J Biomed Inform

Early detection and accurate characterization of disease outbreaks are important tasks of public health. Infectious diseases that present symptomatically like influenza (SLI), including influenza itself, constitute an important class of diseases that are monitored by public-health epidemiologists. Monitoring emergency department (ED) visits for presentations of SLI could provide an early indication of the presence, extent, and dynamics of such disease in the population. We investigated the use of daily over-the-counter thermometer-sales data to estimate daily ED SLI counts in Allegheny County (AC), Pennsylvania. We found that a simple linear model fits the data well in predicting daily ED SLI counts from daily counts of thermometer sales in AC. These results raise the possibility that this model could be applied, perhaps with adaptation, in other regions of the country, where commonly thermometer sales data are available, but daily ED SLI counts are not.1U38 HK000063-01/HK/PHITPO CDC HHS/United StatesP01 HK000086/HK/PHITPO CDC HHS/United StatesP01 HK000086/HK/PHITPO CDC HHS/United StatesR01 LM009132/LM/NLM NIH HHS/United StatesR01 LM009132/LM/NLM NIH HHS/United StatesU38 HK000063/HK/PHITPO CDC HHS/United States2015-10-18T00:00:00Z23501015PMC460954

J Biomed Inform

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising.P01-HK000086/HK/PHITPO CDC HHS/United StatesR01 LM009132/LM/NLM NIH HHS/United StatesR01 LM011370/LM/NLM NIH HHS/United StatesR01-LM009132/LM/NLM NIH HHS/United StatesR01-LM011370/LM/NLM NIH HHS/United States2016-02-01T00:00:00Z25181466PMC444133

Acute oncology service = Acute palliative service? Early palliative care assessment results from a pilot project in South Wales

Background: The National Chemotherapy Advisory Group report 2009 recommends an acute oncology service (AOS) in every UK hospital with an emergency department. Patients discussed in Acute Oncology Service settings are often very unwell, at the start of their cancer journey, and may have multiple challenging symptoms. Aims: Will earlier palliative care intervention in AOS meetings result in an increase in palliative care involvement? As a comparative baseline we used data from Royal Sussex County Hospital, where an acute admission AOS data collection was carried out: in 53 patients with cancer, palliative care were involved in four cases (7.5%). Methods: As part of this project, our palliative care team started to attend AOS meetings at Velindre Cancer Hospital on a daily basis. After initial embedding, a strategy to collect data was designed and an audit cycle was carried out. Results: Through integration of the palliative care team into Acute Oncology Service meetings, key areas of advance care planning were addressed and discussions with patients were planned. Of 100 patients assessed during the AOS reporting period, 80% were not known to a palliative care team/provider. Of all patients analysed, 28% required no palliative input, 29% were signposted to another palliative care team, 27% received same day face to face palliative care review and 16% required verbal advice only to a generalist team member. Advance care planning discussions in the sample of patients who needed some palliative care input were held within a two week time frame in 61% of cases. Discussion: Significant findings included large population (80%) unknown to palliative care services at AOS entry point, but with high level of unmet need. 72% had palliative care needs. 29% had received their cancer diagnosis within the last month. Conclusion: AOS meetings appear to be a valid entry point for referral to palliative services, despite many AOS patients being at the start of their cancer diagnosis