US hospital performance: A dynamic network analysis

Workshop 2013 on Dynamic and Network DEA (January 29-30, 2013)Healthcare is a critical and costly industry. In the U.S. a significant component of healthcare costs are expenses generated in hospitals. This paper reports the results of analyzing 607 U.S. hospitals between 2006-2009 using a dynamic network slack-based Data Envelopment Analysis (DEA) Model. We find accounting for the dynamic and network structure of the hospital lowers efficiency estimates. Further, hospitals are more efficient at providing hospital services compared to hotel services, but the efficiency of hospitals is not correlated with their size. Regarding the dynamic network slack-based DEA Model, we find slack-based approaches combine technical and allocative aspects of inefficiency and thus tend to have significantly lower efficiency levels than just radial technical efficiency measures. Further when applying an envelopment method like DEA, there are some benefits to averaging multiple years of data to remove variation and avoid estimating a frontier based on observations that might have significant noise in their measurement.This workshop is supported by JSPS KAKENHI Grant Number 22310092 under the title “Theory and Applications of Dynamic DEA with Network Structure.

Engineering Incentives in Distributed Systems with Healthcare Applications

U.S. healthcare costs have experienced unsustainable growth, with expenditures of $2.5 trillion in 2009, and are rising at a rate faster than that of the U.S. economy. A major factor in the cost of the U.S. healthcare system is related to the strategic behavior of system participants based on their incentives. This dissertation addresses the challenge of designing incentives to solve problems in healthcare systems. Principal agent theory and Markov decision processes are the primary methods used to construct incentives. The first problem considered is how to design contracts in order to align consumer and provider incentives with respect to preventive efforts. The model consists of an insurer contracting with two agents, a consumer and a provider, and focuses on the trade off between ex ante moral hazard and insurance. Two classes of efforts on behalf of the provider are studied: those which complement consumer efforts, and those which substitute with consumer efforts. The results show that the provider must be given incentives when the consumer is healthy to induce effort, and that inducing provider effort allows an insurer to save on incentives given to the consumer. The insurer can save on the cost of incentives by using a multilateral contract compared to the bilateral benchmark. These savings are illustrated by an example showing which model features affect the savings achieved. The second problem addresses the decision to provide knowledge to consumers regarding the consequences of health behaviors. The model developed to address this second problem extends the literature on incentives in healthcare systems to consider dynamic environments and includes a behavioral model of healthcare consumers. By using a learning model of consumer behavior, a policy maker's knowledge provision problem is transformed into a Markov decision process. This framework is used to solve for optimal knowledge provision policies regarding behaviors affecting coronary health. Sensitivity analysis shows robust threshold features of optimal policies. The results show that knowledge about smoking should be provided at most health and behavior states. As the cost of providing knowledge increases or aptitude for behavioral change decreases, fewer states are in the optimal knowledge provision policy, with healthy consumers dropping out first. Knowledge about diet and physical activity is provided more selectively due to the to uncertainty in the health benefits, and the time delay in accrued rewards

System-Wide Prediction of General, All-Cause, Preventable Hospital Readmissions

Existing studies of hospital readmissions typically focus on specific diagnoses, age groups, discharge dispositions, payer classes, or hospitals, and often use small samples. It is not clear how predictive models generated from such studies generalize across diseases, hospitals, or time periods. In this study, a logistic regression model of readmission risk within 30 days based on hospital administrative data was constructed and validated across hospitals and time periods. The hospitals included both general and specialty hospitals such as long-term care, women’s, and children’s hospitals. The administrative data included information on patient’s demographics, diagnoses, procedures, and discharge disposition. Derivation and validation samples for the cross-hospital analysis yielded C-statistics of 0.722 and 0.706, respectively. The cross-time period analysis yielded C-statistics from 0.736 to 0.755 for five derivation samples, and from 0.681 to 0.701 for fifteen validation samples. The findings indicate that a prediction model can be used with relative success to extrapolate beyond the estimation sample both in terms of hospital and time period. Such risk estimates can be used to inform discharge intervention decisions and increase care coordination

Prospectus, September 6, 1995

https://spark.parkland.edu/prospectus_1995/1019/thumbnail.jp

The Grizzly, March 23, 1999

For 27 Ursinus Students, Spring Break is No Day at the Beach • Former CIA Member to Speak on U.S. Intelligence • French Conducts Meistersingers Concert • A New Reimert for a New Season • UC Students Check Out Hot Internet Sites • Do You Remember Last Wednesday? • Opinion: Warning This Article has Been Censored; A Cruel Second; Cash Equivalency Controversy Revisited • Back in the Swing of Things: UC Baseball Report • Softball Evens Record at 7-7 • Gymnastics Holds Own at Nationals • UC Lax Drops First Two • Men\u27s Tennis Team Splits Matcheshttps://digitalcommons.ursinus.edu/grizzlynews/1437/thumbnail.jp

The Grizzly, April 13, 1999

Recent Violence a Worry for Ursinus Administration • Tuition Increase for Fall 1999 • Former Rendell Aide to Speak • Students Answer Call to Help Refugees • Opinion: Editorial Response; Fight That Led to a Painful Assignment; Bathrooms, Respect Gone Down the Toilet; Why Kosovo is (but Shouldn\u27t be) Another Vietnam and Why our Hands are Tied no Matter What • Softball Steals First Place from Western Maryland • Cheerleading Squad Hosts Basketball Tournament • Lacrosse Remains Undefeated in Centennial Conference • Lax Wins National Recognition • UC Baseball Climbing to Top of Conference • Wiatrak Named CC Player of the Week • Ursinus Golf Challenges Conferencehttps://digitalcommons.ursinus.edu/grizzlynews/1440/thumbnail.jp

The Grizzly, March 2, 1999

Vaccination for Meningitis a Success • Dr. England to Speak on Social Crowds • Middle States Presents Findings • Ursinus Alum Named Principal of Vanguard Group • Ursinus Students Called to Aid Project for Peace • Middle States Team Praises Ursinus Students • Ursinus Students Battle Against Sickness • Dean\u27s Office Recognizes Resident Scholars • Opinion: Lessons From Space Ghost; Higher Education: Meal Ticket or Soul Food?; Dan Quayle: Eight Years Later, What has Changed? • Grizzly Wrestlers to Take Trenton State by Storm • Positive Outlook for New Field Hockey Coachhttps://digitalcommons.ursinus.edu/grizzlynews/1436/thumbnail.jp

Modeling factors influencing the demand for emergency department services in ontario: a comparison of methods

<p>Abstract</p> <p>Background</p> <p>Emergency departments are medical treatment facilities, designed to provide episodic care to patients suffering from acute injuries and illnesses as well as patients who are experiencing sporadic flare-ups of underlying chronic medical conditions which require immediate attention. Supply and demand for emergency department services varies across geographic regions and time. Some persons do not rely on the service at all whereas; others use the service on repeated occasions. Issues regarding increased wait times for services and crowding illustrate the need to investigate which factors are associated with increased frequency of emergency department utilization. The evidence from this study can help inform policy makers on the appropriate mix of supply and demand targeted health care policies necessary to ensure that patients receive appropriate health care delivery in an efficient and cost-effective manner. The purpose of this report is to assess those factors resulting in increased demand for emergency department services in Ontario. We assess how utilization rates vary according to the severity of patient presentation in the emergency department. We are specifically interested in the impact that access to primary care physicians has on the demand for emergency department services. Additionally, we wish to investigate these trends using a series of novel regression models for count outcomes which have yet to be employed in the domain of emergency medical research.</p> <p>Methods</p> <p>Data regarding the frequency of emergency department visits for the respondents of Canadian Community Health Survey (CCHS) during our study interval (2003-2005) are obtained from the National Ambulatory Care Reporting System (NACRS). Patients' emergency department utilizations were linked with information from the Canadian Community Health Survey (CCHS) which provides individual level medical, socio-demographic, psychological and behavioral information for investigating predictors of increased emergency department utilization. Six different multiple regression models for count data were fitted to assess the influence of predictors on demand for emergency department services, including: Poisson, Negative Binomial, Zero-Inflated Poisson, Zero-Inflated Negative Binomial, Hurdle Poisson, and Hurdle Negative Binomial. Comparison of competing models was assessed by the Vuong test statistic.</p> <p>Results</p> <p>The CCHS cycle 2.1 respondents were a roughly equal mix of males (50.4%) and females (49.6%). The majority (86.2%) were young-middle aged adults between the ages of 20-64, living in predominantly urban environments (85.9%), with mid-high household incomes (92.2%) and well-educated, receiving at least a high-school diploma (84.1%). Many participants reported no chronic disease (51.9%), fell into a small number (0-5) of ambulatory diagnostic groups (62.3%), and perceived their health status as good/excellent (88.1%); however, were projected to have high Resource Utilization Band levels of health resource utilization (68.2%). These factors were largely stable for CCHS cycle 3.1 respondents. Factors influencing demand for emergency department services varied according to the severity of triage scores at initial presentation. For example, although a non-significant predictor of the odds of emergency department utilization in high severity cases, access to a primary care physician was a statistically significant predictor of the likelihood of emergency department utilization (OR: 0.69; 95% CI OR: 0.63-0.75) and the rate of emergency department utilization (RR: 0.57; 95% CI RR: 0.50-0.66) in low severity cases.</p> <p>Conclusion</p> <p>Using a theoretically appropriate hurdle negative binomial regression model this unique study illustrates that access to a primary care physician is an important predictor of both the odds and rate of emergency department utilization in Ontario. Restructuring primary care services, with aims of increasing access to undersupplied populations may result in decreased emergency department utilization rates by approximately 43% for low severity triage level cases.</p

Management of infantile hemangiomas during the COVID pandemic

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.The COVID‐19 pandemic has caused significant shifts in patient care including a steep decline in ambulatory visits and a marked increase in the use of telemedicine. Infantile hemangiomas (IH) can require urgent evaluation and risk stratification to determine which infants need treatment and which can be managed with continued observation. For those requiring treatment, prompt initiation decreases morbidity and improves long‐term outcomes. The Hemangioma Investigator Group has created consensus recommendations for management of IH via telemedicine. FDA/EMA‐approved monitoring guidelines, clinical practice guidelines, and relevant, up‐to‐date publications regarding initiation and monitoring of beta‐blocker therapy were used to inform the recommendations. Clinical decision‐making guidelines about when telehealth is an appropriate alternative to in‐office visits, including medication initiation, dosage changes, and ongoing evaluation, are included. The importance of communication with caregivers in the context of telemedicine is discussed, and online resources for both hemangioma education and propranolol therapy are provided

Miscellany

Art Writinghttps://digitalcommons.georgiasouthern.edu/miscell/1000/thumbnail.jp