29 research outputs found
The Second International Conference on Health Information Technology Advancement
TABLE OF CONTENTS
I. Message from the Conference Co-Chairs
B. Han and S. Falan …………………………....….……………. 5
II. Message from the Transactions Editor
H. Lee …...………..………….......………….……….………….... 7
III. Referred Papers
A. Emerging Health Information Technology and Applications
The Role of Mobile Technology in Enhancing the Use of Personal Health Records
Mohamed Abouzahra and Joseph Tan………………….……………. 9
Mobile Health Information Technology and Patient Care: Methods, Themes, and Research Gaps
Bahae Samhan, Majid Dadgar, and K. D. Joshi…………..…. 18
A Balanced Perspective to Perioperative Process Management
Jim Ryan, Barbara Doster, Sandra Daily, and Carmen Lewis…..….…………… 30
The Impact of Big Data on the Healthcare Information Systems
Kuo Lane Chen and Huei Lee………….…………… 43
B. Health Care Communication, Literacy, and Patient Care Quality
Digital Illness Narratives: A New Form of Health Communication
Jofen Han and Jo Wiley…..….……..…. 47
Relationships, Caring, and Near Misses: Michael’s Story
Sharie Falan and Bernard Han……………….…..…. 53
What is Your Informatics Skills Level? -- The Reliability of an Informatics Competency Measurement Tool
Xiaomeng Sun and Sharie Falan.….….….….….….…. 61
C. Health Information Standardization and Interoperability
Standardization Needs for Effective Interoperability
Marilyn Skrocki…………………….…….………….… 76
Data Interoperability and Information Security in Healthcare
Reid Berryman, Nathan Yost, Nicholas Dunn, and Christopher Edwards.…. 84
Michigan Health Information Network (MiHIN) Shared Services vs. the HIE Shared Services in Other States
Devon O’Toole, Sean O’Toole, and Logan Steely…..……….…… 94
D. Health information Security and Regulation
A Threat Table Based Approach to Telemedicine Security
John C. Pendergrass, Karen Heart, C. Ranganathan, and V.N. Venkatakrishnan
…. 104
Managing Government Regulatory Requirements for Security and Privacy Using Existing Standard Models
Gregory Schymik and Dan Shoemaker…….…….….….… 112
Challenges of Mobile Healthcare Application Security
Alan Rea………………………….……………. 118
E. Healthcare Management and Administration
Analytical Methods for Planning and Scheduling Daily Work in Inpatient Care Settings:
Opportunities for Research and Practice
Laila Cure….….……………..….….….….… 121
Predictive Modeling in Post-reform Marketplace
Wu-Chyuan Gau, Andrew France, Maria E. Moutinho, Carl D. Smith, and Morgan C. Wang…………...…. 131
A Study on Generic Prescription Substitution Policy as a Cost Containment Approach for Michigan’s Medicaid System
Khandaker Nayeemul Islam…….…...……...………………….… 140
F. Health Information Technology Quality Assessment and Medical Service Delivery
Theoretical, Methodological and Practical Challenges in Designing Formative Evaluations of Personal eHealth Tools
Michael S. Dohan and Joseph Tan……………….……. 150
The Principles of Good Health Care in the U.S. in the 2010s
Andrew Targowski…………………….……. 161
Health Information Technology in American Medicine: A Historical Perspective
Kenneth A. Fisher………………….……. 171
G. Health Information Technology and Medical Practice
Monitoring and Assisting Maternity-Infant Care in Rural Areas (MAMICare)
Juan C. Lavariega, Gustavo Córdova, Lorena G Gómez, Alfonso Avila….… 175
An Empirical Study of Home Healthcare Robots Adoption Using the UTUAT Model
Ahmad Alaiad, Lina Zhou, and Gunes Koru.…………………….….………. 185
HDQM2: Healthcare Data Quality Maturity Model
Javier Mauricio Pinto-Valverde, Miguel Ángel Pérez-Guardado, Lorena Gomez-Martinez, Martha Corrales-Estrada, and Juan Carlos Lavariega-Jarquín.… 199
IV. A List of Reviewers …………………………..…….………………………208
V. WMU – IT Forum 2014 Call for Papers …..…….…………………20
USING ARTIFICIAL INTELLIGENCE TO IMPROVE HEALTHCARE QUALITY AND EFFICIENCY
In recent years, artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has represented one of the most exciting advances in science. The performance of ML-based AI in many areas, such as computer vision, voice recognition, and natural language processing has improved dramatically, offering unprecedented opportunities for application in a variety of different domains. In the critical domain of healthcare, great potential exists for a broader application of ML to improve quality and efficiency. At the same time, there are substantial challenges in the development and implementation of AI in healthcare.
This dissertation aims to study the application of state-of-the-art AI technologies in healthcare, ranging from original method development to model interpretation and real-world implementation. First, a novel DL-based method is developed to efficiently analyze the rich and complex electronic health record data. This DL-based approach shows promise in facilitating the analysis of real-world data and can complement clinical knowledge by revealing deeper insights. Both knowledge discovery and performance of predictive models are demonstrably boosted by this method.
Second, a recurrent neural network (named LSTM-DL) is developed and shown to outperform all existing methods in addressing an important real-world question, patient cost prediction. A series of novel analyses is used to derive a deeper understanding of deep learning’s advantages. The LSTM-DL model consistently outperforms other models with nearly the same level of advantages across different subgroups. Interestingly, the advantage of the LSTM-DL is significantly driven by the amount of fluctuation in the sequential data. By opening the “black box,” the parameters learned during the training period are examined, and is it demonstrated that LSTM-DL’s ability to react to high fluctuation is gained during the training rather than inherited from its special architecture. LSTM-DL can also learn to be less sensitive to fluctuations if the fluctuation is not playing an important role.
Finally, the implementation of ML models in real practice is studied. Since at its current stage of development, ML-based AI will most likely assistant human workers rather than replace them, it is critical to understand how human workers collaborate with AI. An AI tool was developed in collaboration with a medical coding company, and successfully implemented in the real work environment. The impact of this tool on worker performance is examined. Findings show that use of AI can significantly boost the work productivity of human coders. The heterogeneity of AI’s effects is further investigated, and results show that the human circadian rhythm and coder seniority are both significant factors in conditioning productivity gains. One interesting finding regarding heterogeneity is that the AI has its best effects when a coder is at her/his peak of performance (as opposed to other times), which supports the theory of human-AI complementarity. However, this theory does not necessarily hold true across different coders. While it could be assumed that senior coders would benefit more from the AI, junior coders’ productivity is found to improve more. A further qualitative study uncovers the underlying mechanism driving this interesting effect: senior coders express strong resistance to AI, and their low trust in AI significantly hinders them from realizing the AI’s value