Technology Assisting Cancer Outcomes: Automated Biomarker Abstraction Overcoming Textual Data-Silos

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1 Technology Assisting Cancer Outcomes: Automated Biomarker Abstraction Overcoming Textual Data-Silos Patrick Mergler, MBA PMP CPHIMS DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

2 Conflict of Interest Disclosure Patrick Mergler, MBA PMP CPHIMS Member of GE Healthcare s Cancer Advisory Board 2013 HIMSS

3 Learning Objectives Demonstrate an industry-first, innovative technical solution for converting textual clinical data sources into discrete data Describe an innovative technology implementation for automating manual processes across various IT systems Explain a method for a successful implementation of an innovative technical solution with engaged senior clinical leadership 3

4 University Hospitals (of Cleveland) - History Lakeside Hospital est (later renamed) Lakeside surgery unit (Base Hospital No. 4) was first American military unit in Europe in WWI 7 hospital integrated health system in Ohio Large Academic Center (Case Medical Center) Primary affiliate of Case Western Reserve University School of Medicine 4

5 Original Lakeside Circa 1890 s New Lakeside Hospital Circa 1930 s (in use today) 5

6 UH Seidman Cancer Center - History Cancer program since 1981 National Cancer Institute (NCI) designated CCC (Comprehensive Cancer Center) since 1998 June 2011, opened 1 of 12 NCI-designated CCC, freestanding hospitals US News and World Reports = 18th ranked Cancer Center Nationally (rank increase) 6

7 7

8 UH Seidman Cancer Center - Program Multi-Discipline disease team structure Formal Tumor Board weekly conference Treatment planning recommendations Coordinated care model = higher disease management quality standards Market differentiator + 2nd opinions Continued disease team program expansion 8

9 Tumor Board Key Success Factors Data: Discrete diagnostic data - pathology, radiology and surgery Disease specific biomarkers + genetic data Process: Cycle time - diagnosis to Tumor Board presentation 9

10 MD Requests Patient Tumor Board Review Coord. Loads Patient on Agenda in App. Coord. Loads Data in App. Avg. 42 min per patient Coord. Searches for MRN in Multiple Clinical Systems Search MRN Read Notes Abstract Data Coord. Records Tumor Board Staging & Recommendation Clinical Data Environment Demographics, Pathology, Radiology, Op & Progress Notes 10

11 Tumor Board Informatics Assessment Performed current state process and requirement assessment Bi-weekly project steering meetings with physician and clinical leadership Alignment with Executive Leadership on chosen approach 11

12 Tumor Board Informatics Support Decision = Caisis NCI funded, open source (but project cost not $0).NET + MS SQL, web-based Cancer Data Management application Support for all disease teams + flexibility Data model proven by clinical usage at 70+ Cancer Centers globally over 8 yrs Expandable to meet all requirements via custom code or 3 rd party tools integration 12

13 In-Patient Environment Data Challenges: Allscripts (Eclipsys) Sunrise EMR Cerner CoPath (Pathology Report) No clinical data warehouse IDX Rad (Radiology Report) egate HL7 Interface Engine Hybrid EMR environment (AMB Read EMR Only In Pilot) Web Portal Oncology data >85% textual; not discrete Out-Patient SoftLabs (Lab Results) M-Modal Dictation (Progress Notes, Procedure Notes) 13

14 Automated Data Loading Goals Discrete data loading Disease specific logic + tumor markers Patients presented with existing staff Reduce patient load time Coordinators perform value-added services Data accuracy + completeness Current process = ~10% data loading error 14

15 Integration Solution Challenges Load cohort (cancer) patients only Data = make textual discrete No consistent data dictionary/ ontology Traditional interface approach challenges: HL7 version incompatibilities Demand for interface resources 15

16 Integration Solution Requirements HIPAA data compliant & maintain data provenance Ability to map textual data to discrete data Can extract from variety of clinical system architectures and databases without point-topoint custom code Provide pull data mechanism via scheduler, user initiated, or event trigger 16

17 Proof of Concept (30 days) Aim = Retrieval: - Controlled, prompted Input - Data pulled from 1 system Pilot Build (60 days) End-User Verification Build (120 days) - Map Caisis fields textual clinical source - Develop data collection & abstracting rules - Verify rules with physician leads - Internal + end-user data check Aim = Abstraction & Integration: - Request initiated in Caisis - Multiple source system fetch for 1 disease team - Data loaded in Caisis 17

18 Users 1 Request 2 Data Fetch Cerner CoPath CAISIS IIS 4 Data Loading W E B S E R V E R -IIS es ngines ClearView Software run time engines Data Abstraction Rules Clinical Portal Industry Data Sources NIH MeSH 3 Data Abstraction & Clinical Rules Future Clinical Systems 18

19 Source system Unstructured data Final system Must choose specific value 19

20 Solution Demos 1) End User Experience 2) Behind the Scenes 20

21 Automated Data Loading Results Data loading time = ~50% person effort Disease specific logic + tumor markers Data entry errors Data more complete More accurate discrete data input Disease contextual case abstraction Supports disease specific clinical rules 21

22 Solution Benefits vs. Traditional HL7 1. Data compliance and security Full auditing & HIPAA compliance Honor source system s data access model 2. Flexibility & Scalability Designed for change = new cancer biomarkers Expandability for adding additional sources 3. Automated process monitoring Automatic system administrator notifications 22

23 Maintenance & Monitoring: Monitoring solution metrics (sensitivity) Continuous improvement of disease specific logic Next Phase: Next Steps Automatically populate Tumor Registry data (survivability + treatment) Expand use of standard ontology: ICD, CPT, DRG, SNOMED, LOINC, RxNORM Beta partner for ClearView s next generation NLP 23

24 Next Generation NLP FINAL DIAGNOSIS A. RIGHT MENTAL NERVE, BIOPSY: -- NERVE, NEGATIVE FOR MALIGNANCY. B. RIGHT BUCCAL, GUM, AND MANDIBLE, COMPOSITE RESECTION: -- INVASIVE, MODERATELY TO POORLY DIFFERENTIATED SQUAMOUS CELL CARCINOMA. Site(s) of involvement: Right buccal Histologic grade: Moderately to poorly differentiated Pattern of invasion: Pushing and infiltrative Extent of invasion: Invading into underlying skeletal muscle for distance of 0.4 cm. Tumor does not invade into underlying bone. Margins: All mucosal margins and bone margins are negative for malignancy. Focal area of dysplasia is present. Angiolymphatic Three Yes invasion: Not identified Perineural invasion: Present C. RIGHT SELECTIVE NECK, LYMPH NODE DISSECTION: -- LEVEL 1A: METASTATIC CARCINOMA INVOLVING ONE OF EIGHT LYMPH NODES (1/8), -- LEVEL 2: SIXTEEN LYMPH NODES, NEGATIVE FOR MALIGNANCY (0/16). -- LEVEL 3: SIX LYMPH NODES, NEGATIVE FOR MALIGNANCY (0/6). -- LEVEL 4: SEVEN LYMPH NODES, NEGATIVE FOR MALIGNANCY (0/7). NOTE: The lymph node measures 2.5 cm. No extracapsular tumor extension is identified. Electronically Signed Out By A. L. M.D., PhD./ By the signature on this report, the individual or group listed as making the Final Interpretation/Diagnosis certifies that they have reviewed this case. Source system data Final system needs structured data to answer the below questions Pre-saved questions kept in a config. file How many specimens were taken? Was malignancy detected? If yes, what was the histology? Where were malignancies located? What were the sizes of malignancies any other questions 24

25 Thank You! Patrick Mergler, MBA PMP CPHIMS Manager Cancer Informatics, UH Seidman Cancer Center

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