The current healthcare systems are overwhelmed with ever-expanding advances in the treatment modalities to provide safe and cost-effective patient care. For new digital healthcare technologies to reach their full potential without the need to increase resources, the whole health and care system will need to anticipate and plan for the future. One of the challenges for these integrated digital health systems is supporting perioperative clinical decision-making. A few studies were found to address these challenges with the use of AI Simulation Framework. They were also found to be beneficial in ensuring optimal decision-making including the most complex situations.[5]
Anesthesiologists have always been early adopters of technology to improve patient care and outcomes. In anaesthesiology, AI can be used to develop Clinical Decision Supportive Tools (CDS). Machine learning, a subset of AI that focuses on enabling algorithms to learn from the data provided, gathers insights and makes predictions on previously unanalyzed data using the information gathered. The three basic models of machine learning are supervised, unsupervised, and reinforcement learning. In perioperative practice, the current focus is on using machine learning to develop CDS tools and clinical pathways which have both descriptive and predictive capabilities. Machine learning has certain limitations. The information generated by the machine learning algorithms can be overwhelming. It can be difficult to make meaningful clinical interpretations and to ensure that the data are validated. Nonetheless, despite such limitations, machine learning algorithms seem to lead the future in perioperative medicine.[6]
Digital transformation: are we there yet
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Big data is providing newer biomedical research and discoveries. More and more predictive models in clinical practice are being developed. It is vital that the data used should be relevant for patient safety and outcomes. The machine learning models and algorithms should not only be consistent but should allow meaningful interpretation and free from random coincidence. Luo et al. suggested guidelines to ensure appropriate use of data and thereby applying machine learning models.[13]
The National Cabinet and now the enshrined Australian Digital and Data Council of Ministers have led the way in embracing digital solutions to support and meet the growing expectations of our communities.
The relentless information gathered and analysed by government data scientists (such as in the NSW Data Analytics Centre) from across government and the private sector has increasingly provided citizens up to date information on infection rates, the economic impacts and when best to travel on public transport.
If we do both policy and government reform well, fuelled by digital, we will see rising citizen and business confidence that translates into recovery, investment and jobs enabled by a world class public service. With 30% of the economy tied to government, its transformation will be vital to Australia recovering and coming back stronger.
Abstract:This paper focuses on the knowledge problem of economics by discussing its current status in light of digitalization. This problem highlights the paradox of not having the necessary knowledge to take an economic decision, but pretending to have it and act, hence questioning the legitimacy of governmental decision-making and its impacts on the economy. Current technological developments are challenging this problem. Big Data has been a neglected phenomenon when it comes to its impact on the nature of knowledge and the decision-making processes associated with it, and it is easy to think that Big Data solves this problem. This research gap is evaluated by re-visiting the knowledge problem and evaluating whether the knowledge problem can still be valid in the digital era. The digital governance issue has been largely covered by literature in terms of technical possibilities. However, the main challenge is not the technical one, but rather how to create governance structures to involve people in decision-making processes, and at the same not fall into the trap of the knowledge problem. The sustainable transition from digital government to digital governance is a transition from a technical structure to multiple processes on different levels, and these processes have their own limits.Keywords: digital government; digital governance; knowledge; digitalization; governance; big data
When it comes to the possibilities of IoT, there have been many technological evolutions from which we can learn. Beyond the laws of luminaries such as Moore, Metcalfe, and Bezos, we are seeing a causal relationship between tetration and attestation. To put it another way, the rate at which data is interacting with each other needs to be coupled with the need to guarantee that this layer two thing is truly this thing in context. As things become more homogenous in compute capability, the need for contextual control that is agnostic to location will become the dominant paradigm.
The crawl, walk, run analogy is a great way to summarize expectations from users and business. What is Cisco strategy for all these three different phases. Is there one particular tool/product targeted for all three or there are multiple tools.
An often untapped opportunity for high-impact cost savings is in digitization of field operations. Tech solutions that help operators work smarter rather than harder are proven to yield sustained cost savings. Case studies document how digital oilfield software for field management can reduce OPEX by 15% for mid-sized producers.
Then add in the patchwork of data that comes from specialized solutions for single tasks such as pump off controllers for oil wells, digital chart reading solutions and leak detection for high risk pipeline segments. This results in multiple, disparate solutions that all require their own logins and in many cases, duplicates the data entry work.
There is still a major disconnect between what organizations have, and what they want to accomplish. Larger organizations in the IFS study reported their ERP system was an impediment to digital transformation, yet the larger organizations were also found to be the most likely to invest in sensoring and IoT initiatives involving over 90% of their equipment. So essentially, businesses are investing in IoT hardware when their backend software is viewed as being an impediment that already has an existing set of monumental data challenges.
ERP systems are attempting to rapidly evolve to meet the growing demand for IoT integration. But, are they there yet? For decades, the focus has been on integration and transparency across finance, procurement and supply chain - Core ERP. However, in the past ten years, the focus has begun to shift to operations, asset management and field service management. Companies realized the dollars and cents impact of smart operations and asset management, and in turn, the ERP industry responded. The challenge now is to extend IoT data from the shop floor to the c-suite and every line of management and leadership in-between to make smart, predictive and nimble decisions.
The IFS study found that only 34% of companies were successful in leveraging data from their manufacturing execution systems and that only 19% can correlate that data with ERP systems. To truly realize the value of the digital transformation and the resulting business growth, companies must be able to consume and leverage their IoT data. To do this, integration and out-of-the-box solutions are key. As we push into 2019 and beyond, expect to see much more robust embedded solutions within core ERP offerings supporting the integration with IoT devices and sensors.
In an interview with CMO.com, Ashley Stirrup, CMO of software integration provider Talend, points out that digital transformation is a series of changes. The earliest of these changes are not going to produce results based on traditional KPIs.
Pioneered by WalkMe, the DAP is an algorithmic layer that can be placed on top of existing digital systems. Not only does it provide enterprises with valuable digital adoption insights, it also improves the user experience.
Find out how many of your customers are engaging with your digital system or product, after a launch or other event. Track new users over time and compare with returning engaged users to paint a fuller picture of user adoption.
Using a DAP, you can view digital adoption insights, like platform engagement and progress, per employee. This makes it easy for you to celebrate success and provide additional support to any staff members that need it.
How can we all help shape better policies for better lives? In just 15 minutes, we bring you insightful interviews with OECD and guest experts on such pressing challenges as inequality and inclusive growth, the digital transformation, social change, the environment, international co-operation, and more.
A workable definition is that digital transformation is the transition from manual, non-digital formats and processes to digital. For example, a company goes from handwriting quote indications to using software to process and manage them. Or moving from manual rating processes to using a core rating engine.
Unfortunately, most of the activities under the umbrella of digital transformation are not transformative at all. They are merely incremental. And, while transformation is routinely described as a journey, the end is never in sight. Without a definitive finish line, or with the finish line constantly moving, it is near impossible to establish meaningful goals and measure return on investment (ROI).
The next phase is really evolving current digital systems to modern technologies, such as the cloud and application programming interfaces (APIs). A common example of evolution is replacement of core systems or applications. Whether you are replacing an entire ecosystem or just components, like rating and pricing, you have moved beyond transformation and into the evolution phase. 2ff7e9595c
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