Signal successes of the Harvard Partners National Center for Biocomputing include post-market surveillance studies demonstrating the utility of health system data for detecting adverse events associated with medications in the post marketing phase. The NCBC now extends these approaches in virtual cohort studies elucidating inflammatory pathways and measuring comparative effectiveness. A critical class of data for these studies is (1) an accurate lists of medications a patient is taking and (2) an assessment of the impact-positive and negative-of the medications on biology and health. We propose to build and test infrastructure for capturing new medication sources and patient input across desktop and mobile platforms, to improve medication lists and accounting of medication effects for post-market surveillance and comparative effectiveness studies. The public may believe that the electronic information systems employed by their physicians and pharmacists ensure consistent access to complete medication histories. In practice such comprehensive medication histories either are not present or are not consistently accessed and maintained. This despite substantial fragmentation in care across sites-making the med list at any single institution very likely to be incomplete. In the clinical realm, the practice of merging several sources of medication data into a single comprehensive definitive list of medications the patient should be receiving is called """"""""medication reconciliation."""""""" We implement the software and processes to capture a research grade version of medication reconciliation. And also capacitate patients to self-report medication-related data on reasons for starting and stopping medications, efficacy endpoints, and adverse effects. To do so, we create a general mechanism for patients to participate in patient-centered research and to contribute phenotype data to i2b2. We instantiate our solutions in software to provide a general solution enabling patient contribution to i2b2 data sources, and test them in high priority pediatric chronic disease populations. We extend a national-scale biomedical-computing environment using NCBC computational tools as foundation stones. The goal of this proposal is two-fold: 1) develop novel approaches to capture medication variables for comparative effectiveness and postmarket study;and 2) define the tradeoffs across the least intensive (using the existing EHR data) to the most (acquiring pharmacy data engaging patients to reconcile the medications) so that researchers using our findings may select he method most suited to the objectives and resources of a particular study. In two real world settings, we enroll patient cohorts to assess the value added by pharmacy- sourced, and patient reconciled medication-related data for key variables in clinical effectiveness research and postmarket surveillance.
We focus on a crosscutting theme across clinical, research, and public health disciplines-deficiencies in accurate medication-related data. We borrow and adapt toolkits from all these disciplines to implement a scalable process for engaging patients in solving the problem for clinical and translational research. We align our efforts with existing wor in the NCBC on the safety, efficacy and mechanisms of medications.
|Geva, Alon; Gronsbell, Jessica L; Cai, Tianxi et al. (2017) A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry. J Pediatr 188:224-231.e5|
|Weber, Griffin M; Adams, William G; Bernstam, Elmer V et al. (2017) Biases introduced by filtering electronic health records for patients with ""complete data"". J Am Med Inform Assoc 24:1134-1141|
|Ong, Mei-Sing; Olson, Karen L; Chadwick, Laura et al. (2017) The Impact of Provider Networks on the Co-Prescriptions of Interacting Drugs: A Claims-Based Analysis. Drug Saf 40:263-272|
|Wagholikar, Kavishwar B; Mandel, Joshua C; Klann, Jeffery G et al. (2017) SMART-on-FHIR implemented over i2b2. J Am Med Inform Assoc 24:398-402|
|Homer, Mark L; Palmer, Nathan P; Fox, Kathe P et al. (2017) Predicting Falls in People Aged 65 Years and Older from Insurance Claims. Am J Med 130:744.e17-744.e23|
|Tseng, Yi-Ju; Steinberg, Gregory; Fox, Kathe P et al. (2017) Antihyperglycemic Medications: A Claims-Based Estimate of First-line Therapy Use Prior to Initialization of Second-line Medications. Diabetes Care 40:1500-1505|
|Klann, Jeffrey G; Abend, Aaron; Raghavan, Vijay A et al. (2016) Data interchange using i2b2. J Am Med Inform Assoc 23:909-15|
|Homer, Mark L; Palmer, Nathan P; Bodenreider, Olivier et al. (2016) The Drug Data to Knowledge Pipeline: Large-Scale Claims Data Classification for Pharmacologic Insight. AMIA Jt Summits Transl Sci Proc 2016:105-11|
|Pfiffner, Pascal B; Pinyol, Isaac; Natter, Marc D et al. (2016) C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR. PLoS One 11:e0152722|
|Klann, Jeffrey G; Phillips, Lori C; Turchin, Alexander et al. (2015) A numerical similarity approach for using retired Current Procedural Terminology (CPT) codes for electronic phenotyping in the Scalable Collaborative Infrastructure for a Learning Health System (SCILHS). BMC Med Inform Decis Mak 15:104|
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