Data Mary E. Edgerton, MD, PhD Vice-Chair, CAP Pathology Electronic - PowerPoint PPT Presentation

The CAP Cancer Protocols: Using Data Standards and Minimum Data Sets to Insure Interoperability and Insure Quality of Cancer Diagnostic Data Mary E. Edgerton, MD, PhD Vice-Chair, CAP Pathology Electronic Reporting Committee (PERT) UT MD Anderson Cancer Center

Data Issues in Cancer Research • Data is not shared • Data is not interoperable • EHR data is text based, not discretized • Blue Ribbon Panel Analysis for Moonshots • Build a national cancer data ecosystem Create a national ecosystem for sharing and analyzing cancer data so that researchers, clinicians and patients will be able to contribute data, which will facilitate efficient data analysis.

Where do we start? • A cancer event timeline begins with tissue diagnosis • Pathologic (tissue) diagnosis contains a compilation of phenotypic features that impart information about the tumor • Site/organ of origin: Implies organ based differentiation of aberrant cell • Histologic type: Relates to cell of origin • Behavior: Malignant vs not • Grade: Degree of aggressiveness • Stage: Extent of disease at time of diagnosis (and at subsequent timepoints) • Biomarkers: Refinement of phenotype with molecular information • Genomics: Precise information about the genotype of the tumor

Patient/Disease Attributes Start with Pathology • College of American Pathologists (CAP) Cancer Protocols • Minimum data set to define a cancer diagnosis-first release 1998 • Evolution to synoptic report • Evolution to machine readable content consisting of uniquely coded data elements paired to data values • Can use c-key to identify individual concepts (data elements) paired with c-key for individual values such that a composite gives a unique identifier for question|result or SNOMED codes • Synoptic report with minimum set of required data elements now required by American College of Surgeons (ACOS) Commission on Cancer (CoC)and by College of American Pathologists (CAP) for accreditation • Not required to be in machine readable format • Biomarkers, genomic attributes not included in requirement • Biopsy reports not required to have synopsis

How Is Report Data Be Accessed • Data can be input using forms with queries and controlled values for selection and stored as discretized data • eCC’s are currently in XML, in transition to SDC (structured data capture) • Depending on how the individual LIMS vendor stores the data, it can be queried and exported • Data can be encoded with SNOMED-CT codes • Natural Language Processing (NLP) • In general pathology reports are semi structured into sections, e.g. DIAGNOSIS, and there is a syntax for reporting that can be used to train an NLP engine • Synoptic reports in question: answer format can be parsed • Report data is transformed via human interpretation and re-stated in clinical note in free text format

Preferred Format-eCC • Use of electronic based protocols • Can package in HL7 text message, send, and parse at consumption node • Evolution to SDC with full interoperability • Can query local database

Why SDC and What Is It? • The SDC project was initiated by the Office of the National Coordinator for Health Information Technology (ONC) in early 2013 through its Standards and Interoperability (S&I) Framework initiative. • SDC’s technical workgroups have focused on defining standards by which interoperable forms are defined, rendered, populated and exchanged. • The SDC project was developed in cooperation with Integrating the Healthcare Enterprise (IHE) , a standards organization which focuses on the interoperability of healthcare IT systems, with a focus on combining constrained standards into profiles for interoperable data transmission. • IHE gathers case requirements, identifies available standards, and develops technical guidelines which technical professionals can implement. IHE also hosts yearly “Connectathons” and stages “interoperability showcases” at HIMSS in which vendors assemble to demonstrate the interoperability of their products.

How Does It Work? • Discretized data is collected via forms with queries and (hopefully) choices for data value selections • SDC standardizes the definition of data items of a Data Entry Form (DEF) inside a Form Design File (FDF). • An FDF is an XML description of the data items in a DEF. It is not dependent on the programming language used to create a DEF. In other words, the FDF is technology-agnostic.

Interoperability • CAP PERT Committee participates in Connectathons yearly and has demonstrated interoperability of SDC based electronic CAP cancer protocols

Adoptions and Use of eCC’s • Cancer Care Ontario • California Cancer Registry

Cancer Care Ontario • Since 2004 Cancer Care Ontario has evolved from narrative text to synoptic reporting with use of discretized data elements

Proportion of Ontario Hospitals Reporting Cancer Pathology to Cancer Care Ontario, by Level of Standardization, from Narrative to Synoptic Format LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 4 LEVEL 5 LEVEL 6 REPORTING LEVEL DESCRIPTION Narrative Narrative Level 2+ Level 3+ Level 4+ Level 5+ No CAP content No CAP content Synoptic-like structured format Electronic reporting tools using drop-down menus Standardized reporting language Common data and messaging standards with ckeys, Single text field data Single text field data Data elements stored in discrete data fields SNOMED CT or other encoding 5% 40% 50% 5% 0% 0% % ONTARIO HOSPITALS 2004 - 05 0% 5% 70% 25% 0% 0% % ONTARIO HOSPITALS 2006 - 07 0% 0% 65% 17% 18% 0% % ONTARIO HOSPITALS 2008 - 09 % ONTARIO HOSPITALS 0% 0% 20% 2% 78% 0% 2009 -10 0% 0% 8% 0% 0% 92% % ONTARIO HOSPITALS JANUARY 2012 0% 0% 3% 0% 0% 97% % ONTARIO HOSPITALS MAY 2012 % ONTARIO HOSPITALS 0% 0% 0% 0% 0% 100% OCTOBER 2015

So as of now CCO • 100% Level 6, which is • Common data and messaging standards with c-keys, SNOMED CT or other encoding

Real-time cancer patient data • Allows for much more than direct patient care & surveillance, including: • Analytics • Quality improvement • Change management • Performance analysis • Enabling action to affect patient outcomes

Analysis of structured data can indicate need for action to improve patient care • Surgical Resection Positive Margins § Provide feedback to surgeons with higher than expected positive margin rates • Lymph Node Retrieval Rates § Increase retrieval rate by pathologists / pathology assistants • Frequency of cases meeting ASCO/CAP cold ischemia and fixation times § Feedback provided to surgery or radiology on need for pre-analytic data • Correlation of hormone receptor positivity with histologic type

The US Cancer Registries could be a rich data resource if… • Data input was in real-time, requiring little human input • Data analytics were in place • Governance was in place to provide access for providers, consumers, and payors to PHI-free data

California Cancer Registry • Ongoing project to automate case creation in the registry

CCR History • Case Creation, Validation, Aggregation • Prior to 2018, operations rely upon manual abstraction • Data set is not considered research ready or “complete” until 18-24 months after the date of diagnosis for any case • Pathology (ePath) problematic in the case identification and population process • 8-13K reports processed manually per month to identify unreported cancer cases • ePath reports processed manually at the end of the data aggregation process • Manual intervention required to determine if a report is an actual cancer case • 40%-50% of reports are deleted upon initial review (not cancer) • Narrative text is delaying the ability of the CCR to operate as a real-time surveillance registry and provide additional value to the citizens of CA

Move to Automate Case Creation • Follow lead of CCO-pathologists submit eCC’s, parse at consumption • For pathologists not using eCC’s, use NLP to process reports • Provide portal for pathologists to input data

Pilot Project: St. Joseph Health (SJH) • Integrated Catholic health care delivery system • Organized into three regions § NorCal – Eureka, Santa Rosa, Queen of the Valley § SoCal – Orange, St. Jude, Mission, Laguna Beach, St. Mary’s § TX/ NM - Covenant • 14 acute care hospitals, home health agencies, hospice care, outpatient services, skilled nursing facilities, community clinics, and physician organizations. • 9 laboratories using the CAP eCC • 48 pathologists

Key factors for SJH participating • SJH already using CAP eCC through mTuitive / Meditech • Pathologist buy in & champion identified • Worked with project management on adding this to busy schedule • Executive support obtained • Contracts • Project justification development & submission • Mitigating costs of project § Funding provided by California Department of Public Health to offset costs

Project nuts and bolts Report data Message Pathologist Report data transformed with Data signs out saved as by vendor/ structured instantly cancer structured LIS into data uploaded report via (discrete) interoperable automaticall into CCR CAP eCC in data electronic y sent by SJH database LIS message to CCR Data can be tracked, grouped, analyzed, and shared to improve clinical practice 24