What the future of cloud computing holds for health insurance companies

With the increasing importance of analytics and data management, a cloud-first mentality is ideally suited for health insurers, who are using the technology to streamline operations, reduce costs, and better interact with their customers.

At the same time, the move toward value-based care means claims-based data and analytics can provide a comprehensive view of an insurer’s transactions by aggregating claims across healthcare plans.

The full Healthcare IT News article can be viewed at this link.  

Cleveland Clinic ready to push AI concepts to clinical practice

The Cleveland Clinic’s Center for Clinical Artificial Intelligence (CCAI) will not feature robots greeting visitors at the door, says its new director, but it will leverage new technology to improve diagnosis, prognosis and treatment planning. 

The center is meant to be an international “hub of collaboration,” bringing together experts from pathology, radiology, oncology, information technology, computer science and genetics and providing programmatic and technology support for initiatives in augmented intelligence (AI), often called “artificial intelligence.” 

“We’re not in it because AI is cool, but because we believe it can advance medical research and collaboration between medicine and industry—with a focus on the patient,” said Aziz Nazha, MD, an AMA member and an assistant professor at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University. 

The full American Medical Association (AMA) article can be viewed at this link.  

How Machine Learning Can Help Prevent Hospitalizations

It doesn't take artificial intelligence to tell you that a preventable hospitalization is not good. A hospital is not a bed-and-breakfast. No one says, "hey, for fun, let's get a hospital room overlooking the parking garage this weekend." A preventable hospitalization is by definition one that could have been prevented. Thus, it costs people, insurance companies, businesses, the government, and society considerable time, effort, and resources that could have been diverted to more productive activities. Plus, a hospitalization may expose a patient to potential badness such as hospital food, being separated from friends and family, medication errors, and antibiotic-resistant bacteria. Again human intelligence can tell you all this. Where artificial intelligence may be helpful is in reducing such preventable hospitalizations, and Clover Health is an example of a company aiming to do this.

The full Forbes article can be viewed at this link.  

Blockchain Technology May (Eventually) Fix Healthcare: Just Don't Hold Your Breath

There is a common fallacy that every new technology that skitters across the healthcare plain will have an earth-shattering, and short-term, positive impact on the healthcare system writ large. In fact, when attending the Health Information Management Systems Society’s (HIMSS) annual meeting, you see a vast and growing number of service providers addressing some healthcare-technology need, whether far-reaching, niche, real, or imagined, in the healthcare space. From artificial intelligence (AI) to machine learning to blockchain to care management, the healthcare horizon is rife with new technologies. But these solutions seldom deliver immediate applications or success. Look at IBM Watson’s highly publicized venture into the delivery of cancer-care services. Internal IBM documents showed “multiple examples of unsafe and incorrect treatment recommendations” from the Watson for Oncology system.  Additionally, The Wall Street Journal pointed out that “more than a dozen IBM partners and clients have halted or shrunk Watson’s oncology-related projects.” In a blog post titled “Setting the Record Straight,” IBM responded to some of this media coverage by saying that it is inaccurate to suggest Watson “has not made ‘enough’ progress on bringing the benefits of AI to healthcare.

Is that to say that AI, machine learning, and blockchain will not play a role in the future of healthcare? Certainly not. But it seems reasonable to expect some missteps in the short term. These and other cutting-edge technologies are needed to advance the delivery and coordination of care, squeeze costs out of “the system,” and help ensure repeatable quality-care outcomes. But few technologies are perfect.

The full Forbes article can be viewed at this link.  

Electronic Health Record 'Gag Clauses' May Soon Come Off

Dr. Raj Ratwani, director of the MedStar Health National Center for Human Factors in Healthcare in Washington, D.C., says freer speech is needed to help make electronic records safer and more user-friendly.

"Electronic health records are a positive thing; the majority of clinicians would never want to go back to paper," he says. "Having said that, there are some unintended consequences to the technology, and these gag clauses in particular have prevented us from being able to really quantify that impact."

The full WBUR article can be viewed at this link.  

Chasing Value as AI Transforms Health Care

Business leaders no longer think about artificial intelligence in terms of future impact—they’re seeing the impact today. AI is appearing in all corners of business, transforming the way companies operate. Health care is no exception.

Health care players are using AI to address significant inefficiencies and open up powerful new opportunities. These include everything from the delivery of remote health care services to the early diagnosis of disease and the hunt for new life-saving medicines. Today, the technology is incorporated into heart monitors, smart glucose pumps, and other recently FDA-approved diagnostic devices. Biopharma companies are already using AI to improve the efficiency of R&D; one notable example is through identification of better drug targets.

The ongoing rapid development of AI will trigger a major shift in the value pools across health care. This has serious implications not only for the industry’s four major traditional sectors—biopharma, providers, payers, and medtech—but also for consumers and technology companies. Boston Consulting Group has conducted an in-depth analysis of the potential impact of AI on health care, identifying two prospective scenarios for how value will shift among stakeholders. Under one scenario, much of the value unlocked by AI is retained by players in the four health care sectors and technology companies—while the second scenario sees much of the value flowing directly to consumers.

The full Boston Consulting Group article can be downloaded below. 

CMS launches $1.65 million AI challenge

The CMS on Wednesday launched a $1.65 million contest to develop an understandable artificial intelligence tool that can predict patients' healthcare outcomes and adverse events.

The right AI tool could improve quality and lower administrative burdens for doctors, The agency and the American Academy of Family Physicians, which is supporting the challenge, say the right AI tool could improve care quality and lower doctors' administrative burdens.

The full Modern Healthcare article can be viewed at this link.  

Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches

Prognostic modelling using standard methods is well-established, particularly for predicting risk of single diseases. Machine-learning may offer potential to explore outcomes of even greater complexity, such as premature death. This study aimed to develop novel prediction algorithms using machine-learning, in addition to standard survival modelling, to predict premature all-cause mortality.

A prospective population cohort of 502,628 participants aged 40–69 years were recruited to the UK Biobank from 2006–2010 and followed-up until 2016. Participants were assessed on a range of demographic, biometric, clinical and lifestyle factors. Mortality data by ICD-10 were obtained from linkage to Office of National Statistics. Models were developed using deep learning, random forest and Cox regression. Calibration was assessed by comparing observed to predicted risks; and discrimination by area under the ‘receiver operating curve’ (AUC).

14,418 deaths (2.9%) occurred over a total follow-up time of 3,508,454 person-years. A simple age and gender Cox model was the least predictive (AUC 0.689, 95% CI 0.681–0.699). A multivariate Cox regression model significantly improved discrimination by 6.2% (AUC 0.751, 95% CI 0.748–0.767). The application of machine-learning algorithms further improved discrimination by 3.2% using random forest (AUC 0.783, 95% CI 0.776–0.791) and 3.9% using deep learning (AUC 0.790, 95% CI 0.783–0.797). These ML algorithms improved discrimination by 9.4% and 10.1% respectively from a simple age and gender Cox regression model. Random forest and deep learning achieved similar levels of discrimination with no significant difference. Machine-learning algorithms were well-calibrated, while Cox regression models consistently over-predicted risk.

Machine-learning significantly improved accuracy of prediction of premature all-cause mortality in this middle-aged population, compared to standard methods. This study illustrates the value of machine-learning for risk prediction within a traditional epidemiological study design, and how this approach might be reported to assist scientific verification.

The full article can be downloaded below.  

Personalizing solidarity? The role of self-tracking in health insurance pricing

Can data-driven innovations, working across an internet of connected things, personalize health insurance prices? The emergence of self-tracking technologies and their adoption and promotion in health insurance products has been characterized as a threat to solidaristic models of healthcare provision. If individual behaviour rather than group membership were to become the basis of risk assessment, the social, economic and political consequences would be far-reaching. It would disrupt the distributive, solidaristic character that is expressed within all health insurance schemes, even in those nominally designated as private or commercial. Personalized risk pricing is at odds with the infrastructures that presently define, regulate and deliver health insurance. Self-tracking can be readily imagined as an element in an ongoing bio-political redistribution of the burden of responsibility from the state to citizens but it is not clear that such a scenario could be delivered within existing individual private health insurance operational and regulatory infrastructures. In what can be gleaned from publicly available sources discussing pricing experience in the individual markets established by the Patient Protection and AffordableCare Act 2010 (ACA), widely known as‘Obamacare’, it appears unlikely that it can provide the means to personalize price. Using the case of Oscar Health, a technology driven start-up trading in the ACA marketplaces, I explore the concepts, politics and infrastructures at work in health insurance markets.

The full article can be downloaded below.  

Mobilizing mHealth Data Collection in Older Adults: Challenges and Opportunities

Worldwide, there is an unprecedented and ongoing expansion of both the proportion of older adults in society and innovations in digital technology. This rapidly increasing number of older adults is placing unprecedented demands on health care systems, warranting the development of new solutions. Although advancements in smart devices and wearables present novel methods for monitoring and improving the health of aging populations, older adults are currently the least likely age group to engage with such technologies. In this commentary, we critically examine the potential for technology-driven data collection and analysis mechanisms to improve our capacity to research, understand, and address the implications of an aging population. Alongside unprecedented opportunities to harness these technologies, there are equally unprecedented challenges. Notably, older adults may experience the first-level digital divide, that is, lack of access to technologies, and/or the second-level digital divide, that is, lack of use/skill, alongside issues with data input and analysis. To harness the benefits of these innovative approaches, we must first engage older adults in a meaningful manner and adjust the framework of smart devices to accommodate the unique physiological and psychological characteristics of the aging populace. Through an informed approach to the development of technologies with older adults, the field can leverage innovation to increase the quality and quantity of life for the expanding population of older adults.

The full article can be downloaded below.