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Report cards, performance evaluations, and quality assessments continue to penetrate the lexicon of the healthcare sector. The value of report cards is typically couched as enhancing consumerism among patients, increasing accountability among healthcare providers, and more broadly increasing the transparency of healthcare information. This paper discusses the potential benefits and pitfalls of these performance assessments.

This paper briefly reviews empirical evidence regarding the impact of report cards for healthcare providers and synthesizes the role and limitations of these performance measures into distinct evaluation criteria. The rapid proliferation of report cards for healthcare providers suggests a growing need to develop mechanisms and tools to evaluate their impact. The risks associated with utilizing report cards for provider oversight include the deleterious impact on vulnerable populations and a failure to accurately measure quality of care. The capacity to create report cards should not be the sole criterion to develop and utilize report cards to evaluate healthcare providers. Rather, careful consideration of the benefits and risks should accompany the implementation and utilization of report cards into regulatory processes. This report proposes an evaluation checklist by which to assess the role of report cards in a given healthcare context.

Measures of health status and prior service use have been considered promising predictors of future health expenditures, particularly when used for risk-adjustment models in capitation payment systems. While the use of health status as a future predictor has its difficulty in terms of measurement accuracy and implementation costs, using prior utilization as the base for the calculation of future health expenditures also has its concerns. Based on a three-stage cross-lagged model in a longitudinal study design, this study showed that prior utilization has both a direct and an indirect effect on subsequent utilization. However, the real net effect of prior utilization on subsequent utilization can be overestimated by 25%, if the effect of health status is not taken into account.

The adoption of electronic health records (EHRs) and digitization of health data over the past decade is ushering in the next generation of digital health tools that leverage artificial intelligence (AI) to improve varied aspects of health system performance. The decade ahead is therefore shaping up to be one in which digital health becomes even more at the forefront of health care delivery – demanding the time, attention, and resources of health care leaders and frontline staff, and becoming inextricably linked with all dimensions of health care delivery. In this chapter, we look back and look ahead. There are substantive lessons learned from the first era of large-scale adoption of enterprise EHRs and ongoing challenges that organizations are wrestling with – particularly related to the tension between standardization and flexibility/customization of EHR systems and the processes they support. Managing this tension during efforts to implement and optimize enterprise systems is perhaps the core challenge of the past decade, and one that has impeded consistent realization of value from initial EHR investments. We describe these challenges, how they manifest, and organizational strategies to address them, with a specific focus on alignment with broader value-based care transformation. We then look ahead to the AI wave – the massive number of applications of AI to health care delivery, the expected benefits, the risks and challenges, and approaches that health systems can consider to realize the benefits while avoiding the risks.

This study investigates the stability of the “hospital effect” on performance over time by administrative health data as a source of evidence. Using 78,907 heart failure adult records from 117 hospitals in the Lombardy Region (Northern Italy) over three years (2010–2012), we analyzed hospital performance in terms of 30-day mortality and 30-day unplanned readmissions to gather evidence about the stability of the “hospital effect.” Best/worst performers were identified through multi-level models that combine both patient and hospital covariates. Our results confirm that managerial choices affect hospital performance, and that the “hospital effect” is not, contrary to expectations, stable over the short term. Performance improvement/worsening over the three years has been also analyzed.

The Stanford School of Organizational Sociology has influenced the development and direction of healthcare organizations as a field of research in several very significant ways. This chapter will provide a focused review of the major paradigms to develop from work at Stanford from 1970 to 2000, much of which involved the study of processes and structures within and surrounding healthcare organizations during this period. As a subarea of organizational theory and health services research, healthcare organizations embrace both theory-based research and applied research, and they borrow concepts, theories, and methods from medical sociology, organizational theory, healthcare administration and management, and (to a more limited extent) health economics and decision theory. The bulk of this chapter will focus on four major themes or paradigms from research on healthcare organizations that grew from work by faculty and students within the Stanford School of Organizational Sociology: Health Care Outcomes, Internal Organizational Dynamics, Organizations and Their Environments, and Organizational Systems of Care and Populations of Care Providers. Following our examination of these four paradigms, we will consider their implications for current and future debates in health services research and healthcare policy.

In some contexts, this illiquidity of executive stock options is referred to as non-transferability. In others, the problem is cast in terms of the highly concentrated portfolios that managers hold, an implication of which is that managers could not trade the options to diversify. The notion of option liquidity usually conjures up images of trading pits at the Chicago Board Options Exchange or other exchanges. The existence of an active trading pit gives a powerful visual image of liquidity, but, as evidenced by the success of electronic options exchanges such as New York's International Securities Exchange and Frankfurt's EUREX, a trading pit is hardly a requirement for liquidity. The existence of a guaranteed market for standardized options as implied by options exchanges (whether pit-based or electronic) further gives a misleading appearance of high liquidity. There is also a very large market for customized over-the-counter options. It is a misconception to think that these options are not liquid when they are simply not standardized. If an investor can create a highly customized long position in an option, that investor should be able to create a highly customized short position in the same option at a later date before expiration. If both options are created through the same dealer, they will usually be treated as an offset, as they would if they were standardized options clearing through a clearinghouse. If the two transactions are not with the same dealer, they would both remain alive, but the market risks would offset. Only the credit risk, a factor we ignore in this paper, would remain. Hence, these seemingly illiquid options are, for all practical purposes, liquid.²

The belief-adjustment model has been an integral part of accounting research in belief revision, especially in the examination of order effects. Hogarth and Einhorn ((1992) Cognitive Psychology, 24, 1–55) created the belief-adjustment model to serve as a theoretical framework for studying individuals’ decision-making processes. The model examines several aspects of decision-making, such as encoding, response mode, and task factors. The purpose of this chapter is to provide a comprehensive examination of the accounting studies that have used the theoretical framework of the belief-adjustment model in auditing, tax, and financial accounting contexts. Roberts’ ((1998) Journal of the American Taxation Association, 20, 78–121) model of tax accountants’ decision-making is used as a guideline to organize the research into categories. By using Roberts’ categorization, we can better sort out the mixed results of some prior studies and also expand the review to include a more comprehensive look at the model and its application to accounting. While many variables have been examined with respect to their effect on accounting professionals’ belief revisions, most studies examine them in isolation and do not consider the interaction effects that these variables may have. Our framework also identifies areas of the belief-adjustment model that need further research.