Leveraging Advanced Analytics to Help Hospitals Measure Efficacy of Treatment Standards and Improve Detection of Healthcare Associated Infections

Leveraging Advanced Analytics to Help HospitalsMeasure Efficacy of Treatment Standards and ImproveDetection of Healthcare Associated InfectionsOntological Framework for EHR AnalyticsYugal Sharma, Ph.D. Reechik Chatterjee, M.A.Lead Associate, Advanced Analytics Lead Associate, Enterprise Integration This document is confidential and is intended solely for the use and information of the client to whom it is addressed.

Table of contents• The Challenge• Our Solution• Advanced Analytic Techniques• Results & Applications• Helping Booz Allen’s Clients be Ready for What’s Next 2

Improving the detection and treatment of Severe Sepsis andSeptic Shock is a national healthcare priority• As a result of high mortality rates and an estimated annual cost of $28-$33 billion, the US Department of Health and Human Services has designated the reduction of Healthcare Associated Infections (HAIs) as a major priority.• Severe Sepsis and Septic Shock (S4), a specific type of HAI, has become an area of increased focus due to its extremely high mortality rate, ranging from 30-50%, significant prevalence (> 750k cases diagnosed annually), and high burden of cost (estimated $16.7 billion annually)• The Sisters of Mercy Hospital System (Mercy), with over 400 clinic and hospital locations in the Midwest, has a strong interest in reducing mortality rates associated with S4 and increasing compliance to international standards for treatment of S4 within its hospitals• While Mercy has a vast centralized repository of Electronic Health Records (EHRs), they needed sophisticated analytical expertise to tweeze out the inherent correlations and patterns present in the data to answer their questions

Booz Allen developed a generalizable framework to address thecomplex analytical challenges inherent to addressing S4 • Booz Allen assembled a team of analytics, bioinformatics, and clinical experts and met with Mercy to identify the areas of greatest priority and interest for addressing S4: • Compliance Analysis: To measure hospital-level compliance with SSC guidelines and evaluate the impact of compliance on patient mortality • Early Detection Analysis: To mine the data for potential clinical indicators that could lead to early detection of S4 • We obtained 27,000 de-identified patient EHRs across 4 Mercy hospitals containing structured and unstructured data for analysis. Based on the volume of data and complexity of these tasks, we identified the need for a generalizable analytic framework to apply to this challenge, but one that could be easily repurposed for other similar analyses. • Because the analysis of relationships among events across time is critical to understanding the evolution of a disease, as well as determining efficacy of time- dependent treatment guidelines, we developed a generalizable framework to represent patient EHR data as a series of temporal events. This approach was later dubbed the “Event-Centric Ontology” (ECO) framework. • With the ECO framework as a basis, Booz Allen brought its deep analytics and informatics expertise, in conjunction with Mercy’s vast clinical and domain experience in S4 to bear on the Compliance and Early Detection analyses.

The ECO framework was used as the foundation for the Compliance andEarly Detection analyses A B

ECO framework can be populated with both structured and unstructuredEHR data by leveraging NLP techniques  At the finest level of analysis, our ECO framework captures individual patient- Ontology level event- instances which are documented in the EHR High-level representation of the domain  Natural Language Processing (NLP) of interest aims to extract the entities and events from unstructured text fields which can be matched against standard terminologies enabling further analyses, research, decision support, coding, categorization, and understanding NLP  The extracted entities/events can also be Extraction and instantiated as new propositions or facts interpretation of individual in our ECO framework data-elements from a domain  The source of unstructured text in our study was primarily in the form of doctor and nurse notes

Compliance with 6-hour treatment protocol is correlated with lower mortality. 100.0% % Compliance Note that Hospital 2 had a 90.0% very small compliance sample (n=3) and was• The overall compliance for the 6-hour 80.0% % Mortality removed from our analyses treatment protocol in our population was 70.0% 16.9%, aggregated across the 4 hospitals in 60.0% our population, corresponding to an overall 50.0% mortality rate of 45.2% 40.0% 30.0%• For Hospitals 1, 3, and 4, initial analysis 20.0% indicates a strong negative correlation 10.0% between compliance and mortality (r=-0.82) 0.0% Hospital 1 Hospital 3 Hospital 4• This analysis gives credence to the value of 100.0% the 6-hour treatment protocol and will serve 90.0% as the initial basis of an educational 80.0% campaign to increase compliance across 70.0% Hosp 3 % Mortality hospitals 60.0% 50.0% r = -0.82 (p<0.01)• Current analyses in progress include 40.0% compliance and ICU bed days, and potential 30.0% cost benefit for compliance to individual 20.0% Hosp 1 Hosp 4 bundle elements 10.0% 0.0% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% % Compliance

The early detection model performs significantly better than chance atidentifying septic patients at risk for developing S4• Expert judgment was used to identify a starting set of three clinical indicators: Temperature, Heart Rate, and y=0 is defined as no risk of Respiratory Rate developing Severe Sepsis• Our initial model used input from these y=1 is defined as high risk of developing Severe Sepsis indicators at t (point of diagnosis), t-1 (one hour before), and t-2 (two hours before)• Performance on the test set gives a ROC plot with AUC=0.68 (note: AUC=0.50 gives 50% correct classification or equal to chance), giving Sensitivity=0.54 and Sens=0.54 Spec=0.73 Specificity=0.73 @ Threshold=0.73• The model showed the most significant indicators to be [Temp]t- 1, [HR]t, [Resp]t, [Resp]t-2

Initial results are promising and help to set the stage forimplementation of evidence-based, cost-effective approaches toreducing the severity of and mortality of S4 • Compliance Analysis results demonstrate a strong correlation between increased compliance to SSC guidelines and decreased mortality. Mercy intends to use this data as part of a campaign to increase compliance with treatment guidelines in the Mercy hospital system with the goal of reducing mortality and the burden of cost to due to S4. • Early Detection Analysis results indicate there may be a set of clinical indicators that could be used to identify patients at risk for developing S4, allowing their care to be prioritized. Additional validation will be necessary and these analyses are currently underway. If successful, these results could help to lower S4-related costs by prioritizing the care of high-risk patients earlier, possibly avoiding expensive treatment interventions. • The application of the ECO framework proved a valuable strategy for the analyses mentioned above. Additional NLP work could add further value to the framework by fully leveraging information in unstructured data. The ECO framework has been presented at several conferences as a case study for how to effectively analyze temporal data for measurement of compliance, early detection, or any other time-dependent analysis. 12

Helping Booz Allen’s Clients Be Ready for What’s Next• A 2007 ruling by the Center for Medicaid and Medicare Services (CMS) limited payment to hospitals for certain preventable hospital-acquired infections HAIs. In 2009, CMS added S4 to the list of conditions covered by this ruling.• As a result, the burden of cost of treating S4 is gradually shifting to providers. Further, if private insurance companies, which typically model their guidelines after CMS, come out with similar policies in the future, the burden to providers will be compounded.• The novel, generalizable ECO framework and results from the Compliance and Early Detection Analyses have helped Mercy to begin the process of increasing compliance to SSC guidelines for treatment of S4. Booz Allen will continue to work with Mercy to inform their overarching educational campaign• This work helps Mercy be ready to address specific compliance issues within their system and to be better informed about the efficacy of various components of the SSC treatment protocols, ultimately helping lower mortality and reduce the burden of cost due to non-reimbursement.• This approach can be used for other hospitals similarly burdened the need to increase compliance to treatment standards for HAIs and other diseases

Learn More about our Advanced Analytic Capabilities www.boozallen.com/analytics Yugal Sharma, PhD Reechik Chatterjee, M.A.Lead Associate/ Advanced Analytics Lead Associate/ Enterprise Integration sharma_yugal@bah.com chatterjee_reechik@bah.com Phone (301/251-7158) Phone (202/346-9525) This document is confidential and is intended solely for the use and information of the client to whom it is addressed.

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Leveraging Advanced Analytics to Help Hospitals Measure Efficacy of Treatment Standards and Improve Detection of Healthcare Associated Infections

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