PAVA: physiological and anatomical visual analytics for mapping of tissue-specific concentration and time-course data

Data Science and Geriatric Anesthesia Research: Opportunity and Challenges

With an increase in geriatric population undergoing surgical procedures, research focused on enhancing their perioperative outcomes is of paramount importance. Currently, most of the evidence-based medicine protocols are driven by studies concentrating on adults encompassing all adult age groups. Given the alterations in physiology with aging, geriatric patients respond differently to anesthetics and, therefore, require specific research initiatives to further expound on the same. Large databases and the development of sophisticated analytic tools can provide meaningful insights into this. Here, we discuss a few research opportunities and challenges that data scientists face when focusing on geriatric perioperative research.

Visual Analytics for Electronic Health Records: A Review

The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years, several EHR-based visual analytics systems have been developed to fulfill healthcare experts’ computational and cognitive demands. In this paper, we conduct a systematic literature review to present the research papers that describe the design of EHR-based visual analytics systems and provide a brief overview of 22 systems that met the selection criteria. We identify and explain the key dimensions of the EHR-based visual analytics design space, including visual analytics tasks, analytics, visualizations, and interactions. We evaluate the systems using the selected dimensions and identify the gaps and areas with little prior work.

Obstetric anaesthesia practice: Dashboard as a dynamic audit tool

Rapid advances and improved networking abilities have led to the widespread adoption of technology in healthcare, especially focused on diagnostics, documentation and evaluation, or mining of data to improve outcomes. Current technology allows for rapid and accurate decision-making in clinical care decisions for individual patients, collation and analysis at different levels for administrative and financial purposes, and the ability to visualise, analyse, and share data in real time for departmental needs. The adoption of technology may help to improve efficiency and efficacy of healthcare services. Obstetric anaesthesia is a specialised area that has to address the well-being of the pregnant woman and the unborn baby simultaneously. A shift toward caesarean sections as the major mode of childbirth has led to an increased involvement of anaesthesiologists with childbirth. Decisions are often made in high pressure, time intense situations to protect maternal and foetal health. Furthermore, labour analgesia using various neuraxial and non-neuraxial techniques is being demanded by parturients frequently, and for the materno-foetal safety, risk management is the core issue. Hence, it is essential that obstetric anaesthesia teams regularly audit their outcomes to improve services and to identify potential trouble spots earlier. It may be helpful to have audit parameters displayed as visual data, rather than complex tabular and numerical data, for ease of sharing, analysis, and redressal of problem areas. We describe the design and use of an obstetric anaesthesia dashboard that we have used in our department for the past 5 years.

Safety assessment for ethanol-based topical antiseptic use by health care workers: Evaluation of developmental toxicity potential

Ethanol-based topical antiseptic hand rubs, commonly referred to as alcohol-based hand sanitizers (ABHS), are routinely used as the standard of care to reduce the presence of viable bacteria on the skin and are an important element of infection control procedures in the healthcare industry. There are no reported indications of safety concerns associated with the use of these products in the workplace. However, the prevalence of such alcohol-based products in healthcare facilities and safety questions raised by the U.S. FDA led us to assess the potential for developmental toxicity under relevant product-use scenarios. Estimates from a physiologically based pharmacokinetic modeling approach suggest that occupational use of alcohol-based topical antiseptics in the healthcare industry can generate low, detectable concentrations of ethanol in blood. This unintended systemic dose probably reflects contributions from both dermal absorption and inhalation of volatilized product. The resulting internal dose is low, even under hypothetical, worst case intensive use assumptions. A significant margin of exposure (MOE) exists compared to demonstrated effect levels for developmental toxicity under worst case use scenarios, and the MOE is even more significant for typical anticipated occupational use patterns. The estimated internal doses of ethanol from topical application of alcohol-based hand sanitizers are also in the range of those associated with consumption of non-alcoholic beverages (i.e., non-alcoholic beer, flavored water, and orange juice), which are considered safe for consumers. Additionally, the estimated internal doses associated with expected exposure scenarios are below or in the range of the expected internal doses associated with the current occupational exposure limit for ethanol set by the Occupational Safety and Health Administration. These results support the conclusion that there is no significant risk of developmental or reproductive toxicity from repeated occupational exposures and high frequency use of ABHSs or surgical scrubs. Overall, the data support the conclusion that alcohol-based hand sanitizer products are safe for their intended use in hand hygiene as a critical infection prevention strategy in healthcare settings.

Big data and visual analytics in anaesthesia and health care

Advances in computer technology, patient monitoring systems, and electronic health record systems have enabled rapid accumulation of patient data in electronic form (i.e. big data). Organizations such as the Anesthesia Quality Institute and Multicenter Perioperative Outcomes Group have spearheaded large-scale efforts to collect anaesthesia big data for outcomes research and quality improvement. Analytics--the systematic use of data combined with quantitative and qualitative analysis to make decisions--can be applied to big data for quality and performance improvements, such as predictive risk assessment, clinical decision support, and resource management. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces, and it can facilitate performance of cognitive activities involving big data. Ongoing integration of big data and analytics within anaesthesia and health care will increase demand for anaesthesia professionals who are well versed in both the medical and the information sciences.

A review of analytics and clinical informatics in health care

Federal investment in health information technology has incentivized the adoption of electronic health record systems by physicians and health care organizations; the result has been a massive rise in the collection of patient data in electronic form (i.e. "Big Data"). Health care systems have leveraged Big Data for quality and performance improvements using analytics-the systematic use of data combined with quantitative as well as qualitative analysis to make decisions. Analytics have been utilized in various aspects of health care including predictive risk assessment, clinical decision support, home health monitoring, finance, and resource allocation. Visual analytics is one example of an analytics technique with an array of health care and research applications that are well described in the literature. The proliferation of Big Data and analytics in health care has spawned a growing demand for clinical informatics professionals who can bridge the gap between the medical and information sciences.