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Making Choices Under Fiscal Constraints and Uncertainty: Helping Nursing Students Make Clinical Decisions in Resource Constrained

Environments

Thomas Cox PhD, RN

Sigma Theta Tau International

17th International Nursing Research Conference Focusing on Evidence Based Practice


Overall ObjectivesDescribe the context of forced decision

making in HC settings due to PCIR induced financial instabilities

Describe how to use game theory and simulations to safely expose students to controlled ethical conflicts and resource allocation decisions

Describe a freely available simulation program that helps users experience the nature of conflicts and tradeoffs faced by nurses who choose between services and patients in resource constrained settings


Learner Objectives

Learner Objective #1: The learner will be able to obtain a simulation program designed to model the way nurses distribute scarce resources among a cohort of patients when demands and resources are uncertain

Learner Objective #2: The learner will be able to help students use the simulation program, collect and analyze data, and help students' evaluate their allocation choices


Author Background

NursingMathematicsSocial Work

StatisticsHealth and Social Services Planning

Program EvaluationPsychotherapy

Insurance Ratemaking, Reserving and Reinsurance


Why Is This Necessary?Some health care finance mechanisms shift insurance

risks from health plans to health care providers

“Professional Caregiver Insurance Risk”

Typical insurance risk portfolio transfers:

Capitation agreements

Managed Care

Prospective Payment Systems/DRGs

Prospective nursing unit/division budgets

Managing insurance risks exacerbates extant difficulties managing clinical and financial risks in HCOs

Curriculum focuses on clinically correct decision making – need more work on how to allocate scarce resources


Professional Caregiver Insurance Risk 1

PCIR occurs when HCPs accept insurance risks in prospective payment systems, leading to:

Portfolio size differentials cause inefficiencies

Higher conflagration risk exposures for HCPs

Inadequacy or inefficiency of payment mechanisms

HCPs lack access to internal/external $ resources

Viscosity in resource transfers – difficult to move assets between units, locations, and patients

Violations of basic insurance/accounting principles

Greater difficulties managing nursing environments


PCIR and Clinical Decision Making

Many HCOs reduce supplies, staff, & equipment - BAD

Insurance risk/clinical management requires redundant supplies, staff, and equipment – failure to plan is a different issue than having inadequate resources

Poor planning means that nurses face needless decisions

Student evaluations focus on clinical decisions - time and resource constraints are peripheral

Few tools to develop skills in patient/service trade-offs

Simulated choice making may help improve ability to make and reflect on allocation decisions and processes

Use simulated experiences to review allocation choices, conduct research, and develop theory


Why Simulations?Nurses need to be able to fluidly shift from the

usual, to the unexpected, to the overwhelming

Simulations are a safe way to expose students to forced choice decision-making outside clinical settings

Safely & efficiently encourage reflectiveness on practice and the assessment of how a nurse can improve their decision-making over time.


ConclusionC


Decision Making Under Uncertainty 1I wanted a fun way to expose students to the problem of

allocating insufficient resources to activities and patients

User cares for 8 patients but it is unlikely that all requested services can be provided

User learns patient’s needs when entering their room

Each patient presents 2 options with regard to 10 services:

Provide some or all services needed immediatelyCannot return during the shift

Cannot ‘make up’ later when services are not performed

Unused time cannot be reclaimed – it is lost service potential

Skip to the next patient – provide no services


Decision Making Under Uncertainty 2Service demands are randomly generated at run time

Users have to learn to withhold some services to early patients in order to have time left for their last patients

Users cannot learn how to get around the program by learning a pattern of demand - no two runs are the same

Four 2 hour long shifts

Patient’s needs exceed time and resources available

If user exhausts time - some patients needs will go unmet

Reports on allocations of time to services and patients

User & teacher review decisions and discuss implications


Software DevelopmentVersion 1

FORTRAN in a DOS environment

Clunky

Reviewed by students, colleagues, friends

Future Versions

Visual Basic, Perl, PHP, Python, Java – Web delivery

Freely distributable Open Source code & binaries available under GNU public license

Source code can be altered

FORTRAN compiler is free

(In)formal data collection and research tool

DISCLAIMER: Spaghetti code – no claims to aesthetics


Future Plans 1Windows and Linux GUI environments

Develop clinically meaningful scenarios

Allow return to patients during shift & options to see information on patients at the start of the shift

Prior assessment of user’s anticipated strategy to compare with user performance during simulation

Embedded audio and video materials on simulated patients, ethics, game theory, tips for managing uncertainty…

Manager’s perspective – assignments of nurses to patients

Include optimization software for staff scheduling and delegation

Develop potential to collect data for future research


How to get it?If all else fails, email me at:

[email protected] “tc” underscore “spirit”

Subject Line: “Nursing Simulation Software”

Web Access:

http://drtcbear.servebbs.net:81/STTI2006

http://www.afn.org/~mathstat/STTI2006

Free FORTRAN compiler used for the program

http://gcc.gnu.org/wiki/GFortran

If you improve on it – pass it on to me and others


The Open Source Imperative

Please acknowledge my work but you are free to copy and improve the simulations

Contributions, suggestions, and feedback are encouraged and appreciated

I don’t own it – I just wrote it

Any problems: email [email protected]


ConclusionNurses face increasingly difficult ethical and

resource allocation choices

Applying game theory, uncertainty assessment, and simulated decision-making may help students understand the ethical quandaries that exist in modern health care systems

Data collection and analysis of simulations across students, schools, and health care systems may help researchers and educators with regard to curriculum needs, continuing education issues & helping nurses deal with increasingly complex and difficult choices


Additional Slides

Probably Not Part of Formal Presentation


History of Games & SimulationsChess – 6th century - simulate battles

Prussia - modern war-gaming - "Neue Kriegsspiel" 19th century - replace Chessboard with maps and terrain and pieces that mimic infantry, artillery and cavalry forces

“Simulation” - John von Neumann and Stanislaw Ulam - 1940s - "Monte Carlo analysis“

20th C - large scale, computerized “War Games,” pilot training simulators, government and corporate simulations


Game TheoryInterdisciplinary approach to the study of human behavior

Disciplines most involved in game theory are:

Mathematics

Economics

Social and behavioral sciences

Mathematical game theory formally started by mathematician John von Neumann & mathematical economist, Oskar Morgenstern “The Theory of Games and Economic Behavior”

Copyright 2005 Thomas Cox PhD, RN, BA, BSN, MS, MSW, MSN, CPCU


Issues in Game TheoryWhat are 'rational' strategies if outcomes depend on other player’s strategies and incomplete information?

In "games" with mutual gain (loss) is cooperation "rational" or is individual gain more "rational" regardless of the potential for mutual gain (loss)?

If cooperation is possible, when is it appropriate to be self-serving and when is cooperation appropriate?

How do strategies for ongoing relationships differ from single encounter relationships?

Can rules for cooperation be developed from the plays of rational egoists?

What are "rational" economic behaviors for nurses in resource constrained environments?

Make the consequences of decision-making visible

Copyright 2006 Thomas Cox PhD, RN, BA, BSN, MS, MSW, MSN, CPCU


Rational Decisions & Decision MakersDMs have fixed range of alternatives to choose

from and their choices influence results

Each outcome has numeric value – ‘utility’

Subjective - how much DM desires outcome

Objective - how good outcome is for the DM

Usual assumption:

Rational DMs make decisions that maximize

their expected utility


Types of Decision Situations1. Decision making under certainty: Does DM

prefer $100 or punch in the nose?

2. Decision making under risk: Will DM bet $100 on the flip of a fair coin?

3. Decision making under uncertainty: Will DM go to a movie or study for pop quiz?

The last situation is much harder than the others, but DT uses same basic framework:

Modelling possible decisions and payoffs, and maximizing DM’s utility


Terminology

States of nature: Conditions that exist under particular decisions

Uncertainty: knowing the states of nature before hand

Risk: States of nature are unknown but we can assign probabilities

Uncertainty: probabilities cannot be assigned


Parallels with Other Decision Processes

Investment portfolio management:Investor (nurse) has a fixed amount of money (Time)

to allocate to different investments

Investor (nurse) seeks maximal gain/minimal loss

How should they allocate money (time)?

How do we define ‘good’ or ‘bad’ outcomes?


BackgroundHealthcare organizations face considerable uncertainty about

the adequacy and timeliness of reimbursement for health care services they provide

These uncertainties were present earlier but the expansion of financial risk assumptions in managed care, prospective payment systems, and capitation agreements exacerbates the past impact of these effects in the past, more severely impacting HCOs financially and structurally

Nurses routinely make clinical/financial decisions affecting service quantity and quality for cohorts not individual clients

Simulating these processes will help faculty and students evaluate decisions affecting cohorts, better prepare students for workplace challenges, and lead to better evaluations of choices regarding efficiency, effectiveness, and caring


Professional Caregiver Insurance Risk 2

The Central Limit Theorem explains how insurer risk aggregation and retention reduces overall risk for insurers and policyholders

Insurance risk disaggregation eliminates the social and financial benefit of insurance as a risk management strategy

The loss of insurance benefit increases financial and clinical risks for providers and consumers

No combination of increased management expertise and HC system efficiencies will overcome the inefficiencies introduced by transferring insurance risks to HCPs, but…


Professional Caregiver Insurance RiskInsurance, financial, and clinical risks borne by health care providers (PCs) when they accept insurance risks from 'insurers' under capitation, PPS, Managed Care, and fixed operating budgets

Aggregate risk reduction by insurance is eliminated when public/private insurers cede insurance risks to PCs

PCs have higher risk - adversely affecting PCs, marginalized consumers, and marginalized geographic or social regions, due to limited financial & social capital

PCIR fosters HCP insolvencies, consolidations, lower service capacity, and exacerbates resource shortages, leading to rationing of care on nursing units

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