Analytics

The machine learning model uses 600 variables with patient's data whereas human-constructed models made predictions based on 27, researchers say.

Imagine knowing, in real time, whether a patient will suffer a surgical infection as a surgeon closes up a wound. That's the kind of clinical situation that machine learning is enabling at the University of Iowa Hospitals & Clinics. In a 3-year pilot study ending in 2016, in a subset of general and colorectal surgery, the health system's innovation with AI analytics has led to a 74 percent reduction in surgical site infection. At scale, this would translate to approximately $1.2 million in cost savings – not including savings from value-based purchasing because of the reduced surgical site infection rate. Iowa’s work with comes as more and more hospitals and tech vendors are undertaking innovative initiatives with machine learning and artificial intelligence. Johns Hopkins for instance, is using deep learning to improve how it handles pancreatic cancer and Amazon Web Services is harnessing machine learning to enable customers to better treat depression. Co-developing machine learning The university is co-developing the machine learning technology with vendor DASH Analytics. The system is called the DASH Analytics High-Definition Care Platform, or HDCP. Its proprietary design uses machine learning as it provides valuable data, metrics and decision support at critical moments during the point-of-care timeline. HDCP, the university said, helps lower the rate of surgical infections, reduces the risk of requiring a blood transfusion during surgery, saves lives from brain failure and saves lives from unrecognized sepsis. The technology combines several features, said John Cromwell, MD, associate chief medical officer and director of surgical quality and safety at the University of Iowa Hospitals & Clinics. "The system uses curated knowledge of where and when specific critical decisions that drive outcomes are being made by providers for numerous clinical conditions where there is massive room for improvement," he explained. "It is a machine learning system that integrates with the EHR using industry-standard and vendor-specific APIs and in real time measures individual patient risks and evaluates appropriate best practice based upon these risks." With those two features, HDCP integrates decision support within the provider's EHR workflow, and it generates feedback on how their use of the data changes their patient's outcomes, reinforcing high-value practices, he said. The system works silently in the background, monitoring for specific points in patient care where decision support may improve patient outcomes. At that point in time, the decision support becomes visible to the clinician or other front-line provider within their usual workflows in the EHR. It will present them with the specific risk for their specific patient along with actions to potentially mitigate that risk. "The risks are assessed by using best-in-class machine learning algorithms that use both real-time and historical data on individual patients," Cromwell said. "These risk models are calibrated specifically to patients in each individual hospital using the platform." Here's how it works The surgical site infection reduction module in HDCP is integrated within the World Health Organization Surgical Safety Checklist that virtually all hospitals use during surgery. The module is activated near the completion of a surgery as the circulating nurse is going through his or her routine closing checks. At the time of module activation, real-time data from the EHR such as the surgeon, case duration or estimated blood loss flows into the platform and is combined with historical data on the patient. All of this data then flows into the surgical site infection prediction model. "The machine learning model calculates the infection risk and links this risk to specific interventions that the surgeon may take at the time of wound closure to reduce the infection risk," Cromwell explained. "The risk information and possible interventions are then presented in an interactive interface back to the nurse at her workstation – the whole process takes mere seconds to complete – who then delivers the information to the surgeon." Using a single click, the nurse records whether the surgeon used the decision support recommendations. Ultimately the patient's outcome with respect to surgical site infection is returned to the platform and used to generate an aggregate report for the surgeon regarding his or her outcomes when recommendations were or were not used, thus reinforcing the use of appropriate decisions. "It is very difficult for surgeons to integrate the information necessary to determine whether a patient is at high risk for a surgical site infection," Cromwell said. "There are certainly obvious cases where there is a break in technique, contamination, or very high-risk patient factors, but these are the minority of the cases." There are interventions that can be done at the time of wound closure, but these can be costly or invasive. Would one do these interventions to 100 percent of patients if only a fraction can actually get a surgical site infection? "Selectively using these interventions in patients where it is warranted by objective markers of risk maximize the therapeutic effect, while minimizing the cost and potential risks to patients," Cromwell explained. "In this case, we were able to selectively use negative pressure wound therapy on patients with markers of high risk to achieve the 74 percent reduction. Without the system, we could not have known objectively which patients to use this costly therapy on." Ultimately, machine learning is critical for integrating hundreds or thousands of variables for individual patients in order to objectively measure risk, he added. "Integrating such massive amounts of information that is impossible for any individual caregiver to perform," said Cromwell. "And no matter how much experience one has, the exponential increase in medical knowledge makes it impossible for a caregiver to assimilate all of the data necessary to consistently apply best practices in every situation." A systematic approach to mitigating adverse outcomes or complications requires that one systematically identify the risks, he added. Machine learning algorithms, with few exceptions, are able to do this much more effectively than humans on a consistent basis, he said. "This removes the variation in risk assessment that one may get between different physicians," he said. "Once a provider has an objective assessment of risk, then they may move on to mitigating that risk. When best practices are known and supported by data, machine learning can identify which patients these best practices should be applied to, in a consistent manner. By approaching risks objectively and systematically, we can have an effect greater than any pharmaceutical can provide." Twitter: @SiwickiHealthIT Email the writer: bill.siwicki@himssmedia.com

The Ohio health system implemented analytics and decision support to reduce opioid prescriptions.

The tariffs being proposed by the administration of President Donald Trump, particularly those on certain Chinese imports, could harm the booming cloud computing industry in the United States, undermine U.S. leadership in the sector, and harm innovation – not least in healthcare. That's according to a new report from the Information Technology and Innovation Foundation, the influential nonprofit tech policy think tank, based in Washington, D.C. By stifling the development of fast-evolving cloud technology, the tariffs could put the U.S. at a competitive disadvantage and adversely impact consumers – and, potentially patients, according to the study, which spotlights some specific initiatives that could be affected by import taxes. "For businesses large and small alike, in industries ranging from agriculture and manufacturing to healthcare and transportation, cloud computing has become an indispensable platform technology critical to their success," ITIF researchers wrote in the report. The U.S. economy has become utterly dependent on cloud computing, with 93 percent of businesses nationwide – including most hospitals and health systems – relying on some 3 million data centers, the study showed. And, of course, innovation in the cloud space is enabling huge advances in technology offerings and healthcare outcomes. But Trump's desired tariffs on Chinese imports would put a crimp in the pipeline of key components that fuel that innovation, and could have four specific negative consequences for the industry, the study argues: Higher prices for business and consumers using cloud technology; Fewer new hires, less R&D for product innovation and slowing business expansion as cloud providers seek to cut costs; Higher costs to provide cloud services from the U.S. could leader to other international locations more price-competitive; Disruption of global supply chains for the manufacture of IT – something that "wouldn’t be easily reinvented in the short-term without significant detriment and dislocation to U.S industry." As the report's lead author, Stephen Ezell, ITIF's vice president for global innovation policy, explained: "While the tariffs were proposed to counteract unfair Chinese trade practices and improve U.S. competitiveness, they in fact hurt U.S. cloud computing competitiveness. It’s critically important to contest Chinese innovation mercantilism, but the administration’s proposed tariffs on key capital goods imports are the wrong way to go about it. They threaten U.S. leadership in cloud computing and stunt U.S. economic growth." To help make its point, the ITIF study pointed as an example to San Francisco-based Numerate, which has a healthcare focus, and is using cloud-powered analytics to help improve the drug-design and innovate precision medicine approaches for cardiology. "Numerate’s algorithms provide predictive models for molecular properties with accuracies comparable to laboratory testing, enabling scientists to search through billions of compounds to rapidly and efficiently identify those with the highest probability of activity against a specific  disease target," according to the report. The company's uses cloud computing to help it harness "exploding amounts of empirical data to clear the way for scientists to design new therapies in the cloud," leading to new approaches that could "dramatically reduce the cost of pharmaceutical development and expand the number of therapies that can be created and tested by moving medical research away  from a 'hit-and-hope' world of trial-and-error guesswork." But new tariffs could slow that work, ITIF maintained – and work similar to it at life sciences companies, health IT vendors, hospitals and health systems. "Many Americans will feel the impacts of the proposed tariffs on cloud computing through increased prices, lost jobs, and decreased economic opportunity," said the report's co-author Caleb Foote, ITIF research assistant. "The administration should pursue alternative policy measures that don’t raise the cost of key productivity- and innovation-enhancing capital goods and services." .jumbotron{ background-image: url("http://www.himss.org/sites/himssorg/files/u351641/BigData-Forum2-June2018-712.jpg"); background-size: cover; color: white; } .jumbotron h2{ color: white; } Big Data & Healthcare Analytics Forum The Boston forum to focus on effective pop health management, AI and precision medicine Oct. 22-23. Twitter: @MikeMiliardHITN Email the writer: mike.miliard@himssmedia.com

Deloitte and Vineti announced that they are now working together to integrate and scale their technology platforms – for supply chain and patient engagement, respectively – to give various precision medicine stakeholders easier access to emerging cellular therapies. Vineti, launched as a commercial venture by the Mayo Clinic in partnership with GE, develops  a configurable cloud-based platform designed to improve patient access to cell therapies and genomic medicine. Deloitte's ConvergeHEALTH Patient Connect analytics technology helps connect health systems, pharmaceutical companies, medical device vendors and other groups working to innovate precision medicine. By integrating the Vineti and ConvergeHEALTH technologies, the two companies aim to offer life science researchers, healthcare providers, IT vendors and patients with a more unified platform for personalized medicine. Vineti's software, which is currently deployed at more than 65 leading medical centers, helps users align, manage and integrate major steps in the complex cell and gene therapy process. ConvergeHEALTH Patient Connect, used by 300,000 patients worldwide, is a suite of software tools to help enable actionable insights derived real-world information, evidence and experience. Using both, healthcare and life sciences professionals will be able to innovate and scale up personalized therapies, connect with other health providers and enable broader patient access to precision therapies. "Personalized medicine is changing all aspects of the industry including the way patients, providers and life sciences innovators interact and we believe that a solution that contains both Vineti and PatientConnect will bring an unprecedented level of real-time support and fidelity to control supply chain and care pathway management," said Chris Zant, ConvergeHEALTH chief digital officer, in a statement. .jumbotron{ background-image: url("http://mobihealthnews.com/sites/default/files/u751/Innovation-month-jumbotron-1.jpg"); background-size: cover; color: white; } .jumbotron h2{ color: white; } Focus on Innovation In September, we take a deep dive into the cutting-edge development and disruption of healthcare innovation. Twitter: @MikeMiliardHITN Email the writer: mike.miliard@himssmedia.com

Data scientists from Takeda and institute ConvergeHEALTH by Deloitte are applying artificial intelligence to pools of patient data to test how treatment-resistant depression responds to medication.

A single source of truth is essential for successfully managing population health across large regions.

To VirtualHealth’s Sheela Ramamurthy, technology that works in the background to create a full picture of the patient will greatly impact the sickest, most vulnerable care populations.

Studio H will develop digital tools and enterprise analytics technologies to innovate the healthcare experience for plan members.

Omkar Kulkarni, innovation chief at Children's Hospital Los Angeles, says adoption of tech requires commitment, focus.