The latest Digital Health News industry round up includes news on an automated recruitment platform for clinical studies, an acquisition in the medical imaging field and an Australian company focused on measuring coding launching into the UK. Former NHS leader, Tim Kelsey, has launched an international division of Beamtree into the UK – an Australian company that focuses on measuring coding and the quality of hospital care. Kelsey leads the Australian company, but the new London-based arm will be led by coding policy expert Jennifer Nobbs and former Paterson Inquiry advisor Alex Kafetz. Beamtree works with health organisations around the world in a bid to improve the capture, management and leverage of human expertise. The UK office will focus on AI in health, clinical decision support, data quality and analytics supporting better health outcomes.
AI is being increasingly incorporated by doctors to transcribe, read, analyze, and make predictions based on notes and conversations between physicians and their patients. This opens up new possibilities for care and new concerns about privacy, according to a recent account from Axios. A big and largely invisible contribution AI can make is to capture a physician's written or spoken notes automatically. Spending hours entering data manually into electronic health records (EHRs) is not helpful to medical professionals close to burning out. A recent study from researchers at the University of New Mexico, outlined in EHR Intelligence, found that 13% of stress and burnout self-reported by physicians were directly correlated to EHRs.
Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.
The amount of data that insurance companies, doctors, hospitals and patients have at their disposal is enormous. Payers have access to billions of pieces of claims data from doctors and pharmacies, while healthcare professionals across the country can collectively tap into millions of sophisticated electronic medical records that track patients and their progress. So valuable is all this information that some industry experts talk about data as the "new oil." The analogy is fitting: both are the raw material for creating value. That said, the ability to tie all these disparate pieces of information together is monumentally difficult.
Artificial intelligence (AI) propelled by increasing availability of data and analytics is creating a revolution in the way technology works in solving complex problems. The fact that it utilizes, both structured and unstructured data to deliver powerful, conclusive result makes it highly sought after in areas of healthcare, entertainment, finance, transportation and more. Thanks to AI, the voluminous data which was previously untapped has now been unplugged. Coupled with predictive analysis, through AI massive amounts of data have been scrubbed to produce results that have made a paradigm shift in the way healthcare operates for all – providers, patients and professionals. What is AI actually and how does it work in healthcare?
SAN FRANCISCO--(BUSINESS WIRE)--The following is an opinion editorial provided by Navin Shenoy, executive vice president and general manager of the Data Center Group at Intel Corporation. In the wide world of big data, artificial intelligence (AI) holds transformational promise. Everything from manufacturing to transportation to retail to education will be improved through its application. But nowhere is that potential more profound than in healthcare, where every one of us has a stake. What if we could predict the next big disease epidemic, and stop it before it kills?
Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70-72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level - Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1 + 2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions: This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that "learn" over time.