CAN WE INFER HOSPITAL QUALITY FROM MEDICAL GRADUATES’ RESIDENCY CHOICES?

This paper uses a novel approach to infer hospital technical quality from revealed preferences over residency programs. Specifically, we use Spanish medical graduates’ residency choices made from 1995 to 2000. We start by estimating a model of medical graduates preferences that controls for hospital, proximity, specialty, and gender effects. We interpret the coefficients on the hospital dummy variables as measures of medical graduates’ preferences over hospitals. Our results show that graduates do indeed discriminate between hospitals and that their preferences correlate with hospital‐specific covariates arguably related to hospital training quality. We then show that preferences from medical graduates are positively and statistically significantly correlated with risk‐adjusted hospital rankings based on five alternative outcome measures. Finally, we construct reputation scores for each hospital using news story counts in three media outlets and find that medical graduates’ preferences are especially valuable for inference of hospital technical quality of care as they do not simply reflect well known reputation.     

Life Is All about Timing: An Examination of Differences in Treatment Quality for Trauma Patients Based on Hospital Arrival Time

In health care, most quality transparency and improvement programs focus on the quality variation across hospitals, while we know much less about within‐hospital quality variation. This study examines one important factor that is associated with the fluctuation of quality of care in the same hospital—the timing of patient arrival. We analyze data from the National Trauma Data Bank and find that patients arriving at the hospital during off‐hours (6 PM–6 AM) receive significantly lower quality care than those who arrive during the daytime, as reflected in higher mortality rates, among other measures. More importantly, we try to uncover the mechanism for the quality variation. Interestingly, we find consistent evidence that the inferior care received during off‐hours is not likely due to unobserved heterogeneity, disruptions in circadian rhythms, or delays in receiving treatment. Instead, it is more likely due to the limited availability of high‐quality resources. This leads to a higher surgical complication rate, a higher likelihood of multiple surgeries, and longer patient length of stay in the intensive care unit. These findings have important implications for optimal resource allocation in hospitals to improve the quality‐of‐care delivery.     

Generalized Instrumental Variable Models

This paper develops characterizations of identified sets of structures and structural features for complete and incomplete models involving continuous or discrete variables. Multiple values of unobserved variables can be associated with particular combinations of observed variables. This can arise when there are multiple sources of heterogeneity, censored or discrete endogenous variables, or inequality restrictions on functions of observed and unobserved variables. The models generalize the class of incomplete instrumental variable (IV) models in which unobserved variables are single‐valued functions of observed variables. Thus the models are referred to as generalized IV (GIV) models, but there are important cases in which instrumental variable restrictions play no significant role. Building on a definition of observational equivalence for incomplete models the development uses results from random set theory that guarantee that the characterizations deliver sharp bounds, thereby dispensing with the need for case‐by‐case proofs of sharpness. The use of random sets defined on the space of unobserved variables allows identification analysis under mean and quantile independence restrictions on the distributions of unobserved variables conditional on exogenous variables as well as under a full independence restriction. The results are used to develop sharp bounds on the distribution of valuations in an incomplete model of English auctions, improving on the pointwise bounds available until now. Application of many of the results of the paper requires no familiarity with random set theory.     

Design and Analysis of Hospital Admission Control for Operational Effectiveness

Variability in hospital occupancy negatively impacts the cost and quality of patient care delivery through increased emergency department (ED) congestion, emergency blockages and diversions, elective cancelations, backlogs in ancillary services, overstaffing, and understaffing. Controlling inpatient admissions can effectively reduce variability in hospital occupancy to mitigate these problems. Currently there are two major gateways for admission to a hospital: the ED and scheduled elective admission. Unfortunately, in highly utilized hospitals, excessive wait times make the scheduled gateway undesirable or infeasible for a subset of patients and doctors. As a result, this group often uses the ED gateway as a means to gain admission to the hospital. To better serve these patients and improve overall hospital functioning, we propose creating a third gateway: an expedited patient care queue. We first characterize an optimal admission threshold policy using controls on the scheduled and expedited gateways for a new Markov decision process model. We then present a practical policy based on insight from the analytical model that yields reduced emergency blockages, cancelations, and off‐unit census via simulation based on historical hospital data.     

Benefits of Surgical Smoothing and Spare Capacity: An Econometric Analysis of Patient Flow

In this study, we examine the hospital's ability to admit patients from its emergency department. From a medical perspective, the number of patients being admitted should depend solely on the patients’ clinical conditions. Using a large‐scale econometric study that includes detailed operational and clinical data on all cardiac patient encounters from a set of 128 hospitals over a period of four years, we show that this is not the case. In particular, we find that independent of their medical condition, many emergency patients are denied hospital admission because of a lack of inpatient beds. Our analysis suggests that having one more inpatient bed at the start of a day can increase the likelihood of an emergency room patient admission by around 3% on average. We examine two policies – active discharge and demand smoothing – that can help hospitals improve patient access. We find that some hospitals actively discharge inpatients when beds become scarce; hospitals that follow such an active discharge protocol are, on average, able to admit more patients. We also investigate to what extent the hospital's ability to smooth its surgical schedule impacts hospital admissions. Hospitals tend to schedule their elective patients early in the week (Mondays and Tuesdays), and discharge them by the weekend in order to minimize weekend staffing, effectively maximizing bed occupancy during the middle of the week. This “weekend effect” artificially induces variability, and reduces effective system capacity. We find that by scheduling patients more uniformly over the week, hospitals can dramatically increase patient access, obviating the need for active discharges or additional capacity investment. Our analysis quantifies these effects, and can help hospitals make effective capacity management decisions in order to improve patient flow.     

过度医疗与转诊制:一个排队论下的博弈模型

建立了一个转诊系统中排队和博弈的集成模型, 患者可能有轻微或严重型疾病, 而医生可能将轻微型疾病诊断为严重型疾病, 进行过度治疗.本文探究政府如何通过配置医院规模和调节医疗服务价格影响医患博弈, 使患者倾向于社区首诊、医生选择说实话.研究表明, 社区医院与三甲医院通过规模进行竞争, 社区医院规模越大或三甲医院规模越小, 诱导需求越能得到抑制.轻微型疾病治疗方案的价格较低时, 诱导需求以正概率存在;高于某阈值时, 诱导需求消失.提高严重型和轻微型疾病治疗方案的价差, 能够抑制诱导需求, 但不能完全消除, 也不能改善患者福利;和三甲医院相比, 社区医院在改善患者福利上效果更明显.     

Entropic Latent Variable Integration via Simulation

This paper introduces a general method to convert a model defined by moment conditions that involve both observed and unobserved variables into equivalent moment conditions that involve only observable variables. This task can be accomplished without introducing infinite‐dimensional nuisance parameters using a least favorable entropy‐maximizing distribution. We demonstrate, through examples and simulations, that this approach covers a wide class of latent variables models, including some game‐theoretic models and models with limited dependent variables, interval‐valued data, errors‐in‐variables, or combinations thereof. Both point‐ and set‐identified models are transparently covered. In the latter case, the method also complements the recent literature on generic set‐inference methods by providing the moment conditions needed to construct a generalized method of moments‐type objective function for a wide class of models. Extensions of the method that cover conditional moments, independence restrictions, and some state‐space models are also given.     

医疗信息共享对患者转移数量和服务质量水平影响研究

当前我国医院间存在严重的"信息孤岛"，医院参与医疗信息共享意愿不高，患者的诊疗信息被静态碎片化储存而无法充分有效地利用。考虑到医院进行医疗信息共享将降低患者转移成本，本文构建一个多阶段双寡头动态博弈模型研究医疗信息共享对医院竞争过程中患者转移数量和服务质量水平决策的影响。首先，根据是否存在转移成本，将患者分为新患者和经验性患者，借助Hotelling模型刻画患者的效用函数，分析患者就诊决策。然后，在政府价格规制和不考虑医院利他性情景下，构建了医院累积期望收益目标函数，使用动态规划方法，求解实现医院累积期望收益最大化的服务质量水平，获得了实现患者相互转移且医院在市场中共存的马尔可夫完美均衡。最后，根据医院参与医疗信息共享后患者转移成本降为零，分析与比较信息共享前后患者转移数量和服务质量水平变化。研究发现：在不同医院间本身存在患者转移背景下，医院参与信息共享后，患者转移数量增加但存在一个上限，增加的转移量与患者在医院间的转移成本呈正相关，与初始感知效用的差值范围呈负相关；医院参与信息共享后，均衡状态下的医院服务质量水平高于信息共享前的服务质量水平。因此，在不改变当前医保支付方式下，要加快推进医疗信息共享，政府部门可以根据医院的患者数量和服务质量水平变化对其进行定期补贴，以激励医院积极参与医疗信息共享，本文给出了这个补贴的量化表达。     

Diagnosis-related group system. Can we still earn a living caring for sick people?

Medicare and Medicaid patients are starting to enroll in managed care organizations, as government tries to lower costs of caring for the elderly, disabled, and poor. Because they are the most likely to be sick, taking care of them imposes serious risks on hospitals and medical groups. The federal government uses the diagnosis-related group (DRG) system to reimburse hospitals. The system depends on physicians documenting diagnoses and complications. Physicians see illness as unpredictable, and so feel that detailed documentation of severity is futile. The government and health plans nevertheless follow case mix index and create hospital and physician profiles, relying on the existing imperfect system. For providers to be recognized for the value they add to the care of the sick, physicians must learn to use the DRG system to best advantage, or risk being driven out of business.     

Semiparametric Estimation of a Proportional Hazard Model with Unobserved Heterogeneity

The proportional hazard model with unobserved heterogeneity gives the hazard function of a random variable conditional on covariates and a second random variable representing unobserved heterogeneity. This paper shows how to estimate the baseline hazard function and the distribution of the unobserved heterogeneity nonparametrically. The baseline hazard function and heterogeneity distribution are assumed to satisfy smoothness conditions but are not assumed to belong to known, finite-dimensional, parametric families. Existing estimators assume that the baseline hazard function or heterogeneity distribution belongs to a known parametric family. Thus, the estimators presented here are more general than existing ones.     

Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables

This paper develops a method for inference in dynamic discrete choice models with serially correlated unobserved state variables. Estimation of these models involves computing high‐dimensional integrals that are present in the solution to the dynamic program and in the likelihood function. First, the paper proposes a Bayesian Markov chain Monte Carlo estimation procedure that can handle the problem of multidimensional integration in the likelihood function. Second, the paper presents an efficient algorithm for solving the dynamic program suitable for use in conjunction with the proposed estimation procedure.     

Value-based health care, Part 2. The physician imperative: define, measure, and improve health care quality

Health care is the only major industry that lacks agreed-upon metrics to objectively define the quality of its products and services. The fundamental deficiency has led to the use of price as the de facto metric for patient contracting. Provider selection defaults to price because quality is assumed to be equal across all physicians and hospitals. This assumption is erroneous and now obsolete. Health care quality can be accurately measured using sophisticated illness modeling techniques that objectively risk-adjust patients level data. Providers' clinical data can be correlated with patient self-assessed outcomes for all episodes of inpatient and outpatient care. Physicians and their hospitals currently managing financial risks and those positioning themselves for these opportunities find such tools and techniques to be invaluable. Credible information creates a power shift away from price-based contracting to a true competitive marketplace that rewards quality of care. Purchasers and patients seek providers who objectively demonstrate their value on both quality and price. Part I of this article appeared in the January/February issue of The Physician Executive.     

The Effects of Exposure to "Synthetic" Chemicals on Human Health: A Review

This article examines how scientists use human, animal, and bacterial evidence to develop policy recommendations about the health consequences of human exposure to modern chemicals. Human evidence is limited because many epidemiological studies are contaminated with selection effects or unobserved heterogeneity. Changes in the aggregate incidence of morbidity (such as cancer) in the population over time are not a substitute for the lack of good individual-level data because incidence data are contaminated by the medicalization of cancer. Animal tests are also problematic because the expense of conducting experiments leads researchers to use only enough animals to allow detection of large differences in cancer incidence between controls and experimental animals that can only arise if the exposure doses are large. Predictions about the cancer incidence that would result in humans at much lower exposure levels, thus, require statistical inferences that implicitly make choices between false positive and false negative inference errors. Policy recommendations about carcinogens, therefore, are as much the product of value choices as "scientific" knowledge.     

Binary Response Models for Panel Data: Identification and Information

This paper considers a panel data model for predicting a binary outcome. The conditional probability of a positive response is obtained by evaluating a given distribution function (F) at a linear combination of the predictor variables. One of the predictor variables is unobserved. It is a random effect that varies across individuals but is constant over time. The semiparametric aspect is that the conditional distribution of the random effect, given the predictor variables, is unrestricted. This paper has two results. If the support of the observed predictor variables is bounded, then identification is possible only in the logistic case. Even if the support is unbounded, so that (from Manski (1987)) identification holds quite generally, the information bound is zero unless F is logistic. Hence consistent estimation at the standard pn rate is possible only in the logistic case.     

Development of a Prediction Model for Hospital Closure Using Financial Accounting Data*

Numerous research studies have examined the use of financial accounting data in the prediction of corporate bankruptcy. Partly due to a lack of available data, however, little work has been done in developing a closure prediction model specifically for hospitals. Using cost reports from the Health Care Financing Administration and a sample of 71 closed hospitals and a matched sample of 71 open hospitals, the current study examines the relationship between 21 financial accounting ratios and hospital closure. Univariate logit results indicate that hospital closure is significantly related to 17 of the 21 ratios one year prior to closure. Results are also presented using a multivariate model, and for the relationships two years prior to closure. The current study provides information helpful to users in identifying financial variables which may be important indicators of hospital closure.     

A Bayes Linear Bayes Method for Estimation of Correlated Event Rates

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well‐known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates.     

Nonparametric Estimation in Random Coefficients Binary Choice Models

This paper considers random coefficients binary choice models. The main goal is to estimate the density of the random coefficients nonparametrically. This is an ill‐posed inverse problem characterized by an integral transform. A new density estimator for the random coefficients is developed, utilizing Fourier–Laplace series on spheres. This approach offers a clear insight on the identification problem. More importantly, it leads to a closed form estimator formula that yields a simple plug‐in procedure requiring no numerical optimization. The new estimator, therefore, is easy to implement in empirical applications, while being flexible about the treatment of unobserved heterogeneity. Extensions including treatments of nonrandom coefficients and models with endogeneity are discussed.     

How Important is Selection? Experimental VS. Non‐Experimental Measures of the Income Gains from Migration

How much do migrants stand to gain in income from moving across borders? Answering this question is complicated by non‐random selection of migrants from the general population, which makes it hard to obtain an appropriate comparison group of non‐migrants. New Zealand allows a quota of Tongans to immigrate each year with a random ballot used to choose among the excess number of applicants. A unique survey conducted by the authors allows experimental estimates of the income gains from migration to be obtained by comparing the incomes of migrants to those who applied to migrate, but whose names were not drawn in the ballot, after allowing for the effect of non‐compliance among some of those whose names were drawn. We also conducted a survey of individuals who did not apply for the ballot. Comparing this non‐applicant group to the migrants enables assessment of the degree to which non‐experimental methods can provide an unbiased estimate of the income gains from migration. We find evidence of migrants being positively selected in terms of both observed and unobserved skills. As a result, non‐experimental methods other than instrumental variables are found to overstate the gains from migration by 20–82%, with difference‐in‐differences and bias‐adjusted matching estimators performing best among the alternatives to instrumental variables. (JEL: J61, F22, C21)     

Reinvigorating stalled CQI efforts through physician involvement

Continuous quality improvement (CQI), loosely synonymous with total quality management (TQM), was designed for the comparatively simple world of industry. Now that the gurus of CQI have attempted--originally with full support of the Joint Commission on Accreditation of Healthcare Organizations (JCAHO)--to apply the industrial CQI model to hospitals, they've noticed something: The model doesn't work, and hospital CQI programs are faltering enough to persuade the Joint Commission to scale back its CQI accreditation requirements. One major shortcoming behind the performance to date of CQI in hospitals: Physicians don't fit into the industrial model of CQI. Physicians are too important, and too many programs are stalled, to continue to implement CQI as though physicians don't exist. The techniques described here should help to reinvigorate faltering programs, giving hospital management a chance to "do it right the second time."     

The Role of Clinical and Process Quality in Achieving Patient Satisfaction in Hospitals

Managers constantly struggle with where to allocate their resources and efforts in managing the complex service delivery system called a hospital. In the broadest sense, their decisions and actions focus on two important aspects of health care—clinical or technical medical care that emphasizes “what” the patient receives and process performance that emphasizes “how” health care services are delivered to patients. Here, we investigate the role of leadership, clinical quality, and process quality on patient satisfaction. A causal model is hypothesized and evaluated using structural equation modeling for a sample of 202 U.S. hospitals. Statistical results support the idea that leadership is a good exogenous construct and that clinical and process quality are good intermediate outcomes in determining patient satisfaction. Statistical results also suggest that hospital leadership has more influence on process quality than on clinical quality, which is predominantly the doctors' domain. Other results are discussed, such as that hospital managers must be mindful of the fact that process quality is at least as important as clinical quality in predicting patient satisfaction. The article concludes by proposing areas for future research.