INFORMATION SYSTEM CONTEXTUAL DATA QUALITY: A CASE STUDY

This dissertation describes a case study comparing the effectiveness of twoinformation systems that assess the quality of surgical care, the National SurgicalQuality Improvement Program (NSQIP) and the University HealthSystemConsortium Clinical Database (UHCCD). For the comparison, it develops aframework for assessing contextual data quality (CDQ) from the decision maker\u27sperspective. The differences in quality assessment systems to be studied areposited to be due to the differing contexts in which the data is encoded,transformed and managed impacting data quality for the purpose of surgicalquality assessment.Healthcare spending in the United States has risen faster than the rate of inflationfor over a decade and currently stands at about fifteen percent of the GrossDomestic Product. This has brought enormous pressures on the healthcareindustry to reduce costs while maintaining or improving quality. Numeroussystems to measure healthcare quality have been, and are being, developedincluding the two being studied. A more precise understanding of the differencesbetween these two systems\u27 effectiveness in the assessment of surgical healthcarequality informs decisions nationally regarding hospital accreditation and qualitybasedreimbursements to hospitals.The CDQ framework elaborated is also applicable to executive informationsystems, data warehouses, web portals, and other information systems that drawinformation from disparate systems. Decision makers are more frequently havingdata available from across functional and hierarchical areas within organizationsand data quality issues have been identified in these systems unrelated to thesystem performance from which the data comes.The propositions explored and substantiated here are that workgroup contextinfluences data selection and definition, the data entry and encoding process,managerial control and feedback, and data transformation in information systems.These processes in turn influence contextual data quality relative to a particulardecision model.The study is a cross-sectional retrospective review of archival quality datagathered on 26,322 surgical patients at the University of Kentucky Hospital alongwith interviews of process owners in each system. The quality data includepatient risk/severity factors and outcome data recorded in the National SurgeryQuality Improvement Program (NSQIP) database and the UniversityHealthSystem Consortium Clinical Database (UHCCD)

Empowering Graph Representation Learning with Test-Time Graph Transformation

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems. Nevertheless, the effectiveness of GNNs is immensely challenged by issues related to data quality, such as distribution shift, abnormal features and adversarial attacks. Recent efforts have been made on tackling these issues from a modeling perspective which requires additional cost of changing model architectures or re-training model parameters. In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance. We provide theoretical analysis on the design of the framework and discuss why adapting graph data works better than adapting the model. Extensive experiments have demonstrated the effectiveness of GTrans on three distinct scenarios for eight benchmark datasets where suboptimal data is presented. Remarkably, GTrans performs the best in most cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on three experimental settings

Assessing human resource needs for digital transformation at enterprises and proposing solutions in human resource training for universities

Transforming the educational model for higher education in the context of digital transformation is an inevitable trend. This represents the right direction to take advantage of the 4.0 technology revolution, creating high-quality human resources to meet the requirements of the labor market. The purpose of the study is to analyze the appropriate higher education model in the context of the digital economy towards the development of high-quality human resources to participate in the operation of the digital economy. The requirements for the development of digital universities for countries in general and Vietnam in particular need to constantly innovate and reform the national education systems to further improve the quality and effectiveness of higher education, towards an education that adapts to the context of digital transformation and the covid 19 epidemic. The issue of the quality of human resources must be concerned by education systems to make breakthrough changes and should be prioritized by countries. The article uses qualitative, quantitative, comparative, survey survey and experimental data analysis methods. The survey was conducted on 100 businesses that have cooperation activities with the university through online form via email. The main results of the study provide solutions for adjusting the training methods and models at universities in Vietnam in response to the 4.0 technology revolution and the covid 19 epidemic

Analisis dan Perancangan Interoperabilitas Data Pemonitoran SPM (Standar Pelayanan Minimal) Bidang Kesehatan dengan Web Services

System  interoperability  is  a  key  factor  in  the  transformation  of  the  minimum  service  standards  (SPM)  reporting  system  in  the  health  sector.  This  article  presents  an  in-depth  analysis  to  design  an  Application  Programming  Interface  (API)  model  that  aims  to  increase  the  effectiveness  of  sending  and  reporting  SPM  data  from  primary  health  facilities  under  the  district  health  office.  Through  analytical  studies,  we  identified  system  interoperability  needs  at  the  district  health  department  level,  including  aspects  such  as  electronic  medical  record  (EMR)  data  formats,  minimum  service  standards,  and  patient  identification  systems.  One  of  the  main  challenges  is  the  diversity  of  data  formats  and  sources  that  must  be  integrated. Based  on  this  analysis,  we  designed  an  API  specifically  designed  to  facilitate  the  exchange  of  important  data  related  to  reporting  minimum  service  standards  in  the  health  sector.  The  resulting  API  follows  the  principles  of  RESTful  architecture,  prioritizing  scalability,  flexibility,  and  security.  API  specifications  include  nationally  recognized  data  standards  for  health  reporting  as  well  as  stringent  authentication  and  authorization  systems  to  protect  sensitive  data.  Initial  implementation  and  testing  results  show  that  the  proposed  API  successfully  connects  diverse  health  reporting  systems  with  high  effectiveness.  Evaluation  of  API  performance  through  measuring  response  time  and  resource  usage  indicates  adequate  performance  for  use  in  a  production  environment.  Through  the  design  of  this  API,  it  is  hoped  that  it  can  increase  interoperability  between  minimum  service  standard  reporting  systems  in  the  health  sector,  reduce  data  duplication,  and  speed  up  the  reporting  process. The  conclusions  of  this  study  underscore  the  important  role  of  APIs  in  supporting  healthcare  quality,  data-driven  decision-making,  and  more  efficient  integration  of  medical  systems. 

Urgensi Variabel Kinerja Organisasi dan Faktor-Faktor Penentunya Pada Manajemen Pendidikan Tinggi

Organizational performance variable is an important variable in management studies including education management. Assessment or measurement of organizational performance aims to provide an overview of how the process of achieving organizational goals and quality is realized. This variable has occupied an important position in every study and institutional (management) research. Many researchers are interested in assessing organizational performance from various perspectives. The impact can be seen in the variations of the use of dimensions and indicators in exploring research data. Dimensions such as; effectiveness-efficiency, focus on processes, structural transformation, teamwork and strategy are dimensions often used by researchers in assessing organizational performance. Organizational performance is not an independent variable, but is related to or influenced by other organizational variables such as; leadership, management systems, commitment and culture

Realigning Resources for District Transformation: Using American Recovery and Reinvestment Act Funds to Advance a Strategic Education Reform Agenda

Offers ideas for spending stimulus funds strategically to align and restructure districts' use of resources to improve student performance by assessing current practices, focusing on support for quality instruction, and making transitional investments

Evaluation of strategic information systems planning (SISP) techniques: Driver perspective

Strategic Information Systems Planning (SISP) literature reviews with a focus on the global dimension are considered in this research. The paper counters the evaluation of SISP techniques through information system (IS) strategic drivers. These techniques can be vital contributors in the IS strategy (ISS) designing process. Therefore, categorisation of the techniques of ISS planning will be developed. Keeping in mind the global dimension, the planning team needs to identify how it can cluster an organization’s ISS drivers. This may be achieved by analysing the drivers that can have an effect on IS for the organization, which may support categorisation of drivers against techniques being classified to understand which are needed to fit specific drivers. The contribution of this research is the taxonomy of SISP techniques, with a case study for X international airlines. This classification can benefit evaluation of the ISS planning processes to support decision-makers through the planning process

Business Process Redesign in the Perioperative Process: A Case Perspective for Digital Transformation

This case study investigates business process redesign within the perioperative process as a method to achieve digital transformation. Specific perioperative sub-processes are targeted for re-design and digitalization, which yield improvement. Based on a 184-month longitudinal study of a large 1,157 registered-bed academic medical center, the observed effects are viewed through a lens of information technology (IT) impact on core capabilities and core strategy to yield a digital transformation framework that supports patient-centric improvement across perioperative sub-processes. This research identifies existing limitations, potential capabilities, and subsequent contextual understanding to minimize perioperative process complexity, target opportunity for improvement, and ultimately yield improved capabilities. Dynamic technological activities of analysis, evaluation, and synthesis applied to specific perioperative patient-centric data collected within integrated hospital information systems yield the organizational resource for process management and control. Conclusions include theoretical and practical implications as well as study limitations