Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities

Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

REFORMING THE DELIVERY OF PUBLIC DENTAL SERVICES IN IRELAND: POTENTIAL COST IMPLICATIONS. ESRI RESEARCH SERIES NUMBER 80 APRIL 2019

This report details the results of an analysis of the potential cost implications of proposed changes to aspects of the model of delivery of publicly-financed dental services in Ireland, as set out in the new National Oral Health Policy (Department of Health, 2018b). Currently, dental services in Ireland are financed and delivered in a mixed public-private system, with most individuals paying out-of-pocket fees to independent dental practitioners. The public system currently finances the delivery of dental healthcare services to adult medical cardholders via the Dental Treatment Services Scheme (DTSS); to non-medical cardholder eligible adults via the Treatment Benefit Scheme (TBS); and to children and adults requiring special and complex care via the Public Dental Service (PDS). This report deals with proposed changes to the delivery of preventive dental healthcare services under the DTSS and PDS

Primer on adult patient satisfaction in perioperative settings.

The topic of patient satisfaction has gained increasing importance over the past decade. Due to the impact of patient satisfaction on health care quality, understanding factors that predict satisfaction is vital. The purpose of this review is to examine the literature and identify factors related to patient perioperative satisfaction as well as predictive variables that, if modified, can enhance satisfaction scores of patients undergoing surgery. Our review reports that patient satisfaction scores are affected by modifiable factors such as clinician-patient communication, information provision to patients, and operational function of a hospital. Non-modifiable factors affecting patient satisfaction scores include patient demographics such as gender, age, and education. In order to enhance patient perioperative satisfaction, we suggest that anesthesiologists and surgeons focus their efforts on enhancing their communication skills and providing information that is appropriately tailored to the understanding of their patients

Medical Savings Accounts in Singapore: How much is adequate?

While many studies have examined the cost-containment aspect of Medical savings accounts (MSA), few have investigated the adequacy of MSA to finance the health care expenditure. This paper estimates the present value of lifetime healthcare expenses (PVHE) of the Singaporean male and female elderly upon retirement at age 62. The estimation involves calibrating the stream of future healthcare expenditure; stochastic forecasting of cohort survival probabilities; and discounting the projected lifetime healthcare expenditure. Estimated values of the PVHE under various scenarios are used to assess the adequacy of the government-decreed minimum saving to be maintained in the MSA.medical savings accounts, present value of lifetime health care expense, cohort survival probabilities