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Putting It All Together: Informatics Challenges and Opportunities in Health Outcomes ResearchMichelle Erin Johnson
June 2019 – CHIP Clinical Information Sciences Certificate Practicum
Key Findings
Clinical research can be inefficient due to scientific, technical, policy, and governance challenges
It is complex to develop solutions from clinical research findings that can be implemented into clinical practice
Clinical research informatics (CRI) tools and approaches boost research efficiency, innovativeness, and implementation
Health outcomes research can benefit substantially from CRI tools and approaches
My Background
Research ethics & compliance
Technology transfer
Innovation support
Research project management
Data collection & management
Outcomes Research
Goals of the internship
• Develop comprehensive understanding of clinical research informatics approaches, tools, and methods.
Develop
• Identify common informatics challenges and opportunities faced by health outcomes researchers
Identify
• Develop best practices for data collection and data management in health outcomes research
Develop
• Identify new sources of outcomes data and develop best practices for API integration
Identify
Research with human subjects that is:
1) Patient-oriented research. Research conducted with human subjects (or on human tissues, specimens, and cognitive phenomena) for which an investigator directly interacts with human subjects. Excludes in vitro studies that use human tissues that cannot be linked to a living individual. Includes (a) mechanisms of human disease, (b), therapeutic interventions, (c) clinical trials, or (d) development of new technologies.
2) Epidemiological and behavioral studies.
3) Outcomes research and health services research
*Studies falling under 45 CFR 46.101(b) (4) are not considered clinical research by this definition
More: https://www.hhs.gov/ohrp/regulations-and-policy/decision-charts/index.html
NIH Definitionof ClinicalResearch
Clinical Research InformaticsClinical research informatics focuses on developing theories, tools, and solutions to accelerate the translation of research findings from basic science to clinical research to implementation in health practice
“Clinical researchers are faced with significant and increasingly complex workflow and information management challenges… Due to the preceding characteristics …and the recognition that effective and efficient information access is critical to any solution to the many challenges faced by the domain, there has been a corresponding and rapid evolution of the biomedical informatics methods and tools specifically designed to address clinical research information management requirements.”1
Health Outcomes Research
Health outcomes research seeks to identify and improve the end results (outcomes) of the health care delivery system, including its impact on the well-being of patients and populations.
Value-Based Care Model Measures Outcomes
Outcomes Research: Many Data Sources
INSURANCE CLAIMS
EHR PATIENTS CAREGIVERS
CLINICAL RESEARCH
STAFF
DEVICES AND HEALTH APPS
RESEARCH DATA
REPOSITORIES
METADATA
Outcomes Research: Data Collection Modes
Collected Directly from the Patient
via web-based forms
via direct phone calls
via Interactive Voice Response (IVR) calls
via text messages
via smart phone applications
via in-person interviews
via tablet during clinic visits
via paper forms in the mail
via EHR patient portal
Collected Indirectly by Research Staff
via electronic data management systems
via data entry
Collected Indirectly by APIs
via device API integration
via web application integration
Outcomes Research: Integration with Clinical Workflows
• Outcomes research data EHR
• EHR data research data collection systems
• Both directions?
EHR integration
• Patient recruitment in clinic
• Symptom alerts to clinical staff
• Clinician reports
Clinical workflow
integration
Outcomes Research: Integration with Devices
• Fitbit
• Garmin
• Apple Watch
Wearables / fitness trackers
• Pill dispensers
• Blood pressure monitors
• Sleep trackers
• Glucometers and insulin delivery systems
• Smart scales
• Medical alert devices
Personal health devices
• Fertility apps
• Weight loss apps
• Medication management apps
• Mental health and wellbeing apps
Health applications
• Environmental sensors
• Amazon Alexa
• Robotic assistants
Smart home devices
Outcomes Research: Complex Processes
Data collection schedule and frequency varies
Study arms / randomization / interventions
Device management Multi-site studies and large research teams
Outcomes Research: CRI Opportunities
Recruitment tools
Retention & adherence tools
Electronic data capture
Electronic data management systems
Data quality and monitoring
Secure & compliant data sharing across sites and teams
Integration across different data sources, devices, applications, modes
Outcomes Research: Technological Challenges
• Status discrepancies
• Mid-study data collection instrument changes
• Multi-site studies where “go-live” requirements differ by site
Data systems integration challenges
Spam filtering (affects IVR, phone call, and text messaging interventions)
• Sync errors
• Internet connectivity
• Inexplicable data loss
Device issues
Development time / costs
System user access
Outcomes Research: Scientific Challenges
Using multiple data collection modes and methods leads to data
inconsistencies and errors if SOPs are unclear / protocol
gaps
Misleading or confusingly worded survey questions
Survey questions in which participant responses are
subjective and/or contextual
Sampling bias
• E.g. studies using Apple iPhone apps
• E.g. studies enrolling patients at major academic medical centers
Response bias
• Population not represented in the responses
Device data - Rigor, reproducibility and scientific
validity of devices is not always guaranteed
Outcomes Research: Policy Challenges
Survey instrument copyrights
HIPAA and allowable methods for PHI data transfer/storage
Lack of institutional clarity around research data retention / data destruction policies
Gaps in information security policies
Data access restrictions
No institutional guidance or standardized templates for data collection
No guidance or standardized templates for data storage/management
Outcomes Research: Governance Challenges
Implementation of outcomes solutions is highly dependent on institutional risk-benefit analysis and strategic priorities
Solutions must be scientifically validated and demonstrably feasible prior to implementation
Outcomes solutions in clinical practice tend to have limited scope: clinician administered, department administered, disease and/or project specific
How do I build an electronic system to
do this more efficiently?
How do I work with leadership and stakeholders to
implement a solution into
practice?
What policy and
compliance issues do I
need to keep in mind?
What research methods should I
use? Will my evidence be valid and reliable? Do I
understand the problem I’m trying
to solve?
What tools do I need? What data do I need? How do I put
it all together?
Implementing a CRI Solution: Seeing the Trees instead of the ForestIs this even a problem that needs fixing?
Does it require a high-tech solution?
Forest of Effective Outcomes Projects
Governance Gorge
Feasibility Forest
Hypothesis HollowPolicy Preserve
Solution Summit
Technological Timberline
Workflow Woodlands
Scientific Approach
Pros to this approach:Start with the clinical problem you are trying to solve
Well-defined problem and scope
Apply available technology to try to solve it
Build out an evidence-based solution over time
Cons to this approachLess likely to be aware of available features
Less likely to be familiar with technological limitations or challenges
More likely to use “tried and true” / traditional methods rather than innovative methods
Not knowing what is possible means less likely to consider how new approaches could be useful
Technological Approach
Pros to this approach:“Build it and they will come”
Develop for the market, validate afterwards
Focus on first-to-market
Make a platform that can be used to address many problems
Cons to this approachSolutions in search of a problem
Not always evidence-based
Doesn’t know what the clinical problems are
Scientific Approach Recommendations
Have an understanding of the CRI tools and technologies available to your study
Understand that technologies often progresses faster than research
Understand the complexity of integrating different systems/technologies – especially with the EHR
Meet with systems/technological domain experts early in the research design part of your study
Seek out funding that supports and targets innovative approaches to research questions
Technological Approach Recommendations
Look for the device most likely to have patient impact
Focus attention on developing technology for those devices first
Health insurance may pay for development / implementation if improves patient care
Understand organizational change needed to use your technology
Collaborate with clinical/healthcare domain experts
Governance Challenges: Applicable to Both Approaches
Governance is biggest hurtle to implementing healthcare solutions and effecting change, setting new standards for care, and seeing improved outcomes
Identify institutional stakeholders and strategic priorities
Identify institutional barriers to implementation
What must be done before your solution can be implemented?
Are there any technology limitations/barriers at your institution?
Internship Deliverable: Guide to Data Collection & Management for Health Outcomes Research
What’s next?
Write technical and end user documentation for PRO Core platformWrite
Build new PRO Core APIs to interface with consumer devices and other data collection systemsBuild
Find new ways to integrate outcomes research findings into clinical workflowsFind
Develop predictive algorithms (e.g. chatbot) based on outcomes research data Develop
With gratitude for all the support!
• Antonia Bennett, Faculty Director, PRO Core
• Mattias Jonsson, Director of Systems, PRO Core
References
• Basch E, Abernethy AP, Mullins CD, et al. Recommendations for incorporating patient-reported outcomes into clinical comparative effectiveness research in adult oncology. J Clin Oncol 2012;30:4249–4255. PMID: 23071244. doi:10.1200/JCO.2012.42.5967
• Butte AJ. Translational bioinformatics: coming of age. J Am Med Inform Assoc. 2008;15(6):709–714. doi:10.1197/jamia.M2824
• Embi PJ, Payne PR. Clinical research informatics: challenges, opportunities and definition for an emerging domain. J Am Med Inform Assoc. 2009;16(3):316–327. doi:10.1197/jamia.M3005
• Embi PJ, Payne PR, Kaufman SE, Logan JR, Barr CE. Identifying challenges and opportunities in clinical research informatics: analysis of a facilitated discussion at the 2006 AMIA Annual Symposium. AMIA Annu Symp Proc. 2007;2007:221–225. Published 2007.
• Johnson SB. Clinical Research Informatics: Supporting the Research Study Lifecycle. Yearb Med Inform. 2017;26(1):193–200. doi:10.15265/IY-2017-022
• Lucila Ohno-Machado, Clinical research informatics: a growing subspecialization of biomedical informatics, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Page 223, https://doi.org/10.1093/jamia/ocy008
• Matkar S, Gangawane A. An outline of data management in clinical research. Int J Clin Trials 2017;4(1):1-6.
• Richesson RL, Horvath MM, Rusincovitch SA. Clinical research informatics and electronic health record data. Yearb Med Inform. 2014;9(1):215–223. Published 2014 Aug 15. doi:10.15265/IY-2014-0009
• Saczynski JS, McManus DD, Goldberg RJ. Commonly used data-collection approaches in clinical research. Am J Med. 2013;126(11):946–950. doi:10.1016/j.amjmed.2013.04.016
• Segal, Courtney; Holve, Erin; and Sabharwal, Raj, "Collecting and Using Patient-Reported Outcomes (PRO) for Comparative Effectiveness Research (CER) and Patient-Centered Outcomes Research (PCOR): Challenges and Opportunities" (2013). Issue Briefs and Reports. Paper 10. http://repository.academyhealth.org/edm_briefs/10
• Zerhouni EA. Translational and clinical science—Time for a new vision N Engl J Med 2005;353(15):1621-1623Oct 13.
References (2)
• Huser V, Sastry C, Breymaier M, Idriss A, Cimino JJ. Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM). J Biomed Inform. 2015;57:88–99. doi:10.1016/j.jbi.2015.06.023
• Kahn MG, Weng C. Clinical research informatics: a conceptual perspective. J Am Med Inform Assoc. 2012;19(e1):e36–e42. doi:10.1136/amiajnl-2012-000968
• Richesson RL, Nadkarni P. Data standards for clinical research data collection forms: current status and challenges. J Am Med Inform Assoc. 2011;18(3):341–346. doi:10.1136/amiajnl-2011-000107
• Whitaker TJ, Mayo CS, Ma DJ, et al. Data collection of patient outcomes: one institution's experience. J Radiat Res. 2018;59(suppl_1):i19–i24. doi:10.1093/jrr/rry013
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