Eugene Kroch

Schooling as Human Capital or a Signal: Some Evidence

A new way is proposed to distinguish between the human capital and the signaling theories of the value of education. If education is a signal, then the essence of the signal should be distilled in the position of an individual in the distribution of education for his cohort. Estimating earnings equations that include both absolute (years) and relative (percentile) measures of education provides a test of the two competing theories. Analyzing two separate panel data sources under a range of alternative specifications, we find that the years measure of schooling has a consistently significant positive effect on earnings, but that the rank measure rarely does. This evidence supports the conclusion that human capital rather than signaling is the predominant explanation of schoolings value.

Patient Factors Predictive of Hospital Readmissions Within 30 Days.

Background:Under the Affordable Care Act, the Congress has mandated that the Centers for Medicare and Medicaid Services reduce payments to hospitals subject to their Inpatient Prospective Payment System that exhibits excess readmissions. Using hospital-coded discharge abstracts, we constructed a readmission measure that accounts for cross-hospital variation that enables hospitals to monitor their entire inpatient populations and evaluate their readmission rates relative to national benchmarks. Methods:Multivariate logistic regressions are applied to determine which patient factors increase the odds of a readmission within 30 days and by how much. This study uses deidentified discharge abstract data from a database of approximately 15 million inpatient discharges representing 611 acute care hospitals from Premier healthcare alliance over a 2-year period (2008q4–2010q3). The hospitals are geographically diverse and represent large urban academic centers and small rural community hospitals. Results:This study demonstrates that meaningful risk-adjusted readmission rates can be tracked in a dynamic database. The clinical conditions responsible for the index admission were the strongest predictive factor of readmissions, but factors such as age and accompanying comorbid conditions were also important. Socioeconomic factors, such as race, income, and payer status, also showed strong statistical significance in predicting readmissions. Conclusions:Payment models that are based on stratified comparisons might result in a more equitable payment system while at the same time providing transparency regarding disparities based on these factors. No model, yet available, discriminates potentially modifiable readmissions from those not subject to intervention highlighting the fact that the optimum readmission rate for any given condition is yet to be identified.

The Two Waves of Voucher Privatization in the Czech Republic: A Model of Learning in Sequential Bidding

This study develops a dynamic model of bidding behaviour to investigate the Czech voucher privatization process, which took place in two waves of bidding rounds, the first in 1992 and the second in 1994. It examines the voucher mechanism from the standpoint of investors and the pricing and allocation of shares. Investors could participate as independent individuals or by assigning some of all of their voucher points to investment privatization funds. Principal findings are that individual participants behaved differently from funds and benefited by learning from one round to the next and from observing the behaviour of the funds. An important collateral finding is that the market, though crude, behaved efficiently in the adjustment of share prices over the bidding rounds.

The effect of bivalirudin on costs and outcomes of treatment of ST-segment elevation myocardial infarction

BACKGROUND Bivalirudin is commonly used during percutaneous coronary intervention (PCI) rather than unfractionated heparin. The higher cost of bivalirudin may be offset if it reduces costly bleeding complications and/or length of stay. We sought to assess the effect of using bivalirudin on the costs of care among patients with ST-segment elevation myocardial infarction (STEMI) undergoing PCI. METHODS We analyzed data from 64,872 patients treated in 1 of 278 hospitals. The effect of overall hospital use of bivalirudin on clinical and economic outcomes was assessed using multivariable regression, based on average hospital use of treatments. RESULTS The use of bivalirudin among patients with STEMI treated with PCI varied widely across hospitals, with a median of 6.9% (interquartile range 2.3%-18.6%). After controlling for patient and hospital characteristics, use of bivalirudin rather than heparin and a glycoprotein IIb/IIIa inhibitor reduced bleeding (odds ratio 0.47, P < .001), length of stay (-0.47 days, P < .03), and hospital costs (-14%, P < .04). CONCLUSIONS Use of bivalirudin among patients with STEMI treated with PCI appears to reduce bleeding and overall costs.

Making Hospital Mortality Measurement More Meaningful: Incorporating Advance Directives and Palliative Care Designations

Accounting for patients admitted to hospitals at the end of a terminal disease process is key to signaling care quality and identifying opportunities for improvement. This study evaluates the benefits and caveats of incorporating care-limiting orders, such as do not resuscitate (DNR) and palliative care (PC) information, in a general multivariate model of mortality risk, wherein the unit of observation is the patient hospital encounter. In a model of the mortality gap (observed - expected from the baseline model), DNR explains 8% to 24% of the gap variation. PC provides additional explanatory power to some disease groupings, especially heart and digestive diseases. One caveat is that DNR information, especially if associated with the later stages of hospital care, may mask opportunities to improve care for certain types of patients. But that is not a danger for PC, which is unequivocally valuable in accounting for patient risk, especially for certain subpopulations and disease groupings.

Governing board, C-suite, and clinical management perceptions of quality and safety structures, processes, and priorities in U.S. hospitals.

EXECUTIVE SUMMARY To achieve quality improvement in hospitals requires greater attention to systems thinking than is typical at this time, including a shared understanding across different levels of the hospital of the current state of quality improvement efforts. A self‐administered survey assessed the perceptions of board members, C‐suite executives, and clinical managers regarding quality activities and structures. This instrument, the Hospital Leadership and Quality Assessment Tool (HLQAT), includes 13 domains in six conceptual areas that we believe are major organizational drivers of quality and safety: (1) commitment of senior leaders, (2) a vision of exemplary quality, (3) a supportive culture, (4) accountable leadership, (5) appropriate organizational structures, and (6) adaptive capability. HLQAT survey results from a convenience sample of more than 300 hospitals were linked to performance on the Centers for Medicare & Medicaid Services (CMS) Core Measures. The results show significantly different perceptions between the groups. Higher HLQAT scores for each respondent group were associated with better hospital performance on the CMS Core Measures. There is no magic bullet—no one domain dominates. Leaders in higher‐performing hospitals appear to be more effective at conveying their vision of quality care and creating a culture that supports an expectation that staff and leadership will work across traditional boundaries to improve quality.

Measuring Adverse Events in Hospitalized Patients: An Administrative Method for Measuring Harm.

Context Current methods for tracking harm either require costly full manual chart review (FMCR) or rely on proxy methods that have questionable accuracy. We propose an administrative measure of harm detection that uses electronically captured data. Objective Determine the level of agreement on harm event occurrence when harm is detected based on an administrative harm measurement tool (AHMT) compared with FMCR. Design A retrospective chart review was used to measure the level of agreement in harm detection between an AHMT that uses electronically captured data and a FMCR. Setting The inpatient hospital setting was used. Patients Approximately 771 medical records from 5 hospitals were reviewed. Main Outcome Measures Measures of positive predictive value, negative predictive value, weighted sensitivity, weighted specificity, and concordance were used to evaluate agreement between the 2 methods. Results Although there was agreement at the harm-event level, the results were not all as high as desired: adjusted sensitivity 65%, adjusted specificity 85%, positive predictive value (PPV) 59%, negative predictive value (NPV) 88%, and concordance 75%. The patient-level results show greater agreement: adjusted sensitivity 95%, adjusted specificity 86%, PPV 61%, NPV 99%, and concordance 81%. Conclusion The AHMT is sufficiently accurate for use as a within hospital tool to reliably detect and track harm. Nevertheless, it is not recommended as a tool to make comparisons across institutions, which has policy and payment implications. Further research using administrative harm detection, including the use of a broader set of measures and electronic health records, is needed.

The effectiveness of a multicenter quality improvement collaborative in reducing inpatient mortality.

Motivation and Background This study examines the evidence that a particular quality improvement collaborative that focused on Quality, Efficiency, Safety and Transparency (QUEST) was able to improve hospital performance. Setting The collaborative included a range of improvement vehicles, such as sharing customized comparative reports, conducting online best practices forums, using 90-day rapid-cycle initiatives to test specific interventions, and conducting face-to-face meetings and quarterly one-on-one coaching sessions to elucidate opportunities. Methods With these kinds of activities in mind, the objective was to test for the presence of an overall “QUEST effect” via statistical analysis of mortality results that spanned 6 years (2006-2011) for more than 600 acute care hospitals from the Premier alliance. Results The existence of a QUEST effect was confirmed from complementary approaches that include comparison of matched samples (collaborative participants against controls) and multivariate analysis. Conclusion The study concludes with a discussion of those methods that were plausible reasons for the successes.

Identifying hospital-wide harm: a set of ICD-9-CM-coded conditions associated with increased cost, length of stay, and risk of mortality.

This study identifies an expanded set of hospital-acquired conditions (HACs), using the Present-On-Admission (POA) indicator and secondary diagnoses present on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-coded discharge abstracts and evaluates their association with mortality, length of stay (LOS), and cost. A sample of 500 000 de-identified ICD-9-CM-coded discharge abstracts was randomly drawn from a data set of 11 million. A total of 138 secondary condition clusters were identified as potential inpatient complications (PICs). Regression modeling was used to determine marginal association of each PIC with mortality, LOS, and cost. In all, 16% of hospitalized patients developed 1 or more of these conditions while in the hospital compared with less than 1% of inpatients experiencing HACs defined by the Centers for Medicare and Medicaid Services. Also, 74 PICs were associated with seriously higher mortality rates (5 excess deaths per 1000), significantly LOS (0.4 extra days per discharge), and significantly higher costs (an extra

Development of a Method to Measure and Compare Hospital Waste The Premier Hospital Waste Index

1000 per discharge).