Journal of Health & Medical Informatics

Sharing genomic is important for healthcare but privacy must be protected because links between de-identified genomic data and named persons can be re-established by users with malicious intents. In this paper, a game theoretic approach is developed for quantifiable protections of genomic data sharing. This approach accounts for adversarial behavior and capabilities. The game model is developed to discover the best solution for sharing genomic summary statistics under an economically driven recipient’s (adversary) inference attack based on a Stackelberg game. The inference attack checks if a targeted DNA is in a genome pool with published summary statistics (that is, minor allele frequency of Single Nucleic Polymorphisms).

Objective: The purpose of this paper is to review the PubMed/MEDLINE literature for articles that discuss the use of machine learning (ML) and deep learning (DL) for clinical decision support systems (CDSSs).

Materials and Methods: To identify relevant articles, we searched PubMed/MEDLINE through December 2nd, 2017. We identified a total of 283 studies.

Results: The number of ML and DL associated CDSS articles increased significantly beginning around 2010. The most common type of advanced artificial intelligence (AI) methodologies that the articles evaluated was neural networks also known as DL (n=109) followed by ML (n=86). The most common types of ML algorithm were support vector machines (n=78), logistic regression analysis (n=38), random forest (n=26), decision tree (n=25), and k-nearest neighbour (n=21). Cardiology, oncology, radiology, surgery, and critical care/ED were the most commonly represented specialties. Only 19 out of 283 (6.7%) ML and DL associated CDSS articles reported an effect on the process of care or patient outcomes.

Discussion: The current decade has seen research efforts and attention increase significantly in creating CDSS tools with the advanced AI methodologies of DL and ML. Although the research experiments demonstrate success, the scope of AI technology is still limited to a well-defined task. Also, most of these studies lack patient-oriented outcomes necessary to justify its widespread application in healthcare.

Conclusion: There is a clear upwards trend in ML and DL research in healthcare. However, in order to effectively translate successful AI research into the patient care, more clinically-relevant studies must be pursued.

Background: Laboratory information maximizes effective delivery of care by allowing physicians and other providers to make appropriate diagnostic and therapeutic decisions. Studies exploring the Electronic Health Record (EHR) interconnection with the laboratory information system (LIS) through data processing, reviewing, and sharing capabilities among ambulatory care providers are scarce.

Aim: The aim of this study was to explore the use of electronic laboratory services through the EHR-LIS interconnectivity to access patient laboratory data in meaningful way. This study was further used to evaluate the relationship between practice characteristics and meaningful usage of laboratory functionalities.

Method: Using a nationally representative sample of 44,296 physician responses from National Ambulatory Medical Care Survey (NAMC) data, this study used descriptive statistics to first determine the level of meaningful usage of the EHR-LIS functionalities among ambulatory care physicians. Logistic regression was then used to assess potential effects of factors, such as physician specialty, practice type, practice geographical region, and ownership status on usage of the EHR-LIS functions.

Results: More than two-third of physicians used the EHR-LIS meaningfully. The strongest positive associations (OR=2.64 and 2.42) were found between practice type (solo, non-solo) and electronic reviewing and sharing of laboratory test results with ambulatory physicians in practice group. On the other hand, practice region and ownership status negatively influenced (OR=0.79, 0.94, and 0.77) the electronic sharing of tests results with physicians outside practice groups.

Conclusion: Practice and physician characteristics can significantly affect physician usage of laboratory functionalities.

Health challenges for persons with serious mental illness and comorbid medical conditions are related to application design and usability. An application suite to improve community care was deployed in the community services division of South Beach Psychiatric Center, a large state-run facility in New York, USA. The association between the use of the Medicaid utilization application and changes in the use of Medicaid publicly paid medical inpatient and emergency services was evaluated. A Generalized Linear Mixed Model (GLMM) was developed and applied to assess for longitudinal (5-year) reductions in utilization, the impact of colocation of behavioural and medical health services, and the effects of intra subject correlation, zero inflation, and over-dispersion in the data. Significant reductions in the use of Medicaid paid inpatient and emergency services were observed. Colocation of services was not significant. Significant effects for intra subject correlation and zero inflation were obtained and accounted for in the model.