{"id":32775,"date":"2018-11-13T15:36:01","date_gmt":"2018-11-13T20:36:01","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/the-sentinel-initiative-and-machine-learning\/"},"modified":"2018-11-13T15:41:47","modified_gmt":"2018-11-13T20:41:47","slug":"the-sentinel-initiative-how-the-fda-might-know-why-youre-sick-before-your-doctor-does","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-sentinel-initiative-how-the-fda-might-know-why-youre-sick-before-your-doctor-does\/","title":{"rendered":"The Sentinel Initiative \u2014 How the FDA Might Know Why You\u2019re Sick Before Your Doctor Does"},"content":{"rendered":"

The Sentinel Initiative<\/strong><\/p>\n

The U.S. Food and Drug Administration (FDA) regulates over 25 percent of the American economy and ensures the safety of medical products. Until recently, the FDA manually reviewed all incoming reports to identify potential safety issues (called adverse events) not uncovered during clinical trials. In 2007, Congress directed the FDA to establish a system to actively monitor such events rather than waiting for these reports. In 2008, FDA launched the Sentinel Initiatve\u2014a national electronic system for medical product safety surveillance.<\/p>\n

Fully launched in February 2016, the Sentinel System utilizes data generated from patient interactions with the U.S. health care system.[i]<\/a> Specifically, Sentinel\u2019s \u201cdistributed data infrastructure approach allows the FDA to rapidly and securely access electronic health care data\u201d from over 193 million patients from multiple data partners, mostly comprised of claims data from national health insurers and managed care organizations.[ii]<\/a>[iii]<\/a><\/p>\n

Sentinel currently has over 425 million person-years of observation time.[iv]<\/a>.With over 5,000 adverse event reports filed daily plus the deluge of claims data, Sentinel helps FDA hear the signal through the noise. Machine learning in Sentinel presents an incredible opportunity to improve public health and safety by reducing labor needs, deriving clinical insights, and mitigating risks from drugs even faster.<\/p>\n

 <\/p>\n

Machine Learning in Sentinel<\/strong><\/p>\n

Sentinel is currently exploring applications for machine learning. The integration of a natural language processing (NLP) solution for initial review and classification of reports from the FDA Adverse Events Reporting System (FAERS) and Vaccine Adverse Events Reporting System (VAERS) would significantly reduce the workforce required for reviewing reports. This would allow the FDA to instead focus on the second and third step analyses, which require greater judgement and discretion.<\/p>\n

In a preliminary study, unstructured text from VAERS was used to identify and classify possible incidents of anaphylaxis after vaccination. While promising, the algorithm produced a misclassification rate of 40 percent due to its \u201cinability\u2026 to make the same clinical judgements as human experts.\u201d[v]<\/a> To further explore, the FDA is running a pilot with the Centers for Disease Control and Prevention and IBM Watson to further refine the \u201cNLP techniques used to assess safety reports\u201d received in the FAERS and VAERS.[vi]<\/a> These early results show great promise for the use of NLP in surveillance, but more work is needed around prediction and ETHER<\/strong>: E<\/strong>vent-based T<\/strong>ext-M<\/strong>ining of H<\/strong>ealth E<\/strong>lectronic R<\/strong>ecords.<\/p>\n

 <\/p>\n

Looking Forward<\/strong><\/p>\n

Electronic Health Records<\/u><\/p>\n

The FDA is developing a corpus of validator cases for training around ETHER[vii]<\/a> However, it is critical that they expand to partner with Cerner and Epic, the two largest vendors of electronic health records (EHRs). The limitless potential of Sentinel will not be realized until the system can successfully use NLP to analyze real-time health data pulled from EHRs and catch drug-related adverse events proactively.<\/p>\n

Real World Evidence<\/u><\/p>\n

Congress mandated that the FDA incorporate Sentinel into its human drug review process by 2020.[viii]<\/a> Advanced NLP could further enable Sentinel to serve as a platform to collect real-world evidence, potentially allowing the FDA to approve existing drugs for new indications. Leveraging Sentinel\u2019s scale with appropriate predictive and NLP capabilities could<\/em> significantly reduce the cost of subsequent approvals by eliminating the need for additional clinical trials.[ix]<\/a><\/p>\n

Questions <\/strong><\/p>\n

    \n
  1. What other sources (online patient forums, Twitter, etc) should FDA use to inform its active postmarket surveillance?<\/li>\n
  2. How should FDA balance opening Sentinel to external partners to advance machine learning and derive key insights about a system with huge data fragmentation with the need to maintain capacity for its regulatory and surveillance activities within the system?<\/li>\n<\/ol>\n

    (800 words)<\/p>\n

     <\/p>\n

    [i]<\/a> Coyle, D. T. U.S. Food and Drug Administration Center on Drug Evaluation and Research, Sentinel Operations Center. \u201cSentinel System Overview.\u201d Accessed 9 Nov 2018. https:\/\/www.fda.gov\/downloads\/ForPatients\/About\/UCM595420.pdf<\/a><\/p>\n

    [ii]<\/a> U.S. Food and Drug Administration. \u201cSentinel Initiative \u2013 Transforming How We Monitor Product Safety \u2013 Background.\u201d Accessed 11 Nov 2018. https:\/\/www.fda.gov\/safety\/fdassentinelinitiative\/ucm149340.htm<\/a><\/p>\n

    [iii]<\/a> U.S. Food and Drug Administration. \u201cSentinel Initiative: Final Assessment Report.\u201d September 2017. Accessed 9 Nov 2018.\u00a0https:\/\/www.fda.gov\/downloads\/ForIndustry\/UserFees\/PrescriptionDrugUserFee\/UCM577502.pdf\u00a0<\/a><\/p>\n

    [iv]<\/a> Ibid.<\/p>\n

    [v]<\/a> Ball, R. U.S. Food and Drug Administration, Center for Drug Evaluation and Research, Office of Surveillance and Epidemiology. \u00a0\u201cImproving the Efficiency of Outcome Validation in the Sentinel System: Defining the Problem.\u201d Remarks the Duke Margolis Center for Health Policy. 26 July 2018. Accessed 9 Nov 2018.\u00a0https:\/\/healthpolicy.duke.edu\/sites\/default\/files\/atoms\/files\/planned_next_steps_to_advance_sentinel_system_2018-07-26_slides.pdf<\/a><\/p>\n

    [vi]<\/a> Proestel, S. U.S. Food and Drug Administration, Center for Drug Evaluation and Research. \u201cInvestigation of Artificial Intelligence in the Interpretation of Adverse Event Reports.\u201d 14 Sept 2018. Accessed 9 Nov 2018. https:\/\/www.fda.gov\/downloads\/Drugs\/NewsEvents\/UCM621740.pdf<\/a><\/p>\n

    [vii]<\/a> Brown, J. 性视界 Department of Population Medicine. \u201cNext Steps to Advance the Sentinel System.\u201d Remarks the Duke Margolis Center for Health Policy. 26 July 2018. Accessed 9 Nov 2018. https:\/\/www.sentinelinitiative.org\/sites\/default\/files\/communications\/publications-presentations\/Planned_Next_Steps_to_Advance_Sentinel_System_2018-07-26_Slides.pdf<\/a><\/p>\n

    [viii]<\/a> Public Law 115-52 : Food and Drug Administration Reauthorization Act<\/em> of 2017<\/em>. ( 131 Stat. 1005; Date: 8\/18\/18; enacted H.R. 2430). Accessed 9 Nov 2018. https:\/\/www.congress.gov\/bill\/115th-congress\/house-bill\/2430\/text<\/a><\/p>\n

    [ix]<\/a> Corrigan-Curay J, Sacks L, Woodcock J. \u201cReal-World Evidence and Real-World Data for Evaluating Drug Safety and Effectiveness.\u201d\u00a0Journal of American Medicine.\u00a02018;320(9):867\u2013868. doi:10.1001\/jama.2018.10136. Accessed 13 Nov 2018.\u00a0https:\/\/jamanetwork-com.ezp-prod1.hul.harvard.edu\/journals\/jama\/fullarticle\/2697359<\/a><\/p>\n

     <\/p>\n","protected":false},"excerpt":{"rendered":"

    An examination of the potential to harness big data to improve public health and safety using natural language processing.<\/p>\n","protected":false},"author":11928,"featured_media":32776,"comment_status":"open","ping_status":"closed","template":"","categories":[4781,640,346],"class_list":["post-32775","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-food-and-drugs","category-government","category-machine-learning","hck-taxonomy-organization-food-and-drug-administration","hck-taxonomy-industry-pharmaceutical","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nThe Sentinel Initiative \u2014 How the FDA Might Know Why You\u2019re Sick Before Your Doctor Does - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-sentinel-initiative-how-the-fda-might-know-why-youre-sick-before-your-doctor-does\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Sentinel Initiative \u2014 How the FDA Might Know Why You\u2019re Sick Before Your Doctor Does - 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