4 challenges impacting the healthcare supply chain

COVID-19 has highlighted structural weaknesses in the healthcare system and, most notably, a persistent issue with the capacity and resilience of healthcare supply chains. Between 59-83% of organizations have reported delays or increased lead times in acquiring supplies since the onset of the pandemic. In response, 81 percent of these organizations adjusted their inventories, most by increasing inventory levels, to weather the demand fluctuations and disruptions.

Several major risk factors or underlying challenges have come into focus for healthcare supply chains:

Lack of resilience – COVID-19 exposed a need for greater supply chain resiliency. IDC emphasizes the importance for healthcare organizations to have greater adaptability to shifting pandemic conditions while positioning for the “next normal” post-pandemic. Of course, mitigating supply chain disruptions in healthcare have significant consequences for the bottom-line and patient care. Perhaps that’s why a recent survey showed that supply chain disruptions are now healthcare CEOs second-highest priority behind patient safety.

Lack of visibility – The lack of resiliency across healthcare organizations often stems from poor visibility, specifically a lack of quick access to centralized, consumable, real-time data from dispersed data sources and siloed systems. This makes it difficult to determine what’s needed, what’s in stock, and the scope of future demand. Ultimately, you can’t manage what you can’t see and measure.

Cost management – During the pandemic, as demand increased for personal protective equipment (PPE) and medical supplies, costs soared. Now, supply expenses are forecasted to surpass personnel costs as the biggest expense in healthcare. Studies show that most inventory decisions made to adjust to a disruptionare suboptimal and nearly half are unnecessary. This isn’t surprising, given the lack of visibility and that health systems typically order supplies based on historical models and physician preference, rather than actual utilization and expected demand. This leads to waste, delayed procedures and high inventory and carrying costs.

Integration and interoperability – Integration challenges— from an organizational, process, and technology perspective — also contribute to cost increases and visibility issues. Data integration across disparate ERP, legacy supply chain systems, and external sources, along with interoperability across tools such as RFID barcode readers that feed product data back into these systems, is needed to connect the dots.

Integration challenges are further exacerbated by mergers and acquisitions (M&A) – and the fallout from COVID-19 is expected to accelerate M&A in healthcare. M&A provides significant upside potential for growth and cost savings, but it also leads hospital systems to manage fragmented teams, technologies, and processes. Improved integration and interoperability enable better decision-making and allow an organization to better collaborate and get ahead of disruptions.

Despite these challenges, advanced technology solutions offer an expedited path to transformation and resiliency. Healthcare and supply chain leaders are already acting to address these challenges and improve their organizations in the process. According to IDC, 62 percent of hospitals increased spend on supply chain applications and 64 percent of executives called their cloud-based supply chain management applications “business-critical.”

AI-enabled supply chain control towers are garnering particular interest. In fact, 88 percent of healthcare executives identified AI as a ‘critical’ technology for their supply chains in the next three years. AI-backed control towers can help an organization gain dynamic visibility across the network, improve the ability to sense demand changes and disruptions, and support inventory management and decision-making to improve patient care.

AI-enabled supply chain control towers can help meet the challenges presented by the pandemic by leveraging five key capabilities:

◉ End-to-end visibility

◉ Intelligent forecasting and demand sensing

◉ Touchless planning and improved productivity

◉ Elevated planning and automation

◉ Creating a collaborative ecosystem

Deploying control towers is not just about meeting the challenges exposed by the pandemic. They’re also a key component in transitioning to a more digital and data-driven environment and meeting the challenges and opportunities of the future. Combining visibility, automation and integration across the supply chain with a control tower unlocks the next frontier: a healthcare ecosystem where supply chain connectivity and collaboration make it possible to better manage through, and even get ahead of future crises and healthcare challenges.

Source: ibm.com

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AI and crowdsourcing to help physicians diagnose epilepsy faster

Epilepsy, a chronic neurological disorder, always causes unprovoked, recurrent seizures — but the experience can be very different from person to person.

A highly individualized condition, epilepsy is extremely difficult to diagnose uniformly or at scale, which is further complicated by the fact that disease expressions change over time. One development in helping us better understand epilepsy is that researchers have been collecting electroencephalography (EEG) data about patients for quite some time.

In a new paper in The Lancet’s EBioMedicine journal, “Evaluation of Artificial Intelligence Systems for Assisting Neurologists with Fast and Accurate Annotations of Scalp Electroencephalography Data,” we describe the design and implementation of a new open hybrid cloud platform to manage and analyze secured epilepsy patient data. We also present the findings from a related crowdsourced AI challenge we launched to encourage IBMers across the globe to use Temple University epilepsy research data and our new platform to develop an automatic labelling system that could potentially help reduce the time a clinician would need to read EEG records to diagnose patients with epilepsy.

The results indicate deep learning can play an extremely important role in patient-specific seizure detection using EEG data, gathered using small metal discs—called electrodes—attached to a patient’s scalp to detect electrical activity of the brain. We found that deep learning, in combination with a human reviewer, could serve as the basis for an assistive data labelling system that combines the speed of automated data analysis with the accuracy of data annotation performed by human experts.

The challenges within the Challenge

The IBM Deep Learning Epilepsy Challenge, as described in the EBioMedicine paper, asked participants to develop AI algorithms that could automatically detect epileptic seizure episodes in a large volume of EEG brain data collected by the Neural Engineering Data Consortium at Temple University Hospital (TUH). For this application, operating at high sensitivity (~75%) while maintaining a very low false alarm rate is crucial. IBM researchers who participated as competitors were provided with an ecosystem that allowed them to efficiently develop and validate detection models. Importantly, competitors did not have direct access to the dataset nor were they able to download the data.

The IBM-TUH challenge organizing team processed participant responses through objective and predetermined evaluation metrics. One of our goals was to lower the barrier of entry in using AI model development platforms. We turned to crowdsourcing because it let us draw from a larger pool of talent across the company, essentially turbocharging the discovery process.

As organizers of the challenge, our big test was finding a way to exploit the “wisdom of the crowd” while keeping highly sensitive medical data secured and private. IBM’s hybrid approach to cloud infrastructure played a pivotal role in meeting that challenge, enabling the broader research community to participate in crowdsourced model development, all while keeping patient data secured and preventing it from being downloaded or directly accessed by participants. Our challenge platform infrastructure was housed and hosted data behind a secure firewall, allowing participants to test and submit models and then to receive feedback about the performance of their algorithms.

Close to one hundred IBM researchers participated in the challenge. The criteria for evaluation of submitted models were fairly straightforward—detect a seizure when there is one, without producing a lot of false positives that would undermine confidence in the model being judged. The best performing model in the challenge would have, if used in the real world, decreased the amount of data a doctor would have had to manually review by a factor of 142. That means that instead of having to manually review 24 hours of raw EEG data, using the models developed in the challenge, a doctor would only have to review 10 minutes of data. The key wasn’t just speeding up analysis, but also accurately labeling and reducing the amount of data a doctor would need to review.

After close to two years building the platform, the challenge served as a showcase for its capabilities. The platform facilitated the use of Temple University’s data to develop an effective detection system that we hope can one day assist neurologists to improve the efficiency of EEG annotation. Ultimately, this lays the foundation for clinicians to develop more accurate, personalized and precise treatment plans for epilepsy patients.

We’ve continued to develop our deep learning platform and have already made an updated version available for a public crowdsourced challenge project with MIT. The platform will be eventually open sourced, a move we anticipate will open the door to even more exciting deep learning projects.

Helping physicians to improve patient care

Our work is part of IBM’s larger mission to build a digital health platform that can analyze a range of biomarkers—including sleep, movement and pain—and use those metrics to help physicians better understand, monitor and treat diseases. Deep learning—and the AI models the method creates—can potentially complement doctors’ clinical assessments to help them provide faster and more accurate diagnoses and treatments.

Additionally, IBM Research and Boston Children’s Hospital will soon publish joint work in which AI is used to study epilepsy. The paper showcases AI models that can detect the largest range of epileptic seizure types yet in pediatric patients—including seizure types that have never before been able to be detected automatically using technology. The AI algorithms use temperature, electrodermal activity and accelerometer data from commercially available wearable devices (such as smartwatches) to detect and identify epileptic seizures. This work was also showcased recently at the American Epilepsy Society Annual Meeting (AES) and through PAME (Partners Against Mortality in Epilepsy) Recognition in the Clinical Research Category.

This work is part of IBM Research’s use of AI to better understand a range of diseases and conditions through the analysis of natural, minimally invasive biomarkers such as speech, language, movement, sleep, pain, stress levels and mood. This includes work to use these data points to help better monitor, measure and predict events for conditions such as chronic pain, Alzheimer’s, Parkinson’s and Huntington’s diseases, as well as psychiatric disorders such as schizophrenia and addiction.

Source: ibm.com

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Tackling global uncertainty with agility and technology

The technological and economic transformations of the past decade have been some of the most dramatic in history—and they help explain much of the uncertainty business leaders are now confronting.

To succeed in the decade ahead, leaders must be prepared to act and adapt in new, yet sometimes daunting, ways. Technology will both drive this change and help address it. The biggest opportunities lie in AI and data to put a seamless focus on a business’s big picture and its details.

Yet the greatest investments will be in people. It will take a new kind of workforce to truly harness these technologies, a new kind of leadership to capitalize on it and a renewed focus on global issues to satisfy key constituencies.

As we kick off a new decade, here are six trends driving business in 2020 and beyond. These will have an impact on how businesses are run day-to-day, how things need to adapt ahead of these changes and what smart leaders need to do to prepare.

Uncertainty demands agility

The global economy is in a period of historic transformation, with moments of uncertainty likely to persist for some time. Industrial demand is evolving as consumers expect full experiences, not just physical products. Global trade tensions heighten the differences between capitalistic and state-sponsored enterprises. Shifts in workforce demographics reveal skill gaps and fuel the race for talent.

Business leaders need to understand that the economic transformation is impacting their organizations as well as workers and consumers. They should respond by becoming more agile and adaptable to the disruption, and they should continue to identify sources of competitive advantage to deliver to customers and clients the value that they’re looking for.

Tech’s impact goes beyond its application

It’s easy to be concerned about this transition, but what’s happening today fits a recognizable pattern of technology’s historic impacts. Looking back to the First Industrial Revolution starting in the 18th Century, machine-made production boosted productivity in textile manufacturing, agricultural harvesting and food processing. From a socio-economic perspective, technology led to population redistribution from rural areas to factory towns, and the rise of the working class.

Today we’re on the cusp of the Fourth Industrial Revolution. Information technology has bolstered productivity across business processes and brought trade to the fingertips of every consumer. New technologies like AI, IoT, 5G and Blockchain will further accelerate a data explosion. Business leaders need to invest in human capital and intellectual property alongside technology to facilitate the new business processes, new products, new services, and new business models success will require.

AI changes how work gets done

AI is well suited to perform certain tasks that draw on experiential learning, like scheduling equipment maintenance and defect detection. Human workers bring softer skills to the equation, like using intuition, applying creativity and creating innovative designs. IBM’s research at the MIT-IBM Watson AI Lab shows that AI adoption is gaining momentum by performing a small portion of AI-suitable tasks across many occupations.

This is creating a new and growing set of requirements for workers – not less work but different work. Occupations and tasks are being reorganized. This means organizations must reskill workers to work with AI and look for additional opportunities to improve productivity. We are still in the very early days, but the pace is quickening.

Data is a raw material—it must be refined

We often say data is the newest natural resource. Just as oil is only useful in its applications—for heat, energy, plastic products, etc.—the same goes for data. It’s all in how you use it. The challenge for business leaders is to organize their data and all its disparate sources, whether it’s IoT, financial, customer or HR data. Companies must display the data in a timely, clear manner, and not only understand performance but also how to improve performance.

AI is one solution. It provides the benefit of anticipating future needs, whether for consumers, workers or decision makers. And AI is different because of its ability to draw on vast pools of unstructured data, solve nonlinear optimization problems, and use fast, inexpensive computing power.

Sustainability beyond the consumer

In a number of markets, we have consumers expressing a desire for more sustainable practices and products—particularly younger consumers. Workers have begun demanding it, too. Increasingly, we’re seeing investors becoming concerned with sustainability. Some want to minimize corporate liability, but they also want more social responsibility.

Traditional tools remain insufficient to tackling such globe-spanning issues, which are bigger than any one organization. That’s where technology like blockchain and AI can break down barriers to tackle serious issues, like product transparency or carbon credits. Solutions are out there, and they’re being driven by innovations and conversations.

Agile leaders make resilient companies and communities

The Business Roundtable made its recent announcement that “companies should benefit all stakeholders.” It’s becoming the norm. The kind of long-term value that shareholders require is going to take a commitment to a broader set of constituents, including customers, employees, suppliers and especially the communities we live and work in.

It’s going to require a reallocation of capital to talent, research, sustainability and social responsibility. Agile leaders are the ones who help companies and communities navigate the transformation ahead. It’s not just in business but the nature of work, education, public policy—everything. Companies are driving this transformation.

Source: ibm.com

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Making the move to value-based health

A few years ago, the IBV Institute for Business Value published a report which predicted the convergence of population health management and precision medicine into a new healthcare model we called Precision Health and Wellness. We believed a key component of that model would be a continued transition to outcome-based results and lower costs.

Fast forward to 2019 and our latest research found that not only had those predictions become real but that the speed of change had increased. Globally, healthcare systems are looking at how to maintain access, quality, and efficiency. Emphasis has shifted from volume of services toward patient outcomes, efficiency, wellness, and cost savings. And there is a recognition that the focus on “care” alone will be not deliver the degree of outcome improvement and cost reduction needed by providers or payers. Instead they will need engage the individuals, employers, communities, and social organizations as key partners in the process.

By using collaboration models, shared information, and innovative technology solutions across these stakeholders, better outcomes can be achieved across the whole lifespan of the individual—not just in doctors’ offices and hospitals, but in their daily lives, homes, and communities. It is this extension of health and wellness beyond the traditional clinical environment that takes care to the next level – value-based health.

Value -based health entails keeping individuals healthy and well even when not receiving healthcare services. Engaging people and communities in health, identifying and addressing social determinants of health, and making sure community resources are available and accessible are cornerstones of value-based health.

In order to determine what is needed to transition from traditional value-based care towards value-based health, we spoke with a thousand healthcare executives in payer and provider organizations around the world.

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