Snow Disease Surveillance System Study (Snow)
|ClinicalTrials.gov Identifier: NCT01232686|
Recruitment Status : Completed
First Posted : November 2, 2010
Last Update Posted : February 16, 2017
|Condition or disease||Intervention/treatment|
|Communicable Diseases||Other: Online disease surveillance data access|
We will collect data from general practitioners (GP) offices by installing local data extraction solutions. Each installation will build a local anonymous database of GP consultations extracted from the local electronic patient record (EPR) system. These anonymous data records will be used to produce local disease statistics before they are exported to a centralized server available in the Norwegian Health network. The centralized server will produce daily reports about the epidemiological situation in the patient population. We will combine the syndromic data from the GP offices with data from the microbiology laboratories on the hospitals that covers the study areas. The epidemiological data will be made available to the intervention areas in the study through web based and customized client applications.
By using data extracted from the GP offices EPR databases and the microbiology laboratories we will investigate the study hypothesis.
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||200 participants|
|Intervention Model:||Parallel Assignment|
|Masking:||None (Open Label)|
|Primary Purpose:||Health Services Research|
|Official Title:||Snow Disease Surveillance System Study|
|Study Start Date :||October 2010|
|Primary Completion Date :||December 2012|
|Study Completion Date :||December 2012|
No Intervention: Control area
In the control areas we will monitor the prevalence and treatment of communicable diseases without giving the participants online access to disease surveillance information
Experimental: Intervention area
In these areas we will give study participants online access to epidemiological data for communicable diseases
Other: Online disease surveillance data access
In the intervention areas we will give the study participants online access to the Snow disease surveillance system. The system will provide data about the incidents of respiratory and gastrointestinal communicable diseases in the patient population.
- Earlier diagnosis and treatment for communicable diseases [ Time Frame: Measured at the end of the data collection period, approx. 1.5 year. (December 2012) ]
General practitioners (GP) have three possible decisions in a consultation with a patient; 1) treat on suspicion, 2) take a sample, 3) wait and see whether the patient recovers or get worse, or 4) a combination of 1 and 2.
In situations with decision 3 (wait and see) the patient may return to a consultation later on.
The hypothesis is that online access to epidemiological data from the local patient population will enable GPs to make the right decision more often based on knowledge about the epidemiological situation in the patient population.
- Earlier detection of local disease outbreaks [ Time Frame: Measured at the end of the data collection period, approx. 1.5 year. (December 2012) ]Syndromic surveillance enables earlier detection of local disease outbreaks compared to traditional laboratory based surveillance. We will record the time of disease outbreak detection in both intervention and control areas and compare.
- Lower number of infected during disease outbreaks [ Time Frame: Measured at the end of the data collection period, approx. 1.5 year. (December 2012) ]We will compare the number of infected in the intervention and control areas. The hypothesis is that the intervention areas will have fewer infected compared to the control areas.
- Impact on health service costs [ Time Frame: Measured at the end of the data collection period, approx. 1.5 year. (December 2012) ]We will measure the cost related to communicable diseases in the control and intervention areas. Our prediction is that it will change.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01232686
|Principal Investigator:||Johan Gustav Bellika, PhD||University of Tromsø, Department of Computer Science|