Healthcare

Many mobile applications retrieve content from remote servers via user generated queries. Processing these queries is often needed before the desired content can be identified. Processing the request on the mobile devices can quickly sap the limited battery resources. Conversely, processing user-queries at remote servers can have slow response times due to communication latency incurred during transmission of the potentially large query.  We evaluate a network-assisted mobile computing scenario where mid-network nodes with "leasing" capabilities are deployed by a service provider.

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.

Patient access to healthcare is a major problem area due to inadequate supplies and misallocation of resources including physicians, nurses, and hospital beds. Increasing patient demands due to an aging and more chronically ill population will exacerbate this situation, leading to longer delays for care, hurried treatment times, and adverse clinical outcomes. Though there is a significant operations literature focused on methods to mitigate these effects, suggested remedies may be ineffective due to adaptive behavior by both physicians and patients.

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.

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.

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.