scholarly journals Doctor at Your Fingertips: An Exploration of Digital Visits from Stakeholders’ Perspectives

Life ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Selina Demi ◽  
Samir Hilmy ◽  
Christina Keller
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Empirical Data ◽  
Mobile Technologies ◽  
Structured Interviews ◽  
Time Data ◽  
Technological Advances ◽  
Real Time Data

Technological advances and the evolution of mobile technologies enable patients to meet their doctors through their smartphones. While offering the opportunity of digital visits to patients, there are also challenges for this development. The purpose of this study is to enhance the understanding of digital visits, as perceived by experts working in telemedicine companies. To serve this purpose, the authors conducted semi-structured interviews with managers and employees in eight telemedicine companies. The analysis of the empirical data confirmed the importance of digital visits and their efficiency. The potential of digital visits is expected to increase significantly, should they make use of remote devices to transfer real-time data from patients to physicians. In such a case, digital visits are expected to cover approximately 70–75% of medical cases. However, the use of remote devices must be taken with caution and specific conditions need to be taken into account. We encourage researchers to perform research on promising technologies such as artificial intelligence and remote diagnostic devices, which could make more diagnoses and conditions possible to be treated by digital visits. This is even more important in light of the ongoing Covid-19 pandemic.

A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
Nagaraju Reddicharla ◽  
Subba Ramarao Rachapudi ◽  
Indra Utama ◽  
Furqan Ahmed Khan ◽  
Prabhker Reddy Vanam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Test Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Real Time Data ◽  
Well Tests

Abstract Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

scholarly journals Artificial Intelligence assisted Prediction of COVID-19 Hotspots in Third Wave using EHTERAZ

2021 ◽  
Author(s):  
Mustafa Ahmed ◽  
Mohammed Houkan ◽  
Kishor Kumar Sadasivuni
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Mobile Technology ◽  
Hot Spots ◽  
Mobile Application ◽  
Local State ◽  
Third Wave ◽  
Time Data ◽  
Policy Makers ◽  
Real Time Data

The present study conceptualizes fusion of artificial intelligence and contract tracking mobile application for predicting the COVID-19 hot spots. The findings suggest that mobile technology can be used to provide real-time data on the national and local state of the pandemic, enabling policy makers to make informed decisions in a quickly moving pandemic.

scholarly journals Mobile technologies for disease surveillance in humans and animals

2014 ◽  
Vol 81 (2) ◽  
Author(s):  
Mpoki Mwabukusi ◽  
Esron D. Karimuribo ◽  
Mark M. Rweyemamu ◽  
Eric Beda
Keyword(s):  
Developing Countries ◽  
Human Health ◽  
Real Time ◽  
Disease Surveillance ◽  
Open Data ◽  
Weather Conditions ◽  
Mobile Technologies ◽  
Surveillance Systems ◽  
Time Data ◽  
Real Time Data

A paper-based disease reporting system has been associated with a number of challenges. These include difficulties to submit hard copies of the disease surveillance forms because of poor road infrastructure, weather conditions or challenging terrain, particularly in the developing countries. The system demands re-entry of the data at data processing and analysis points, thus making it prone to introduction of errors during this process. All these challenges contribute to delayed acquisition, processing and response to disease events occurring in remote hard to reach areas. Our study piloted the use of mobile phones in order to transmit near to real-time data from remote districts in Tanzania (Ngorongoro and Ngara), Burundi (Muyinga) and Zambia (Kazungula and Sesheke). Two technologies namely, digital and short messaging services were used to capture and transmit disease event data in the animal and human health sectors in the study areas based on a server–client model. Smart phones running the Android operating system (minimum required version: Android 1.6), and which supported open source application, Epicollect, as well as the Open Data Kit application, were used in the study. These phones allowed collection of geo-tagged data, with the opportunity of including static and moving images related to disease events. The project supported routine disease surveillance systems in the ministries responsible for animal and human health in Burundi, Tanzania and Zambia, as well as data collection for researchers at the Sokoine University of Agriculture, Tanzania. During the project implementation period between 2011 and 2013, a total number of 1651 diseases event-related forms were submitted, which allowed reporters to include GPS coordinates and photographs related to the events captured. It was concluded that the new technology-based surveillance system is useful in providing near to real-time data, with potential for enhancing timely response in rural remote areas of Africa. We recommended adoption of the proven technologies to improve disease surveillance, particularly in the developing countries.

The Twenty-Third-Century Solution

2021 ◽  
pp. 119-144
Author(s):  
David A. Patterson Silver Wolf
Keyword(s):  
Substance Use ◽  
Real Time ◽  
Substance Use Disorders ◽  
Addiction Treatment ◽  
Healthcare Systems ◽  
Mobile Technologies ◽  
Third Century ◽  
Time Data ◽  
Face To Face ◽  
Real Time Data

In light of the issues identified and problems discussed in the preceding chapters, a new addiction treatment is proposed. The COVID-19 pandemic has vividly illustrated how a crisis can lead to disruption and change, including the new and creative use of mobile technologies to bring and keep people together. Using telecom and other Internet-based technologies throughout healthcare systems was once thought to be in our far-off future. However, in response to this pandemic, with little preparation or planning, we have quickly transformed many face-to-face services into virtual/remote ones. These innovations can be leveraged to bring real-time data and better outcomes to the treatment of substance use disorders.

scholarly journals Enhancing Diabetes Self-Management Through Collection and Visualization of Data From Multiple Mobile Health Technologies: Protocol for a Development and Feasibility Trial (Preprint)

2019 ◽  
Author(s):  
Ryan J Shaw ◽  
Angel Barnes ◽  
Dori Steinberg ◽  
Jacqueline Vaughn ◽  
Anna Diane ◽  
...  
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Management Strategies ◽  
Mobile Technologies ◽  
Feasibility Trial ◽  
Self Management ◽  
Time Data ◽  
Body Scale ◽  
Level Data ◽  
Real Time Data

BACKGROUND Self-management is integral for control of type 2 diabetes mellitus (T2DM). Patient self-management is improved when they receive real-time information on their health status and behaviors and ongoing facilitation from health professionals. However, timely information for these behaviors is notably absent in the health care system. Providing real-time data could help improve patient understanding of the dynamics of their illness and assist clinicians in developing targeted approaches to improve health outcomes and in delivering personalized care when and where it is most needed. Mobile technologies (eg, wearables, apps, and connected scales) have the potential to make these patient-provider interactions a reality. What strategies might best help patients overcome self-management challenges using self-generated diabetes-related data? How might clinicians effectively guide patient self-management with the advantage of real-time data? OBJECTIVE This study aims to describe the protocol for an ongoing study (June 2016-May 2019) that examines trajectories of symptoms, health behaviors, and associated challenges among individuals with T2DM utilizing multiple mobile technologies, including a wireless body scale, wireless glucometer, and a wrist-worn accelerometer over a 6-month period. METHODS We are conducting an explanatory sequential mixed methods study of 60 patients with T2DM recruited from a primary care clinic. Patients were asked to track relevant clinical data for 6 months using a wireless body scale, wireless glucometer, a wrist-worn accelerometer, and a medication adherence text message (short message service, SMS) survey. Data generated from the devices were then analyzed and visualized. A subset of patients is currently being interviewed to discuss their challenges and successes in diabetes self-management, and they are being shown visualizations of their own data. Following the data collection period, we will conduct interviews with study clinicians to explore ways in which they might collaborate with patients. RESULTS This study has received regulatory approval. Patient enrollment ongoing with a sample size of 60 patients is complete, and up to 20 clinicians will be enrolled. At the patient level, data collection is complete, but data analysis is pending. At the clinician level, data collection is currently ongoing. CONCLUSIONS This study seeks to expand the use of mobile technologies to generate real-time data to enhance self-management strategies. It also seeks to obtain both patient and provider perspectives on using real-time data to develop algorithms for software that will facilitate real-time self-management strategies. We expect that the findings of this study will offer important insight into how to support patients and providers using real-time data to manage a complex chronic illness. INTERNATIONAL REGISTERED REPOR DERR1-10.2196/13517

Integrated E-Marketing-A Strategy-Driven Technical Analysis Framework

2011 ◽  
pp. 317-327
Author(s):  
Simpson Poon ◽  
Irfan Altas ◽  
Geoff Fellows
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Empirical Data ◽  
Integrated Approach ◽  
Strategy Formulation ◽  
Time Data ◽  
Data Mining Techniques ◽  
Marketing Campaign ◽  
Potential Problems ◽  
Real Time Data

E-marketing is considered to be one of the key applications in e-business but so far there has been no sure-fire formula for success. One of the problems is that although we can gather visitor information through behaviours online (e.g., cookies and Weblogs), often there is not an integrated approach to link up strategy formulation with empirical data. In this chapter, we propose a framework that addresses the issue of real-time objective-driven e-marketing. We present approaches that combine real-time data packet analysis integrated with data mining techniques to create a responsive e-marketing campaign. Finally, we discuss some of the potential problems facing e-marketers in the future.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data
Sign in / Sign up

Export Citation Format

Share Document