Data Analytics Platform Helps Hospitals Reduce Cost Of Patient Care

Rising pharmaceutical drug prices and declining reimbursements have made it difficult for US-based hospitals to prescribe medicines that their uninsured or under-insured patients can afford. Even with the government’s 340B program, which requires drug makers to discount some of their prescription medicines, hospitals struggle with rigorous regulatory requirements and complex procurement contracts, which make it hard for them to qualify for the discount program. It’s also hard for them to determine which therapies have the highest success rates and then identify which pharmacies have the necessary discounted drugs available.

Enter healthcare data analytics company Sentry Data Systems, which works with hundreds of hospitals and clinics “that serve some of the neediest and most vulnerable patient populations in the country,” says founder and CEO Travis Leonardi. “Our mission is to ensure that hospitals, physicians, and safety-net providers, not drug companies or pharmacies, remain in control of how to best care for their patients,” he says.

Sentry’s healthcare analytics software-as-a-service platform continuously captures terabytes of data containing patients’ protected healthcare information, and then categorizes that data into demographic profiles, conditions, treatments, and invoices. The company now pulls that data daily from the financial, clinical, and pharmacy systems of more than 11,000 US-based hospitals, health clinics, pharmacies, and specialty treatment centers, and then makes it available in its prebuilt analytical models for healthcare administrators to determine which type of treatment would be the most cost-effective for each patient’s specific condition. 

By analyzing patient populations across a range of variables, such as age, gender, and disease, “we’re helping hospitals make informed decisions based on real-world evidence, not anecdotal observations or scientific hypotheses,” says Dr. William Kirsh, Sentry’s chief medical officer. 

The full Forbes article can be read at this link.  

Opioid Prescribing and Physician Autonomy: A Quality of Care Perspective

The public health and social harms resulting from misuse of opioids, particularly substance use disorders related to prescription opioids, have been under intense scrutiny in recent years. Some individuals who receive prescription opioids also use heroin, which has additional risks due to unknown potency and adulteration. Center for Disease Control and Prevention (CDC) statistics indicate that 115 Americans die of an opioid overdose each day. Although the dominant media narrative assigns much of the blame to overprescribing or misprescribing by physicians, the news media less than 20 years ago frequently castigated physicians for failing to provide sufficient pain control and dismissed or ignored the possibility that inappropriate deployment of opioids could lead to addiction. As one article published in American Family Physician in 2000 stated: “Despite recent advances in the understanding of pain management, patients continue to suffer needlessly, primarily because of improper management and inadequate pain medication”. In 2001, a story appeared in the Chicago Tribune reporting that “[a] jury awarded $1.5 million to the family of an 85-year-old man who accused his doctor of not prescribing enough pain medication during his final days”.

Since then, the narrative has changed. Headlines such as “Who Is Responsible for the Pain-Pill Epidemic?” and “Doctors Increasingly Face Charges for Patient Overdoses” have led physicians in many cases to be extremely cautious in prescribing opioids and lawmakers to impose highly specific restrictions on treatment use of opioids. Legal actions seeking to impose liability on opioid manufacturers for their marketing practices have focused on the alleged effects of those marketing efforts on physician prescribing. Yet, nothing can relieve the responsibility of the physician as the party ultimately responsible for the decision to prescribe a controlled substance. That responsibility is grounded in professional obligations to treat patients appropriately and according to accepted standards of care, violation of which can lead to professional discipline, peer review actions, and quality-assurance measures. This article evaluates the effectiveness of recent legislative mandates and restrictions on opioid prescribing and proposes alternative frameworks for combatting and preventing harms caused by the misuse of prescribed opioids.

The full article can be downloaded below.  

2019 HEALTHCARE TRENDS FORECAST: THE BEGINNING OF A CONSUMER-DRIVEN REFORMATION 

The well-known proverb “may you live in interesting times” is considered by many to be a blessing, yet others believe it is actually an ancient curse. People’s interpretation tends to correspond with their own appetite for change, as well as their comfort (or lack of) in the face of ambiguity. Regardless of the adage’s origin and intent, many would agree that these are interesting times for healthcare. In fact, at HIMSS we believe healthcare is currently undergoing a period of reformation on a scope and scale that is unmatched.

A perfect storm of factors – including the shift toward value-based care, rising costs, health system consolidation, the approaching silver tsunami, regulatory pressures, increased consumerization, major technology players entering the market and the ever-expanding potential of digital health tools – are coalescing and fundamentally disrupting business models. Traditional healthcare institutions are in reactive mode. Upstarts are finding that healthcare is not as easily disrupted as industries like retail. For nearly everyone, there are more questions than answers.

At HIMSS, we believe it is our responsibility to help the industry make sense of these changes and tap into the promise and potential of information and technology. With that premise, we are introducing an annual forecast report. Bringing together insights from leadership across HIMSS and our subsidiaries, we’re aiming to shine a light around the corner and help illuminate the path to clinical and financial health.

Read on for our predictions for the industry in 2019.

The full article can be downloaded below.  

Integrating Clinical and Mental Health: Challenges and Opportunities

Nearly 45 million American adults suffered from some form of mental illness in 2016. Although there is little change in the estimates of those with mental illness over the last few years, rates of death due to drugs, alcohol, and suicide are increasing. In 2016, about 45,000 Americans age 10 and over died by suicide. Twenty-five states experienced at least a 30 percent increase in suicide rates between 2014 and 2016.

The national opioid crisis has resulted in significant attention to federal policy associated with substance use disorder (SUD). Mental illness along with SUD comprise a broad category of illness commonly referred to as “behavioral health.” In 2016, 44.7 million American adults experienced a mental illness, 20.1 million experienced a SUD, and 8.2 million experienced both—and these numbers are likely underestimated due to lack of identification and issues of stigma. Collectively, more than 1 million people have died from drugs, alcohol, and suicide over the past decade. If these trends continue, the death rate could grow to claim 2 million more lives by 2025.

The purpose of this paper is to examine the barriers to the integration of clinical health care and mental health services, and to identify policy options for consideration in advancing integration of services. In 2018, the Bipartisan Policy Center hosted a series of public and private discussions on this topic. As part of this research, BPC consulted patient advocates; clinical and behavioral health care providers; federal, state, and county agency officials and staff; insurers; academics; and other experts. The goal was to identify barriers to integration caused by federal policy, to identify policy options to mitigate or remove those barriers, and, through policy changes, to advance evidence-based treatment for mental health in the United States.

The full paper can be downloaded below.  

Can Digital Medicine Improve Drug Adherence?

Lack of proper adherence to medications has been called a national epidemic. To help address the adherence problem multiple digital health solutions have been proposed with some concerns that there has been limited large-scale evidence to support adoption. A new program focused on oncology patients is attempting to make a difference using a value-based payment structure between a health system and technology company.

The full Forbes article can be viewed at this link.  

MEDICAL DEVICE AND HEALTH IT JOINT SECURITY PLAN

The Healthcare and Public Health Sector Coordinating Council (HSCC) is a coalition of private sector, critical healthcare infrastructure entities organized under Presidential Policy Directive and the National Infrastructure Protection Plan to partner with government in the identification and mitigation of strategic threats and vulnerabilities facing the sector’s ability to deliver services and assets to the public. The HSCC Joint Cybersecurity Working Group (JCWG) is a standing working group of the HSCC, composed of more than 200 industry and government organizations working together to develop strategies to address emerging and ongoing cybersecurity challenges to the health sector. This Medical Device and Health IT Joint Security Plan is the product of a task group established under the auspices of the HSCC JCWG and composed of medical technology, health IT and health delivery organizations, as well as the FDA, to address a major recommendation of the Health Care Industry Cybersecurity Task Force report from June 2017 calling for a cross-sector strategy to strengthen cybersecurity in medical devices.

The full plan can be downloaded below.  

What would happen if hospitals openly shared their prices?

Hospitals have resisted disclosing prices, leading policymakers to consider laws requiring price transparency. This issue has taken on increasing urgency, as patients face increasing out-of-pocket costs. In addition, prices vary widely across hospitals. The same lower limb MRI can cost US$700 at one hospital and $2,100 at another. This means that there are large potential savings if patients switched to less expensive options.

There was a tiny step in this direction on Jan. 1, when all hospitals in the U.S. were required to post their charge prices. However, the list of over 15,000 procedures is notoriously incomprehensible, even for medical professionals. What exactly is a “HC PTC CLOS PAT DUCT ART,” a procedure listed by one Tennessee hospital? Perhaps more importantly, patients’ out-of-pocket costs often depend on the specifics of their insurance plan and the prices that are negotiated by their insurer, meaning the listed prices do not reflect what they actually pay.

For these reasons, many researchers and commentators, including myself, believe that this approach is unlikely to have a meaningful effect on health care costs.

That does not mean that price transparency is hopeless. Recent research shows that price transparency tools that actually have useful, easy-to-use information can benefit patients and reduce health care costs.

The full The Conversation article can be viewed at this link.  

Patients Suffer As Insurers And Big Health Systems Spar For Market Share​

Contract disputes between insurers and medical providers have been a regular feature of the national health industry for a long time. But the stakes have risen as big players on both sides have expanded to gain market share and leverage in network negotiations.

Most negotiations are completed before the old contract expires, and consumers usually don't hear about these behind-the-scenes disagreements. But when insurers and providers fail to reach an agreement on time, it can force patients to pay higher prices for care that is no longer covered by their health plans. At the least, it can cause considerable anxiety.

The full NPR article can be viewed at this link.  

Filling the gaps in a patient’s medical data

MIT researchers have developed a model that can assimilate multiple types of a patient’s health data to help doctors make decisions with incomplete information.

The field of “predictive analytics” holds promise for many health care applications. Machine learning models can be trained to look for patterns in patient data to predict a patient’s risk for disease or dying in the ICU, to aid in sepsis care, or to design safer chemotherapy regimens.  

The process involves predicting variables of interest, such as disease risk, from known variables, such as symptoms, biometric data, lab tests, and body scans. However, that patient data can come from several different sources and is often incomplete. For example, it might include partial information from health surveys about physical and mental well-being, mixed with highly complex data comprising measurements of heart or brain function.

Using machine learning to analyze all available data could help doctors better diagnose and treat patients. But most models can’t handle the highly complex data. Others fail to capture the full scope of the relationships between different health variables, such as how breathing patterns help predict sleeping hours or pain levels.

In a paper being presented at the AAAI Conference on Artificial Intelligence next week, MIT researchers describe a single neural network that takes as input both simple and highly complex data. Using the known variables, the network can then fill in all the missing variables. Given data from, say, a patient’s electrocardiography (ECG) signal, which measures heart function, and self-reported fatigue level, the model can predict a patient’s pain level, which the patient might not remember or report correctly.

The full MIT News article can be viewed at this link.  

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidencebased clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.

The full article can be downloaded below.