In health care, the days of business as usual are over. Around the world, every health care system is struggling with rising costs and uneven quality despite the hard work of well-intentioned, well-trained clinicians. Health care leaders and policy makers have tried countless incremental fixes--attacking fraud, reducing errors, enforcing practice guidelines, making patients better "consumers," implementing electronic medical records--but none have had much impact. At its core is maximizing value for patients: that is, achieving the best outcomes at the lowest cost. We must move away from a supply-driven health care system organized around what physicians do and toward a patient-centered system organized around what patients need. We must shift the focus from the volume and profitability of services provided--physician visits, hospitalizations, procedures, and tests--to the patient outcomes achieved. And we must replace today's fragmented system, in which every local provider offers a full range of services, with a system in which services for particular medical conditions are concentrated in health-delivery organizations and in the right locations to deliver high-value care. Making this transformation is not a single step but an overarching strategy. We call it the "value agenda." It will require restructuring how health care delivery is organized, measured, and reimbursed. In 2006, Michael Porter and Elizabeth Teisberg introduced the value agenda in their book Redefining Health Care. Since then, through our research and the work of thousands of health care leaders and academic researchers around the world, the tools to implement the agenda have been developed, and their deployment by providers and other organizations is rapidly spreading. The transformation to value-based health care is well under way. Some organizations are still at the stage of pilots and initiatives in individual practice areas. Other organizations, such as the Cleveland Clinic and Germany's Schön Klinik, have undertaken large-scale changes involving multiple components of the value agenda. The result has been striking improvements in outcomes and efficiency, and growth in market share. There is no longer any doubt about how to increase the value of care. The question is, which organizations will lead the way and how quickly can others follow? The challenge of becoming a value-based organization should not be underestimated, given the entrenched interests and practices of many decades. This transformation must come from within.
To doctors, pajama time means homework. In fact, it's a common phrase describing the nighttime ritual of finishing up clinical notes about the patients they saw earlier that day. As demands for notes and data to chronicle patient interactions from hospital administration and insurance industry payers have increased, the amount of time physicians spend on the computer has squeezed their already tight schedules. A 2017 study published in Annals of Family Medicine found that primary care physicians spend nearly six hours a day interacting with their electronic health records systems during and after clinic hours. Amid pandemic burnout, the stress is enough for doctors to hand over the work of writing clinical notes to an AI-based tool, even if it could create patient data privacy risks and medical errors.
Have you ever found yourself sitting for hours in a busy waiting room at the ER? It's stressful, you're in physical pain and everyone is rushing around you, focused on the massive number of other patients waiting, too. Luckily, hospitals are beginning to use machine learning solutions to streamline the waiting room process. The healthcare industry is introducing AI to combat many issues in the field. AI in hospitals can not only ease hospital patient flow, but it can also help develop pharmaceutical drugs, keep and analyze data and patient records, and even help diagnose illnesses like cancer. When people spend less time in the ER waiting room, more people can be treated in a timely manner.
Terence Mills, CEO of AI.io, a data science & engineering company that is building AI solutions that solve business problems. Have you ever found yourself sitting for hours in a busy waiting room at the ER? It's stressful, you're in physical pain and everyone is rushing around you, focused on the massive number of other patients waiting, too. Luckily, hospitals are beginning to use machine learning solutions to streamline the waiting room process. The healthcare industry is introducing AI to combat many issues in the field. AI in hospitals can not only ease hospital patient flow, but it can also help develop pharmaceutical drugs, keep and analyze data and patient records, and even help diagnose illnesses like cancer. When people spend less time in the ER waiting room, more people can be treated in a timely manner.
Most likely, you're familiar with Watson from the IBM computer system's appearance on Jeopardy! in 2011, when it beat former champions Ken Jennings and Brad Rudder. Watson Health was supposed to change health care in a lot of important ways, by providing insight to oncologists about care for cancer patients, delivering insight to pharmaceutical companies about drug development, helping to match patients with clinical trials, and more. It sounded revolutionary, but it never really worked. Recently, Watson Health was, essentially, sold for parts: Francisco Partners, a private equity firm, bought some of Watson's data and analytics products for what Bloomberg News said was more than $1 billion. On Friday's episode of What Next: TBD, I spoke with Casey Ross, technology correspondent for Stat News, who has been covering Watson Health for years, about how Watson went from being the future of health care to being sold for scraps.
Artificial intelligence (AI) is a formidable tool for supply chain management and procurement services for healthcare systems. Just about any product can be sourced/evaluated with the highest efficiency using AI--for example, personal protective equipment (PPE), medical devices and equipment, supplies, components, contracts (including terms and costs), and even third-party vendors for particular services--all within a matter of hours. AI is amazingly cost-effective in that it saves healthcare systems large amounts of time and billions of dollars through improved efficiencies. For example, AI can process hundreds of thousands of products or contracts in one business day or less--an achievement that would take years if it was a manual task. AI helps hospitals control costs by making their procurement processes more cost-efficient and productive, ultimately enhancing the patient experience.
The pandemic is now two years old. A population the size of Finland has so far died from COVID-19 and tens of millions more are dealing with its side-effects. Even for those who haven't fallen seriously ill, nearly every aspect of our lives has been disrupted by COVID-19: from how we socialize and communicate, to how we study and work. We are all familiar with the crisis, but how has it impacted innovation, especially in the health and healthcare sector? Most obviously, the industry has seen a massive wave of investment, innovation and new entrants from the technology, telecom and consumer industries.
Adverse drug reactions / events (ADR/ADE) have a major impact on patient health and health care costs. Detecting ADR's as early as possible and sharing them with regulators, pharma companies, and healthcare providers can prevent morbidity and save many lives. While most ADR's are not reported via formal channels, they are often documented in a variety of unstructured conversations such as social media posts by patients, customer support call transcripts, or CRM notes of meetings between healthcare providers and pharma sales reps. In this paper, we propose a natural language processing (NLP) solution that detects ADR's in such unstructured free-text conversations, which improves on previous work in three ways. First, a new Named Entity Recognition (NER) model obtains new state-of-the-art accuracy for ADR and Drug entity extraction on the ADE, CADEC, and SMM4H benchmark datasets (91.75%, 78.76%, and 83.41% F1 scores respectively). Second, two new Relation Extraction (RE) models are introduced - one based on BioBERT while the other utilizing crafted features over a Fully Connected Neural Network (FCNN) - are shown to perform on par with existing state-of-the-art models, and outperform them when trained with a supplementary clinician-annotated RE dataset. Third, a new text classification model, for deciding if a conversation includes an ADR, obtains new state-of-the-art accuracy on the CADEC dataset (86.69% F1 score). The complete solution is implemented as a unified NLP pipeline in a production-grade library built on top of Apache Spark, making it natively scalable and able to process millions of batch or streaming records on commodity clusters.
"Too many meanings, and it's seen as the hammer that can solve everything," he said. "I think'innovation' needs to go away in the context of what we use these days because everything that we do has innovation built into that," said Anil Bhatt, CIO at Anthem Inc. "We want to make sure that we are being more real about things as we move forward." Citigroup Inc. CIO of its Global Consumer Bank Shadman Zafar requests that everyone deprogram from language best suited to machines, like "sync up." "Let's use more human terms to describe human interactions," he said. Another one he would not miss is "actionable insights." "Any other kind would be useless so we don't need to waste time on those other insights, and we can drop the'actionable' adjective!" he said.
According to the Food and Drug Administration (FDA), the term real-world data (RWD) refers to routinely collected data relating to patient health status and the delivery of healthcare services, and real-world evidence (RWE) is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from the analysis of RWD. Both RWD and RWE have increasingly attracted attention in the healthcare industry for years now, and rightly so, considering that the healthcare analytics market is expected to expand at a compound annual growth rate of 28.9% between now and 2026. There's no doubt that within this massive data trove, there exist countless insights that could streamline care delivery, help physicians diagnose disease faster, and improve treatment strategies – if only we could identify them. This data revolution we are experiencing in the healthcare industry necessitates the appropriate tools and approaches to work with higher dimensional data sources to truly harvest the insights buried in RWD. Machine learning, an area of artificial intelligence (AI) consisting of a collection of methodologies that focus on algorithmically learning efficient representations of data and extracting insights from data, offers promise and has consistently been gaining traction within the industry in the context of RWD.