Management of Emergency Services in Lombardy during COVID-19 epidemic using Business Intelligence

Abstract Background In Italy on the 20th of February, the first Italian patient was tested positive for Coronavirus Disease 2019 (COVID-19) in the Lombardy region. The Regional Emergency Medical Services (EMS) Trust (Azienda Regionale Emergenza Urgenza, AREU) of the Lombardy region decided to apply a Business Intelligence (BI) System to take timely decisions on the management of EMS and to monitor the spread of the disease in the region in order to better respond to the outbreak. Methods Since the beginning of the COVID-19 outbreak, AREU developed a BI System to track the daily number of first aid requests received from 1.1.2. (Public Safety Answering Point 1). BI evaluates the number of requests that have been classified as respiratory and/or infectious episodes during the telephone dispatch interview. Moreover, BI analyses the pattern of the epidemic, identifying the numerical trend of episodes in each municipality (increasing, stable, decreasing). Currently, AREU is still implementing the BI as the epidemic is still ongoing. Results In the Lombardy region on the 20th of February the number of the first aid requests for respiratory and/or infectious episodes were 314. This figure increased sharply during the month of February and March reaching its peak on the 16th of March with 1537 episodes. In the area around Bergamo, this number experienced a greater rise compared to the rest of the Lombardy territory, going from 74 episodes on the 20th of February to 694 on the 13th of March. Therefore, AREU decided to reallocate in the territory the resources (ambulances and human resources) based on the real-time data elaborated by the BI system. Conclusions The BI System has been of paramount importance in taking timely decisions on the management of EMS during the COVID-19 outbreak in the Lombardy region. Indeed, BI can be usefully applied to promptly identify the trend of the COVID-19 epidemic and, consequently, make informed decisions to improve the response to the outbreak. Key messages The Emergency Medical Services Trust of the Lombardy region applied a Business Intelligence System to promptly respond to the outbreak of COVID-19 and reallocate the resources based on real-time data. AREU used a Business Intelligence System to track the daily number of first aid requests that have been classified as respiratory and/or infectious episodes during the telephone dispatch interview.

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Business Intelligence applied to Emergency Medical Services in the Lombardy region during SARS-CoV-2 epidemic (Preprint)

10.2196/preprints.19062 ◽

2020 ◽

Cited By ~ 1

Author(s):

Giuseppe Maria Sechi ◽

Maurizio Migliori ◽

Gabriele Dassi ◽

Andrea Pagliosa ◽

Rodolfo Bonora ◽

...

Keyword(s):

Emergency Medical Services ◽

Business Intelligence ◽

First Aid ◽

Medical Services ◽

Evidence Based ◽

Time Data ◽

Lombardy Region ◽

Emergency Medical ◽

Real Time Data ◽

The Impact

BACKGROUND Background: In Italy on the 21st of February, the first patient was tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Codogno hospital in the Lombardy region. From that date, the Regional Emergency Medical Services (EMS) Trust (Azienda Regionale Emergenza Urgenza, AREU) of the Lombardy region decided to apply Business Intelligence (BI) to the management of EMS during the epidemic. OBJECTIVE Objective: The aim of the study is to assess in this context the impact of BI on EMS management outcomes. METHODS Methods: Since the beginning of the coronavirus disease 2019 (COVID-19) outbreak, in February 2020, AREU is using BI daily to track the number of first aid requests received from 112 (Public Safety Answering Point 1). BI analyses the number of requests that have been classified as respiratory and/or infectious episodes during the telephone dispatch interview. Moreover, BI allows analysing the pattern of the epidemic, identifying the numerical trend of episodes in each municipality (increasing, stable, decreasing). RESULTS Results: AREU decides to reallocate in the territory the resources based on real-time data recorded and elaborated by BI. Indeed, based on that data, the numbers of vehicles and personnel have been implemented in the municipalities that registered more episodes and where the clusters are supposed to be. BI has been of paramount importance in taking timely decisions on the management of EMS during COVID-19 outbreak in the Lombardy region. CONCLUSIONS Conclusion: Even if there is little evidence-based literature focused on BI impact within the health care, this study suggests that BI can be usefully applied to promptly identify clusters and patterns of the SARS-CoV-2 epidemic and, consequently, make informed decisions that can improve the EMS management response to the outbreak.

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Real-Time Data Transmission and Visualization as a Powerful Technology to Reduce Non-Productive Time During Drilling Operations: Present Day Capabilities, Limitation, and Future Development

Scientific Contributions Oil and Gas ◽

10.29017/scog.43.3.515 ◽

2020 ◽

Vol 43 (3) ◽

pp. 135-142

Author(s):

Yustian Ekky Rahanjani ◽

Budhi Nugraha

Keyword(s):

Real Time ◽

Data Transmission ◽

Network Connectivity ◽

Local Network ◽

Time Data ◽

Literature Study ◽

Intelligence System ◽

Real Time Data ◽

Drilling Operations ◽

Productive Time

This paper primarily is focusing on presenting the non-productive time overview and any kind of non-productive time that can be reduced by real-time data technology, real-time data transmission and visualization infrastructure which supports the processes of aggregation, transmission, and visualization; the example of multipurpose implementation and further innovation and improvements that can be made within the real-time data transmission and visualization, such as real-time reservoir footage calculation during geosteering and drill-time calculation to pick the formation tops and casing point; the challenges and limitation while using real-time data, such as VSAT and local network connectivity issue; and future target and improvement of real-time data usage especially to make an artifi cial intelligence system to predict the potential feature, such as formation or drilling problem while drilling. All of those stuff s could be found by literature study and direct professional experience while handling real-time data system. This technology will inspire the user to design their own solution for their operations. Despite the signifi cant advances on real-time data transmission and visualization, there is signifi cant room to fully use itspotential for advanced workfl ows and the usage of real-time data technology which was proven to reduce the Non-Productive Time that could save the operational cost. We believe that the utilization of real-time data transmission and visualization will defi nitely increase the effi ciency of the drilling operations, especially for multiple wells operations.

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Real-Time Analysis of Online Sources for Supporting Business Intelligence Illustrated with Bitcoin Investments and IoT Smart-Meter Sensors in Smart Cities

Electronics ◽

10.3390/electronics9071101 ◽

2020 ◽

Vol 9 (7) ◽

pp. 1101 ◽

Cited By ~ 1

Author(s):

Iván García-Magariño ◽

Moustafa M. Nasralla ◽

Shah Nazir

Keyword(s):

Data Management ◽

Real Time ◽

Business Intelligence ◽

Smart Cities ◽

Online Information ◽

Smart Meter ◽

Time Data ◽

Real Time Analysis ◽

Real Time Data ◽

Historic Data

Real-time data management analytics involve capturing data in real-time and, at the same time, processing data in a light way to provide an effective real-time support. Real-time data management analytics are key for supporting decisions of business intelligence. The proposed approach covers all these phases by (a) monitoring online information from websites with Selenium-based software and incrementally conforming a database, and (b) incrementally updating summarized information to support real-time decisions. We have illustrated this approach for the investor–company field with the particular fields of Bitcoin cryptocurrency and Internet-of-Things (IoT) smart-meter sensors in smart cities. The results of 40 simulations on historic data showed that one of the proposed investor strategies achieved 7.96% of profits on average in less than two weeks. However, these simulations and other simulations of up to 69 days showed that the benefits were highly variable in these two sets of simulations (respective standard deviations were 24.6% and 19.2%).

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