Advances in Intelligent Systems and Computing 594

Jussi Ilari KantolaTibor BarathSalman Nazir Editors

Advances in Human Factors, Business Management and LeadershipProceedings of the AHFE 2017 International Conferences on Human Factors in Management and Leadership, and Business Management and Society, July 17−21, 2017, The Westin Bonaventure Hotel, Los Angeles, California, USA

Advances in Intelligent Systems and Computing

Volume 594

Series editor

Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Polande-mail: kacprzyk@ibspan.waw.pl

About this Series

The series “Advances in Intelligent Systems and Computing” contains publications on theory,applications, and design methods of Intelligent Systems and Intelligent Computing. Virtuallyall disciplines such as engineering, natural sciences, computer and information science, ICT,economics, business, e-commerce, environment, healthcare, life science are covered. The listof topics spans all the areas of modern intelligent systems and computing.

The publications within “Advances in Intelligent Systems and Computing” are primarilytextbooks and proceedings of important conferences, symposia and congresses. They coversignificant recent developments in the field, both of a foundational and applicable character.An important characteristic feature of the series is the short publication time and world-widedistribution. This permits a rapid and broad dissemination of research results.

Advisory Board

Chairman

Nikhil R. Pal, Indian Statistical Institute, Kolkata, Indiae-mail: nikhil@isical.ac.in

Members

Rafael Bello Perez, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cubae-mail: rbellop@uclv.edu.cu

Emilio S. Corchado, University of Salamanca, Salamanca, Spaine-mail: escorchado@usal.es

Hani Hagras, University of Essex, Colchester, UKe-mail: hani@essex.ac.uk

László T. Kóczy, Széchenyi István University, Győr, Hungarye-mail: koczy@sze.hu

Vladik Kreinovich, University of Texas at El Paso, El Paso, USAe-mail: vladik@utep.edu

Chin-Teng Lin, National Chiao Tung University, Hsinchu, Taiwane-mail: ctlin@mail.nctu.edu.tw

Jie Lu, University of Technology, Sydney, Australiae-mail: Jie.Lu@uts.edu.au

Patricia Melin, Tijuana Institute of Technology, Tijuana, Mexicoe-mail: epmelin@hafsamx.org

Nadia Nedjah, State University of Rio de Janeiro, Rio de Janeiro, Brazile-mail: nadia@eng.uerj.br

Ngoc Thanh Nguyen, Wroclaw University of Technology, Wroclaw, Polande-mail: Ngoc-Thanh.Nguyen@pwr.edu.pl

Jun Wang, The Chinese University of Hong Kong, Shatin, Hong Konge-mail: jwang@mae.cuhk.edu.hk

More information about this series at http://www.springer.com/series/11156

Leadership and Safety Management

Applying Decision Analysis to ProgramManagement and Leadership Issues

at the U.S. Veterans Health Administration

John Celona(&)

Decision Analysis Associates, LLC, San Carlos, CA, USAJCelona@DecisionAA.com

Abstract. The Veterans Health Administration (VHA) has long been a leaderin developing and applying safe patient handling methods and equipment toreduce caregiver injuries and patient complications. Following a very successfulinitial program, the VHA faced the challenges of determining if additionalefforts were needed and, if so, what form they should take, what it would cost,and the benefits to justify additional investment. By applying Decision Analysis,the VHA was able to create a detailed and compelling new program recom-mendation and justify the cost. The potential benefits are on the order of$2 billion per year, which constitute a 4% reduction in the approximately$50 billion annual expenditures at the VHA.

Keywords: Decision analysis ! Safe patient handling ! Strategy development !Risk analysis ! Health care

1 Introduction

The physical demands of nursing, especially from assisting, moving, and liftingpatients have long been known to result in musculoskeletal injuries. These injuries tendto build over time, with smaller strains leading to more serious injuries and, in somecases, inability to perform critical nursing tasks. Efforts over the last thirty years toreduce these injuries through training in body mechanics and proper lifting techniquesand elimination of “weaker” staff failed to improve outcomes.

Beginning approximately seventeen years ago, the U.S. Veterans Health Admin-istration (VHA) and, specifically, the VHA’s Tampa Patient Safety Center of Inquirydeveloped a program using specialized equipment (slings, portable and overhead liftingequipment, etc.) to help caregivers mobilize patients [1]. Following successful initialprogram results, the VHA issued in 2008 an Executive Decision Memo (EDM) [2, 3]providing $205 million to fund safe patient handling initiatives at VHA medical cen-ters. These funds were supplemented with monies provided by the individual VHAmedical centers. As shown in Fig. 1, implementation of this program produced amarked reduction in musculoskeletal injuries in nursing occupations from lifting orrepositioning patients. The EDM was followed in 2010 by an Executive Directiveexpanding the program to all patient care areas in the VHA, but did not provideadditional funding to do so.

© Springer International Publishing AG 2018J.I. Kantola et al. (eds.), Advances in Human Factors, Business Managementand Leadership, Advances in Intelligent Systems and Computing 594,DOI 10.1007/978-3-319-60372-8_35

2 Program Management and Leadership Challenges

When incremental funding under the EDM ended, it was up to individual VHAfacilities to continue or expand safe patient handling programs. Capital expenditureswere required for purchase of equipment. In particular, lifts attached to tracks installedin ceilings (“ceiling lifts”) are particularly helpful for mobilizing patients in high acuity,cramped areas (such as intensive care units) but are expensive to purchase and install,often requiring structural retrofits. Ongoing expenses include consumable items (slings,slide sheets, etc.), training, and program management personnel.

Once incremental funding from VHA headquarters ended, budget-constrainedfacilities had to allocate limited available funds among all competing programs,including safe patient handling. It was difficult to obtain funding for new purchases andequipment maintenance. Program management personnel were stretched, often needingto handle very large or multiple facilities. VHA personnel were concerned thathard-won gains in patient and nurse safety were being eroded by budget pressure.

In addition to funding challenges in maintaining the existing program, the VHAfaced a fundamental question: where to go next with the program? The VHA was therecognized leader in safe patient handling, so there was no generally accepted pro-gression to follow in continuing the program.

Lastly, personnel changes at VHA headquarters had left the safe patient handlingprogram without a strong, senior-level advocate. The VHA is the largest integratedhealth care system in the U.S., providing care at 168 VA Medical Centers and 1,053outpatient sites of varying complexity and serving 8.9 million Veterans every year.Among the many priorities facing the VHA, it was hard to argue that, following a verysuccessful initial program, safe patient handling should remain a top priority.

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Fig. 1. Reduction in musculoskeletal injuries among nursing occupations at VHA medicalcenters from lifting or repositioning patients following implementation of a safe patient handlingprogram with funding individual medical centers and from the 2008 Executive Decision Memo.

Applying Decision Analysis to Program Management and Leadership Issues 363

VHA personnel became aware of the successful application of Decision Analysis tosafe patient handling in general [4] and at Stanford University Medical Center inparticular [5]. They decided to pursue the application of Decision Analysis to meetingsafe patient handling program management and leadership challenges at the VHA.

3 Decision Analysis

Decision Analysis is a better way of making high-quality decisions in uncertain orcomplex situations [6–8]. As illustrated in Fig. 2, decision analysis works by struc-turing the interchange between the two thinking modes we are all equipped with [9]:our intuitive, fast thinking and logical, deliberative slow thinking.

Our intuition furnishes the framework for the decision: the alternatives, informa-tion, and preferences. We use logic to formulate these into a quantitative model.Analysis on the model provides insight as to which alternative is best and why.

People typically make decisions with heavy reliance on intuition. Unfortunately,intuition all to predictably and repeatedly leads us astray [9]. Hence the need for aformal approach to integrate effectively the two modes of thinking. These elements ofgood decision and their integration are illustrated in Fig. 3.

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Fig. 2. Decision analysis frames the interchange between intuition and logic to create acompelling rationale for which alternative is best and why.

364 J. Celona

Making a good decision maximizes your prospects of a good outcome, but doesn’tguarantee it—although people commonly evaluate whether a decision was good or badbased on the outcome. From a decision analytic approach, we say that a decision wasgood if all the elements of the decision stand up well to scrutiny at the time the decisionis made. All looks clear in hindsight; making good decisions with opaque futureprospects is a far more difficult challenge. Decision analysis meets this challenge sowell that it is standard and required in high-risk, capital-intensive industries, such asdrug development and oil and gas exploration [10].

4 Alternatives, Information and Preferences for Safe PatientHandling at the VHA

Decision Analysis employs a number of tools to structure the elements of a gooddecision and the interchange between intuitive and logical thinking. The VHA projectstarted by employing several of these tools to structure the alternatives.

First, we created a decision hierarchy to sort the possible decisions into threecategories of Policy Decisions (taken as given), Strategic Decision (which need to bemade now), and Tactical Decisions (which are important, but can be made later). Thedecision hierarchy for safe patient handling at the VHA appears in Fig. 4.

For each strategic decision identified, there were a number of specific decisionsrequired, as shown in Fig. 5.

To develop alternative strategies, we developed a Strategy Table. In a strategytable, each decision heads a column and alternatives for that decision are listedunderneath. We identify comprehensive strategy definitions by making one or morechoices in each column. Reading the selections across the columns specifies thestrategy. For convenience, the first column lists a short name for each strategy andcolor-coded boxes show the selections.

Defining the strategies for safe patient handling at the VHA was a complex process,requiring a number of workshops with participants from across the U.S. The final

Alternatives

Logic Outcome ? Decision Information

Preference

Fig. 3. The elements of a good decision integrate intuitive and logical thinking.

Applying Decision Analysis to Program Management and Leadership Issues 365

Fig. 4. The decision hierarchy for safe patient handling at the VHA sorts the possible decisionsinto three categories.

Scope of Program Facilities

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Equipment Additional Equipment

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Communications Communications Out

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Web-based Communications

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Data Acquisition Surveys

Fig. 5. General and specific strategy decisions for safe patient handling at the VHA.

366 J. Celona

strategy table was equally complex, comprising a 17-column table with pages ofexplanatory footnotes. For brevity, only the first three columns are shown in Fig. 6.

We developed three alternative strategies: Status Quo (no new efforts), Finishimplementing the Executive Decision Memo (EDM) with some expansion, and fullyimplement the Executive Directive. For the latter two strategies, we calculated the costto implement them without specifying how much funding (if any) VHA headquarterswould provide.

In contrast to many other decision-making methodologies, in Decision Analysis weidentify and evaluate the “do nothing” or Status Quo alternative. Deciding to donothing is also a decision. Quantifying the effects of the Status Quo often furnish animportant baseline for quantifying the incremental costs and benefits of doing some-thing, as in this case.

Once the strategies have been defined, we need to structure the risks relevant to thevalue of each strategy. For this purpose, we use a Relevance Diagram in whichdecisions are specified in boxes, value by a hexagon, uncertainties by ovals, and arrowsshow what is relevant to what, as shown for this project in Fig. 7.

The Relevance Diagram is part of the information for a good decision. We alsogathered all relevant historical data for each uncertainty and assessed the uncertainfuture prospects for how each uncertain factor could be impacted by choice of strategy.For this process, a key distinction in Decision Analysis is the use of subjective,Bayesian probabilities to capture a person or persons’ best judgment as to how thingsmight turn out.

For the VHA, assessing the uncertainties was likewise an intensive process inwhich we gathered a team of approximately thirty VHA experts for a series of all-day

CandidateStrategies

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FinishImplementingEDM, expandprogram to coverother areas withinfacility notoriginallyincluded andinclude bariatrics

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Add bariatrics and areas notincluded in EDM to go facility-wide

Fully Implement DirectiveAdd Rehab Facilities, PrimaryCare Centers, CBOC’s, Homebased Health, Assisted LivingFacilities, Domiciliaries

Ensure sustainability of PLProgram

Funding for Future Growth

SQ: no specialized Programs;research on SCI patienthandling needs

Add Bariatric Program

Add Falls Prevention Program

Add Wound Care Program

Add Research to further definepatient population needs andimpact on quality of PatientCare

Fig. 6. The first three columns of the strategy table show strategy themes in the first column,followed by selections for the scope of program decisions.

Applying Decision Analysis to Program Management and Leadership Issues 367

workshops in which we reviewed the available data and assessed the uncertainties. Thefinal uncertainty assessments covered approximately sixty variables.

A key insight which emerged from the assessments was that we needed to considerthe potential strategy impacts in terms of the compliance rate: the proportion that safepatient mobilization equipment and methods should be and were actually used. Wecould then think about the compliance rate achievable in each strategy and the possibleeffects from a given compliance rate. These assessments are shown in Fig. 8.

This key insight allow the assembled experts to think carefully about programimpacts at 0% and 100% compliance rates for key factors, as shown in Fig. 9. Actualimpact for any intermediate result could be estimated by interpolation.

As the assessments are made, they are incorporated into a quantitative model, oftenprogrammed in Excel. For analysis, the model must include means of switchingbetween alternatives and assessed values so that any scenario can be calculated for anyalternative. In this case, we showed strategy values as the incremental value over the

Value ofProgram

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Fig. 7. The Relevance Diagram for safe patient handling at the VHA shows the relationshipsbetween the strategy definitions, the uncertainties, and the value of each alternative strategy.

Fig. 8. Assessed ranges of uncertainty for safe patient handling compliance at the VHA.

368 J. Celona

Status Quo alternative. The incremental value of the Status Quo alternative is then zeroby definition.

Year-by-year values are summarized by a net present value, in this case over a10-year period. The discount rate captures the preference for time value of money.A risk tolerance describes an exponential utility function and captures the risk pref-erence [6–8]. For this analysis, we used risk-neutral results because possible values,although large, are small relative to the size and means of the U.S. Government.

5 Analysis

While building the model, the analyst typically ensures that scenario switching worksand that results in various scenarios make sense. The next step in debugging andanalysis is to run and plot the values for a particular alternative with each uncertaintyset one at a time to its low and high values. This is called deterministic sensitivityanalysis. Plotting the variation as bars arranged from largest to smallest yields thecharacteristic “tornado graph,” as shown in Fig. 10 for the most important uncertaintiesfor the fully implement Executive Directive alternative. (The complete chart displayedresults for all sixty uncertainties.)

Typically, the most important uncertainties account for more than 90% of the valuerisk. These uncertainties for each alternative go into a decision tree which calculates allpossible scenario values for each strategy. The strategy values are then summarized in acumulative probability distribution as shown in Fig. 11.

Fig. 9. Part of the Bayesian assessments for the VHA.

Applying Decision Analysis to Program Management and Leadership Issues 369

6 Insights and Conclusions

As analysis is completed and we’re satisfied that the results make sense and can beexplained (both in terms of the rationale for the inputs and how they flow to theresults), the task becomes how communicating the insights and recommendations to thedecision makers.

Fig. 10. Tornado chart for the most important uncertainties for the incremental value of fullyimplementing the Executive Directive over the Status Quo.

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Fig. 11. Cumulative probability distributions for the incremental net present value of eachalternative over the Status Quo.

370 J. Celona

One major insight flowed from the compliance rate assessments in Fig. 8: there wastremendous uncertainty about how widespread the current utilization of safe patienthandling equipment and methods was, but that utilization was expected to drop off inthe coming years without further support. As was already beginning to become evidentin the data reviewed at the time, there was a real danger that existing progress would beseriously eroded. This effect stemmed from factors like program leaders being stretchedthin, equipment not being maintained as functional (including batteries charged, etc.),slings not being available to accomplish lifts, etc.

On the other hand, a new program could dramatically increase program utilizationand narrow the uncertainty (a risk reduction). This could reduce nurse injuries,hospital-acquired pressure ulcers (HAPU), patient falls and associated injuries, averagelength of stay (ALOS), and nurse turnover. The incremental benefits in each of these

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Fig. 12. Waterfall chart for sources of incremental value and cost for fully implementing theExecutive Directive (ED) over the Status Quo.

Fig. 13. Base case net annual impact on costs at the VHA for either fully implementing theDirective or the Executive Decision Memo (EDM).

Applying Decision Analysis to Program Management and Leadership Issues 371

measures, the associated additional labor and equipment cost, and how they add up to abase case net present value (uncertainty not considered) is shown in Fig. 12.

As shown in Fig. 13, the annual impact on the overall cost of care at the VHAcould be on the order of $2 billion per year. With an annual VHA budget in 2013 of$53 billion, this initiative could reduce the overall cost of care at the VHA by about 4%while increasing the quality of care.

Taking a closer look at the preferred alternative of fully implementing the Directive,Fig. 14 shows the annual base case incremental net benefits and the cost ofimplementation.

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Fig. 14. Base case change in the annual cost of care and the cost of implementation for fullyimplementing the Executive Directive over the Status Quo.

Fig. 15. Mean incremental benefits and program costs for both alternatives over five- andten-year time frames.

372 J. Celona

Although these base case results are useful for showing year-by-year results anddetailed breakdowns, we also need to consider overall prospects with the uncertainty.These are shown in Fig. 11, but we sometimes show the mean net present values insimpler form. Figure 15 shows the mean incremental values and program costs for bothfully implementing the Executive Decision Memo (EDM) and fully implementing theDirective over both five- and ten-year time frames.

This study took some time to complete, mainly due to the time to gather data andschedule the necessary all-day workshops. The results were presented at a dramatic5 pm Friday meeting at the VHA Central Office on K Street in Washington, D.C.—theonly time all the senior decision-makers were available.

The results were clear and startling. There was a path forward and much more couldbe done, even though the VHA was already the recognized leader in safe patienthandling. The costs were minimal relative to the benefits. And the potential benefitswere staggering. The results were taken into the complex decision-making and prior-itization process at VHA. A new program would not be easy to sell, but if offered theprospect of systemic and significant improvements across the entire VHA.

Acknowledgments. The author would like to acknowledge the efforts of the many dedicatedand hardworking personnel at the VHA who made this work possible.

References

1. Nelson, A., Baptiste, A.: Evidence-based practices for safe patient handling and movement.Online J. Issues Nurs. 9(3) (2004). Manuscript 3. www.nursingworld.org/ojin/topic25/tpc25_3.htm

2. VHA Executive Decision Memo on Safe Patient Movement, 1 July 20083. VHA Directive 2010-032: Safe Patient Handling Program, 28 June 20104. Celona, J.N.: 2010 Guidelines for the Design and Construction of Health Care Facilities. The

Facilities Guidelines Institute, Dallas (2010)5. Celona, J.N., Driver, J., Hall, E.: Value-driven ERM: making ERM an engine for

simultaneous value creation and value protection. J. Healthcare Risk Manage. 30(4), 15–33(2011). Jossey-Bass, San Francisco

6. Celona, J.N.: Winning at Litigation through Decision Analysis. Springer, New York (2016)7. McNamee, P.C., Celona, J.N.: Decision Analysis for the Professional, 4th edn. SmartOrg,

Inc., Menlo Park (2007)8. Howard, R.A., Abbas, A.E.: Foundations of Decision Analysis. Pearson, London (2016)9. Kahneman, D.: Thinking, Fast and Slow. Farrar, Straus and Giroux, New York (2011)10. How Chevron Makes Decisions. https://www.youtube.com/watch?v=JRCxZA6ay3M

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