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.
|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; Mendis, Michael; Phillips, Lori C et al. (2015) Taking advantage of continuity of care documents to populate a research repository. J Am Med Inform Assoc 22:370-9|
|Mandl, Kenneth D; Kohane, Isaac S (2015) Federalist principles for healthcare data networks. Nat Biotechnol 33:360-3|
|Taylor, Patrick L; Mandl, Kenneth D (2015) Leaping the Data Chasm: Structuring Donation of Clinical Data for Healthcare Innovation and Modeling. Harvard Health Policy Rev 14:18-21|
|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|
|Mandl, Kenneth D; Mandel, Joshua C (2015) Building a self-measuring healthcare system with computable metrics, data fusion, and substitutable apps. BMJ Outcomes 2015:6-13|
|Mandl, Kenneth D; Mandel, Joshua C; Kohane, Isaac S (2015) Driving Innovation in Health Systems through an Apps-Based Information Economy. Cell Syst 1:8-13|
|Bourgeois, Florence T; Olson, Karen L; Poduri, Annapurna et al. (2015) Comparison of Drug Utilization Patterns in Observational Data: Antiepileptic Drugs in Pediatric Patients. Paediatr Drugs 17:401-10|
|Ong, Mei-Sing; Kohane, Isaac S; Cai, Tianxi et al. (2014) Population-level evidence for an autoimmune etiology of epilepsy. JAMA Neurol 71:569-74|
Showing the most recent 10 out of 14 publications