Clinical Informatics, CER, and PCOR: Building Blocks for Meaningful Use of Big Data in Health Care

Transcription

1 EDM Forum EDM Forum Community Webinars Events Clinical Informatics, CER, and PCOR: Building Blocks for Meaningful Use of Big Data in Health Care Mark Frisse Vanderbilt University Adam Wilcox Columbia University Dean Sittig The University of Texas Health Science Center at Houston Michael Kahn University of Colorado, Denver Marianne Hamilton Lopez AcademyHealth Follow this and additional works at: Part of the Health Information Technology Commons, and the Health Services Research Commons Recommended Citation Frisse, Mark; Wilcox, Adam; Sittig, Dean; Kahn, Michael; and Hamilton Lopez, Marianne, "Clinical Informatics, CER, and PCOR: Building Blocks for Meaningful Use of Big Data in Health Care" (2012). Webinars. Paper 6. This Video/Media is brought to you for free and open access by the Events at EDM Forum Community. It has been accepted for inclusion in Webinars by an authorized administrator of EDM Forum Community.

2 Clinical Informatics, CER, and PCOR: Building Blocks for Meaningful Use of Big Data in Health Care Mark Frisse, MD, MBA, Vanderbilt University Adam Wilcox, PhD, Columbia University Dean Sittig, PhD, The University of Texas Health Science Center at Houston Michael Kahn, MD, PhD, University of Colorado, Denver Marianne Hamilton Lopez, MPA, AcademyHealth October 31, 2012

3 Welcome Erin Holve, Ph.D., M.P.H., M.P.P. Director of Research & Education, AcademyHealth Principal Investigator of the EDM Forum

4 AcademyHealth: Improving Health & Health Care AcademyHealth is a leading national organization serving the fields of health services and policy research and the professionals who produce and use this important work. Together with our members, we offer programs and services that support the development and use of rigorous, relevant and timely evidence to: 1. Increase the quality, accessibility and value of health care, 2. Reduce disparities, and 3. Improve health. A trusted broker of information, AcademyHealth brings stakeholders together to address the current and future needs of an evolving health system, inform health policy, and translate evidence into action.

5 New Resource from the EDM Forum Patient Engagement Framework Outlines key opportunities for engagement Available for download to your ipad through the itunes app store Go to itunes Preview

6 Learning Objectives: Trick or Treat?? Compare six different clinical informatics technology platforms used for research (e.g. i2b2 and CER Hub); Discuss the tradeoffs inherent to data collection across a variety of strategies, including mobile devices; Explore the current peer-reviewed literature at the intersection of clinical informatics and CER; Describe the desirable characteristics of data models that can be used for CER.

7 Today s Faculty Mark Frisse, MD, MS, MBA, Vanderbilt University Michael Kahn, MD, PhD, University of Colorado, Denver Marianne Hamilton Lopez, MPA, AcademyHealth Adam Wilcox, PhD, Columbia University Dean Sittig, PhD, The University of Texas Health Science Center at Houston

8 Building the Informatics Infrastructure for Comparative Effectiveness Research (CER): A Review of the Literature Marianne Hamilton Lopez, MPA Senior Manager AcademyHealth

9 Understanding the Literature: Why Now? Technological advances in clinical informatics have made large amounts of data accessible and potentially useful for research. An influx of new research is likely to result in new scholarship. Charting the progress of this emerging scientific endeavor promises: Unique and interesting challenges, and New opportunities for discovery.

10 Literature Review Aims Develop and implement a systematic search strategy for identifying relevant articles at the intersection of CER & CI Develop codes to classify the literature Perform an in-depth literature synthesis/analysis Review themes in the selected body of work Identify current gaps

11 Identifying Articles at the Intersection of CER & CI Clinical Informatics Comparative Effectiveness Research

12 A Curated Approach to Identifying Relevant Articles PubMed Searches: MeSH search: clinical informatics and CER (68) KW search: Learning Healthcare System (7) Reference Searches PROSPECT, DRN, & Enhanced Registries (1,500) HIT for Actionable Knowledge Annotated Bibliography (40) 2010 AMIA Symposium (2) Review of Projects (818): DARTNet DEcIDE HMORN i2b2 OMOP PhysioMIMI and VISAGE RedCAP Sentinel/Mini-Sentinel SHARP Program TRIAD (OSU CTSA) VINCI and VA informatics idash

13 Refining the Set Search Strategy 1 PubMed Searches N = 75 Search Strategy 2 Reference Searches N = 1,542 Search Strategy 3 Review of Projects N = 818 Exclusions upon Citations Review by biomedical informatician N = 2,035 Combined Total Citations N = 2,435 Abstracts Reviewed N = 400 Exclusions upon Abstracts Review N = 253 Full-Text Articles Reviewed N = 147 Exclusions upon Full-Text Articles Review N = 15 Articles Meeting Criteria N = 132

14 Coding the Primary Folder of Articles on CER & CI Context Platforms, Projects Clinical Informatics Platforms Clinical Informatics Projects Natural Language Processing Data use and quality Research Networks Standardized data collection Identifiers and De- Identification Metadata Patient Involvement IRBs Governance Library of Phenotypes The Learning Healthcare system and CER Single Point Access Cloud Computing Security Cohort Identification

15 Three Types of Articles Identified as Major Areas of Focus 1. Historical context or frameworks for using clinical informatics for research 2. Platforms, projects, and networks 3. Issues, challenges and applications of natural language processing (NLP)

16 Two Cross-Cutting Themes: 1. Standardization: Differences in ontologies, informatics platforms, and data entry practices contribute to the complexity of collecting and analyzing multi-site electronic data for research. 2. Governance: CER based on ECD presents unique data governance concerns related to: Transfer and storage, De-identification, and Access of ECD.

17 Three Gaps Identified in the Literature 1. Cohort Identification 2. Single Point Access 3. Cloud Computing

18 State of the Literature Cross-cutting themes reflect a nascent, but rich, discussion Efforts continue to expand to develop CI platforms, models, and tools to support new infrastructure and studies Breadth of perspectives in this growing community of scientists are engaged in expanding the current paradigm of effectiveness research

19 Available Resources and Looking Ahead Products from the literature review: A set of PubMed search terms A list of cross-cutting codes An abstraction form An annotated bibliography A glossary of relevant terms Next Steps: Updated peer-reviewed search Grey literature review Provide Input: Submit comments Sign-up for updates Join the Discussion Sign up at edmforum@academyhealth.org

20 Informatics platforms enable distributed W E L C O M E comparative effectiveness research using multi-institutional heterogeneous clinical data Event or Meeting Title Dean F. Sittig, PhD

21 20 Compare and contrast 6 large informatics platforms for CER Today: most data manipulations performed using noncoordinated applications with disjointed institutional control New informatics platform designs provide access to electronic clinical data and the governance required for inter-institutional CER platform is a suite of interconnected, coordinated applications, together with the operational environment that hosts those applications Focus on specific CER projects that provide implementations of informatics platforms and highlight design requirements and solutions

22

23 CER requires comprehensive data on many patients 22 Enormous amounts, large variety of data types from different sources to create complete medical history Need in- & out-patient EHRs (free text); billing, laboratory, pharmacy, and radiology Document patients actually receive care ordered; pharmacy dispensing and patient-reported data Data is nearly always incomplete; methods must be appropriate for measuring health status and care events

24 CER requires data on populations from multiple organizations 23 Identify small differences, bias, subgroup analyses, generalizability, evaluation of demographic & geographic variation, rare events Include data from multiple organizations, nontraditional data sources, long-term care facilities, home and public health agencies, ascertain patients socio-economic status

25 CER requires data on populations from multiple organizations 24 Need to merge data from the same patient who has received healthcare services and had clinical data captured at multiple institutions Requires a community-wide MPI - identifies patients based on multiple demographic data (e.g., first name, last name, date of birth, gender, social security or telephone numbers) Only health information exchanges for patient care have tackled this extraordinarily difficult problem; will be a critical success factor.

26 CER requires data extraction, modeling, aggregation and analysis 25 Design and development of mapping apps is big challenges in multi-institutional research Difficult for researchers to appreciate local idiosyncratic data issues without active engagement of local experts Informaticians working to create powerful, userfriendly tools for data extraction, manipulation, and analysis Developing tools to process clinically-rich free-text notes documenting patient care

27 CER must conform to local IRB rules and local and federal legislation 26 Social, legal, ethical, and political challenges in CER must not be underestimated organizations are understandably reluctant to move data beyond their own boundaries absent a clear and specific need to do so, and patients will be less likely to consent to allow this to happen. One design is to retain physical control of raw data while providing for their aggregation as limited data sets to answer specific questions

28 CER must conform to local IRB rules and local and federal legislation 27 Other governance solutions: Standardizing data models across the project Limiting access to authorized personnel while facilitating remote access Restricting types of queries that can be executed and masking patient-specific, identifiable data Logging all data transactions and access activities As rules evolve CER platforms and governance processes must evolve accordingly

29 Summary and Conclusion 28 CER transform healthcare identifying therapies, procedures, preventive tests, and healthcare processes most effective based on cost, quality, and safety State-of-the-art informatics platforms are necessary to carry out this type of research 6 generic steps in CER: data identification, extraction, modeling, aggregation, analysis, and dissemination

30 Data Model Considerations for Clinical Effectiveness Researchers Michael G. Kahn 1,3,4, Deborah Batson 4, Lisa Schilling 2 1 Department of Pediatrics, University of Colorado, Denver 2 Department of Medicine, University of Colorado, Denver 3 Colorado Clinical and Translational Sciences Institute 4 Department of Clinical Informatics, Children s Hospital Colorado Electronic Data Methods (EDM) Forum Clinical Informatics Webinar 31 October2012 Michael.Kahn@ucdenver.edu Funding was provided by a contract from AcademyHealth. Additional support was provided by AHRQ 1R01HS (Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings), AHRQ 1R01HS (Scalable Architecture for Federated Translational Inquiries Network), and NIH/NCRR Colorado CTSI Grant Number UL1 RR (Colorado Clinical and Translational Sciences Institute).

31 Disclosures Presentation based on EDM Forum commissioned paper: Medical Care 50(9) 2012 S60-S67 DOI: /MLR.0b013e318259bff4. 30

32 What is a data model & why should I care? A data model determines: What data elements can be stored What relationships between data can be represented Technical stuff: data type, allowed ranges, required versus optional (missingness) You should care because it determines: How easy can data be recorded How easy can data be extracted Contributes to data quality 31

33 Visit- versus Patient-centric data models 32

34 Query Complexity: For each patient, how many medications where filled over a period of time? Four-table join Three-table join 33

35 Query Complexity: Average number of prescriptions written per visit? Two-table join Three-table join + Date comparisons 34

36 Key questions for a data model From Jeff Brown regarding FDA Sentinel Initiative*: 1. What does the system need to do? 2. What data are needed to meet system needs? 3. Where will the data be stored? 4. Where will the data be analyzed? 5. Is a common data model needed, and if so, what will the model look like? *Brown JS, Lane K, Moore K, Platt R. Defining and evaluating possible database models to implement the FDA Sentinel initiative. U.S. Food and Drug Administration; May

37 Additional Data Model Requirements for SAFTINet Create patient-level analytic data sets Calculate ages to the year for adults, and to smaller units of measurement for children Calculate prescribed drug intervals (drug exposures) Link data across disparate data sources Use standardized terminologies to take advantage of conceptual hierarchies and relationships Identify a patient as being part of a defined cohort Support limited data sets compliant with HIPAA 36

38 37

39 Potential data models considered by SAFTINet Name Developing entity Initial Purpose Observational Medical Outcomes Project (OMOP) Virtual Data Warehouse (VDW) Foundation of the NIH HMO Research Network Comparative Drug Outcomes Studies Distributed data warehouse to allow comparative studies across collaborating sites: HMORN, CRN, Oregon CTRI i2b2 Partners Healthcare Informatics framework for clinical and biological data integration OpenMRS Regenstrief Institute Open source enterprise medical record system platform OpenEHR OpenEHR Foundation Semantically-enabled open source health computing platform 38

40 Summary Findings None of the existing publicly-available data models met all requirements License-free, flexibility, active community and willingness to collaborate were key features for SAFTINet Each project has different requirements and priorities. There is no best model for all potential CER uses 39

41 Data Model Considerations for Clinical Effectiveness Researchers Questions? Funding was provided by a contract from AcademyHealth. Additional support was provided by AHRQ 1R01HS (Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings), AHRQ 1R01HS (Scalable Architecture for Federated Translational Inquiries Network), and NIH/NCRR Colorado CTSI Grant Number UL1 RR (Colorado Clinical and Translational Sciences Institute).

42 Adam Wilcox, PhD Columbia University

43 Necessary Need data to be stored electronically Transcription is expensive Could collect electronically

44 BUT, Tools for electronic data collection have been cumbersome Some studies say they are useful Still do not outweigh the ease of use of paper

45 HOWEVER, Recent developments in consumer electronic devices How will this influence electronic primary data collection?

46

47

48 Looked at 5 case studies, drawn from the EDM Forum projects Performed semi-structured interview about primary data collection tasks Questions about Workflow Connectivity Security Data integration

49

50 Assessed each method in terms of Ease of use Development experience needed Instrument and distribution costs Instrument flexibility Speed of data entry Accuracy Data loss potential Need for technical support Hardware/software requirements

51 Integrating data from clinical and research centers at 4 academic institutions Supporting cross-institution analysis Web-based forms Occasionally collect with paper, then enter after Advantages Data collection validation Rapid quality assurance

52 Expanding a state-wide health information exchange network Indiana Network for Patient Care Scannable forms and a barcode scanner tool (catrack) Data collection for biological samples Information collected in structured paper forms that are scanned, and linked with a barcode Advantages Avoid human error

53 Patient research registry linking electronic health record data in different delivery sites Quality improvement using both EHR and registry data Qualitative interviews with some questionnaires Paper Advantages Flexible Portable

54 Distributed health data network supporting CER and quality improvement for safety-net populations Patient surveys connected with the patient encounter Structured forms within the EHR Similar to web-based form, but links to patient data Advantages Data automatically connected to patient

55 Integrating patient data from multiple sites of care with community surveys Tablet computers Some with direct internet connection, others with data storage on the device and uploading to cloud Advantages Portable Low levels of user training and technical support Integration of components Camera GPS

56 COMET Indiana PROSPECT Pediatric Enhanced Registry SAFTINet WICER Purpose Case report forms during clinical visits Collecting information on tissue samples that will be linked to clinical data Qualitative interviews with patients and family members Administering validated instrument as part of a patient assessment Survey about health related issues for individuals within a community Data collection site Clinical research site; direct patient entery could be done at clinic or home Clinical research site Clinical site, research center, or patient s home Clinical site Patient s home Data collection workflow Collected and entered by research coordinator; pursuing direct patient data entry Collected and entered by research coordinator, both for mobile devices and scannable forms Collected and entered by research coordinator during qualitative interviews Collected and entered by clinical research coordinator as part of clinical visit Collected and entered by clinical research coordinator during interview Data entry approach Web-based forms during interview Scannable paper forms (with attached barcodes) Paper forms and audio recordings Form template within EHR (similar to webbased form) Tablet computer form template

57 Category Paper forms (Pediatric Enhanced Registry) Scannable forms (Indiana PROSPECT) Web-based forms (COMET) Form template in EHR (SAFTINet) Tablet computer (WICER) Ease of use Experience required of designer End user training Cost Flexibility Speed of entry Accuracy/error rate Potential for data loss Need for technical support Equipment/software requirements

58 Huge breadth in data collection methods used Not variation off the same themes Specific to the project needs Trade-offs of different technologies Project setting and goals most significant in defining what technology is used Need: Decision tree algorithm Best practices for each approach

59

60 Continue the Discussion! Medical Care supplement Issue Briefs: Meaningful Engagement ARRA Infrastructure Investments CER Project Profiles Descriptions of ehealth data initiatives for research & QI Event archives Wiki glossary Upcoming Webinars December 18 th : Tackling Practical Methodological Challenges of Using Electronic Data for CER & PCOR Sign up at edmforum@academyhealth.org

61 Thank You Please take a moment to fill out the brief evaluation which will appear in your browser.