scholarly journals Measuring outcomes in healthcare economics using Artificial Intelligence: With application to resource management

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Chih-Hao Huang ◽  
Feras A. Batarseh ◽  
Adel Boueiz ◽  
Ajay Kulkarni ◽  
Po-Hsuan Su ◽  
...  
Keyword(s):  
Decision Making ◽  
Traveling Salesman ◽  
Data Driven ◽  
Resources Allocation ◽  
Healthcare Economics ◽  
Economic Aspects ◽  
Internal Factors ◽  
Measuring Outcomes ◽  
Optimum Plan

Abstract The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e., Covid-19) and natural disasters (such as hurricanes and earthquakes). In most cases, such events lead to critical uncertainties in decision-making, as well as in multiple medical and economic aspects at a hospital. External (geographic) or internal factors (medical and managerial) lead to shifts in planning and budgeting, but most importantly, reduce confidence in conventional processes. In some cases, support from other hospitals proves necessary, which exacerbates the planning aspect. This paper presents three data-driven methods that provide data-driven indicators to help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing. Conventional decision-making methods fall short in recommending validated policies for managers. Using reinforcement learning, genetic algorithms, traveling salesman, and clustering, we experimented with different healthcare variables and presented tools and outcomes that could be applied at health institutes. Experiments are performed; the results are recorded, evaluated, and presented.

scholarly journals Measuring Outcomes in Healthcare Economics using Artificial Intelligence: with Application to Resource Allocation

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Feras A. Batarseh ◽  
Chih-Hao Huang
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Traveling Salesman ◽  
Healthcare Economics ◽  
Economic Aspects ◽  
Internal Factors ◽  
Measuring Outcomes ◽  
Optimum Plan ◽  
Intelligent Methods

The quality of service in healthcare is constantly challengedby outlier events such as pandemics and naturaldisasters. In most cases, such events lead to critical uncertaintiesin decision making, as well as in multiple medicaland economic aspects of a hospital. External (geographical)or internal factors (medical and managerial) at hospitals,lead to shifts in planning, budgeting, and confidencein conventional processes. In some cases, support fromother hospitals becomes inevitable. This manuscript presentsthree intelligent methods that provide data-drivenindicators to help healthcare managers organize their economicsand identify the most optimum plan for resourceallocation and sharing. Using reinforcement learning, geneticalgorithms, traveling salesman, and clustering, weexperimented with different healthcare variables and presentedtools and outcomes that could be applied at healthinstitutes. In this poster, initial experiments are performed;the results are recorded, evaluated, and illustrated.

scholarly journals Implementing Big Data Analytics in Marketing Departments: Mixing Organic and Administered Approaches to Increase Data-Driven Decision Making

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 66
Author(s):  
Devon S. Johnson ◽  
Debika Sihi ◽  
Laurent Muzellec
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Survey Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Top Management ◽  
Data Driven Decision Making ◽  
Marketing Department

This study examines the experience of marketing departments to become fully data-driven decision-making organizations. We evaluate an organic approach of departmental sensemaking and an administered approach by which top management increase the influence of analytics skilled employees. Data collection commenced with 15 depth interviews of marketing and analytics professionals in the US and Europe involved in the implementation of big data analytics (BDA) and was followed by a survey data of 298 marketing and analytics middle management professionals at United States based firms. The survey data supports the logic that BDA sensemaking is initiated by top management and is comprised of four primary activities: external knowledge acquisition, improving digitized data quality, big data analytics experimentation and big data analytics information dissemination. Top management drives progress toward data-driven decision-making by facilitating sensemaking and by increasing the influence of BDA skilled employees. This study suggests that while a shift toward enterprise analytics increases the quality of resource available to the marketing department, this approach could stymie the quality of marketing insights gained from BDA. This study presents a model of how to improve the quality of marketing insights and improve data-driven decision-making.

scholarly journals A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

2021 ◽  
Vol 11 (15) ◽  
pp. 6967
Author(s):  
Marco Cipriano ◽  
Luca Colomba ◽  
Paolo Garza
Keyword(s):  
Decision Making ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Real Data ◽  
Frequent Pattern ◽  
Data Driven ◽  
System Usability ◽  
Bike Sharing ◽  
Data Driven Approach

Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist two different categories of bike sharing systems: station-based systems and free-floating services. In this paper, we concentrate our analyses on station-based systems. Such systems require periodic rebalancing operations to guarantee good quality of service and system usability by moving bicycles from full stations to empty stations. In particular, in this paper, we propose a dynamic bicycle rebalancing methodology based on frequent pattern mining and its implementation. The extracted patterns represent frequent unbalanced situations among nearby stations. They are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. Experiments performed on real data of the Barcelona bike sharing system show the effectiveness of the proposed approach.

Evaluation of the Academic Setting in Spain 1This study was financed by the University of Oviedo.

1995 ◽  
Vol 11 (2) ◽  
pp. 133-137 ◽  
Author(s):  
Juan Fernández ◽  
Miguel A. Mateo ◽  
José Muñiz
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Social Climate ◽  
Three Dimensions ◽  
Academic Setting ◽  
Teaching And Research ◽  
Evaluation Questionnaire ◽  
University Teachers ◽  
The University

The conditions are investigated in which Spanish university teachers carry out their teaching and research functions. 655 teachers from the University of Oviedo took part in this study by completing the Academic Setting Evaluation Questionnaire (ASEQ). Of the three dimensions assessed in the ASEQ, Satisfaction received the lowest ratings, Social Climate was rated higher, and Relations with students was rated the highest. These results are similar to those found in two studies carried out in the academic years 1986/87 and 1989/90. Their relevance for higher education is twofold because these data can be used as a complement of those obtained by means of students' opinions, and the crossing of both types of data can facilitate decision making in order to improve the quality of the work (teaching and research) of the university institutions.

Kegunaan Informasi Akuntansi dalam Pengambilan Keputusan Internal oleh Unit Akuntansi Kuasa Pengguna Anggaran (UAKPA)

2017 ◽  
Vol 2 (4) ◽  
pp. 73-91
Author(s):  
Karno Pandu Wibowo
Keyword(s):  
Decision Making ◽  
Accounting Information ◽  
Information Use ◽  
External Factors ◽  
Accrual Accounting ◽  
Related Factors ◽  
Internal Factors ◽  
Level Of Use ◽  
Accrual Basis ◽  
Cash Basis

This study aims to determine the usefulness level of accounting information (on cash basis and accrual basis) in central goverment agencies internal decision-making process. In addition, this study also aims to determine the influence of the organization’s external factors, the organization's internal factors and Individual Actor’s Characteristic Related Factors on the level of use of accounting information in central goverment agencies internal decision making.The research show that  level of accrual accounting information use in the context of internal decision making is high. In addition it showed differences between  level of cash-based accounting information use and accrual-based accounting information use. This study also addressed that the organization’s external factors, the organization's internal factors and individual actor’s characteristic related factors significantly influence both level of accounting information use  on cash basis and accrual basis in the internal decision-making. Except for the organization’s external factors  did not significantly affect the level of accrual accounting information use  in the internal decision-making.   Abstrak Penelitian ini bertujuan untuk mengetahui tingkat kegunaan informasi akuntansi (berbasis kas dan akrual) dalam proses pengambilan keputusan internal Unit Akuntansi Kuasa Pengguna Anggaran (UAKPA). Selain itu penelitian ini juga bertujuan untuk mengetahui pengaruh faktor eksternal organisasi, faktor internal organisasi dan faktor karakteristik individu pengguna terhadap tingkat penggunaan informasi akuntansi dalam pengambilan keputusan internal Unit Akuntansi Kuasa Pengguna Anggaran (UAKPA).Temuan peneliti menunjukan bahwa tingkat penggunaan informasi akuntansi berbasis akrual tinggi dalam rangka pengambilan keputusan internal. Selain itu penelitian menunjukan adanya perbedaan tingkat penggunaan antara informasi akutansi berbasis kas dan informasi akutansi berbasis akrual. Penelitian juga menujukan bahwa faktor eksternal organisasi, faktor internal organisasi dan faktor karakteristik individu pengguna berpengaruh signifikan terhadap tingkat penggunaan informasi akuntansi (berbasis kas dan akrual) dalam pengambilan keputusan internal. Kecuali untuk faktor eksternal organisasi tidak berpengaruh signifikan terhadap tingkat penggunaan informasi akuntansi akrual dalam pengambilan keputusan internal.

Why Is Safe Science Good Science?

2018 ◽  
Author(s):  
Camilla Kao ◽  
Che-I Kao ◽  
Russell Furr
Keyword(s):  
Decision Making ◽  
Design Thinking ◽  
Good Science ◽  
Safety Issues ◽  
Safety Requirements ◽  
The Arts ◽  
Hands On ◽  
Obvious Benefit ◽  
New Ideas

In science, safety can seem unfashionable. Satisfying safety requirements can slow the pace of research, make it cumbersome, or cost significant amounts of money. The logic of rules can seem unclear. Compliance can feel like a negative incentive. So besides the obvious benefit that safety keeps one safe, why do some scientists preach "safe science is good science"? Understanding the principles that underlie this maxim might help to create a strong positive incentive to incorporate safety into the pursuit of groundbreaking science.<div><br></div><div>This essay explains how safety can enhance the quality of an experiment and promote innovation in one's research. Being safe induces a researcher to have <b>greater control</b> over an experiment, which reduces the <b>uncertainty</b> that characterizes the experiment. Less uncertainty increases both <b>safety</b> and the <b>quality</b> of the experiment, the latter including <b>statistical quality</b> (reproducibility, sensitivity, etc.) and <b>countless other properties</b> (yield, purity, cost, etc.). Like prototyping in design thinking and working under the constraint of creative limitation in the arts, <b>considering safety issues</b> is a hands-on activity that involves <b>decision-making</b>. Making decisions leads to new ideas, which spawns <b>innovation</b>.</div>

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