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Better Outcomes. Delivered. 37th Annual InAHQ Quality In Healthcare Conference Information Query: Tools to Successful Population Health Management- Director of Population Health - Drew Richardson May 13th, 2016 Copyright © 2015 Indiana Health Information Exchange, Inc • Overview of IHIE • Partnership with Regenstrief • How does a HIE work? • Definition of Population Health • Why is a HIE involved in Population Health • IQ • Demo • Ndepth • Demo • Case Studies - Our Experience Agenda www.ihie.org IHIE Background • Nation’s largest HIE • >100 hospitals (38 health systems) • >25,000 clinicians • Payors • Labs and Imaging Centers • Public Health • Long-term/Post-acute care (LTPAC) • Founded in February 2004 • 501c3 not-for-profit organization • Regenstrief Institute partnership • Mission: Through information exchange we improve health and healthcare www.ihie.org HIE Infographic http://www.himss.org/library/health-information-exchange/improving-patient-care?navItemNumber=47236 Copyright © 2010 Indiana Health Information Exchange, Inc www.ihie.org Population Health What is the definition of Population Health? My simplistic definition Health status and health conditions of an aggregate population. This allows organizations, communities, providers, and public health resources to allocate resources to overcome the problems that drive poor health conditions in the population. www.ihie.org Information Query • Population health query solution that enables healthcare administrators, quality teams, and providers to improve outcomes across safety, quality, clinical and operational domains. • Rapidly search health quality, treatment information, and limitless other data points in real-time. Identify specific population groups faster, more reliably, and less costly than existing solutions. Copyright © 2010 Indiana Health Information Exchange, Inc www.ihie.org Labs Structured Data ICD-9/10 Meds CPTs Echo’s Op Reports Radiology Unstructured Data Pathology Provider notes Endoscopy Finding patients is complicated and time consuming Due to limited data visibility and informatics resources Requires engaging data managers because the data is complicated to retrieve Request is added to the queue and often requires refining in order to achieve www.ihie.org Complex patient queries can take hours or days to complete Yesterday’s technology and infrastructure Complex patient queries can take hours or days to complete can’t effectively handle the deep analysis of structured patient data – and many current EMR’s provide a patient view, not a population view. Queries are filtered using discrete values such as: medications, lab/test results, diagnosis and procedure codes, and demographics www.ihie.org Complex patient queries can take hours or days to complete Yesterday’s technology and infrastructure can’t effectively handle the deep analysis of structured patient data – and many current EMR’s provide a patient view, not a population view. These complex queries require extensive time and processing power to retrieve, sometimes taking hours or days instead of minutes. www.ihie.org Empower non-technical users to find patients quickly and easily IQ is population health query solution that enables clinicians, researchers, and quality teams to quickly search, identify, and engage groups of patients to transform population health. www.ihie.org Empower non-technical users to find patients quickly and easily Access population statistics anytime, without waiting for a data request, or learning complex search patterns Create actionable lists for faster patient engagement Easily identify opportunities for patient intervention and disease management Share and reuse validated population queries Customize and export specific patient related information www.ihie.org Let’s go find some patients…. • Building custom queries • Using the Library • Nested queries • Taking action www.ihie.org IQ DEMO Copyright © 2010 Indiana Health Information Exchange, Inc www.ihie.org 80% of patient data is locked away in free text documents Unlocking data buried within electronic health records and other clinical documents is the key to quality improvement, research and outcomes analysis. Provider notes Operative notes Admission and discharge summaries Caths and Echos Radiology and Pathology reports Electronic, PDF, and Word documents Copyright © 2010 Indiana Health Information Exchange, Inc www.ihie.org Traditional Analytics nDepth Labs Structured Data ICD-9/10 Meds CPTs Echo’s Op Reports Radiology Unstructured Data Pathology Provider notes Endoscopy Getting this data is time consuming and expensive Highly qualified clinical staff manually extract discrete information from patient charts for quality measures, research, clinical improvements and outcomes analysis. http://www.healthcareitnews.com/infographic/infographic-clinical-claims-data-what-lies-beneath www.ihie.org Use nDepth to tap new sources of data nDepth harvests discrete information including hard-to-find patient characteristics such as social behaviors, symptoms and family history. www.ihie.org Use nDepth to tap new sources of data Enable deep population exploration by trained medical staff as well as non-technical subject matter experts, bringing rich clinical content to your fingertips. www.ihie.org How does nDepth work? nDepth searches vast collections of data for indicators hidden in free-text. These indicators, called phenotypes, are a set of characteristics that identify a specific condition or population. There is a fine-tuned a library of complex and highly accurate phenotypes using the Indiana Network for Patient Care. This library of reusable queries is included with nDepth and continuously updated. www.ihie.org Find more patients with higher accuracy using nDepth In a 2015 study on peripheral artery disease, nDepth was able to find 400% more patients with greater accuracy than using either lab reports or structured electronic health record (EHR) data. Specificity of the detection algorithms was a combined 96%. Copyright © 2010 Indiana Health Information Exchange, Inc www.ihie.org Why nDepth is Different Complex NLP phenotypes rendered as simple recipes Streamlined integration between structured and unstructured data Unparalleled user experience Integrated validation platform Integrated machine learning Real-time surveillance Deliver to downstream data warehouse Trained on world’s largest HIE www.ihie.org Easily identify opportunities to improve population health Spend less time finding patients, including hard-to-find characteristics such as social behaviors, symptoms, and family history Identify larger populations by using unstructured clinical documents User friendly interface to support data exploration by non-technical users Enable health systems, health information exchanges, and other entities to get the most value from patient data www.ihie.org Example nDepth Projects Extract LVEF values for MSSP-ACO-33 Find patients with metastatic melanoma Identify atypical hip fractures Capture hypoglycemic events Identify patients w/ family history of lung cancer Map patient trajectory following cancer treatment Detect treatment failure in insomnia Find reasons for refusal of osteoporosis medications Identify ‘triple negative’ breast cancers www.ihie.org Lets take a closer look…. Find patients using recipes Refine using terminology search and filter by report/institution/date/etc. Visualize results Define cohort/Perform Deep Text Analytics and advanced information extraction Compare and explore cohorts Validate Results Add to library Copyright © 2010 Indiana Health Information Exchange, Inc www.ihie.org Find patients using recipes Simple and intuitive “google” interface Immediate access to complex queries Automatically performs deep analysis and extraction so you don’t have to www.ihie.org Refine using terminology search and filters Easily find more patients faster Enable deep exploration using recommended synonyms Supports standard terminologies; SNOMED, MEDDRA, and LOINC www.ihie.org Fast and powerful text retrieval Search hundreds of millions of records at light speed Query deep below the surface of electronic health data Identify, extract, and analyze data and relationships www.ihie.org www.ihie.org Visualize results www.ihie.org Perform deep text analytics Tuned for human error detection Misspellings, Punctuation, grammar Sentence detection Context www.ihie.org Advanced information extraction Family history Negation Value extraction Cancer and biomarker staging Karnofsky scores www.ihie.org Integrated machine learning Ngram analysis of terms in Lung Cancer discovery Analysis of most common 3-word sets (trigrams) from 153,000 visit notes for patients with an ICD-9 diagnosis of lung cancer www.ihie.org Compare and explore cohorts Explore intersection of cohorts Save and export new cohorts www.ihie.org Built-in validation Build studies to review results of query Assign to team members to review results Randomly selects records to represent study Highlights key words for easy chart review www.ihie.org Reusable query library When you get it just right… Rinse and reuse proven queries Share with teams or other institutions Speeds workflow of quality, safety, research, and clinical teams. www.ihie.org Finalize and Apply Analytics Final Phenotype Real-Time Surveillance Write observation back to EDW Clinical Decision Support Epidemiology Observational Research Trial Recruitment Prospective Studies Patient Care Quality Metrics EDW www.ihie.org nDepth Demo Copyright © 2010 Indiana Health Information Exchange, Inc www.ihie.org Save hundreds of hours extracting data for quality measurements. nDepth Case Study The collection and use of data to drive healthcare improvements has accelerated in the past few years, and along with it the need for quality measurements. Compiling measurements for programs such as Medicare Shared Savings Program (MSSP) and Hospital Acquired Conditions (HAC) is very labor intensive, requiring detailed review of clinical charts by experienced staff. The Challenge In order to qualify for Medicare reimbursements, health systems and ACOs provide data for a variety of patient experience, safety, preventive health and at-risk measurements for a randomly selected group of over 600 patients. Each year, clinicians spend significant time carefully sorting through patient information in order meet these criteria. In the case of measurement ACO-33, abstractors typically need 45 minutes or more on average per patient in order to extract the LVEF value. Left Ventricle Ejection Fraction (LVEF) refers to the fraction or percentage of blood that is pumped out by the left ventricles of the heart. This measurement provides an assessment of cardiovascular limitations and indicators for health failure. The Solution A local partner chose to pilot nDepth™ to more efficiently report quality measures. nDepth extracted hard-to-find data from unstructured documents quickly and accurately. This automated extraction improved quality workflow allowing valuable resources to focus on validation instead of manual chart abstraction. Using nDepth, we have demonstrated the ability to extract the LVEF values per CMS-MSSP measure ACO-33 at the scale of an entire health system in minutes. Copyright © 2010 Indiana Health Information Exchange, Inc www.ihie.org