An Essential Ingredient for a Successful ACO: The Clinical Knowledge Exchange

Transcription

1 An Essential Ingredient for a Successful ACO: The Clinical Knowledge Exchange Jonathan Everett Director, Health Information Technology Chinese Community Health Care Association Darren Schulte, MD, MPP Chief Medical Officer Apixio Third National ACO Congress October 31, 2012

2 Private Silicon Valley company founded in 2009 Provide data aggregation and Big Data analytic solutions for healthcare organizations 2

3 ACO Core Principles 1. Provider led organizations collectively accountable for quality and total per capita costs across the full continuum of care for a population of patients 2. Payments are linked to quality improvements that also reduce overall costs 3. Reliable and progressively more sophisticated performance measurement, to support improvement and demonstrate that savings are achieved through improvements in care 10/31/ /31/ Proprietary. Copyright Proprietary. Copyright

4 Critical Activities for an ACO Decision Support Risk Adjustment Population Management Utilization Management Quality & Outcomes Measurement 4

5 ACO Challenges Clinical integration across multiple data sources, applications, providers, and sites of care Multiple health information technologies & applications in use or under various stages of implementation within organization Data model and IT infrastructure support for analytics and reporting Clinician resistance to workflow & culture changes Short time horizon to demonstrate results 10/31/2012 5

6 Clinical Informatics Maturity Continuum Are you effectively performing the following activities? 1. Pre ACO ACO Participation ACO Performance 58% 59% 28% 26% 28% 20% 26% 20% 13% Less sophisticated (1) PwC Health Research Institute Clinical Informatics Survey, More sophisticated 6

7 How Does an ACO Address Data Challenges? Data Volume Text, scanned documents, lab results, billing data, images, device data, genomics Data Variety Structured data (e.g., CCD, HL7, claims) Unstructured data from dictations, encounters, transcription, photos, images Data Value Aggregate and analyze data from various databases mobile; claims, free text, scanned documents, and archived images Data Velocity Scalable data storage and retrieval infrastructure with parallel computing capability Real time analysis across population for decision support 7

8 Volume of Data in a Mid Sized Health System 200,000 patients 5 years of data (discrete data, text, scanned notes) Nearly 10 Terabytes = Entire print collection of US Library of Congress 8

9 More Unstructured Data is Being Captured in Electronic Records >90% of patient data is represented in unstructured text and scanned documents 9

10 Data Variety Can Break the Data Model Data handling challenges: Varying degree of resolution e.g. duration in CCD vs. CCR Duplicate, inconsistent, and conflicting information Multiple coding systems e.g. standard & proprietary lab codes Extracting meaning and facts from clinical text e.g. presence of a hypertension care plan 10

11 Most Clinical Information is Hidden from Mission Critical Systems >63% of key clinical data is NOT coded* *Apixio client case study & published studies 11

12 Problem List Inaccuracy Example Severe Coronary Disease Coded Data Free Text Scanned Documents Other Provider EHRs 10/31/2012 Proprietary. Copyright

13 Decision Support Fails Without Access to Required Clinical Data Decision Support Fails Without Access to Required Clinical Data % with Pneumococcal Vaccine 54% 17% 3 x lower Coded History No Coded History in EHR 10/31/

14 Required Clinical Facts for Quality Measures Often Not Accessible in Coded Data 31% 35% 69% 65% 37% 63% 14

15 Leveraging Unstructured Data for Quality Reporting Not Effectively Addressed Physicians required to enter these data into EHR templates Nurses or administrators audit chart and abstract required data from the clinical narrative Organizations accept under performing measures Report upon quality measures based only upon coded data 15

16 The Current State of Data Exchange and Accessibility Application EHR (Acute) EHR (Clinic) Care Mgmt Platform Quality Analytics/ Reporting HIE Portal PHR Aggregator Coded & Text data Coded data & docs Coded data HIE Text data Coded Data Images & doc Coded data Coded & Text data Coded data Coded data Analytic Vendor Coded data Coded data Data Source EHR (Acute) EHR (Clinic) PHR PACS Lab Claims 16

17 Apixio Clinical Knowledge Exchange: A Foundation for Analytics and a Virtual & Intelligent HIE Care Provider or Manager Coder Application Apixio EHR Quality Analytics/ Reporting Care Mgmt Platform Billing System Apixio CKX Stack API integrate & reconcile index, OCR, meta tag data interface Data Source EHR (Acute) EHR (Clinic) PACS Lab Claims 17

18 High Performance Analytics Using Distributed Computing for Scalability & Real Time Results Queries Job Queue Results Patient Data Store Patient Data Store Patient Data Store Analytics Engine Analytics Engine Analytics Engine Patient Objects Patient Objects Patient Objects & Distribute analytic workloads Real time read/ write NoSQL data store 10/31/ /31/2012 Proprietary. Copyright

19 Clinical Knowledge Exchange: Connectivity with Multiple EHRs UI and DB layers) 19

20 Flexible Ontology: Establish Relationships Between >4.5M Clinical Concepts & Codes Existing Ontologies (e.g. ICD9, SNOMED) Public Documents Apixio Knowledge Graph* Echo Retinal Eye Exam CPT 83036, Glucose DM 2 Diabetes Type 1 Dictionary Extraction & Association Discovery Diabetes Type 1 Diabetes Type 2 Clinical Documents Glucose Hemoglobin A1c Retinal Eye Exam Echo *Patent pending 10/31/2012 Proprietary. Copyright

21 Apixio: Powerful Pipeline for Knowledge Assembly* Source Data Simvastatin Zocor Liver function test LDL Cholesterol LDL Chol Fam Fam Hx Hx of of Stroke MI Concept Meta Tagging Pt ID NDC CAD Hyperlipidemia Simvastatin Rx Provider ID 3456 Hyperlipidemia CAD LFT CPT Hepatitis LDL CPT Hyperlipidemia CPT CAD Negated concept Intelligent Run Time Query Analysis Text Mining/ NLP Data Mapping Inferencing Modeling *Patent pending 21

22 Who Is CCHCA? We are an non profit IPA in San Francisco, CA with about 200 providers and 15 specialties Focused on serving the Chinese community 80 clinics and 6 affiliated hospitals. Care for over 75K patients, 30K of which are managed care patients. Dedicated to new technologies and innovation Recognized and awarded by HIMSS, ONC, and others within the HIT Industry 22

23 IT Problems Faced by CCHCA to Create ACO Infrastructure Sharing meaningful data between our IPA s healthcare providers Important data lost in the clinical narrative free text and scanned notes Even if relevant data is located, how do you get it to work for us? 23

24 Towards an ACO Infrastructure Blue Shield of California awarded us $971,000 in 2011 to help build the required infrastructure for our ACO. We decided to use Apixio s Clinical Knowledge Exchange and expand/ enhance NextGen s EHR as the foundation of improving care coordination amongst our care teams. 24

25 What is the CCHCA IT infrastructure? Apixio Clinical Knowledge Exchange (platform) Patient Analyzer (application) HCC Optimizer (application) NextGen EMR 70+ Providers (35%) on our system and growing rapidly Enterprise Chart, KBM 8.0 Laboratory and PBM data interfaces Clinical decision support alerts Quality measurement tool (HQM) Patient Portal beginning an internal pilot Advanced Audit Utility 25

26 Apixio Patient Analyzer: Intelligent Data Access & Search within NextGen EHR 10/31/2012 Proprietary. Copyright

27 Apixio Patient Analyzer: Consistent View of Longitudinal Record 3 data sources aggregated for this patient Patient history organized by clinical categories 27

28 Apixio Patient Analyzer: Care Gap Analysis 28

29 Patient Analyzer: Timely Access to Consulting Physician Notes 29

30 Data Mining & Analytics EHR s are great when used properly, but most often they are not. Important data gets lost in scanned notes Into the abyss, never to be seen or used again Providers free text instead of enter discrete data Busy day in the office, no time to enter data in discrete field You need to report on risk adjustment (HCC) and quality measures, but you CAN T! The data you need to improve these items is buried in the narrative. 30

31 Solution: Optimal HCC Risk Optimization Adjustment beta (HCC) for ACOs: Mine & (Coder Conditions Solution) Found in Clinical Narrative 10/31/ % of documented conditions found to be not coded. Tool increased average RAF and potential reimbursement for 8,400 Medicare Advantage patients by $2.3 million 31

32 Using Advanced Text Mining to Improve Risk Adjustment and Problem List Accuracy Encounter Notes Coded Data code term ICD ICD ICD Chronic Diseases Pertinent for Risk Adjustment Conditions to Consider Coding for Risk Adjustment Diabetes Heart Failure CAD Stroke Renal Failure Heart Failure Renal Failure 10/31/2012 Proprietary. Copyright

33 Identify Facts for a Quality Measures Using Computer Assisted Clinical Text Mining Diabetes Need a Annual Diabetic Foot Exam * Diabetic foot exam * CPT G /31/2012 Proprietary. Copyright

34 Improved Quality Measure Sensitivity Using Computer Assisted Text Mining Textual + Coded Data NQF 0061: Diabetes Urine Screening (evidence of nephropathy) 45% (166/364) Coded Data 22% (26/144) Apixio Case Study 10/31/ Proprietary. Copyright 2012.

35 A Process for Optimizing Quality Analysis and Reporting Aggregated Coded Data (e.g. ICD9, CPT) Automated Text Mining Facts (Optional Step) Quality Measure Analysis Codes Results Reports Population Mgmt Tools Submission for P4P Validation & Audit Results 10/31/2012 Proprietary. Copyright

36 CCHCA: National awards for recognition and HIT innovation CCHCA has been selected as a HIMSS 2012 Stories of Success for the implementation of a searchable Health Information Exchange using NextGen s Enterprise Chart and Apixio s Clinical Knowledge Exchange. Pictured below: Dr. Farzad Mostashari, Chair of the ONC and Aneesh Chopra, former CTO of US Pictured above: Dr. Gustin Ho (CCHCA Provider) and a patient in picture 36

37 Apixio: Clinical Knowledge Exchange Connect multiple systems and aggregate/ index data within 3 months Assemble and provide intelligent and instant access to the complete patient record from within your clinical workflow Mines and analyzes unstructured data for quality, cost, utilization, risk adjustment calculations Application interfaces to connect with third party systems Scalable and cost effective solution Only 20 hours of client IT resources required 10/31/ Proprietary. Copyright 2012.

38 Questions? Contact Info: Jonathan Everett Manager, Health Information Technology Chinese Community Health Care Association Darren Schulte, MD, MPP Chief Medical Officer Apixio