Breaking the Cycle: How the News and Markets Created a Negative Feedback Loop in COVID-19
New research from CBS Professor Harry Mamaysky reveals how negativity in the news and markets can escalate a financial crisis.
New research from CBS Professor Harry Mamaysky reveals how negativity in the news and markets can escalate a financial crisis.
Adapted from “Global Value Chains in Developing Countries: A Relational Perspective from Coffee and Garments,” by Laura Boudreau of Columbia Business School, Julia Cajal Grossi of the Geneva Graduate Institute, and Rocco Macchiavello of the London School of Economics.
Adapted from “Online Advertising as Passive Search,” by Raluca M. Ursu of New York University Stern School of Business, Andrey Simonov of Columbia Business School, and Eunkyung An of New York University Stern School of Business.
This paper from Columbia Business School, “Meaning of Manual Labor Impedes Consumer Adoption of Autonomous Products,” explores marketing solutions to some consumers’ resistance towards autonomous products. The study was co-authored by Emanuel de Bellis of the University of St. Gallen, Gita Johar of Columbia Business School, and Nicola Poletti of Cada.
Co-authored by John B. Donaldson of Columbia Business School, “The Macroeconomics of Stakeholder Equilibria,” proposes a model for a purely private, mutually beneficial financial agreement between worker and firm that keeps decision-making in the hands of stockholders while improving the employment contract for employees.
At Columbia Business School, our faculty members are at the forefront of research in their respective fields, offering innovative ideas that directly impact the practice of business today. A quick glance at our publication on faculty research, CBS Insights, will give you a sense of the breadth and immediacy of the insight our professors provide.
As a student at the School, this will greatly enrich your education. In Columbia classrooms, you are at the cutting-edge of industry, studying the practices that others will later adopt and teach. As any business leader will tell you, in a competitive environment, being first puts you at a distinct advantage over your peers. Learn economic development from Ray Fisman, the Lambert Family Professor of Social Enterprise and a rising star in the field, or real estate from Chris Mayer, the Paul Milstein Professor of Real Estate, a renowned expert and frequent commentator on complex housing issues. This way, when you complete your degree, you'll be set up to succeed.
Columbia Business School in conjunction with the Office of the Dean provides its faculty, PhD students, and other research staff with resources and cutting edge tools and technology to help push the boundaries of business research.
Specifically, our goal is to seamlessly help faculty set up and execute their research programs. This includes, but is not limited to:
All these activities help to facilitate and streamline faculty research, and that of the doctoral students working with them.
We developed a predictive statistical model to identify donor–recipient characteristics related to kidney graft survival in the Chilean population. Given the large number of potential predictors relative to the sample size, we implemented an automated variable selection mechanism that could be revised in future studies as more national data is collected. Materials and Methods: A retrospective multicenter study was conducted to analyze data from 822 adult kidney transplant recipients from adult donors between 1998 and 2018.
Emergency Departments (EDs) typically have multiple areas where patients of different acuity levels receive treatments. In practice, different areas often operate with fixed nurse staffing levels. When there are substantial imbalances in congestion among different areas, it could be beneficial to deviate from the original assignment and reassign nurses. However, reassignments typically are only feasible at the beginning of 8-12-hour shifts, providing partial flexibility in adjusting staffing levels.
Background
Equitable protocols to triage life-saving resources must be specified prior to shortages in order to promote transparency, trust and consistency. How well proposed utilitarian protocols perform to maximize lives saved is unknown. We aimed to estimate the survival rates that would be associated with implementation of the New York State 2015 guidelines for ventilator triage, and to compare them to a first-come-first-served triage method.
Methods
The COVID-19 pandemic has caused great disruption to the service sector, and it has, in turn, adapted by implementing measures that reduce physical contact among employees and users; examples include home-office work and the setting of occupancy restrictions at indoor locations.
Service systems are typically limited resource environments where scarce capacity is reserved for the most urgent customers. However, there has been a growing interest in the use of proactive service when a less urgent customer may become urgent while waiting. On one hand, providing service for customers when they are less urgent could mean that fewer resources are needed to fulfill their service requirement. On the other hand, using limited capacity for customers who may never need the service in the future takes the capacity away from other more urgent customers who need it now.
We examine how organizations of different types --public, non-profit and for-profit -- engage in consumer-benefiting misconduct (CBM) by examining which patients benefit from hospitals of the three types gaming the market for liver transplants. Consistent with our theory, we find that public firms are the least likely of the three organization types to engage in CBM.
We estimated the effects of information avoidance and information seeking among parents of children diagnosed with ASD on age of diagnosis. An online survey was completed by 1,815 parents of children with ASD. Children of parents who self-reported that they had preferred "not to know," reported diagnoses around 3 months later than other children.
Many service systems are staffed by workers who work in shifts. In this work, we study the dynamic assignment of servers to different areas of a service system at the beginning of discrete time-intervals, i.e., shifts. The ability to reassign servers at discrete intervals, rather than continuously, introduces a partial flexibility that provides an opportunity for reducing the expected waiting time of customers.
Patients whose transfer to the Intensive Care Unit (ICU) is unplanned are prone to higher mortality rates and longer length-of-stay than those who were admitted directly to the ICU. Recent advances in machine learning to predict patient deterioration have introduced the possibility of proactive transfer from the ward to the ICU. In this work, we study the problem of finding robust patient transfer policies which account for uncertainty in statistical estimates due to data limitations when optimizing to improve overall patient care.
Objectives:
To examine whether and how step-down unit admission after ICU discharge affects patient outcomes.
Design:
Retrospective study using an instrumental variable approach to remove potential biases from unobserved differences in illness severity for patients admitted to the step-down unit after ICU discharge.
Setting:
Ten hospitals in an integrated healthcare delivery system in Northern California.
Patients:
Objectives:
To examine whether and how step-down unit admission after ICU discharge affects patient outcomes.
Design:
Retrospective study using an instrumental variable approach to remove potential biases from unobserved differences in illness severity for patients admitted to the step-down unit after ICU discharge.
Setting:
Ten hospitals in an integrated healthcare delivery system in Northern California.
Patients:
We estimate the benefit of life-extending medical treatments to life insurance companies. Our main insight is that life insurance companies have a direct benefit from such treatments as they lower the insurer's liabilities by pushing the death benefit further into the future and raise future premium income. We apply this insight to immunotherapy, treatments associated with durable gains in survival rates for a growing number of cancer patients. We estimate that the life insurance sector's aggregate benefit from FDA approved immunotherapies is $9.8 billion a year.
Admission control and service rate speedup may be used during periods of congestion to minimize customer waiting in different service settings. In a healthcare setting, this can mean sending patients to alternative care facilities that may take more time and/or provide less ideal treatment. While waiting can be detrimental to patient outcomes, strategies used to control congestion can also be costly. In this work, we examine a multi-server queueing system that considers both admission control and speedup.
Many service systems have servers with different capabilities and customers with varying needs. One common way this occurs is when servers are hierarchical in their skills or in the level of service they can provide. Much of the literature studying such systems relies on an understanding of the relative costs and benefits associated with serving different customer types by the different levels of service. In this work, we focus on estimating these costs and benefits in a complex healthcare setting where the major differentiation among server types is the intensity of service provided.
Many service systems have servers with different capabilities and customers with varying needs. One common way this occurs is when servers are hierarchical in their skills or in the level of service they can provide. Much of the literature studying such systems relies on an understanding of the relative costs and benefits associated with serving different customer types by the different levels of service. In this work, we focus on estimating these costs and benefits in a complex healthcare setting where the major differentiation among server types is the intensity of service provided.
Objective:
We sought to build on the template-matching methodology by incorporating longitudinal comorbidities and acute physiology to audit hospital quality.
Study Setting:
Patients admitted for sepsis and pneumonia, congestive heart failure, hip fracture, and cancer between January 2010 and November 2011 at 18 Kaiser Permanente Northern California hospitals.
Study Design:
Unplanned transfers of patients from general medical-surgical wards to the Intensive Care Unit (ICU) may occur due to unexpected patient deterioration. Such patients tend to have higher mortality rates and longer lengths of stay than direct admits to the ICU. A new predictive model, the EDIP2, was developed with the intent to identify patients at risk for deterioration, which in some cases could trigger a proactive transfer to the ICU. While it is conceivable that proactive transfers could improve individual patient outcomes, they could also lead to ICU congestion.
Objective:
To understand the impact of adding a medical step-down unit (SDU) on patient outcomes and throughput in a medical intensive care unit (ICU).
Design:
Retrospective cohort study.
Setting:
Two academic tertiary care hospitals within the same health-care system.
Patients:
Adults admitted to the medical ICU at either the control or intervention hospital from October 2013 to March 2014 (preintervention) and October 2014 to March 2015 (postintervention).
This pilot study estimates the effects of family structure on age of diagnosis, with the goal of identifying factors that may accelerate or delay diagnosis. We conducted an online survey with 477 parents of children with autism. In addition, we carried out novel, follow-up surveys of 196 "friends and family," who were referred by parents. Family structure and frequency of interactions with family members have significant effects on age of diagnosis (p < 0.05).
In hospitals, Step Down Units (SDUs) provide an intermediate level of care between the Intensive Care Units (ICUs) and the general medical-surgical wards. Because SDUs are less richly staffed than ICUs, they are less costly to operate; however, they also are unable to provide the level of care required by the sickest patients. There is an ongoing debate in the medical community as to whether and how SDUs should be used. On one hand, an SDU alleviates ICU congestion by providing a safe environment for post-ICU patients before they are stable enough to be transferred to the general wards.
In order for a patient to be discharged from a hospital unit, a physician must first perform a physical examination and review the pertinent medical information to determine that the patient is stable enough to be transferred to a lower level of care or be discharged home. Requiring an inspection of a patient's "readiness for discharge" introduces an interesting dynamic where patients may occupy a bed longer than medically necessary.
In order for a patient to be discharged from a hospital unit, a physician must first perform a physical examination and review the pertinent medical information to determine that the patient is stable enough to be transferred to a lower level of care or be discharged home. Requiring an inspection of a patient's "readiness for discharge" introduces an interesting dynamic where patients may occupy a bed longer than medically necessary.
Rationale
Hospitals are increasingly using critical care outreach teams (CCOTs) to respond to patients deteriorating outside intensive care units (ICUs). CCOT staffing is variable across hospitals and optimal team composition is unknown.
Objectives
To assess whether adding a critical care medicine trained physician assistant (CCM-PA) to a critical care outreach team (CCOT) impacts clinical and process outcomes.
Methods
Mainstream queueing models are frequently employed in modeling healthcare delivery in a number of settings, and further are used in making operational decisions for the same. The vast majority of these queueing models ignore the effects of delay experienced by a patient awaiting care. However, long delays may have adverse effects on patient outcomes and can potentially lead to longer lengths of stay (LOS) when the patient ultimately does receive care. This work sets out to understand these delay issues from an operational perspective.
We consider a system of parallel queues where arriving service tasks are buffered, according to type. Available service resources are dynamically configured and allocated to the queues to process the tasks. At each point in time, a scheduler chooses a service configuration across the queues, in response to queue backlogs. Switching from one service configuration to another incurs a setup time, during which idling occurs and service bandwidth is lost. Such setup times are inherent in manufacturing and computer systems.
Objectives: To employ automated bed data to examine whether ICU occupancy influences ICU admission decisions and patient outcomes.
Design: Retrospective study using an instrumental variable to remove biases from unobserved differences in illness severity for patients admitted to ICU.
Setting: Fifteen hospitals in an integrated healthcare delivery system in California.
Patients: Seventy thousand one hundred thirty-three episodes involving patients admitted via emergency departments to a medical service over a 1-year period between 2008 and 2009.
Objectives: To employ automated bed data to examine whether ICU occupancy influences ICU admission decisions and patient outcomes.
Design: Retrospective study using an instrumental variable to remove biases from unobserved differences in illness severity for patients admitted to ICU.
Setting: Fifteen hospitals in an integrated healthcare delivery system in California.
Patients: Seventy thousand one hundred thirty-three episodes involving patients admitted via emergency departments to a medical service over a 1-year period between 2008 and 2009.
This work examines the process of admission to a hospital’s intensive care unit (ICU). ICUs currently lack systematic admission criteria, largely because the impact of ICU admission on patient outcomes has not been well quantified. This makes evaluating the performance of candidate admission strategies difficult. Using a large patient-level data set of more than 190,000 hospitalizations across 15 hospitals, we first quantify the cost of denied ICU admission for a number of patient outcomes.
The development of predictive models in healthcare settings has been growing; one such area is the prediction of patient arrivals to the Emergency Department (ED). The general premise behind these works is that such models may be used to help manage an ED which consistently faces high congestion. In this work, we propose a class of proactive policies which utilizes future information of potential patient arrivals to effectively manage admissions into an ED while reducing waiting times for patients who are eventually treated.
I investigate whether diseases subject to more rapid pharmaceutical innovation experienced greater declines in Americans’ disability days and use of medical services during the period 1997–2010, controlling for several other factors, using data from the Medical Expenditure Panel Survey. The mean number of work loss days, school loss days, and hospital admissions declined more rapidly among medical conditions with larger increases in the mean number of new (post-1990) prescription drugs consumed.
This work examines the process of admission to a hospital’s intensive care unit (ICU). ICUs currently lack systematic admission criteria, largely because the impact of ICU admission on patient outcomes has not been well quantified. This makes evaluating the performance of candidate admission strategies difficult. Using a large patient-level data set of more than 190,000 hospitalizations across 15 hospitals, we first quantify the cost of denied ICU admission for a number of patient outcomes.
In a number of service systems, there can be substantial latitude to vary service rates. However, although speeding up service rate during periods of congestion may address a present congestion issue, it may actually exacerbate the problem by increasing the need for rework. We introduce a state-dependent queuing network where service times and return probabilities depend on the “overloaded” and “underloaded” state of the system.
We use longitudinal, disease-level data to analyze the impact of pharmaceutical innovation on longevity and medical expenditure in Sweden, where mean age at death increased by 1.88 years during the period 1997-2010. Pharmaceutical innovation is estimated to have increased mean age at death by 0.60 years during the period. The estimates indicate that longevity depends on the number of drugs to treat a disease, not the number of drug classes.
The U.S. government has mandated that, in a catastrophic event, metropolitan areas need to be capable of caring for 50 burn-injured patients per million population. In New York City, this corresponds to 400 patients. There are currently 140 burn beds in the region, which can be surged up to 210. To care for additional patients, hospitals without burn centers will be used to stabilize patients until burn beds become available.
The U.S. government has mandated that, in a catastrophic event, metropolitan areas need to be capable of caring for 50 burn-injured patients per million population. In New York City, this corresponds to 400 patients. There are currently 140 burn beds in the region, which can be surged up to 210. To care for additional patients, hospitals without burn centers will be used to stabilize patients until burn beds become available.
This work examines the impact of discharge decisions under uncertainty in a capacity-constrained high-risk setting: the intensive care unit (ICU). New arrivals to an ICU are typically very high-priority patients and, should the ICU be full upon their arrival, discharging a patient currently residing in the ICU may be required to accommodate a newly admitted patient. Patients so discharged risk physiologic deterioration, which might ultimately require readmission; models of these risks are currently unavailable to providers.
Since its inception in 2006, the New York City (NYC) Task Force for Patients with Burns has continued to develop a city-wide and regional response plan that addressed the triage, treatment, transportation of 50/million (400) adult and pediatric victims for 3 to 5 days after a large-scale burn disaster within NYC until such time that a burn center bed and transportation could be secured. The following presents updated recommendations on these planning efforts. Previously published literature, project deliverables, and meeting documents for the period of 2009–2010 were reviewed.
I investigate the contribution of pharmaceutical innovation to recent longevity growth in Germany and France. First, I examine the effect of the vintage of prescription drugs (and other variables) on the life expectancy and age-adjusted mortality rates of residents of Germany, using longitudinal, annual, state-level data during the period 2001–2007. The estimates imply that about one-third of the 1.4-year increase in German life expectancy during the period 2001–2007 was due to the replacement of older drugs by newer drugs.
This paper analyzes longitudinal state-level data during the period 1995–2004 to investigate whether use of newer prescription drugs has reduced the ratio of the number of workers receiving Social Security Disability Insurance benefits to the working-age population (the “DI recipiency rate”).
Nursing care is arguably the single biggest factor in both the cost of hospital care and patient satisfaction. Inadequate inpatient nursing levels have also been cited as a significant factor in medical errors and emergency room overcrowding. Yet, there is widespread dissatisfaction with the current methods of determining nurse staffing levels, including the most common one of using minimum nurse-to-patient ratios. In this paper, we represent the nursing system as a variable finite-source queuing model.