NEW TECHNOLOGY


Machine learning may help in early identification of severe sepsis A machine-learning algorithm has the capabil- ity to identify hospitalized patients at risk for severe sepsis and septic shock using data from electronic health records (EHRs), according to a study presented at the 2017 American Thoracic Society International Conference. Sepsis is an extreme systemic response to


infection, which can be life-threatening in its advanced stages of severe sepsis and septic shock, if left untreated. “We have developed and validated the first machine-learning algorithm to predict severe sepsis and septic shock in a large academic multi-hospital healthcare system,” said lead author Heather Giannini, MD, of the Hospital of the University of Pennsylvania. “This is a breakthrough in the use of machine-learning technology, and could change the paradigm in early intervention in sepsis.” Machine learning is a type of artificial intel-


ligence that provides computers with the abil- ity to learn complex patterns in data without being explicitly programmed, unlike simpler rule-based systems. Earlier studies have used electronic health record data to trigger alerts to detect clinical deterioration in general. The researchers developed a machine-


learning algorithm to predict patients most at risk for severe sepsis or septic shock, and to use their electronic health record to alert the care team. To develop the algorithm, they trained a random forest classifier, an approach to classify a wide range of data, to sort through electronic health record data for 162,212 patients discharged between July 2011 and June 2014 from three University of Pennsylvania Health System acute care hospitals. The algorithm was able to examine hun-


dreds of variables on a continuous basis. Pa- tients with severe sepsis or septic shock were labeled as such 12 hours before the actual onset of severe sepsis or septic shock. The onset was determined based on lab results and physiological data, such as blood pres- sure. A total of 943 patients in the database met the assigned lab or physiological criteria. The algorithm was validated in real time


between October and December 2015 with 10,448 patients while they were cared for in the study hospitals, using a “silent mode” of electronic health record sampling. Approxi- mately 3 percent of all acute care patients screened as positive, and 10 alerts were sent each day across the three hospitals. “We were hoping to identify severe sepsis


or sepic shock when it was early enough to in- tervene and before any deterioration started,” said senior author Craig Umscheid, MD, of the Hospital of the University of Pennsylvania. “The algorithm was able to do this.“


Photo courtesy: Ryder 40 July 2017 • HEALTHCARE PURCHASING NEWS • hpnonline.com Page 42


PRODUCTS & SERVICES How tight a ship do


for sending and receiving freight by Rick Dana Barlow


f space is the final frontier for manag- ing inventory, freight and shipping costs must represent the toll booths along the way. On the journey of getting something


I


from there to here or vice versa, these tolls can add up — both in frequency and amount. Over the years, Healthcare Purchasing


News has explored the breadth and depth of healthcare providers managing the costs and practices of freight and ship- ping. Historically, the bulk of HPN’s coverage focused on the tips for and traps of controlling costs and improving how Supply Chain oversees inbound and outbound freight and shipping. This year remains no exception as healthcare organizations continue to struggle with freight and shipping practices — from making it a budgetary, and therefore a management, priority to relying on outside resources for vary- ing degrees of control. Those outside resources typically include third-party logistics (3PL) companies as well as main- line distributors with 3PL “boutique” operations.


One noteworthy trend emerges: Soft-


ware. Designed to help Supply Chain departments manage costs by tracking usage and expenses, these applications automate portions of the process, func- tioning sort of like a “Freight-Shipline. com,” but without the clever commercials and corny pitchmen. Regardless of outsourcing all or part


of the freight and shipping function to a third party or using a software package internally, Supply Chain executives and professionals continue to wrestle with some of the subtle nuances of freight and shipping contracts and requirements.


Red flag rising To wit, HPN asked a variety of freight/ shipping executives to reveal and ex- plain some of the contractual red flags that Supply Chain pros should spot and understand, including such examples as early termination addendums, actual versus billed weight discrepancies and minimum billable weight determina- tions.


Contractual red flags are difficult to identify because typically they may be


you run? Supply Chain pros don’t want to pay the freight


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