AI News, The Health Care Benefits of Combining Wearables and AI artificial intelligence

Patients’ views of wearable devices and AI in healthcare: findings from the ComPaRe e-cohort

Our results may explain the high drop-out of participants in the first large-scale implementations of digital monitoring strategies (90% incomplete follow-up for MyHeart Counts and 55% incomplete follow-up data for the Healthy Pregnancy Research Program).25,26,27 Our results highlight that patients intuitively think that AI should help clinicians “predict” outcomes, but that decisions, actions, and recommendations should remain a human task.

Technology would be like as a “driver assistance” for clinicians.12 Even among patients who were the most ready for the use of technology in their care, they would only see AI as a complement—and not as replacement—for human care for situations related to sensitive topics (cancer) or which involved lasting interventions (monitoring for chronic conditions).

First, patient-reported data (qualitative or quantitative) were collected in some studies evaluating digital technologies and AI-based tools.13,29,30 However, these results are specific to both a given intervention and a given context and do not reflect patients’ uptake of these interventions if they had to be scaled up.20 Second, a handful of studies have explored patients’ perceptions of wearable devices and IA outside of the context of an ongoing digital-tool evaluation study.19,20,21,31,32,33,34 However, these studies were often limited in sample size and participant diversity or focused on a specific subject.

This study from Syneos Health Communications involved 800 patients with atrial fibrillation, type 2 diabetes mellitus, and breast cancer and showed that 16–19% of participants were “excited” about use of AI in healthcare and 32 to 42% were “unexcited”.35 Our results provide the largest and most comprehensive view of chronic patients’ perspectives of the use of these technologies in healthcare.

Healthcare systems in high-income countries such as France strive to care for patients with chronic conditions within overburdened practices and consultations constrained to short visits.36,37 There is a mismatch between what care systems can and need to deliver.38 Therefore, many clinicians, researchers and decision makers are looking to BMDs and AI to find the “magic bullet” to transform healthcare.

Artificial Intelligence in Insurance – Three Trends That Matter

Readers should note that auto insurance is more than 40% of the insurance industry as a whole.  (For readers with a strong interest in other financial applications of AI, please refer to our full article on machine learning applications in finance.) Trends that business leaders should know about.

In this article we look at three key ways that AI will drive savings for insurance carriers, brokers and policyholders, plugging into existing transformations within the insurance industry: Insurance as a global marketplace tends to be associated with public distrust (one Australian poll ranked sex workers as more trusted than the insurance industry), and this may present unique challenges to technology innovations –

We’ll begin with “behavioral pricing”: IoT data is opening a slew of  are three key ways that IoT data will enable personalized insurance pricing: Hypothesis: IoT disrupts insurance the same way that data science has been disrupting finance: moving analysis from proxy to source data.

Today: IoT sensors allow insurance carriers to price coverage based on real events, in real time, using data linked to individuals rather than samples of data linked to groups.

Telematics sensors allows real-time tracking of an underlying asset (cars) allowing for the roll-out of a new product line in the related insurance market (auto insurance) by personalizing the risk of the event being insured (a car accident).

A 2017 report from the National Association of Insurance Commissioners noted: “…UBI is an emerging area and thus there is still much uncertainty surrounding the selection and interpretation of driving data and how that data should be integrated into existing or new price structures to maintain profitability.”  But most customers who tried it seem to have loved it.

Associates found: “…UBI participants provided more positive recommendations and more often indicated that these recommendations resulted in a friend, relative or colleague purchasing from their insurer compared with those customers who did not use a UBI program.” Some insurers offer discounts for participation in usage-based insurance programs to collect thousands of miles worth of monitored driving data.

21% of customers declined to participate in a UBI program when it was available and 81% of those respondents did so because they didn’t want their driving monitored, didn’t think they’d save money, or didn’t think their premiums would decrease.

That’s where platform marketplaces like Next Generation Platform (NGP) by Octo Telematics comes in, providing auto insurance carriers with an Application Platform Interface (API) for driver behavior scores, crash and claim analysis alongside specialized risk analytics for fleet managers and car rental companies.

The 2017 Excellence in Risk Management report found “…an apparent lack of awareness among many risk professionals on existing and emerging technologies including telematics, sensors, the Internet of Things (IoT), smart buildings and robotics, and their associated risks.” Markets could start moving fast as consumers trade IoT data for lower premiums.

As managing director of Corporate Finance for KPMG Joe Schneider wrote, detailing the shift in the auto insurance industry: “Once the massive market disruption begins and traditional insurance business models are flipped upside down, we expect significant turmoil.” It’s all about the sensor data.

Anyone trying to benchmark legacy players versus newcomers should answer this question: How well are a company’s business lines positioned to take advantage of sensor data originating from their policies’ underlying assets?

Here are the three key ways that AI will enhance the insurance buying experience: (Readers with an explicit interested in conversational interfaces may want to read our full article about 7 chatbot use cases that are working now.) You can now buy insurance with a selfie.

Speed and success in settling claims is a critical factor for insurance business efficiencies, as well as for Here are two key ways that AI will improve customer satisfaction after filing a claim: AI’s advantage seems to be most obvious in claims settlement.

An April 2017 Accenture survey found that 79% of insurance executives believe that: “…AI will revolutionize the way insurers gain information from and interact with their customers.” AI will likely bring faster claims settlement with decreased fraud.

That’s one reason why fraud detection is among the fastest areas of tech adoption in the insurance industry, with over 75 per cent of the industry reporting to have used an automated fraud detection technology in 2016.

The April 2017 Accenture survey found that this opinion is widespread: ”Insurance executives believe that artificial intelligence (AI) will significantly transform their industry in the next three years.” Whether telematics, autonomous vehicles, chatbots or customization platforms, the market will likely move towards firms that are able to best harness AI to improve the customer on-boarding and claim management process.

Health Care Benefits of Combining Wearables and AI

In southeast England, patients discharged from a group of hospitals serving 500,000 people are being fitted with a Wi-Fi-enabled armband that remotely monitors vital signs such as respiratory rate, oxygen levels, pulse, blood pressure, and body temperature.

pilot at the Dartford and Gravesham hospitals, for instance, home monitoring had involved dispatching hospital staffers to drive up to 90 minutes round-trip to check in with patients in their homes about once per week.

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