Christian Wernz

Multiscale decision-making: Bridging organizational scales in systems with distributed decision-makers

Decision-making in organizations is complex due to interdependencies among decision-makers (agents) within and across organizational hierarchies. We propose a multiscale decision-making model that captures and analyzes multiscale agent interactions in large, distributed decision-making systems. In general, multiscale systems exhibit phenomena that are coupled through various temporal, spatial and organizational scales. Our model focuses on the organizational scale and provides analytic, closed-form solutions which enable agents across all organizational scales to select a best course of action. By setting an optimal intensity level for agent interactions, an organizational designer can align the choices of self-interested agents with the overall goals of the organization. Moreover, our results demonstrate when local and aggregate information exchange is sufficient for system-wide optimal decision-making. We motivate the model and illustrate its capabilities using a manufacturing enterprise example.

International study of technology investment decisions at hospitals

Purpose – Healthcare costs have increased considerably over the past decades around the world. Major contributors to this trend are expensive medical technologies. The purpose of this paper is to use a case study approach to understand how organizational and country level factors influence hospital investment behavior. Design/methodology/approach – The paper developed a conceptual framework based upon decision theory and institutional theory from which key questions were derived. The paper conducted semi-structured group interviews with relevant stakeholders in six hospitals located in five countries (Germany, India, Thailand, South Korea, USA). Findings – The paper found that the investment decisions of the interviewed hospitals are primarily affected by the healthcare system, the socio-economic and cultural context, and the organizations mission. Most of the interviewed hospitals consider multiple criteria in their decision-making framework and share similar organizational processes. Practical implicat...

Unifying temporal and organizational scales in multiscale decision-making

In enterprise systems, making decisions is a complex task for agents at all levels of the organizational hierarchy. To calculate an optimal course of action, an agent has to include uncertainties and the anticipated decisions of other agents, recognizing that they also engage in a stochastic, game-theoretic reasoning process. Furthermore, higher-level agents seek to align the interests of their subordinates by providing incentives. Incentive-giving and receiving agents need to include the effect of the incentive on their payoffs in the optimal strategy calculations. In this paper, we present a multiscale decision-making model that accounts for uncertainties and organizational interdependencies over time. Multiscale decision-making combines stochastic games with hierarchical Markov decision processes to model and solve multi-organizational-scale and multi-time-scale problems. This is the first model that unifies the organizational and temporal scales and can solve a 3-agent, 3-period problem. Solutions can be derived as analytic equations with low computational effort. We apply the model to a service enterprise challenge that illustrates the applicability and relevance of the model. This paper makes an important contribution to the foundation of multiscale decision theory and represents a key step towards solving the general X-agent, T-period problem.

A multiscale decision theory analysis for revenue sharing in three-stage supply chains

Revenue sharing is an effective mechanism for coordinating decisions in a supply chain. For a three-stage supply chain, we explore how revenue-based incentives can be used by the stage 1 supply chain agent (retailer) to motivate cooperative behavior from its two upstream partners with conflicting interests. To illustrate our analysis, we provide a food supply chain example, with retailer, processor and farmer. Compared to the frequently studied two-stage problem, a three-stage supply chain leads to a more complex decision and incentive problem. To model and solve this more complex problem, we apply multiscale decision theory (MSDT), a novel approach for multi-level system analysis. MSDT enables us to account for uncertainties at all stages of the supply chain, not just at the final stage, and to derive analytic solutions. Results show and quantify the extent to which contracting and information sharing facilitate chain-wide cooperation. Further, it determines optimal decisions and incentives for agents at each stage. This paper is the first to apply MSDT to supply chains and contributes to its theory by advancing MSDT modeling and analysis capabilities. The modeling and solution approach can be applied to decision and inventive problems in other multi-level enterprise systems.

Service convergence and service integration in medical tourism

Purpose – The purpose of this paper is to introduce and discuss the concepts of service convergence and service integration, illustrate them in the context of the medical tourism industry, and link them to factors that contributed to the success of a medical tourism firm. Design/methodology/approach – The basis for the conceptual development of service convergence and service integration is an in-depth case study of Bumrungrad International Hospital (BIH) in Thailand. Based on semi-structured interviews and archival data, BIHs business model is analyzed and factors are identified that led to its success in the industry. Findings – BIHs success can be attributed to nine key initiatives that enhanced customer focus, operational efficiency, and service quality. These initiatives supported BIHs twofold business model of product differentiation and globally competitive prices. The firms activities led to the integration of medical and hospitality services resulting in a new, enhanced product. Competitors a...

Managerial decision-making in hospitals with real options analysis

We present the application of real options analysis (ROA) to a managerial decision-making problem. A case study was developed to illustrate the mathematical steps required to apply ROA. The results of this model show that a net present value analysis, which is most often used in practice, would have led to a sub-optimal decision, as it does not take into account the value of future options and managerial flexibility. Hospitals rarely use quantitative methods like ROA to address their managerial decision-making problems. Usually, simple cost-benefit analysis and subjective assessment are used instead of sophisticated analysis methods and objective data. This paper aims to show the capabilities of ROA and provides details on how to apply it in practice. We discuss the kind of data that is needed to carry out the analysis and how ROA can be integrated into the organizational decision process. To do this, we propose a data-to-decision (D2D) framework. The D2D framework consists of two components: data-to-information (D2I) and information-to-decisions (I2D). D2I suggests the use of quantitative methods, such as ROA, to extract decision-relevant information from data. Based on the generated information, the second component, I2D, supports executives in selecting the best course of action given multiple organizational objectives.

Decision Analysis of Dynamic Spectrum Access Rules

A current trend in spectrum regulation is to incorporate spectrum sharing through the design of spectrum access rules that support Dynamic Spectrum Access (DSA). This paper develops a decision- theoretic framework for regulators to assess the impacts of different spectrum access rules on both primary and secondary operators. We analyze access rules based on sensing and exclusion areas, which in practice can be enforced through geolocation databases. Our results show that receiver-only sensing provides insufficient protection for primary and co-existing secondary users and overall low social welfare. On the other hand, using combining sensing information of the transmitter and receiver of a communication link provides dramatic increases in system performance. The performance of using these link end points is relatively close to that of using many cooperative sensing nodes associated to the same access point and large link exclusion areas. These results are useful to regulators and network developers in understanding in developing rules for future DSA regulation.

Modeling and designing health care payment innovations for medical imaging

Payment innovations that better align incentives in health care are a promising approach to reduce health care costs and improve quality of care. Designing effective payment systems, however, is challenging due to the complexity of the health care system with its many stakeholders and their often conflicting objectives. There is a lack of mathematical models that can comprehensively capture and efficiently analyze the complex, multi-level interactions and thereby predict the effect of new payment systems on stakeholder decisions and system-wide outcomes. To address the need for multi-level health care models, we apply multiscale decision theory (MSDT) and build upon its recent advances. In this paper, we specifically study the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs) and determine how this incentive program affects computed tomography (CT) use, and how it could be redesigned to minimize unnecessary CT scans. The model captures the multi-level interactions, decisions and outcomes for the key stakeholders, i.e., the payer, ACO, hospital, primary care physicians, radiologists and patients. Their interdependent decisions are analyzed game theoretically, and equilibrium solutions - which represent stakeholders’ normative decision responses - are derived. Our results provide decision-making insights for the payer on how to improve MSSP, for ACOs on how to distribute MSSP incentives among their members, and for hospitals on whether to invest in new CT imaging systems.

Improving Technology Investment Decisions at Hospitals through System Dynamics and Decision Analysis

Medical technologies represent a major share of hospital budgets and have been driving up healthcare costs in the United States. Typically, hospitals have limited resources and have to be selective with their purchases. Despite the importance and complexity of investment decisions, most hospitals do not use structured methods or comprehensive data. In response, we developed and field-tested a data-to-decision (D2D) approach. We interviewed executives and physicians from two hospitals to learn about the role of technology and capital investment decision processes. We developed a system dynamics (SD) model for the adoption of a Da Vinci® surgical system. Hospital executives evaluated this technology along with other alternatives in a session we conducted using the Simple Multi-Attribute Rating Technique (SMART).

Improving Electronic Health Record Downtime Contingency Plans with Discrete-Event Simulation

Healthcare has become reliant on electronic health record systems to support patient treatment. Despite all of the benefits of these electronic systems, they have one major flaw: they can go offline, leaving healthcare workers forced to employ contingency plans and procedures. The procedures are poorly regulated and rarely practiced, introducing the potential for significant increase to patient risk. Simulation methods provide a means to examine the problem and develop data derived solutions to make downtime safer. By creating a general simulation model, future study of specific real-world hospitals models and improvements to healthcare organizations can be expedited.