Introduc;ons (and disclaimers)

Transcription

1 Got Smart Data? Trailblazing the Path from Insights to Ac;ons in Radiology RSNA 2015 Refresher Course, MSAS22, Room S105AB Monday, 11/30/15 10:30 AM - 12:00 PM (Sponsored by the Associated Sciences Consor;um) (An Interac;ve Session) URL for Handouts: hop:// Introduc;ons (and disclaimers) Jon Hernandez Started in the healthcare industry in the early 1990 s as a sotware developer and database architect co- building and developing an early- stage claims clearinghouse based out of Colorado Springs, CO. Throughout the years, Jon Hernandez has co- founded or invested in over a dozen healthcare based organiza;ons. These organiza;ons, along with strong strategic partnerships, offer a wealth of data and resources delivering unique insights, trends, and eventually the connec;ons needed to accomplish the op;mal Triple Aim results for our communi;es. Nicole Newsom VP of Product Management for AdvancedBI, a healthcare analy;cs company specializing in business intelligence solu;ons. Nicole also serves as a Director for MSN Healthcare Solu;ons, an industry- leading radiology billing and management company. With an MHA and 14 years experience, Nicole has focused the last nine of those on the business of radiology. She shepherded the development of AdvyzeBI, a BI solu;on with a custom Radiology Intelligence Pla_orm designed to provide physicians and administrators with valuable, evidenced- based insights. Nicole is an ac;ve member of the RBMA and its Data Collec;on and Repor;ng CommiOee, and sits on its DataMAXX Task Force. Philip Heckendorn COO of American Radiology Associates, P.A. in Dallas, TX. Mr. Heckendorn holds an MBA in Finance and has 19+ years of management and opera;ons experience in the healthcare industry, including extensive radiology management experience in inpa;ent, outpa;ent and ancillary services. Prior to joining ARA, Mr. Heckendorn served as CEO of RadCare, EmCare s radiology prac;ce management division as well as Execu;ve VP of BIDON Companies where he developed and implemented the company s outpa;ent diagnos;c imaging division. 2 1

2 Objec;ves Recognize the importance of innova;ve solu;ons in value- based care delivery. Describe Business Intelligence and differen;ate the concept of smart meaningful data in radiology informa;cs. The value equa;on will be explored. Examine prac;cal applica;ons of radiology insights that drive quality efficiency and collabora;on. 3 What is the Target? EPISODE of CARE Why do we need data? Improve Outcomes Reduce Costs Increase Value Is your Data Smart enough to reach the Target? How can your data help others reach the Target? 4 2

3 Data Revolu;on Due to advancements in algorithmic programming combined with processing power, a complex problem that would have taken a computer 7 years to solve in 1991 can now be solved in under 1 SECOND. different data different users different times different quality different clinicians different facilities But...healthcare s;ll lives in a different world! Filling the gaps data security data-centered care 5 Data Libera;on Securely accessing disparate data in comprehensive mashups to share and collaborate at will, in real- ;me Connect Share Collaborate CreaEng insights from historical to predic;ve to adap;ve Learn Iterate Plan Capturing the Network Effect driving data commerce Dynamic Cubes Protect High Performance Computing 6 3

4 The Value Equa;on Triple Aim VALUE OUTCOME CLINICAL OUTCOMES PATIENT SATISFACTION Post- Op Mobility Quality of Life Total Cost of Surgery COST 7 Inges;ng Data to Create Insights Physician Radiology Hospital System Adaptive Intelligence Platform Cloaked Data Equipment Rejected Data Data Cloaking Clinical Data Administrative Data Social Data Self (e.g. Sensor) Data 8 4

5 Insights Turned Into Analy;cs Descrip;ve Describe Data Historical Trends Benchmarking Key Performance Indicators Predic;ve Predict the future Risks Outcomes Impacts PaOerns Prescrip;ve Prescribe AcEon Goals and Targets Process Modifica;on Op;miza;on 9 How do you start? Phase 1: Define Requirements & Develop Tools ideate design build pilot Approach: CogniEve Agile needs assessment: focus on business first, not technology Establish goals Define objectives Understand requirements & constraints Identify data drivers Interactive Visualization ensures human-centric usability Develop data and system architecture Design wireframes Establish design parameters along human and creative factors Iterative build frameworks encourage high-speed development Bring together all data sources Create single or drill-down views (Insight Cards) to answer questions Develop dashboards with live/ dynamic data Implement pilot Select pilot facility and physicians Identify pilot population Evaluate results and incorporate feedback Finalize roll-out plan 10 5

6 What s next? Phase 2: Integrity and Trust expand educate deploy support Connection Develop comprehensive educational program Incremental deployment of new users Maintain high level of user satisfaction Getting the most of the system How to implement changes within your practice Continue to enhance the platform with additional value added metrics and capabilities On-going user training Monitor user activity and identify use trends Proactive reach out programs to all users LISTEN to our users 11 Are Care Providers ready for engagement? Providers need to get in front of the engagement revolu;on: - Collabora;on, Data Engagement, Pa;ent Engagement, Point- of- Care Solu;ons Having a clear understanding of your data will be very important, and how/when pa;ents will be connected FHIR Standard: Pa;ents will be connected with their financial and clinical informa;on more than ever before hop:// 12 6

7 Posi;oning Your Organiza;on for Change Clear vision for how disparate data is interpreted into predic;ve and prescrip;ve models Specialty and sub- specialty data Financial, clinical, and outcomes data Hospital and ambulatory data Access to analy;cs exper;se within the healthcare industry It s not just about spreadsheets and dashboards anymore Healthcare change is happening, and beoer decisions are needed 13 One PracEce s Data Journey 14 7

8 The Prac;ce Profile American Radiology Associates, Dallas, TX - Hybrid Prac;ce Private Prac;ce with Academic Orienta;on AAARad [1] Member 58 Radiologists, 3 PA s, 28 Residents & 5 Fellows Serving quaternary medical center, specialty hospitals, community hospitals, 16 staffed O/P loca;ons and ~ 40 remote loca;ons Highly sub- specialized prac;ce model 45 docs on any given day covering ~25 loca;ons through sub- specialty team approach. No islands. Sub- special;es: Body Imaging, MSK, Neuroradiology, VIR, NIR, Breast Imaging, Nuclear/Pet 15 Starts with a Data Champion and a Vision Physician Leadership: President (MD) of the prac;ce passionate about evidenced- based decision making. Leadership Alignment: President and COO aligned on data- driven, strategic management of the prac;ce 16 8

9 Driving factors for Business Intelligence Solu;on Prac;ce Evolved - could not manage what we could not measure Prac;ce growth - Rad growth - Subspecialty growth (subspecialty dedica;on) Difference in work dura;on & intensity among docs? Among specialty divisions? Track and trend wrvus accurately for all volume, regardless of billing source Contractual compliance Data driven decisions new contract evalua;on, hiring, benchmarking Strive towards even distribu;on of workload in subspecialty prac;ce paradigm Historical wrvu trend by reading sta;on Physicians want to know How did I do today?, Did I pull my weight? Set reading sta;on daily benchmarks Compensa;on modeling do wrvu s belong in a compensa;on plan? Dura;on & Intensity differences Crea;ng strategic value- proposi;on: : opemally performing radiology prac;ce that is posi;oned to be a collabora;ve, value- add partner to it s health systems For ARA this required wherewithal to disclose data to health system. Show me what that looks like 17 Barriers to Consider Access to data Data sources are oten owned by the facility Physician buy- in, throughout the en;re process Resource alloca;on Time commitment Data Culture Will prac;ce go where data leads it? Data Conspicuity Who will have access to what level of data? Built in security feature for blinded repor;ng is very useful. Access to exper;se in healthcare informa;cs: engineering and data science 18 9

10 Phasing in the Deliverables, I Sprint I Integrate all outside RIS / PACS data sources along with data from the billing company to complete the full picture Track and trend wrvus for poten;al change in physician compensa;on model Vet data integrity Gain physician buy- in Vet data integrity.repeat 19 Phasing in the Deliverables, II Sprint II Implement new compensa;on model developed during Sprint I Heightened priority and implemented Also added junior data analyst onsite to work with and engage physicians in data insights U;liza;on of data spans business team And increased access to data fully func;onal mobile applica;on deployed 20 10

11 Phasing in the Deliverables, III Sprint III Integrate ;mes stamps at the accession number level TAT Analysis / Contract Compliance Reading Sta;on Analysis Establish historical trend Establish reading sta;on benchmarks Are there boolenecks in work load at certain ;mes/ loca;ons for certain shits or ;me slots. Should we schedule based on demand? 21 Phasing in the Deliverables, What s Next Future IniEaEves Predic;ve Scheduling Model physician staff schedule using seasonality trends and historical ;me slot produc;on poten;al for integra;on with electronic scheduling (based on Phase III) Hospital/Client Collabora;on: Work with all clients to isolate processes that poten;ally contribute to workload boolenecks Transparency with produc;vity & collec;ons data? Data is just data. The value of data is in the design and applica;on delivery. The u;liza;on and applica;on of data is an evolving process enjoy the ride! 22 11

12 Trailblazing the Path from Insights to AcEon 23 Viewing Big Data as a Journey 11:34 am ET Nov 19, 2015 The Big Data Business Adoption Journey By Randy Bean Big Data represents a business adop;on paradox: It promises speed, but successful business adop;on takes ;me. encourage organizaeons to view business transforming inieaeves like Big Data as a journey. Success ul;mately depends upon organiza;onal alignment, process change, and people. True there are opportuni;es for quick wins to ensure credibility, build organiza;onal support, establish momentum, and secure funding but for the most part, pa;ence and persistence are essen;al. hop://blogs.wsj.com/cio/2015/11/19/the- big- data- business- adop;on- journey/ 24 12

13 What is a data journey? Data Big Data Measure, Refine Insights Ac;on Analy;cs Smart Data 25 To navigate, we must be able to visualize. Turning Data Into Insights. Turn Insights Into Ac;on. Data must be accessible and intelligent to navigate today s changing radiology climate

14 Data Warehouse: Integrates Disparate Data Integrating data sources for true business intelligence. RIS & PACS Dicta;on with NLP technolgy Billing System Scheduling Accoun;ng HIS / EHR Peer Reviews Marke;ng Data 27 Smart Data More Than an Insigh_ul App Must be able to manage the pulse of your prac;ce. Must be designed to answer ques;ons we haven t yet asked. Dashboards, KPIs Scorecards, Reports Alerts and Messaging ü ü ü ü ü Interac;ve, adhoc insights End- user control, custom analy;cs Na;onal Benchmarking & Trending of Findings & Financials Leveraging muleple data sources NLP 28 14

15 Other Technology: Ar;ficial Intelligence What is Natural Language Processing? SyTrue s definieon: The ability for the computer to read, process, understand and extract unstructured clinical informa8on 29 NLP Methodologies New GeneraEon of NLP More Common Approach Legacy text parsers (some date to 1990 s) Slow and costly to extend Black box: no customer access to technology Deployed on premise Ver;cally integrated State of the art Rapid extensions White box: customer can develop new NLP modules Cloud- based Data- as- a- Service Opera;ng System Mul;- tenant REST APIs 30 15

16 NLP OS as a Method for Unified BI Solu;on NLP Processing Medical Reports Analyzers ClassificaEon Terminology Mapping Rules Structured Output for BI End User Pla]orm VisualizaEon & IntegraEon 31 Beyond Repor;ng, Benchmarking - It s out there! > Data extracted from tens of millions of radiology reports: ReporEng & Benchmarking Posi;ve, nega;ve, or uncertain findings Cri;cal results and follow up communica;on Rules for Laterality, Gender, and more Diagnos;c tests, history, and more Enabling Radiology Pa;ent Care (RPC) Indices First findings- based na;onal benchmarking metrics for radiology imaging Suppor;ng Global Prac;ce Informa;on (GPI) Repor;ng First all- in- one analy;cs tool driving measurable clinical and opera;onal value through objec;ve review of the Imaging Service Line to improve scheduling, departmental throughput, u;liza;on and cost management 32 16

17 Technology: Ex. of Predic;ve Analy;cs at POC > Dicta;on: Pa;ent admioed with possible sinusies. Appearing lethargic and disoriented. Posi;ve gram + spinal fluid culture. Suspect meningies NLP OS NLP Extracts codified working diagnosis Meningi;s due to unspecified bacterium AnalyEcs Vendor PredicEve algorithm > > establishes risk For ICU admissions in the next 4 hours Physician is alerted, engaged, and physician s ac;on is recorded to measure clinical impact 33 What Story Does The Data Tell? Compare U;liza;on Benchmarking & Trends Compare Billing Metrics and wrvus across large sample size Improve Follow- up and Findings Communica;on Improve TAT Improve Prac;ce Workflow Efficiency Predict Staffing Needs Analyze and mone;ze workflow hour- by- hour and by read sta;on Iden;fy paoerns of and mone;ze poten;al over- imaging in the ED Iden;fy when prior or subsequent imaging is performed 34 17

18 A Look at Radiology Insights & Analy;cs Slides of Radiology Intelligence Pla_orm excluded from handouts. Please presenter(s) for addi;onal informa;on. 35 Thank you. URL for Handouts: hop:// Jon Hernandez e: Phil Heckendorn e: Nicole Newsom e: 18