Journal of Health & Medical Informatics

This study addresses Brain-Computer Interface (BCI) systems meant to permit communication for those who are severely locked-in. The current study attempts to evaluate and compare the efficiency of different translating algorithms. The setup used in this study detects the elicited P300 evoked potential in response to six different stimuli. Performance is evaluated in terms of error rates, bit-rates and runtimes for four different translating algorithms; Bayesian Linear Disciminant Analysis (BLDA), Linear Discriminant Analysis (LDA), Perceptron Batch (PB), and nonlinear Support Vector Machines (SVMs) were used to train the classifier whilst an N-fold cross validation procedure was used to test each algorithm. A communication channel based on Electroencephalography (EEG) is made possible using various machine learning algorithms and advanced pattern recognition techniques. All algorithms converged to 100% accuracy for seven of the eight subjects. While all methods obtained fairly good results, BLDA and PB were superior in terms of runtimes, where the average runtimes for BLDA and PB were 13 ± 2 and 15.6 ± 6 seconds, respectively. In terms of bit-rates, BLDA obtained the highest average value (22 ± 12 bits/minute), where the average bit-rate for all subjects, all sessions, and all algorithms was 18.76 ± 10 bits/minute.

The purpose of this research was to develop a Health Learning Center (HLC) System for provincial hospitals in
Thailand. The methodology of this study using R&D processes, consisted of four steps: (1) studying the factors relating
to the states and problems of existing HLC system, (2) developing the HLC system prototype from obtained information
and validating the prototype, (3) implementing the HLC system prototype in three provincial hospitals, and (4) certifying
the HLC system and proposing the final HLC system. Qualitative instruments included a reflection log, a semi-structure
interview guide and an observation guide.
The research findings revealed that the HLC system for provincial hospitals in Thailand comprised four main
systems as follow: (1) HLC Establishing System, (2) HLC Planning System, (3) HLC Operating System: including six
sub-systems: (3.1) knowledge corner development sub-system, (3.2) health learning activity sub-system, (3.3) health
education and behaviors network supporting sub-system, (3.4) production and procurement of educational media subsystem,
(3.5) health knowledge database sub-system and (3.6) health information management sub-system, and (4)
HLC Evaluation System.
This HLC System was developed as management tools for staff at all levels in order to develop on health learning
center for their clients and the general public at risk of various diseases through individual and group active learning.
Replication of this HLC system can be modified to be implemented in provincial hospitals. In addition, this system was
a model of HLC System of hospitals in the district level throughout Thailand as well.

Primary health care in Canada has entered a period of transformational change with a key initiative including support for implementation of electronic medical records. Lack of engagement by end-users has been found to bethe key factor contributing to failed implementations of EMR in the healthcare setting. Change models have been found to be effective tools to bring about organizational transformations. In this study, Kotter’s 8 Step Process for Leading Change is implemented to digitally transform a community-based three-surgeon orthopaedic surgical practice in Toronto, Canada. Having identified the residual paper-based operational tasks employed by the partner surgeons and staff, and having implemented the digital alternative for these tasks through the use of a comprehensive EMR computer program, Kotter’s 8-Step Process for Leading Change is successfully applied to the EMR adoption process leading to digital transformation above a 95% threshold.

Aim: To clarify the developing mechanism of regular uterine contractions in the labor.
Methods: The similarity of normal regular labor contractions of the uterus to the electrical oscillation was studied.
The electrical oscillation technique was adapted to regular labor contraction of the uterus, where innervations between
the uterus and brain were reported in animals in four papers.
Results: Interval between peaks of contractions was 2 min and the amniotic pressure was 40 mmHg in typical labor
contractions. Labor contractions were repeated from the onset of labor until the delivery of fetus. Attachment of reverse
contraction curve to normal contraction curves formed sine wave-like repetition, similar closely to electrical oscillation
waves. A positive feed-back was estimated from the uterus to the amplifying input
Comments: A biological amplifier is the hypothalamic center-hypophysis-oxitocin secretion, its output is uterine
labor contraction, and its positive feed-back loop to the amplifier input is the innervations between the uterus and
hypothalamus, of which presence was reported in animals. The biological positive feed-back loop produces oscillationlike
regular uterine contractions, of which wave length is 2 min, frequency 0.008 Hz and amplitude 40 mmHg.
Conclusion: Regular stable labor contractions of uterus is formed in the oscillation by the positive feed-back system
composed of hypothalamus-hypophysis-oxitocin secretion, uterine contractions and uterus-brain innervations.

Objectives: We examine the emerging health analytics field by describing the different health analytics and
providing examples of various applications.
Methods: The paper discusses different definitions of health analytics, describes the four stages of health analytics,
its architectural framework, development methodology, and examples in public health.
Results: The paper provides a broad overview of health analytics for researchers and practitioners.
Conclusions: Health analytics is rapidly emerging as a key and distinct application of health information technology.
The key objective of health analytics is to gain insight for making informed healthcare decisions.