EHR Stakeholder Workshop: Toward New Interaction Models

1. EHR Stakeholder Workshop:Toward New Interaction Models Two Illustrative Instances and a Suggested Framework Charles N Mead, MD, MSc Chief Technology Officer National Cancer Institute Washington, DC (USA) Senior Associate Global Health Group Booz Allen Hamilton “For every 25% increase in complexity, there is a 100% increase in effort” – Scott Woodfield “What’s right is what’s left when you’ve done everything else wrong.” – Robin Williams

2. Example 1: The caBIG™ Program: “Connecting the cancer community…from bedside to bench and back.” • Now in 4th year: will release ‘first version of a product’ Feb 1, 2008 • Connecting cancer researchers (basic science) and clinical trialists (clinicians) across • 60+ cancer centers • Multiple ‘cooperative groups’ (e.g. Centers of Excellence) • Multiple vendors • Initially focused on static data exchange at a computational semantic interoperable level • Top-down governance • Bottom-up input • Standards-based application development

3. Example 1: The caBIG™ Program: Some Lessons Learned • Computable Semantic Interoperability is hard but achievable • In addition to the software engineering concerns, CSI must be based on • Human Semantic Interoperability • the BRIDG Model (and its siblings) • Centralized vocabulary management • Organizational Semantic Interoperability • Data sharing and Intellectual Property in the clinical research setting • Learning to collaborate in a competitive environment • The “Common Cause” philosophy • Motivators of change: conflicts-of-interest vs conflicts-of-conscience

4. Example 1: The caBIG™ Program: Some Lessons Learned • Stepwise (iterative/incremental) approaches are essential • Connecting the ‘bench’ to the ‘clinical research’ domain was the first step • Connecting to the clinical care community is the next step • Pilot program in breast cancer enabling ‘single source’ EHR/CRF data entry now under consideration • Robust IT infrastructure (e.g. HL7 RIM, V3 data types, standardized terminologies, run-time wizards to prompt clinicians for appropriate data to satisfy CRF in the context of clinical care

5. Example 2: The Health Service Specification Project (HSSP) • Joint effort by HL7 EHR Technical Committee (EHR TC) and the Object Management Group (OMG) • HL7 responsibility: produce semantically robust service specifications • Driven by business cases • Manifest as Interface specifications (implementation-independent) bound to “semantic signafiers” (e.g. BRIDG Model, terminologies, etc.) • OMG responsibility: manage an RFP process that results in the implementation of the specified service • Overall goal is to produce a set of standardized services that can be deployed by multiple vendors across the life sciences/clinical research/clinical care continuum • E.G. Clinical Research Filtered Query (CRFQ) service

6. List Qualified Protocol Interface I/E criteria I/E criteria Qualified protocols CRFQ client (clinician, caregiver, patient C R F Q P2 P3 P4 P1 P2 P4 P1 I/E criteria Clinical data set I/E criteria List Qualified Patients Interface Pt data Pt data C R F Q P3 Qualified patients CRFQ client (trial sponsor, CRO, Pharma) Pt data Protocol I/E criteria/ Safety criteria Pt data CRFQ and its clients…

7. An Exemplar Scenario… A Trial Sponsor has developed a new intervention for Type I diabetes and has developed a clinical trial protocol to test this new intervention. A repository containing the Electronic Health Records (EHRs) for a number of patients is available to the Sponsor as a possible source of subjects for the protocol. The Trial Sponsor would like to compare the protocol’s inclusion/exclusion (I/E) criteria against patient-specific data in the EHR repository to see how many patients could be potentially eligible to participate in the intervention study.

8. This should be easy except for issues of… • Security and Access to EHR repository • Consent of individual patient (not necessarily the same as the previous point) • Non-standard expression of I/E criteria • Non-standard expression of patient-specific data

9. And those were just the ‘easy’ limitations. Also there are… • Many additional steps involved in ‘recruiting a subject for a trial’ including • More finely granulated analysis of data (beyond I/E criteria) • Lack of standards for automating this analysis, i.e. every recruitment is a one-off process • Multitude of regulatory hurdles to cross • Local/State/regional • National/International • Multiple stakeholders (with multiple value propositions) working from within multiple systems. For eachsystem involved: • Who mandates a system? • Who pays for a system? • Who uses (primary and secondary) the system? • Who builds the system? • Who regulates the system? • Differing levels of organization maturity

10. Complexity • “Complicated”, “Multi-faceted”, “Multi-factorial”, “Multi-layered” • Ivar Jacobson (paraphrase): “A multi-leveled, vertically hierarchical organization whose products of value are produced through one or more horizontal processes that cross vertical organizational lines.” • With cross-organization processes – whether they involve people or systems –syntactic and semantic problems occur at the vertical boundaries. • Cumulative experience in industry, art, and (cognitive) science has repeatedly shown that the best way to deal with complexity is through abstraction, layering, and the use of standards.

11. The Communication Pyramid Standardized Models (UML) ` Non-standard Graphics ad hoc Drawings Problem Space Solution Space Implementation-Independent Abstraction Implementation-Specific Structured Documents Free-text Documents Discussions Communication

12. Concept 1 Concept 2 “We need to sign off on the protocolby Friday” • “Protocol XYZ has enrolled 73 patients” Thing 1 (Document) Thing 2 (Study) Concept 3 Thing 3 (Plan) “Per the protocol, you must be at least 18 to be enrolled” “Protocol” – a ‘commonly used’ term… Symbol “Protocol” Ogden/Richards (Mead/Speakman) Source: John Speakman

13. A New Interaction Model • What is “An Interaction Model”? • Candidate definition (CNM): A formal representation of a a set of activities and deliverables that occur as the result of one or more participating entities requesting or responding to well-defined events in a control flow. A given interaction has well-defined • pre- and post-conditions • Inputs and outputs • If this sounds like empiric process and/or software engineering, it is… • …but only because software engineering addresses complexity management in situations of equivalent complexity to the proposed goals of this conference • Best represented in visual diagrams augmented by text (rather than the inverse)

14. A Formal Representation of an Interaction Use Case 2 – Load Lab Data

15. A New Interaction Model: Critical Components • Identify stakeholders by role • Capability, Capacity, Competency • Stakeholders can be systems, organizations, or persons • Many-to-many relationships are common • Five ‘types’ of stakeholders, multiple instances of each type • Apply ongoing risk management strategies • Static identification on a regular (e.g. weekly) basis • Integration of risk mitigation strategies into project planning • Proceed iteratively and incrementally • Apply project management Best Practices and avoid the Waterfall • RUP • Agile • Scrum • Etc.

16. Summary • The problem we are trying is the embodiment of a (hyper) complex system  apply the appropriate tools, techniques, expertise, etc. • “You can’t build a skyscraper by nailing together doghouses.” • The problem will not be solved ‘bottom up’ – a meaningful solution will require top-down mandates to focus bottom-up and middle-out efforts – they will not succeed on their own • Success will only occur iterative and incrementally – any attempt to solve this problem with Waterfall approaches is doomed to failure • Think architecture: business first, technology second • Success in a layered, I/I approach involves • Continuous risk identification and management • Multi-disciplinary teams • Identification of discipline-specific value propositions for all stakeholders • Prioritization of project goals and realistic expectation settting • The is a hard problem, but it is a solvable one if approached correctly

17. QUESTIONS & ANSWERS

18. Complex problems require the application of complex cognitive processes in order to achieve meaningful solutions Cognitive processes must apply layering and chunking (“the law of 7 +- 2”) All disciplines that routinely deal with complex problems develop either formal or de facto approaches to Layering and Chunking Cyclical application of core process of definition, discovery, intervention, (re)evaluation (re-definition)  “iterative/incremental process” The Nursing Process as a model of complex problem solving Cumulative experience in industry, art, and (cognitive) science has repeatedly shown that the best way to deal with complexity is iteratively, using abstraction and layering http://www.chambers.com.au/glossary/chunk.htm

19. Organizational Maturity • Level 1: Heroism and Passion (no defined process) • Level 2: A Set of Directions (minimal ability to deal with unexpected) • Level 3: A Map (unexpected events can be managed) • Level 4: Gathering Process Variance (parallel process improvement) • Level 5: Using Process Variance data to drive Process Improvement • Everyone wants to be Level 5 • Progression to the ‘next level’ is stepwise • Level 1 does not mean incompetence! It just doesn’t scale well over time

20. Complexity • “Complicated”, “Multi-faceted”, “Multi-factorial”, “Multi-layered” • Ivar Jacobson (paraphrase): “A multi-leveled, vertically hierarchical organization whose products of value are produced through one or more horizontal processes that cross vertical organizational lines.”