that are available by virtue of regulatory authority without information blocking for purposes of public health surveillance . Over time , we can advance to an additional way in which the data can be obtained and used for surveillance , and that is where we are able to query FHIR servers for some of the data for HAI and AUR numerator , denominator , and line-level patient data .”
Pollock continued , “ This can enhance development of HAI metrics , such that we can use elements to develop our predictability models that are an integral part of the SIR or our risk adjusted HAI measures . So , in practice , what we are envisioning is an NHSN that delivers FHIR-based HAI and AU data collection and reporting requirements to the hospital systems where they are ingested , and the hospital systems in turn deliver in accordance with specifications , numerator , denominator and supplementary data to NHSN . The supplementary data is patient-level data ; NHSN can produce data sets , dashboards and other data provisioning that can go to local , state and federal partners , and we have features in NHSN already built that enable partners to gain direct access to data reported in to NHSN . An essential part of this whole process is our use and contribution to data standards , and that enables us to work in a vendor-neutral , standards-based approach with HL7 and health IT implementation vendors to create , use and update standards that are an essential part of automation .”
“ We are at a point in the evolution of NHSN and the evolution of healthcare IT where there is opportunity to grow , to move beyond some of the present limitations , and use healthcare IT and automatable data more purposefully for surveillance and quality measurement ,” Pollock emphasized in his Decennial Day presentation . “ We have key strategies for doing so , including using Natural Language Processing ( NLP ) — a computational technique for extracting concepts , such as medical conditions , from free text documents — as an aid to infectious disease and non-infectious disease case finding , that would otherwise require a great deal of labor and effort . NLP is a tool for getting at small data and using it for some conditions in ways for surveillance in ways that have been elusive for public health surveillance – both for ID and non-ID case finding . Using NLP , we can greatly reduce the time and effort required to identify such conditions such as lower respiratory tract infections , infectious endocarditis , venous thromboembolism , and adverse drug events . Paying attention to small data that can make big data richer and readily usable is an essential part of our vision for NHSN , forging a partnership with healthcare facilities , clinical communities , and healthcare IT vendors . What we are envisioning are the enormous opportunities for advances in healthcare surveillance and quality measurement that can come as a result of the attention given to big data ; once we have those data available in a large , open information space , referred to as a data lake , we can apply big data in predictive analytic tools that can enable predictions and real-time analytic capabilities such as for risk adjustment purposes of our clinical quality measures , for identifying instances that have eluded surveillance in the past , so in order to make all of this happen , we need to place special emphasis right now on the small data and the veracity of those data ; the value of those data will be enhanced as we increase their validity , their accuracy , their social relevance – all of that is important work for us to do as we move forward . “
The Missing Pieces of the Puzzle
For all the efforts devoted to infection surveillance and reporting , what if healthcare institutions were missing wide swaths of other key conditions that did not fall under mandatory reporting rules ?
Posing just this scenario was Michael Klompas MD , MPH , FIDSA , FSHEA , a professor of infectious disease at Harvard Medical School and a hospital epidemiologist at Brigham and Women ’ s Hospital . In his Decennial Day 2021 presentation , “ Ongoing Evolution of Healthcare Information Technology and Opportunities for Application to HAI Surveillance ,” Klompas noted , “ In practice , I think most U . S . hospitals focus the bulk of their reporting efforts on CAUTI , CLABSI , SSI , HO-MRSA and HO-CDI , largely because those are the conditions we are required to report to CMS . I ’ d argue that this particular focus is very much a legacy of some of the history of infection control . About 50 years ago now , the CDC kicked off the landmark SENIC study , a study on the efficacy of nosocomial infections and the mission of that study was to describe U . S . infection control programs , measure nosocomial infections rates , and determine if implementing infection surveillance and control programs lead to lower HAI rates . This landmark study involving many , many hospitals , was a resounding success and set the framework for infection control for the next 50 years .”
Klompas pointed to the key findings of the SENIC study , including the use of detailed chart reviews to estimate an HAI rate of 5.7 events per 100 admissions and finding UTI 42 percent , SSI 24 percent , HAP 10 percent , bacteremia 5 percent and other sites 19 percent . He also pointed to parallel data that came out around the same time as demonstrating that devices , procedures , and intensive care accounted for the majority of nosocomial infections . “ In doing so , the SENIC study set multiple precedents that have carried through to the present day , including that infection control programs can be very impactful and should be a required program for hospitals ; that we should focus our attention on a small set of high-yield infections ( SSI , CAUTI , CLABSI , HAP ); and that surveillance requires detailed chart reviews by trained experts applying clinically nuanced definitions .”
Klompas continued , “ The question is , is this still the right approach for the current era ? Are we capturing the right events ( completeness , morbidity )? Are we conducting surveillance in an efficient and objective fashion ? Is our approach commensurate with current
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About 50 years ago now , the CDC kicked off the landmark SENIC study , a study on the efficacy of nosocomial infections and the mission of that study was to describe U . S . infection control programs , measure nosocomial infections rates , and determine if implementing infection surveillance and control programs lead to lower HAI rates .
www . healthcarehygienemagazine . com • june 2021
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