. Through 

such partnerships, funding agencies can stimulate wider interest in creating an ideal health 

care delivery system and can engage these diverse individuals knowledgeable about ISyE and 

health care in creating breakthrough change. 

Examples of partnership organizations (listed alphabetically): 

a.	 AcademyHealth 

b.	 American Hospital Association (AHA) 

c.	 American Medical Association (AMA) 

d.	 American Society for Quality (ASQ) 

e.	 Association for Computing Machinery (ACM) 

f.	 Centers for Disease Control and Prevention (CDC) 

g.	 Department of Defense (DoD) 

h.	 Healthcare Information and Management Systems Society (HIMSS) 

i.	 Human Factors and Ergonomics Society (HFES) 

j.	 Institute of Electrical and Electronics Engineers (IEEE) 

k.	 Institute for Healthcare Improvement (IHI) 

l.	 Institute for Operations Research and the Management Sciences (INFORMS) 

m. Institute of Industrial Engineers (IIE) 

n.	 Institute of Medicine (IOM) 

o.	 Joint Commission 

p.	 Medical Device Manufacturers Association 

q.	 Medical Group Manufacturer’s Association 

r.	 National Academy of Engineering (NAE) 

s.	 National Academy of Sciences (NAS) 

t.	 Production and Operations Management Society (POMS) 

u.	 Society for Medical Decision Making (SMDM) 

v.	 Society of General Internal Medicine (SGIM) 

w.	 World Health Organization (WHO) 

46
 
In addition to the organizations listed above, there was some debate among workshop 

participants as to whether or not Federal agencies should partner directly with vendors. Some 

workshop participants identified vendors such as General Electric and Intel as desirable 

partners, whereas other participants noted that the conflicting motivations between Federal 

agencies and such vendors make these forms of partnerships less desirable. 

4.	 Professional home: We recommend support for the creation of a common professional 

association or coordination of existing relevant societies for professionals working at the 

intersection of ISyE and health care. Creation of a professional home for this intersection (or 

creation of coordinating mechanisms between multiple professional homes) would facilitate 

effective and sharing of new knowledge. Such coordination is particularly needed between 

ISyE and health care organizations. 

Education and Training 

We recommend support for the creation of programs and initiatives to facilitate the 

development of professionals with a deep understanding of both ISyE and health care. 

1.	 Higher education: We recommend support for the creation of more academic programs and 

interdisciplinary courses that train undergraduate and graduate ISyE student in health care 

and health care professionals in ISyE. Creation of these programs will ensure a growing 

supply of professionals who are working knowledge of both content areas. 

2.	 Professional development: We recommend support for the creation of cross-training short 

courses, seminars, and online trainings to make currently practicing ISyE and health care 

professionals aware of the knowledge of the other discipline. 

3.	 Fellowships: We recommend support for the establishment of early and mid-career awards 

for ISyE professionals to spend time within a health care environment and for health care 

professionals to spend time working with ISyE professionals. Such awards would enable 

professionals from one discipline to obtain fluency in the language and mental models of the 

other discipline. 

4.	 Case studies: We recommend support for the compilation of a library of case studies 

demonstrating the application of ISyE knowledge to solving problems within the health care 

delivery system. This library would serve to facilitate teaching of ISyE knowledge to health 

care professionals and health care knowledge to ISyE professionals. It could also serve to 

stimulate engineering interest in the problems of health care, resulting in increased 

recruitment of industrial and systems engineers in health care. 

Funding 

We recommend direct support for research activities at the intersection of ISyE and health 

care. 

47
 
1.	 Investigator initiated projects: We recommend support for investigator initiated projects 

that are short-term (1 to 2 years) at the intersection of ISyE and health care. Such projects 

could, for example, focus on testing the appropriateness of an existing method within the 

context of health care or focus on creating methodological knowledge (e.g., a model) that has 

applicability within a particular health care domain. 

2.	 Methodological innovation centers (MICs): We recommend support for the development 

of centers that are responsible for the conduct of long-term research (3 years or greater) at the 

intersection of ISyE and health care. These centers should facilitate alignment of research 

within health care delivery systems by establishing an enduring relationship with a health 

care delivery system as a laboratory for testing and refining of new discoveries. This may be 

accomplished in part by creating partnerships with and capitalizing upon resources of 

existing centers. MICs should not, however, be focused on existing analytical challenges 

faced by these centers, but should focus on creating new methodological knowledge at the 

intersection of ISyE and health care. MICs may be anchored to specific geographical areas or 

may span several geographical sites. 

a.	 An effective way to develop MICs may exist in the identification of “Learning 

HealthCare Systems.” Envisioned as an enduring research-delivery system alliance, 

these institutions would integrate basic research in ISyE, a working health care 

delivery system to serve as a test-bed for new ideas and a demonstration site for ISyE 

methods integration, and an educational partner (e.g., university, academic health 

science center) for training the next generation of health-care professionals shaped by 

ISyE knowledge and ISyE researchers and analysts well-versed in the issues of health 

care. 

3.	 Multidisciplinary grantees: We recommend support for the requirement of grantee teams to 

be multidisciplinary, bringing together not only ISyE and health care professionals, but also 

professionals from fields such as social science in order to receive funding. Mutlidisplinary 

work should be encouraged both for investigator-initiated and center-based projects. 

Dissemination 

We recommend support for the creation of programs and initiatives that encourage the timely 

and effective dissemination of research at the intersection of ISyE and health care. 

1.	 Publication: We recommend support for the publication of funded research in practice 

journals and public media in addition to traditional research journals. Furthermore, funders 

should support the creation of a high-impact research journal designed specifically for 

publication of research conducted at the intersection of ISyE and health care. 

2.	 Networking: We recommend support for the dissemination of research conducted at the 

intersection of ISyE and health care through IT-enabled social networks to promote rapid 

transfer of knowledge. Support could take the form of online forums, virtual networks, and 

virtual conferences. 

48
 
3.	 Demonstration projects We recommend support for the testing of solutions developed at the 

intersection of ISyE and health care within physical test beds or real health care 

organizations. Such testing would help facilitate development of best practices knowledge. 

Administration 

We recommend support for administrative changes to promote high quality research at the 

intersection of ISyE and health care. 

1.	 Joint solicitations and collaborative funding: We recommend support for joint solicitations 

and collaborative funding to drive the research agenda proposed in this report. For example, 

NSF and AHRQ could extend a joint solicitation. NSF could be responsible for the 

evaluation of the engineering knowledge contained within the proposal and AHRQ could be 

responsible for the evaluation of the value of the proposal to solving problems within the 

health care delivery system. 

2.	 Faster proposal cycle: We recommend support for the acceleration of the cycle of proposals 

for grants relevant to the research agenda in this report to ensure a timely realization of the 

vision of an ideal health care delivery system. 

49
 
 

Chapter 7: Conclusion 

It is clear that the current U.S. health care delivery system is suboptimal. Lack of efficiency 

and effectiveness of the system has resulted in health care that is high in cost but not consistently 

high in quality. Continued operation of such a health care delivery system that is inefficient and 

costly is unwise, particularly given the unprecedented financial distress being experienced by the 

country. Solutions are needed which will result not only in incremental change to the health care 

delivery system (e.g., decreased wait times, more efficient scheduling), but breakthrough 

changes which will lead to an ideal health care delivery system. Unfortunately, current solutions 

to the problems of the health care delivery system are often politically motivated, not grounded 

in systematic development of alternatives, and focused on incremental change at the level of the 

subsystem. 

The field of industrial and systems engineering has the potential to make significant 

contributions to achieving an ideal health care delivery system through use of tools, techniques, 

and methods that allow for systematic development of solutions. To date, however, efforts at 

implementing ISyE knowledge to solving the problems of the health care delivery system have 

failed to realize breakthrough change. 

This project takes an important first step toward determining how the field of ISyE can 

significantly contribute to breakthrough change in the health care delivery system by: 

1.	 Explicating a vision of an ideal health care delivery system, as new, engineered, and 

patient-centered, 

2.	 Identifying nine barriers and four facilitators to achieving tractable change in the health 

care delivery system using current ISyE methods, and 

3.	 Establishing a research agenda focused primarily on knowledge innovation , and to a 

lesser extent on knowledge transfer and meta-knowledge integration and an action 

agenda focused on collaboration, education and training, funding, dissemination, and 

administration needed to enable ISyE to meaningfully contribute to the realization of an 

ideal health care delivery system. 

Much work, however, remains to be accomplished. Three areas warranting further 

investigation are described below: 

1.	 Policy and incentives. The scope of application of ISyE knowledge is generally confined 

within a specified policy environment. Consequently, in this project, we treated all 

policy-related topics, including the topic of incentives as a constraint. Future 

investigations, however, should explore and address the misaligned incentives that exist, 

for example, between providers and payers or academia and industry. 

2.	 The role of axiomatic research. The field of health care relies heavily on empiricism. 

Whereas an empirical approach is grounded in data, an axiomatic approach is grounded 

in first principles. Such first principles are valuable because they serve as a framework 

for interpretation of data. The field of ISyE serves as a bridge between fields such as 

physics and biology that rely heavily on first principles and fields such as the social 

sciences in which such principles, because of the nature of personalities, politics, and 

51
 
people do not always hold. Future investigations should explore the tensions between 

these two approaches and address the role of ISyE in capitalizing upon this tension. 

3.	 Role of health IT. The systematic, purposeful application of health IT will aid in the 

realization the goal realizing an ideal health care delivery system through the application 

of ISyE knowledge. Development and implementation of new health IT knowledge will, 

therefore, play an important role in achieving both the research and action agenda at the 

intersection of ISyE and health care. Future investigations should focus on creating a 

research agenda that further explicates the type of health IT knowledge needed. 

Both ISyE and health care professionals should work together and team with other 

stakeholders to accomplish the research agenda outlined in this report. It is recommended that 

appropriate funding agencies support the successful execution and implementation of the results 

of the research agenda as outlined in the action agenda. It is also recommended that these 

communities work together to continue to build upon the research and action agendas by 

addressing the areas of further investigation outlined above. By solidifying the partnership with 

health care and by focusing on the development of new tools, techniques, and methods, and how 

health IT may facilitate development and use of this new knowledge, the field of ISyE can help 

realize the vision of an ideal health care delivery system. 

52
 
 

 

 

 

References 

1.	 Commission on Systemic Interoperability. 

Ending the document game: Connecting and 

transforming your healthcare through 

information technology. Washington, DC: 

U.S. Government Printing Office; 2005. 

2.	 Donaldson MS, Mohr JJ. Exploring innovation 

and quality improvement in health care microsystems: a cross-case analysis. Washington, 

DC: Institute of Medicine; 2001. A technical 

report for Institute of Medicine Committee on 

the Quality of Health Care in America. 

3.	 Institute of Medicine. Crossing the quality 

chasm: A new health system for the 21st 

century. Washington, DC: National Academies 

Press, 2001. 

4.	 Institute of Medicine. Engineering a learning 

healthcare system: A look at the 

future/Preliminary draft. Work in progress, 

2008. 

5.	 Institute of Medicine. The learning healthcare 

system: Workshop summary. L Olsen, D 

Aisner, JM McGinnis, eds. Washington, DC: 

National Academies Press, 2007. 

6.	 Institute of Medicine. Learning healthcare 

system concepts v. 2008/Annual report. 

Washington, DC: National Academies Press, 

2008. 

7.	 Institute of Medicine. To err is human: 

Building a safer health system. LT Kohn, JM 

Corrigan, MS Donaldson, eds. Washington, 

DC: National Academies Press, 2000. 

8.	 Institute of Medicine and National Academy 

of Engineering. Building a better delivery 

system: A new engineering/health care 

partnership. PP Reid, WD Compton, JH 

Grossman, G Fanjiang, eds. Washington, DC: 

National Academies Press, 2005. 

9.	 McClellan MB, McGinnis JM, Nabel EG, et 

al. Evidence-based medicine and the changing 

nature of health care. Washington, DC: 

Institute of Medicine; 2008. 

10.	 National Research Council. Computational 

technology for effective health care: 

Immediate steps and strategic directions. WW 

Stead & HS Lin, eds. Washington, DC: 

National Academies Press; 2009. 

11.	 Nelson EC, Batalden, PB, Godfrey MM, et al. 

Microsystems in health care: The essential 

building blocks of high performing systems. 

Princeton (NJ): Robert Wood Johnson 

Foundation; 2001. RWJ Grant Number 

036103. 

12.	 Rardin RL. Research agenda for healthcare 

systems engineering/Final report. Arlington, 

VA: National Science Foundation, February, 

2007. NSF Grant No. 0613037. 

13.	 Roberts S, Uzsoy R, Ivy J, Denton B. 

Workshop: Healthcare engineering and health 

services research: Building bridges, breaking 

barriers/Final report. Arlington, VA: National 

Science Foundation, April, 2008. NSF Grant 

NO. 0817223. 

53
 
 

Appendix A: Background Report 

Industrial and Systems Engineering and Health Care: 

Critical Areas of Research 

Prepared for: 

Agency for Healthcare Research and Quality 

U.S. Department of Health and Human Services 

540 Gaither Road 

Rockville, MD 20850 

www.ahrq.gov 

Contract No. 290-09-00027U 

Prepared by: 

University of Wisconsin-Madison, Madison, WI 

Authors: 

Rupa Sheth Valdez 

Industrial and Systems Engineering 

University of Wisconsin-Madison 

Patricia Flatley Brennan 

Professor and Chair 

Industrial and Systems Engineering 

Professor, School of Nursing 

University of Wisconsin-Madison 

AHRQ Project Officer: 

Teresa Zayas-Caban 

This report is also available as a standalone document on the AHRQ NRC Web 

site at http://healthit.ahrq.gov/engineeringhealthbackgroundreport 

(AHRQ Publication No. 09-0094-EF, September 2009) 

A-1
 
Contents 

Executive Summary ........................................................................................................ A-5 

Introduction............................................................................................................... A-5 

Purpose of This Meeting and Report ........................................................................ A-5 

Key Themes .............................................................................................................. A-6 

Chapter 1: Summary of Past Reports and Identification of Common Themes............... A-7 

Report Summaries ..................................................................................................... A-7 

Common Themes.................................................................................................... A-14 

Chapter 2: Vision of an Ideal Health Care Delivery System........................................ A-15 

Summary ................................................................................................................. A-15 

Discussion............................................................................................................... A-15
 

Chapter 3: Barriers to Realization of This Vision ........................................................ A-17 

Summary ................................................................................................................. A-17 

Discussion............................................................................................................... A-18
 

Chapter 4: New Industrial and Systems Engineering Tools To Realize This Vision ... A-21 

Summary ................................................................................................................. A-21 

Discussion............................................................................................................... A-22
 

Chapter 5: Questions To Stimulate Workshop Discussion ........................................... A-23 

Chapter 6: Conclusion................................................................................................... A-25 

References..................................................................................................................... A-27
 

A‐3
 
Executive Summary 

Introduction 

There is no question that the current health care delivery system is suboptimal. Problems 

related to the system’s efficiency, effectiveness, accessibility, safety, and other characteristics 

have been copiously documented in reports issued by both public and private agencies and in 

numerous journal articles. Acutely aware of the current political climate surrounding health care 

reform, the popular press daily inundates the public with stories of yet another deficiency in, or 

failure of, the system. In just one day (September 7, 2009), three prominent news sources 

reported the following shortcomings: 

Chicago Tribune: There is concern that the proposals in Congress for health care reform will 

threaten the funding and future of the country’s safety net hospitals (Johnson, 2009) 

Wall Street Journal: A child died from a medical error caused by a lack of communication 

between physicians (Landro, 2009) 

New York Times: Sixty-two percent of bankruptcies this year will be medical. Of those, 

three-quarters had insurance, at least when they initially got sick (Underwood, 2009) 

Leaders in the health care community are deeply aware that change is necessary. Myriad 

approaches to improving the health care delivery system have been offered and remain at 

different stages of implementation. Prominent solutions to improving health care delivery 

include suggestions grounded in education, incentives, research funding, information technology, 

and systems engineering. Yet, despite consciousness of the problems and the many initiatives 

targeted at addressing them, the health care delivery system remains replete with flaws. 

Purpose of This Meeting and Report 

Beginning with the report Building a Better Delivery System (Institute of Medicine and 

National Academy of Engineering, 2005), there has been heightened interest in solving problems 

in the health care delivery system using industrial and systems engineering tools. Approaches to 

date envision improvements in the known health care system though the application of industrial 

and systems engineering approaches. However, the absence of progressive improvement in 

health care suggests the need to reframe the discussion, beginning first with a vision of an 

optimal health care system then specifying the industrial and systems engineering methods 

needed to insure the realization of that future. 

The primary purpose of this meeting is to develop a research agenda at the intersection of 

industrial and systems engineering and health care. To achieve this primary purpose, meeting 

participants will be asked to engage in three activities: 

A‐5
 
1.	 Define a vision of an ideal health care system. 

2.	 Critically examine the reasons for which fundamental change to the health care 

delivery system, including through the use of industrial and systems engineering 

tools, remains intractable. 

3.	 Develop a prioritized list of new industrial and systems engineering tools that must be 

developed to realize the vision of an ideal future. 

This background report provides a critical summary of the discourse salient to each element 

above. Documents generated by conferences, workshops, and working groups sponsored by 

various national bodies (National Academy of Sciences, Institute of Medicine, National 

Academy of Engineering, National Science Foundation) serve as the corpus upon which this 

report rests. By providing an overview of the main themes and identifying points of 

inconsistency and limitation in the current disquisition, this report seeks to provoke discussion 

among meeting participants. As such, it seeks not to be exhaustive but stimulating. 

There are 13 reports that form the basis for this background report. These reports may be 

conceptualized as belonging to three categories: (1) reports that “set the stage” for discussions 

about improving the health care delivery system by drawing national attention to the need for 

change, (2) reports that directly explore the intersection between industrial and systems 

engineering and health care delivery, and (3) reports that represent the discourse related to 

specific mechanisms for improving the health care delivery system. Reports belonging to this 

final category primarily advocate one of three solutions to the deficiencies of the health care 

delivery system: (1) information technology, (2) evidence-based medicine, or (3) a 

“microsystems” approach. We purposely excluded reports that focused on a narrow aspect of the 

health care delivery system. Thus, for example, we excluded reports that focused solely on 

documenting specific demand for evidence, adoption challenges related to electronic health 

records, and a research agenda for consumer health information technology. 

This background report has three parts. Chapter 1 presents a brief summary of each of the 13 

reports that form the basis of this background report and presents a list of 7 themes common to 

these reports. Chapters 2-4 present summaries and discussion points related to the three meeting 

action items. Chapter 5 presents questions that may be used to instigate discussion among 

meeting participants, and Chapter 6 provides a conclusion. 

Key Themes 

Review of the 13 reports resulted in identification of seven common themes: 

1.	 The current health care delivery system is both unsustainable in terms of cost and 

suboptimal in terms of value. 

2.	 The current health care delivery system cannot adequately respond to changes in the 

larger environment and within the medical sciences. 

3.	 Solving the problems of the health care delivery system is complex and will require 

approaches that are multidimensional, multileveled, and inclusive of multiple 

stakeholders. 

4.	 Information technology will play a key role in the future health care delivery system. 

A‐6
 
5.	 Incentives are needed to promote change, including the use of systems engineering tools, 

information technology, and evidence-based medicine. 

6.	 Opportunities are needed for cross-education and collaboration between health care 

professionals and scientific and technical professionals such as engineers and computer 

scientists. 

7.	 Research funding is needed to explore the intersections between health care and the use 

of systems engineering tools, computer science methodologies, and information 

technology. 

Additionally, review of the 13 reports resulted in a short summary and discussion related to 

each of the three meeting action items: 

Define a vision of an ideal health care delivery system: Current visions of an ideal health 

care delivery system are primarily descriptive rather than prescriptive. When prescriptive 

guidance is provided, it is partial rather than comprehensive, focusing only on select aspects of 

an ideal health care delivery system such as the role of evidence-based medicine or information 

technology. If conceptualized in terms of an engineering design process, the vision or “design” 

of an ideal health care delivery system must be comprehensively specified prior to realization. 

Relying on only partial specification may be risky because it constrains the vision and increases 

the likelihood of unintended and potentially undesirable consequences. 

Determine why fundamental change to the health care delivery system remains intractable: 

Solutions advanced to fixing problems of the health care delivery system are not aligned with the 

theoretical recognition that solving the problems of the health care delivery system requires a 

multidimensional solution not grounded in current realities. Furthermore, current approaches to 

changing the health care delivery system emphasize local optimization rather than system-wide 

optimization. Systems engineering tools have also been used to promote local optimization. This 

is reasonable, given the nature of systems engineering tools, the culture of health care, and the 

systems currently in place to support the use of systems engineering tools. 

Determine what new forms of industrial and systems engineering tools are needed to arrive 

at the vision of an ideal health care delivery system: Although these reports mention several 

existing industrial and systems engineering tools that would be useful for local optimization, 

there is only minimal discussion of the new types of industrial and systems engineering tools that 

will be necessary. Those that are provided are a positive step in the right direction as they push 

the boundary beyond local optimization. One limitation of the new tools mentioned, however, is 

that they remain grounded in the current health care delivery system. What is needed is a set of 

new industrial and systems engineering tools that are grounded not in the current health care 

delivery system, but in the vision of an ideal health care delivery system. 

A‐7
 
Chapter 1: Summary of Past Reports and 

Identification of Common Themes 

Report Summaries 

In this section, a brief summary is provided of each of the 13 documents that form the 

foundation of this background report. The corpus is arranged alphabetically for ease of access to 

the source documents. 

Commission on Systemic Interoperability. Ending the document game: Connecting and 

transforming your healthcare through information technology. Washington, DC: U.S. 

Government Printing Office; 2005. 

This report advances the idea of information technology as a solution to many of the 

problems present within the current health care delivery system. Specifically, it is noted that 

increased interconnectivity will facilitate communication, security and confidentiality, and 

recordkeeping and prevent medical errors. Three steps are presented as a means of creating a 

nationwide system of health information: adoption, interoperability, and connectivity. The 

authors note that realization of such a system will involve multiple changes, including 

reformulated financial incentives, regulatory reform, changes in workforce requirements, data, 

privacy and authentication standards, a national health information network, and legal 

protections for consumers. 

Donaldson MS, Mohr JJ. Exploring innovation and quality improvement in health care 

micro-systems: a cross-case analysis. Washington, DC: Institute of Medicine; 2001. A 

technical report for Institute of Medicine Committee on the Quality of Health Care in 

America. 

This purpose of this report is to define the concept of a microsystem and to determine which 

characteristics of a microsystem enable it to improve the quality of care received by patients. The 

authors note that the concept of the microsystem is drawn for the manufacturing and service 

industry, where it has been successfully applied. In this report, the concept of the microsystem 

in health care is defined as a consisting of (1) a core team of health care professionals, (2) a 

defined population they care for, (3) an information environment to support the work of 

caregivers and patients, and (4) support staff, equipment, and a work environment. Examples of 

microsystems within health care provided by the report include a dialysis unit, an emergency 

room in a community hospital, and a hospice. Qualitative interviews with representatives of 43 

purposely selected high-performing microsystems yielded eight themes which provide a 

framework for conceptualizing how microsystems function: (1) integration of information, (2) 

measurement, (3) interdependence of care team, (4) supportiveness of larger system, (5) 

constancy of purpose, (6) connection to community, (7) investment in improvement, and (8) 

alignment of role and training. The authors note that these findings may be used to develop tools, 

which may be used by other microsystems to replicate and extend the high levels of performance 

found within this sample. 

A‐9
 
Institute of Medicine. Crossing the quality chasm: A new health system for the 21st century. 

Washington, DC: National Academies Press, 2001. 

This report serves as a complement to To Err is Human (Institute of Medicine, 2000), 

documenting not only quality concerns caused by errors but by an entire spectrum of causes. The 

authors acknowledge that although the health care system does, at times, perform admirably, 

there are large variations in care, and many do not receive the care that they need. Thus, there is 

recognition that “trying harder” within the current system is not the solution; a new system is 

needed. Recommendations include a systems approach to creating a health care system that is 

timely, effective, patient-centered, efficient, equitable, and safe. In advocating a systems 

approach, the authors acknowledge the need for change at the level of health care providers but 

also at the level of health care organizations, professional groups, public and private purchasers, 

and other government bodies. Environmental changes in four areas are proposed as a means of 

enabling larger system change: infrastructure that supports the dissemination and application of 

new clinical knowledge and technologies, the information technology infrastructure, payment 

policies, and preparation of the health care workforce. Funding for recommended to support 

projects targeted towards achieving the six aims and/or producing substantial improvement in 

quality for priority conditions. 

Institute of Medicine. Engineering a learning healthcare system: A look at the 

future/Preliminary draft. Work in progress, May, 2008. 

The authors of this report note that systems engineering tools have transformed multiple 

industries and question whether these methods may be useful in creating a learning health care 

system. Ten common themes are documented in this report: (1) center the system’s processes on 

the right target—the patient experience, (2) system excellence is created by the reliable delivery 

of established best practice, (3) complexity compels reasoned allowance for tailored adjustments, 

(4) emphasize interdependence and tend to process interfaces, (5) teamwork and cross-checks 

trump command and control, (6) performance transparency and feedback serve as engines for 

improvement, (7) expect errors in the performance of individuals, perfection in the performance 

of systems, (8) align rewards on key elements of continuous improvement, (9) develop education 

and research to facilitate understanding and partnerships between engineering and the health 

professions, and (10) foster a leadership culture, language, and style that reinforces teamwork 

and results. Finally, the report identifies five points of followup for members of the roundtable 

including clarifying terms, identifying best practices, exploring changes to health professions 

education, advancing the science of payment for value, and exploring development of a science 

of waste and engagement. 

Institute of Medicine. The learning healthcare system: Workshop summary. Olsen L, 

Aisner D, McGinnis JM, eds. Washington, DC: National Academies Press, 2007. 

The learning healthcare system is a system within which evidence is generated and applied 

within the decisionmaking process. This report highlights how evidence-based medicine could 

serve as a rigorous scientific basis for the medical profession and highlights approaches currently 

used by health care organizations to achieve this aim. Thus, the primary purpose of this report is 

to document how information could be better generated and applied to improving health care. 

Twelve needs are identified that must be addressed to move toward a learning healthcare system. 

These needs include: (1) adaptation to the pace of change, (2) stronger synchrony of efforts, (3) 

A‐10
 
culture of shared responsibility, (4) new clinical research paradigm, (5) clinical decision support 

systems, (6) universal electronic health records, (7) tools for database linkage, mining, and use, 

(8) notion of clinical data as public good, (9) incentives aligned for practice-based evidence, (10) 

public engagement, (11) trusted scientific broker, and (12) leadership. Mechanisms of 

addressing these needs and current progress towards meeting these needs are presented, 

including realigned incentives, revised medical education, and building upon current 

infrastructure and resources. There is also a brief mention of the potential of methodologies such 

as mathematical modeling, Bayesian statistics, and decision modeling as effective mechanisms 

for assessing interventions. 

Institute of Medicine. Learning healthcare system concepts v. 2008/Annual report. 

Washington, DC: National Academies Press, 2008. 

This report presents a summary of the key issues identified during the first 2 years of work 

by the Institute of Medicine Roundtable on Evidence-Based Medicine. The authors emphasize 

that our Nation is failing to deliver the value that should be expected from received care and that 

the purpose of the roundtable is to accelerate the delivery of such value, particularly in terms of 

effectiveness and efficiency, by creating a learning health care system. There is mention of the 

fact that the context within which change to the health care delivery system is needed is 

daunting, given, for example, constantly changing medical interventions, the increasing 

complexity of decisions, and geographical variations in spending. A learning health care system 

within which new evidence is constantly produced and applied through real-time learning and 

use is presented as a solution to these problems. The report also documents the need to provide 

incentives for high-performing caregivers, to develop capacity to generate evidence, and to 

generate and disseminate high-quality data. There is acknowledgment that any effective solution 

will require the participation of multiple stakeholders, including caregivers, health care 

organizations, patients, health care product companies, researchers, regulators, and payers and 

purchasers. 

Institute of Medicine. To err is human: Building a safer health system. Kohn LT, Corrigan 

JM, Donaldson MS, eds. Washington, DC: National Academies Press, 2000. 

This report calls attention to the fact that between 48,000 and 96,000 individuals die each 

year as a result of medical errors in our health care system. Such errors are described as 

expensive to the system, resulting in rework and opportunity costs as well as intangible ones, 

such as those related to employee and patient satisfaction and morale. Multiple reasons are 

provided for the silence that surrounds the issue of medical errors: (1) consumers believe they 

are protected, (2) providers fear legal repercussions from systematically uncovering and 

addressing errors, (3) the fragmented system prevents an understanding of root causes, and (4) 

purchasers have not demanded better quality and safety conditions. This report states that success 

has been achieved by other industries in reducing errors and that the lessons learned in these 

industries should be applied to health care. In this report, the problem of medical errors is viewed 

as a systems problem, and one that can only be solved be implementing a multifaceted, 

multilevel response. Specifically, the report calls for the number of errors to be reduced through 

the implementation of regulatory, educational, and engineering mechanisms, and at the level of 

the provider, health care organization, and national agencies. Funding for a center dedicated to 

A‐11
 
improving patient safety though goal setting, tracking, research, and dissemination is 

recommended. 

Institute of Medicine and National Academy of Engineering. Building a better delivery 

system: A new engineering/health care partnership. Reid PP, Compton WD, Grossman JH, 

Fanjiang G, eds. Washington, DC: National Academies Press, 2005. 

This report is divided into two sections: the consensus report and a series of individual 

articles by leaders in the fields of health care delivery or systems engineering. The authors 

explore the potential of systems engineering tools to the improvement of the health care delivery 

system. In the consensus report, a framework is presented in which the health care system is 

conceptualized at consisting of four levels: the patient, the care team, the organization, and the 

environment. Systems engineering tools are identified that could help the health care system 

overcome difficulties at each of these four levels. Additionally, action steps are presented which 

would promote both the awareness and use of systems engineering tools at each of these four 

levels. These action steps include the dissemination of current systems engineering tools by both 

governmental and private organizations, development of information and communication 

technologies, and multidisciplinary research and educational programs. The second half of this 

report contains short articles by leaders in the fields of health care delivery and systems 

engineering, each of which focus on a particular area of health or a particular class of systems 

engineering tools. 

McClellan MB, McGinnis JM, Nabel EG, et al. Evidence-based medicine and the changing 

nature of health care. Washington, DC: Institute of Medicine; 2008. 

This report is the 2007 annual report of the Institute of Medicine roundtable on evidencebased medicine. It notes that there is a need for better evidence to guide health care decisions and 

that a mechanism is needed to both develop and apply evidence naturally during the care process. 

Nine common themes were identified related to the discussion of evidence-based medicine: (1) 

increasing complexity of health care, (2) unjustified discrepancies in care patterns, (3) 

importance of better value from health care, (4) uncertainty exposed by the information 

environment, (5) pressing need for evidence development, (6) promise of health information 

technology, (7) need for more practice-based research, (8) shift to a culture of care that learns, 

(9) new model of patient-provider partnership, and (10) leadership that stems from every quarter. 

It is noted that a multidimensional approach is needed to implement evidence-based care. Such 

an approach is described as involving multiple stakeholders such as patients, provider, payers, 

industry, and policymakers and involving realignment of incentives to support the use of 

evidence-based care. 

National Research Council. Computational technology for effective health care: Immediate 

steps and strategic directions. Stead WW and Lin HS, eds. Washington, DC: National 

Academies Press; 2009. 

This report centers around two questions: (1) How can today’s computer science-based 

methodologies and approaches be applied more effectively to health care? and (2) What are the 

limitations of these methodologies? and How can they be overcome through additional research 

and development? Answers to these questions are obtained through site visits to eight medical 

centers, literature review, and committee expertise. The authors conclude that the current focus 

A‐12
 
on information technology efforts within health care is insufficient to drive needed change. 

Recommendations include the implementation of both evolutionary and radical change and to 

expand research along two dimensions: (1) the extent to which new, fundamental, generalpurpose research is needed and (2) the extent to which new research specific to health care and 

biomedicine is needed. As in other reports, there is acknowledgement that multiple stakeholders, 

including government, the computer science community, and health care institutions must 

participate for meaningful change to be realized. There is clear communication that the scope of 

this report is limited, focusing primarily on the role of clinicians in large health care institutions, 

and only peripherally touching on the larger economic, political, and cultural context within 

which health care reform must occur. 

Nelson EC, Batalden, PB, Godfrey MM, et al. Microsystems in health care: The essential 

building blocks of high performing systems. Princeton (NJ): Robert Wood Johnson 

Foundation; 2001. RWJ Grant Number 036103. 

This report presents the concept of the microsystem as an opportunity to think about 

transforming health care from the front line of service delivery. The authors note that the idea of 

transforming health care via optimization of the microsystem relies on three primary assumptions 

about the structure of the health care system: (1) bigger systems (i.e., microsystems) are made of 

smaller systems, (2) these smaller systems (i.e., microsystems) produce quality, safety, and cost 

outcomes at the front line of care, and (3) the outcomes of the macrosystems can be no better 

than the microsystems of which it is a part. Thus, microsystems are conceptualized as the 

building blocks of the health system. The authors note that the concept of the microsystem has 

been successfully been used by service organizations such as FedEx®, McDonald’s, and 

Nordstrom. This report then presents the results of a study in which 20 high-performing clinical 

microsystems were studied to uncover nine characteristics of success: (1) improvement methods, 

(2) staff focus, (3) performance results, (4) information and information technology, (5) patient 

focus, (6) leadership, (7) interdependence of care team, (8) culture, and (9) organizational 

support. The authors conclude that the role of the clinical microsystem has been ignored to date 

and should be attended to in the future as a means to transform the health care system. 

Rardin RL. Research agenda for healthcare systems engineering/Final report. Arlington, 

VA: National Science Foundation, February, 2007. NSF Grant No. 0613037. 

This report serves two primary functions: it (1) proposes a research agenda for health care 

systems engineering and (2) documents the funding challenges and potential funding solutions 

for health systems engineering. In this report, the health care system is conceptualized as 

consisting of six levels: patient, population, team, organization, network, and environment. The 

field of health care systems engineering is conceptualized as consisting of three domains: 

technology, model-based, and practice-based. A research agenda is outlined at each health care 

system level and the potential for advances is evaluated for each of the three health care systems 

engineering domains. Top research priorities identified include treatment optimization, 

personalized, preventive care, information rich and configurable operations management, 

collaboration within networks, and large-scale delivery system design. The report also notes that 

health care systems engineering has no funding “home” and calls for the establishment of a 

health care engineering alliance to support such research. 

A‐13
 
Roberts S, Uzsoy R, Ivy J, Denton B. Workshop: Healthcare engineering and health 

services research: Building bridges, breaking barriers/Final report. Arlington, VA: 

National Science Foundation, April, 2008. NSF Grant No. 0817223. 

This report in organized around answering four questions at the intersection of health 

services research and health care systems engineering: (1) What do health services research and 

health care systems engineering have in common?; (2) What can health care systems engineering 

learn from health services research?; (3) What can health services research learn from health care 

systems engineering?; and (4) Why is the VA so important to health care engineering? 

Commonalities identified include shared understanding of problems, shared common intellectual 

assets, shared belief in data-driven analysis and decisions, and complementary research methods 

and tools. Seven recommendations to the National Science Foundation (NSF) are presented, 

which emphasize the need for NSF to encourage and fund interdisciplinary projects at the 

intersection of these fields. These recommendations include calls for both educational and 

research initiatives at the intersection of health care engineering and health services research. 

Common Themes 

The common themes presented below are not necessarily present in all of the 13 reports 

included in this background report, but may be found in a large majority of the reports reviewed. 

•	 The current health care delivery system is both unsustainable in terms of cost and 

suboptimal in terms of value. 

•	 The current health care delivery system cannot adequately respond to changes in the 

larger environment and within the medical sciences. 

•	 Solving the problems of the health care delivery system is complex and will require 

approaches that are multidimensional, multi-leveled, and inclusive of multiple 

stakeholders. 

•	 Information technology will play a key role in the future health care delivery system 

•	 Incentives are needed to promote change, including the use of systems engineering tools, 

information technology, and evidence-based medicine. 

•	 Opportunities are needed for cross-education and collaboration between health care 

professionals and scientific and technical professionals such as engineers and computer 

scientists. 

•	 Research funding is needed to explore the intersections between health care and the use 

of systems engineering tools, computer science methodologies, and information 

technology. 

A‐14
 
Chapter 2: Vision of an Ideal Health Care Delivery 

System 

Summary 

In the current discourse, comprehensive visions of an ideal health care delivery system are 

descriptive rather than prescriptive. For example, in Crossing the Quality Chasm (Institute of 

Medicine, 2001), the ideal health care delivery system is described as embodying six attributes: 

safety, effectiveness, efficiency, equity, timeliness, and patient-centeredness. Other attributes of 

an ideal health care delivery system include the capacity to learn and therefore continuously 

improve (Institute of Medicine, 2007) and cost-effectiveness (Institute of Medicine, 2008a). 

Finally, an ideal health care system should also be able to accommodate both environmental 

changes, such as an aging population and the shift in disease burden from acute to chronic illness 

(National Research Council, 2009) and advances in both technology and medical science, 

including new medications, devices, diagnostics, biologics, and procedures (Institute of 

Medicine, 2008b). 

Limited attempts have been made to prescriptively specify components of the ideal health 

care system. Such attempts have primarily focused on detailing the role of information 

technology in the future. For example, in Ending the Document Game (Commission on 

Systemic Interoperability, 2005), the authors advance a vision of the future in which information 

technology is used to manage health care information, enabling a medical record to be available 

wherever and whenever it is needed and authorized. Similarly, reports focusing on evidencebased medicine (e.g., Institute of Medicine, 2007, 2008b; McClellan et al., 2008) detail how a 

vision of a learning health care system may be achieved by promoting knowledge bases other 

than randomized controlled trials and by collecting and disseminating data in real-time via 

information technology. 

In Crossing the Quality Chasm (Institute of Medicine, 2001), there is an explicit refusal to 

specify a vision not only of an ideal future health care system, but also of a 21st century health 

care system. The committee argues that such an exercise would be neither useful nor possible. 

They further argue that imagination and valuable pluralism abound at the local level and, 

consequently, offer a set of rules the may be used to implement innovation and achieve 

improvement at this local level. 

Discussion 

The potential risk inherent in the current approach of prescriptively specifying only select 

aspects of a vision of an ideal health care delivery system may be best explored by considering 

an engineering design process. At the beginning of a design process, a designer establishes an 

understanding of the functional requirements that a product, service, or system must be meet. 

This results in a uniquely descriptive vision of this product, service, or system. Endless 

possibilities exist as to how these functional requirements may be realized. 

A‐15
 
After exploring the infinite possible design solutions, a designer must create concrete 

alternatives, evaluate these alternatives, and choose one with which to proceed. Thus, the 

designer must select the means that will be used to achieve the functional requirements. There is 

definitive movement from a descriptive solution to a prescriptive solution. No aspect of the 

design solution is left unspecified. 

There is a simple reason for which an engineering design process requires comprehensive 

specification before advancing to development. Partial specification is unpredictable. Without 

ensuring that all of the pieces of the final design fit together, there is no assurance that the bridge 

will not collapse, that circuit will not short. It is not enough to determine what material should be 

used to build the bridge or build the circuit. Such limited prescriptive specification only serves 

to constrain the final solution; it does not present a final solution. 

The same principles hold for “designing” an ideal health care delivery system. A uniquely 

descriptive solution is important at the beginning of the process. It allows for application of 

“imagination and valuable pluralism” (Institute of Medicine, 2001). Yet, this phase of the design 

process cannot last indefinitely. To realize an ideal health care system, a prescriptive solution 

must be defined. Precluding a prescriptive solution, it is impossible to determine whether we are 

moving toward the desired destination. Furthermore, such a solution must be comprehensive. At 

present, the prescriptive solution is only piece-meal. There are loud calls for the use of evidencebased medicine and information technology and even many details about how these tools may be 

used to improve health care delivery (e.g., Commission on Systemic Interoperability, 2005; 

Institute of Medicine, 2007, 2008b; McClellan et al., 2008; National Research Council, 2009). At 

the same time, however, there is little prescriptive specification as to how other aspects of the 

health care delivery system will be redesigned, eliminated, or added to fit with these 

recommended improvements. 

One final point should be made about the level at which a prescriptive solution for the health 

care delivery system is specified. In Crossing the Quality Chasm (Institute of Medicine, 2001), 

the authors imply that such prescriptive specification should take place at the local level. Yet, in 

other publications such as Building a Better Delivery System (Institute of Medicine and National 

Academy of Engineering, 2005), the ideal health care system is envisioned as something 

transformed from “an underperforming conglomerate of independent identities into a highperformance ‘system’ in which participating units recognize their interdependence and the 

implications and repercussions of their actions on the system as a whole (p. 2).” 

We are not arguing that a comprehensive prescriptive solution is possible, or even desirable 

at present. It may be that, as a Nation, we still need time to explore options, to draw lines in the 

sand. We only wish to draw attention to two realities: (1) there is inherent risk in prescriptively 

defining only certain dimensions of the ideal health care delivery system, and (2) a 

comprehensive prescriptive solution is important for ensuring purposeful movement toward an 

ideal health care delivery system. 

A‐16
 
Chapter 3: Barriers to Realization of This Vision 

Summary 

Unquestionably, “solving health care” is viewed as a complex problem. This complexity is 

recognized in each of the 13 reports that serve as the basis for this background report. 

Particularly within To Err is Human (Institute of Medicine, 2000), Crossing the Quality Chasm 

(Institute of Medicine, 2001), and Building a Better Delivery System (Institute of Medicine and 

National Academy of Engineering, 2005), there is recognition that improving the health care 

delivery system will require a multifaceted response, involving numerous stakeholders such as 

providers, health care organizations, public and private purchasers, professional groups, and 

national agencies. Mechanisms for change advocated in these reports include: 

•	 Financial mechanisms. Create financial incentives, including payment reform, for the use 

of quality improvement techniques and evidence-based medicine. Implement financial 

penalties for preventable medical errors. Increase research funding to investigate 

potential solutions to problems in the health care delivery system. 

•	 Educational mechanisms. Spread knowledge related to tools and techniques that may be 

used to improve safety, effectiveness, and efficiency. Ensure that all providers are 

prepared to respond to the changing environment. Create multidisciplinary learning 

environments to foster innovative solutions to the problems of the current health care 

delivery system. 

•	 Engineering mechanisms. “Design out” problems that contribute to problems such as 

medical errors. Create new structures that support evidence-based medicine and the use 

of information technology. 

•	 Regulatory and market-based mechanisms: Develop performance standards and 

expectations both for the health care professional and health care organization. 

•	 Provider-based mechanisms: Rely on the intrinsic motivation of providers as a force for 

improving the health care delivery system. 

Thus, philosophically, there is consensus that an ideal health care system will not be realized 

through incremental improvements of the current system. This sentiment is most directly 

expressed in Crossing the Quality Chasm (Institute of Medicine, 2001): “The current care 

systems cannot do the job. Trying harder will not work. Changing systems of care will” (p. 4). 

A second common theme among the reports is that improving individual components of the 

health care delivery system will lead to improvement of the overall, or whole, health care 

delivery system. This theme is most clearly expressed within two reports (Donaldson & Mohr, 

2001; Nelson et al., 2001) that detail the microsystem philosophy. In this view, smaller parts of 

an organization (microsystems) are seen as having semipermeable boundaries with other 

microsystems, the whole of which is embedded in an environment. This broader environment 

consists of dimensions such as the payment environment, the regulatory environment, and the 

cultural–socio-political environment (Nelson et al., 2001). Examples of a microsystem include a 

dialysis unit, an emergency room within a community hospital, or a hospice care center 

A‐17
 
(Donaldson & Mohr, 2001). The microsystem philosophy states that (1) bigger systems are made 

of smaller systems, (2) the smaller systems produce quality, safety, and cost outcomes at the 

front line of care, and (3) the outcomes of the macrosystems can be no better than the 

microsystems that comprise it (Nelson et al., 2001). Thus, proponents of this philosophy 

maintain that improving individual microsystems will lead to improvement of the overall health 

care delivery system. 

Finally, a third common theme among reports is that systems engineering approaches are a 

novel approach to changing the health care delivery system and should be adopted (e.g., Institute 

of Medicine, 2000, 2008a; Institute of Medicine and National Academy of Engineering, 2005; 

Rardin, 2007; Roberts et al., 2008). A common rationale presented for adopting systems 

engineering tools is that they have been successfully applied within other industries such as 

banking, manufacturing, and aviation. The reports reviewed suggest that the reason that systems 

engineering tools have not had a larger impact on changing the health care delivery system is that 

(1) knowledge of their existence is not widespread, (2) there are no incentives in place for either 

providers or health care organizations to use these methods, and (3) little funding exists to 

conduct research on the intersection of health care delivery and systems engineering. As a result, 

several reports advocate for increased cross-education between health care and engineering 

professionals (e.g., Institute of Medicine and National Academy of Engineering, 2005; Roberts et 

al., 2008) and incentives for implementing systems engineering tools within health care 

organizations (e.g., Institute of Medicine, 2008a; Institute of Medicine and National Academy of 

Engineering, 2005), and additional research funding (e.g., Institute of Medicine and National 

Academy of Engineering, 2005; Roberts et al., 2008) to encourage the use of these approaches. 

Discussion 

There is a contradiction between the philosophical view that solving health care is a complex 

problem that cannot have a solution grounded in the current system and many of the solutions 

presented. For example, there is a belief (although not shared by all) that certain solutions will be 

close to panaceas. In other words, there is a tendency to oversimplify the problem. Such 

sentiment is found, for example, in Ending the Document Game (Commission on Systemic 

Interoperability, 2005). In this report, the authors argue that many of the problems within health 

care, for example, problems related to cost, safety, efficiency, and effectiveness, could be solved 

through the adoption of information technology. The rationale behind this belief is that 

information technology will enable systems to fundamentally retain their current structure but 

better perform necessary tasks. 

Furthermore, despite acknowledgement that fundamental changes to the delivery system are 

necessary, many of the changes proposed remain grounded in the current system. Thus, 

suggestions for increased research funding assume that Federal agencies will remain responsible 

for disseminating funds. Similarly, suggestions for the use of evidence-based medicine assume 

that care will primarily continue to be delivered in clinics and solutions for education assume 

that academic disciplines will, fundamentally, retain their existing boundaries. 

A similar problem exists with the idea that implementing change at the level of the 

microsystem will result in meaningful change at the level of the macrosystem or the overall 

A‐18
 
health care delivery system. Proponents of the microsystem viewpoint maintain that outcomes of 

the macrosystems can be no better than the microsystems that comprise it (Nelson et al., 2001). 

Although this is a reasonable statement, modest reflection reveals that its converse is more 

powerful. In other words, microsystems can be no better than the macrosystem in which they are 

embedded. It is the macrosystem which constrains what the microsystem is capable of achieving. 

Thus, the microsystem approach to improving the health care delivery system is also grounded 

within the current macrosystem reality. 

The reflections above highlight that the solutions advocated contradict the understanding that 

improving the health care delivery system is complex and requires a fundamentally new solution. 

Answers proposed often either oversimplify the problem and/or remain grounded to current 

realities. Achieving true reform of the health care delivery system will require solutions that are 

true to our theoretical understanding of the problem as multidimensional and requiring a 

paradigm shift. 

Unfortunately, the answer of systems engineering has, to date, also failed to yield any 

fundamental change. Common wisdom suggests that this failure is due to the lack of use, instead 

of any inherent limitation, of these methods. Failure to use these tools has been attributed to a 

lack of awareness, resources, or motivation to implement systems engineering knowledge. 

There is reason to believe, however, that this failure is also the result of a combination of 

assumptions about systems engineering tools, of the structures in place to support their use, and 

of the traditional focus of both systems engineering tools and the health care delivery system. 

Although each of these factors will be examined in turn, the primary problem seems to be that 

there has been a focus on local instead of system-wide optimization. 

A pervasive assumption exists that systems engineering tools have been useful in solving 

problems in other fields and will, therefore, be successful in the field of health care. The 

determination of whether systems engineering tools have been successful in other fields may 

depend upon the level of examination. Thus, for example, it may be argued that systems 

engineering tools such as human factors engineering have been useful for the redesign of the 

cockpit, preventing some pilot errors. Yet, a more microlevel examination may lead to a different 

conclusion. Aviation, banking, and manufacturing, all industries for which there has been a claim 

of success for systems engineering tools, are suffering deeply. A more accurate assertion, 

therefore, may be that systems engineering tools have been successful in solving microlevel 

problems, but have not been successful at solving many of the microlevel problems pervasive in 

each of these industries. 

The support structures that have been built to support the use of systems engineering tools 

are similarly focused on local optimization. For example, organizations such as the Institute for 

Healthcare Improvement and the Leapfrog Group advocate the use of systems engineering tools 

at the level of a practice or a health care organization. Both the measures and tools presented for 

use assume that the overall health care delivery system will remain constant, and that it is the 

prerogative of each individual practice or institution to optimize their performance within this 

relatively fixed environment. 

A‐19
 
The culture of health care also promotes local optimization. Silos exist both at the level of the 

practice, the “microsystem” or subspecialty, and at the level of the health care organization. 

Each practice has its own panel of patients, each “microsystem” or subspecialty has its own 

specialized knowledge, and each health care organization has its own market share. At each 

level, therefore, there is incentive to optimize locally to preserve whatever advantage one 

currently enjoys. Particularly at the level of the health care organization, there is little incentive 

to join with another health care organization and to optimize at the level of joint operations. Such 

a maneuver may be an anathema to profit-driven health care organizations, which, in a marketbased health care system, compete with one another for survival. 

Finally, by their very nature, systems engineering tools are best suited for local optimization. 

Although systems engineering tools are meant to provide a means for obtaining a holistic 

perspective about and solution to a given problem, the “systems” that these methods were 

originally designed for were relatively small-scale systems such as a manufacturing plant floor or 

perhaps even an organization. Thus, it could be argued that the use of systems engineering tools 

at the level of a practice, “microsystem,” or even health care organization is sensible. The 

methods are being used at the level for which they were originally designed. 

If this is the case, what is the role of systems engineering tools in creating a vision of the 

future that is not grounded in the current system? To date, the relationship between systems 

engineering tools and the health care delivery system has been to use current systems 

engineering tools to optimize the current health care delivery system. In the future, this 

relationship will need to change such that new systems engineering tools are developed to 

facilitate the creation of an ideal health care delivery system. 

A‐20
 
Chapter 4: New Industrial and Systems Engineering 

Tools To Realize This Vision 

Summary 

There is very little discourse related to what new industrial and systems engineering tools 

must be created to realize a vision of an ideal health care delivery system. Although several 

reports mention that systems engineering tools must be adopted for use in health care or that new 

systems engineering tools must be developed for health care (e.g., Institute of Medicine, 2007; 

Institute of Medicine and National Academy of Engineering, 2005) only one report (Institute of 

Medicine and National Academy of Engineering) contains an in-depth discussion related to this 

topic. This report, Building a Better Delivery System, is divided into two parts. The first part 

contains a consensus report and the second, articles written by individuals. Little is mentioned in 

the consensus report about the need for new systems engineering tools. Only in the individual 

articles is there some detailed discussion about the new types of systems engineering tools that 

must be developed to improve the health care delivery system. 

Examples of the new industrial and systems engineering tools proposed are presented below. 

Many of these methods are presented as also necessary to address complex problems within 

other fields such as manufacturing: 

1.	 Methods of modeling and optimizing supply chains where demand is a function of 

multiple variables. Within health care, demand is a function of multiple variables 

including the types of treatment available and the insurance coverage available. Models 

are needed which can account for demand that does not have a single determinant. 

2.	 Models of modeling and optimizing supply chains within which the actions of one party 

affect the options available to other parties. The activities of stakeholders in the health 

care system are interdependent. For example, the coverage decisions made by an 

insurance company may affect the treatment decisions made by a provider. 

3.	 Methods of analyzing large-scale systems. Industrial and systems engineering tools 

contain methods such as value-stream mapping and facilities layout tools that may be 

used to analyze small-scale systems. These tools may be useful for optimizing a clinic or 

unit but are not as likely to be useful for optimizing an entire system. 

4.	 Methods of modeling which replace the need for clinical trials. Developing knowledge 

via randomized controlled trials is considered time consuming and costly. Computer 

modeling techniques may be a useful means of generating the necessary evidence in a 

more efficient manner. 

5.	 Methods of modeling and optimizing activities of multiple, independent agents. Health 

care consists of multiple, independent agents such as health care providers, health care 

systems, health care payers, and regulatory agencies working independently to optimize 

their position. 

A‐21
 
Discussion 

The methods listed above provide a glimpse into the types of new industrial and systems 

engineering tools that may be needed to move beyond current improvement efforts. These 

methodological visions are a step in the right direction to move the tools available beyond local 

optimization. It should be noted, however, that many of these tools are also grounded in the 

assumption that the current health care delivery system will retain many of its features such as 

multiple, independent players and demand that is informed by the actions of insurance 

companies. 

This latter point emphasizes the need also for new industrial and systems engineering tools 

that are grounded not in the current reality but in the vision of an ideal health care delivery 

system. The new tools created should be those that will be necessary to both create and optimize 

this vision. Thus, determining which new industrial and systems engineering tools are necessary 

must be the last step in the process. Without specifying a vision, it will be unclear what new 

types of tools will be needed to realize the vision. 

A‐22
 
 

Chapter 5: Questions To Stimulate Workshop 

Discussion 

1.	 Are current systems engineering techniques scalable to be effectively used at levels higher 

than an organization? 

2.	 Are we asking the right question? Instead of asking how the health care delivery system 

should respond to environmental changes such as the rising need for chronic care, should we 

be asking why there is a rising need for chronic care? In an article in the New York Times this 

week, Michael Pollan noted that one reason for rising chronic care is the rise in obesity due 

to the American diet. Similarly, asthma has increased due to environmental conditions. 

Instead of placing such emphasis on redesigning the health care delivery system, should we 

instead focus on preventing these environmental changes? 

3.	 There is a pervasive assumption that providers are intrinsically motivated to deliver the best 

possible care. There is also an assumption that financial incentives are needed for providers 

to implement systems engineering tools and to use evidence-based medicine. This suggests 

that providers are also driven by other motivations. What are the implications of this apparent 

contradiction? 

4.	 Given that the microlevel system constrains the microlevel systems, is there reason to believe 

that creating change at lower levels will lead to fundamental change for the entire delivery 

system? 

5.	 There is a tension between revolutionary and evolutionary change. Historically, systems 

engineering tools have been used within the field of health care to create evolutionary 

change. How can systems engineering tools be used to create revolutionary change? 

6.	 Many hold fast to the belief that information technology will solve many of the problems 

inherent in the current health care delivery system. Why does such an assumption exist? 

What are the pitfalls of such an assumption? 

7.	 What is the meaning of “best practice”? How should we define “best practice,” given that the 

definition may differ depending upon the point of view (patient, practitioner, payer)? 

8.	 How do we systematically balance the need for evidence-based care and the individuality of 

patients? 

9.	 Who will use the newly developed industrial and systems engineering tools? If they are not 

focused on local optimization, who will be responsible for implementing them in practice? 

A‐23
 
 

 

Chapter 6: Conclusion 

There is consensus among the reports reviewed that change to the current health care delivery 

system is necessary. An understanding exists at a theoretical level that changes to the system 

must be revolutionary, not simply grounded in current realities. Visions of an ideal health care 

delivery system are primarily descriptive, although some elements of the vision, such as the role 

of information technology, have been prescriptively defined. 

Efforts at change, including the use of systems engineering methods have, however, 

remained grounded in current realities. The focus has been on locally optimizing elements of the 

entire system, such as a practice, unit, or organization. The use of systems engineering tools in 

this context is understandable given that (1) the culture of health care emphasizes local 

optimization, (2) existing systems engineering methods were created for local optimization, and 

(3) structures supporting the use of systems engineering methods promote local optimization. 

To create a revolutionary new future, however, there is a need for new industrial and systems 

engineering tools that have a focus beyond local optimization. The reports reviewed contain a 

few suggestions of new industrial and systems engineering tools that have this broader focus. 

Several of these tools, however, are still grounded in the realities of the current health care 

delivery system. There is a need to create industrial and systems engineering tools that are 

grounded not in the current reality, but in the vision of an ideal health care delivery system. 

A‐25
 
 

 

 

 

 

 

References 

1.	 Commission on Systemic Interoperability. 

Ending the document game: Connecting and 

transforming your healthcare through 

information technology. Washington, DC: U.S. 

Government Printing Office; 2005. 

2.	 Donaldson MS, Mohr JJ. Exploring innovation 

and quality improvement in health care microsystems: a cross-case analysis. Washington, DC: 

Institute of Medicine; 2001. A technical report 

for Institute of Medicine Committee on the 

Quality of Health Care in America. 

3.	 Institute of Medicine. Crossing the quality 

chasm: A new health system for the 21st century. 

Washington, DC: National Academies Press, 

2001. 

4.	 Institute of Medicine. Engineering a learning 

healthcare system: A look at the 

future/Preliminary draft. Work in progress, 2008. 

5.	 Institute of Medicine. The learning healthcare 

system: Workshop summary. Olsen L, Aisner D, 

McGinnis JM, eds. Washington, DC: National 

Academies Press, 2007. 

6.	 Institute of Medicine. Learning healthcare 

system concepts v. 2008/Annual report. 

Washington, DC: National Academies Press, 

2008. 

7.	 Institute of Medicine. To err is human: Building 

a safer health system. Kohn LT, Corrigan JM, 

Donaldson MS, eds. Washington, DC: National 

Academies Press, 2000. 

8.	 Institute of Medicine and National Academy of 

Engineering. Building a better delivery system: 

A new engineering/health care partnership. Reid 

PP, Compton WD, Grossman JH, Fanjiang G, 

eds. Washington, DC: National Academies 

Press, 2005. 

9. Johnson CK. Will safety net hospitals survive 

health reform? Chicago Tribune. Available at: 

http://www.chicagotribune.com/news/chi-ap-ushealthcare-safety,0,5110897.story. Accessed 

September 7, 2009. 

10. Landro L. A fight against fatal error. Wall Street 

Journal September 7, 2009. Available at: 

http://online.wsj.com/article/SB10001424052970 

203706604574378762626910076.html. 

Accessed September 7, 2009. 

11.	 McClellan MB, McGinnis JM, Nabel EG, et al. 

Evidence-based medicine and the changing 

nature of health care. Washington, DC: Institute 

of Medicine; 2008. 

12.	 National Research Council. Computational 

technology for effective health care: Immediate 

steps and strategic directions. Stead WW and Lin 

HS, eds. Washington, DC: National Academies 

Press; 2009. 

13.	 Nelson EC, Batalden, PB, Godfrey MM, et al. 

Microsystems in health care: The essential 

building blocks of high performing systems. 

Princeton (NJ): Robert Wood Johnson 

Foundation; 2001. RWJ Grant Number 036103. 

14.	 Rardin RL. Research agenda for healthcare 

systems engineering/Final report. Arlington, VA: 

National Science Foundation, February, 2007. 

NSF Grant No. 0613037. 

15.	 Roberts S, Uzsoy R, Ivy J, Denton B. Workshop: 

Healthcare engineering and health services 

research: Building bridges, breaking 

barriers/Final report. Arlington, VA: National 

Science Foundation, April, 2008. NSF Grant No. 

0817223. 

16.	 Underwood A. Insured, but bankrupted anyway. 

New York Times September 7, 2009. Available 

at: 

http://prescriptions.blogs.nytimes.com/2009/09/0 

7/insured-but-bankruptedanyway/?scp=1&sq=medical%20bankruptcy&st 

=cse. Accessed September 7, 2009. 

A‐27
 
Appendix B: Workshop Participants
 

Patricia Flatley Brennan, RN, PhD, 

FAAN (Chair) 

Professor and Chair, Industrial & Systems 

Engineering 

College of Engineering 

Lillian L. Moehlman Bascom Professor 

School of Nursing 

University of Wisconsin-Madison 

pbrennan@engr.wisc.edu 

Maulik S. Joshi, DrPH 

(Morning Keynote Speaker) 

President, Health Research & Educational 

Trust 

Senior Vice President of Research, 

American Hospital Association 

Aneesh Chopra 

(Dinner Keynote Speaker)
 

United States Chief Technology Officer 

Associate Director 

Office of Science and Technology Policy 

Oguzhan Alagoz, PhD 

Assistant Professor 

Department of Industrial & Systems College 

of Engineering 

University of Wisconsin-Madison 

alagoz@engr.wisc.edu 

Nilay Tanik Argon, PhD 

Assistant Professor 

Department of Statistics & Operations 

Research 

University of North Carolina at Chapel Hill 

nilay@email.unc.edu 

James Benneyan, PhD 

Executive Director, New England VA 

Healthcare Engineering Partnership 

Director, Healthcare Quality & Productivity 

Lab 

Northeastern University 

benneyan@coe.neu.edu 

Brian Denton, PhD 

Assistant Professor 

Edward P. Fitts Department of 

Industrial & Systems Engineering 

North Carolina State University 

bdenton@ncsu.edu 

Robert Dittus, MD, MPH 

Albert & Bernard Werthan Professor of 

Medicine 

Chief, Division of General Internal 

Medicine 

Director, Institute for Medicine & Public 

Health 

Director, Center for Health Services 

Research 

Vanderbilt University 

robert.dittus@Vanderbilt.Edu 

Ilaina Edison, RN, MBA 

Vice President for Operations 

Visiting Nurse Service of New York 

Ilaina.edison@vnsny.org 

Seth A. Eisen, MD 

Director of Health Services Research & 

Development 

Department of Veterans Affairs 

seth.eisen@va.gov 

John Fowler, PhD 

Editor, IIE Health Systems 

Professor of Industrial Engineering 

Arizona State University 

john.fowler@asu.edu 

B-1 
Charles Friedman, PhD 

Deputy National Coordinator for Health IT 

U.S. Department of Health & Human 

Services 

charles.friedman@hhs.gov 

Stephanie Guerlain, PhD 

Associate Professor 

Department of Systems & Information 

Engineering 

University of Virginia 

guerlain@virginia.edu 

Isaac Kohane, MD, PhD 

Associate Professor of Pediatrics and Health 

Sciences & Technology 

Director, Children's Hospital Informatics 

Program 

Harvard University 

isaac_kohane@harvard.edu 

Ross Koppel, PhD 

Sociology Department & Center for 

Epidemiology & Biostatistics 

School of Medicine 

University of Pennsylvania 

rkoppel@sas.upenn.edu 

Sally Lundeen, PhD, RN, FAAN 

Professor and Dean 

College of Nursing 

University of Wisconsin - Milwaukee 

slundeen@uwm.edu 

Sanjay Mehrotra, PhD 

Professor 

Department of Industrial & Management 

Systems Engineering 

Northwestern University 

mehrotra@iems.northwestern.edu 

Edmond Ramly 

Industrial & Systems Engineering 

University of Wisconsin-Madison 

edmond.ramly@gmail.com 

Marilyn Rantz, PhD, RN, FAAN 

Professor, Sinclair School of Nursing 

Helen E. Nahm Chair, Sinclair School of 

Nursing 

University Hospitals & Clinics Professor of 

Nursing 

Executive Director, Aging In Place 

Associate Director, Interdisciplinary Center 

on Aging 

University of Missouri 

rantzm@health.missouri.edu 

Michelle Rogers, PhD 

Assistant Professor 

College of Information Science & 

Technology 

Drexel University 

michelle.l.rogers@drexel.edu 

Sarah Root, PhD 

Assistant Professor of Industrial Engineering 

University of Arkansas 

seroot@uark.edu 

Vinod Sahney, PhD 

Senior Vice President and Chief Strategy 

Officer 

Blue Cross Blue Shield of Massachusetts 

vinod.sahney@bcbsma.com 

Eugene S. Schneller, PhD 

Professor 

Dean's Council of 100 Distinguished 

Scholar 

Health Sector Supply Chain Initiatives 

School of Health Management & Policy 

W. P. Carey School of Business 

Arizona State University 

gene.schneller@asu.edu 

Julie Swann, PhD 

Associate Professor of Industrial & Systems 

Engineering 

Georgia Institute of Technology 

jswann@isye.gatech.edu 

B-2 
 

James M. Walker, PhD, FACP 

Chief Health Information Officer 

Geisinger Health System 

jmwalker@geisinger.edu 

Merry Ward, PhD, MS 

Healthcare Informatics Research 

Department of Veterans Affairs 

merry.ward@va.gov 

Steven Witz, PhD, MPH 

Director, Regenstrief Center for Healthcare 

Engineering 

Purdue University 

witz@purdue.edu 

Shinyi Wu, PhD 

Assistant Professor 

Daniel J. Epstein Department of 

Industrial & Systems Engineering 

University of Southern California 

shinyiwu@usc.edu 

José L. Zayas-Castro, PhD 

Professor and Chair 

Department of Industrial & Management 

Systems Engineering 

University of South Florida 

josezaya@eng.usf.edu 

David Zimmerman, PhD 

Professor 

Director, Center for Health Systems 

Research & Analysis 

University of Wisconsin-Madison 

davidz@chsra.wisc.edu 

B-3 
 

 

Appendix C: Agenda 

DAY ONE: WHAT ENGINEERING KNOWLEDGE IS NEEDED TO CREATE A 

DESIRED FUTURE FOR HEALTH CARE? 

8:00 am Registration and Continental Breakfast 

8:30 am Welcome 

Patricia Flatley Brennan, Lillian L. Moehlman Bascom Professor, School of 

Nursing and College of Engineering, University of Wisconsin-Madison 

8:35 am Welcome and Comments by AHRQ & NSF 

Teresa Zayas-Cabán, Senior Manager, Health IT, Agency for Healthcare 

Research and Quality 

Cerry Klein, Program Director, Service Enterprise Systems and 

Manufacturing Enterprise Systems, National Science Foundation 

9:00 am Chair’s Presentation 

Patricia Flatley Brennan 

9:30 am Keynote Presenter 

Maulik Joshi, President, Health Research & Educational Trust 

SVP of Research, American Hospital Association 

10:00 am Plans for the Day 

Patricia Flatley Brennan 

10:10 am Break 

10:30 am Vision Statements 

1. Information Technology/Finance and Quantitative Decision Making 

2. Systems Analysis, Change and Implementation 

Theories 

3. Materials Management and Production Processes 

4. Human Factors and Sociotechnical Systems 

5. Quality Engineering 

12:00 pm Lunch 

1:00 pm Breakout Groups: 

C-1
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 Breakout A: Information Technology/ Finance and Quantitative 

Decision Making 

Session Co-Chairs – James Walker, Chief Health Information

 Officer, Geisinger Health Systems; Brian Denton, Assistant Professor, 

North Carolina State University 

 Breakout B: Systems Analysis, Change and Implementation 

Theories 

Session Chair – José L. Zayas-Castro, Professor & 

Chairperson, University of South Florida 

 Breakout C: Materials Management and Production Processes

 Session Chair – Eugene S. Schneller, Ph.D., Professor, School 

of Health Management and Policy, W. P. Carey School of 

Business Arizona State University 

 Breakout D: Human Factors and Sociotechnical Systems

 Session Chair – Stephanie Guerlain, Associate Professor, 

 Department of Systems and Information Engineering, University 

of Virginia 

Breakout E: Quality Engineering

 Session Chair – James Benneyan, Executive Director, New 

England VA, Healthcare Engineering Partnership, Director, 

 Quality and Productivity Laboratory, Northeastern University 

2:30 pm Open Discussion 

3:30 pm Break 

3:45 pm Cross-cutting Groups: 

Breakout A: Managing acute illness and disease 

 Breakout B: Creating effective models of health promotion

 and disease prevention

 Breakout C: Insuring chronic disease management 

 Breakout D: Enhancing the end-of-life experience

 Breakout E: Facilitating public health

 Breakout F: Accelerating discovery 

5:15 pm Chair’s Summary 

C-2
 

 

 

 

 

 

 

 

 

 

 

Patricia Flatley Brennan 

5:30 pm Adjourn 

6:00 pm Working Dinner 

Address by Aneesh Chopra, Chief Technology Officer & Associate Director of 

Technology, Office of Science and Technology Policy, United States 

DAY TWO: FROM DISCUSSION TO ACTION: A PLAN FOR MOVING AHEAD 

8:00 am Working breakfast 

Policy Discussion (research in an ever-changing world) 

Patricia Flatley Brennan 

8:45 am Plans for the Day 

Patricia Flatley Brennan 

9:00 am Breakout Groups: 

 Breakout A: Information Technology/ Finance and Quantitative 

Decision Making 

Session Co-Chairs – James Walker, Chief Health Information

 Officer, Geisinger Health Systems; Brian Denton, Assistant Professor, 

North Carolina State University 

 Breakout B: Systems Analysis, Change and Implementation 

Theories 

Session Chair – José L. Zayas-Castro, Professor & 

Chairperson, University of South Florida 

 Breakout C: Materials Management and Production Processes

 Session Chair – Eugene S. Schneller, Ph.D., Professor, School 

of Health Management and Policy, W. P. Carey School of 

Business Arizona State University 

 Breakout D: Human Factors and Sociotechnical Systems

 Session Chair – Stephanie Guerlain, Associate Professor, 

 Department of Systems and Information Engineering, University 

of Virginia 

 Breakout E: Quality Engineering

 Session Chair – James Benneyan, Executive Director, New 

England VA, Healthcare Engineering Partnership, Director, 

 Quality and Productivity Laboratory, Northeastern University 

C-3
 
 

 

10:00 am Break 

10:15 am Presentation: Research Agenda 

11:30 am Update and Revise Research Agenda (Group Work) 

12:15 pm Lunch 

1:00 pm Roundtable Discussion 

Led by Patricia Flatley Brennan 

3:00 pm Concluding Remarks 

Patricia Flatley Brennan 

3:10 pm Thanks and Logistics 

Patricia Flatley Brennan 

3:30 pm Adjourn 

C-4
 
Appendix D: ISyE Small Group Assignments 

Breakout Group Assignments 

1:00 PM – 2:30 PM 

Monday, September 21, 2009 

9:00 AM – 12:15 PM 

Tuesday, September 22, 2009 

Breakout A – Information Technology/Finance & Quantitative Decisionmaking 

Session Co-Chairs – James Walker and Brian Denton 

Facilitator - Jacquie Munson-Gaines 

Note Taker – Perci Abu 

1. Oguzhan Alagoz 

2. Ilaina Edison 

3. Charles Friedman 

4. Isaac Kohane 

5. Sarah Root 

Breakout B – Systems Analysis, Change and Implementation Theories 

Session Chair – José L. Zayas-Castro 

Facilitator – Frank Carlos Gihan 

Note Taker – Paolo Gonzalez 

1. Robert Dittus 

2. Sally Lundeen 

3. Sanjay Mehrotra 

4. Steven Witz 

Breakout C – Materials Management and Production Processes 

Session Chair – Eugene Schneller 

Facilitator – Nhan Tran 

Note Taker – Don Cunningham 

1. John Fowler 

2. Edmond Ramly 

3. Julie Swann 

Group D – Human Factors and Sociotechnical Systems 

Session Chair – Stephanie Guerlain 

Facilitator – Jerry Stayton 

Note Taker – John Ryan 

D-1 
1. Ross Koppel 

2. Marilyn Rantz 

3. Michelle Rogers 

4. Vinod Sahney 

Breakout E – Quality Engineering 

Session Chair – James Benneyan 

Facilitator – Jennifer Raviv 

Note Taker – Sean Fox 

1. Nilay Tanik Argon 

2. Seth Eisen 

3. Merry Ward 

4. Shinyi Wu 

5. David Zimmerman 

D-2 
Appendix E: Health Care Challenge Area Small Group 

Assignments 

Cross-Cutting Group Assignments 

3:45-5:15 PM
 

Monday, September 21, 2009 

Group A - Managing Acute Illness and Disease 

Facilitator – Jacquie Munson-Gaines 

Note Taker – Perci Abu 

1. Oguzhan Alagoz 

2. James Benneyan 

3. Ross Koppel 

4. James Walker 

5. Merry Ward 

Group B - Creating Effective Models of Health Promotion and Disease Prevention 

Facilitator – Pam Wilson 

Note Taker – Randi Henderson 

1. Edmond Ramly 

2. Sarah Root 

3. Vinod Sahney 

4. Steven Witz 

Group C - Insuring Chronic Disease Management 

Facilitator – Frank Carlos Gihan 

Note Taker – Paolo Gonzalez 

1. Brian Denton 

2. Eugene Schneller 

3. Julie Swann 

4. David Zimmerman 

E-1 
 

Group D - Enhancing the End-of-life Experience 

Facilitator – Nhan Tran 

Note Taker – Don Cunningham 

1. Ilaina Edison 

2. Stephanie Guerlain 

3. Marilyn Rantz 

4. Shinyi Wu 

Group E - Facilitating Public Health 

Facilitator – Jerry Stayton 

Note Taker – John Ryan 

1. Seth Eisen 

2. Sally Lundeen 

3. Sanjay Mehrotra 

4. José L. Zayas-Castro 

Group F - Accelerating Discovery 

Facilitator – Jennifer Raviv 

Note Taker – Sean Fox 

1. Nilay Tanik Argon 

2. Robert Dittus 

3. John Fowler 

4. Charles Friedman 

E-2