Healthcare

This paper examines the role of a hospital Step-Down Unit (SDU) on patient flows and patient outcomes. An SDU provides an intermediate level of care for semi-critically ill patients who are not sick enough to require intensive care but not stable enough to be treated in the general medical/surgical ward (ward). Using data from 10 hospitals from a single hospital network, we use an instrumental variable approach to estimate the impact on patient outcomes of routing patients to the SDU following Intensive Care Unit (ICU) discharge.

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.

I examine the relationship across diseases between the long-run growth in the number of publications about a disease and the change in the age-adjusted mortality rate from the disease. The diseases analyzed are almost all the different forms of cancer, i.e. cancer at different sites in the body (lung, colon, breast, etc.). Time-series data on the number of publications pertaining to each cancer site were obtained from PubMed. For articles published since 1975, it is possible to distinguish between publications indicating and not indicating any research funding support.