Outcomes-based Pricing Not A Panacea For High Priced Drugs

In the battle over the pricing of new drugs, manufacturers and payers have sought to come up with novel approaches to pricing that can be tolerated by both parties. One such approach has been to provide a type of money-back guarantee for expensive new drugs. In what are called outcomes-based agreements, prices are agreed to by payers with the caveat that if the drug doesn’t perform up to patients’ and physicians’ expectations, the company will refund part, if not all, of the cost of the drug.

The full Forbes 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.  

North Dakota doesn’t have enough psychiatrists. Telemedicine is helping to fix that

Until recently, when the North Dakota human services agency had an opening for a mental health provider, months might go by before a single application came in.

But that’s started to change as the state boosts telemedicine as an option for mental health care. The department has started allowing providers who serve patients through its health centers to live in some of the state’s bigger cities — or even move out of state — and deliver mental health care to rural areas through video calls. The University of North Dakota’s medical school has started training its psychiatry residents to treat rural patients by computer.

“Telepsychiatry really is integral to our ability to provide that equitable access to psychiatric services across the state, regardless of rural and urban environment,” said Dr. Laura Kroetsch, a psychiatrist who works as the field medical director of the human services department.

The full STAT article can be viewed at this link.  

With the Silver Tsunami on its way, telehealth is ready for its moment

Whether it's after-hours coverage at skilled nursing facilities or connected tools for home health monitoring, remote care technology is reaching maturation just when it will be needed most.

Grant Chamberlain, managing director in the corporate finance healthcare practice at Ziegler, has long experience of telehealth-focused investments. He's advised major health systems (Baylor Health, Cedars-Sinai, Sharp) and vendors such as AirStrip, MDLive and Voalte.

He's also director at the ATA and serves on the board of the not-for-profit MAVEN Project, which helps deliver care to underserved populations with volunteer physicians affiliated with Harvard, Yale, Stanford, UCLA and others.

Chamberlain agrees that telehealth maturity, acceptance and adoption have been a long time coming – and he thinks we still have a way to go before its benefits are fully felt.

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

Precision prevention: A focused response to shifting paradigms in healthcare

Human health and disease are defined at the intersection of molecules, environment, and lifestyle, which combined determine phenotypic outcomes. To date, most clinical applications in the precision medicine space have focused on DNA, with lesser attention given to the biology encoded by RNA molecules, or the complexity of biological regulation at the level of proteins and metabolites. The totality of this information must be integrated in ways that allow for implementation of knowledge-based health promotion and prevention strategies that can help address current limitations in healthcare delivery. This review describes recent advances in the development of diagnostic tools for early detection and stratification of individuals suffering from chronic obstructive pulmonary disease and their heightened risk for the development of lung malignancies.

The full article can be downloaded below.