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Additional file 1: Figure S1. Overview of the tokenization and linkage flow within PCORnet. The following steps are used to complete the linkage tasks: 1) Run Datavant tokenize. Each site runs Datavant application in tokenize mode on-premises to generate tokens in their own site-specific token encryption scheme. Every site’s tokens are unique. A security breach at one site would not propagate across other sites in the ecosystem. No linking can happen without a site’s permission. 2) Run Datavant transform-tokens. Each site runs Datavant application in transform-tokens to mode to prepare tokens for sending to the Coordinating Center (CC); tokens are uniquely encrypted in transit. 3) Run Datavant transform-tokens. CC runs Datavant application in transform-tokens from to transform tokens into a common CC encryption scheme. Thus, tokens can only be linked at the CC. 4) CC Performs Overlaps. CC runs Datavant Match to determine overlap among records. Figure S2. Overall data flow for the linkage query. Partners generate de-identified tokens from PII held within their source systems (a). The Token Team of the Coordinating Center distributes a SAS query to extract tokens from the HASH_TOKEN table of the CDM (b). Partners execute the query against their CDM and upload the results to a Secure File Transfer location. These tokens are processed by the Datavant software solution (c) and then a Match Index is generated by executing the Datavant Match software (d). A version of this Index is passed to the Query Team of the Coordinating Center that does not include the underlying tokens (e). The Query Team distributes a SAS query to extract Demographic data. Partners execute it against the CDM and the results are returned to a second Secure File Transfer location (f). The results are pulled down by the Query Team and then combined with the token-less Match Index to complete the overlap analysis and to generate the summary demographic table (g). For this study, DCRI acted as the Token Team of the Coordinating Center and HPHCI as the Query Team. The number of participating Partners in this initial pilot was 4. Table S1. Illustration of the content of the HASH_TOKEN extract. There is one row per patient. DMID_PATID corresponds to the ID of the contributing DataMart and PATID corresponds to a pseudoidentifier used to link across all information belonging to a patient within the DataMart’s CDM. Table S2. Example of matching output. MATCH_ID is used to denote patients that match across DMs. Each MATCH_ID corresponds to a unique patient. DMID_PATID is the internal DM identifier (but does not contain patient identifiers). The TOKEN columns are populated with encrypted hash tokens. MATCH_ID and DMID_PATID are needed to perform the overlap analysis and generate the de-duplicated patient demographic characteristics table.

Additional file 1.

Supplemental material, Appendix_A._Patient-Caregiver_Educational_Materials for Prescribing cascades in persons with Alzheimer’s disease: engaging patients, caregivers, and providers in a qualitative evaluation of print educational materials by Sarah Bloomstone, Kathryn Anzuoni, Noelle Cocoros, Jerry H. Gurwitz, Kevin Haynes, Vinit P. Nair, Richard Platt, Paula A. Rochon, Sonal Singh and Kathleen M. Mazor in Therapeutic Advances in Drug Safety

Supplemental material, Appendix_B._Provider_Educational_Materials for Prescribing cascades in persons with Alzheimer’s disease: engaging patients, caregivers, and providers in a qualitative evaluation of print educational materials by Sarah Bloomstone, Kathryn Anzuoni, Noelle Cocoros, Jerry H. Gurwitz, Kevin Haynes, Vinit P. Nair, Richard Platt, Paula A. Rochon, Sonal Singh and Kathleen M. Mazor in Therapeutic Advances in Drug Safety

Supplemental material, Supplemental_materials for Role of health plan administrative claims data in participant recruitment for pragmatic clinical trials: An Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-term Effectiveness (ADAPTABLE) example by Qian Shi, Sonali Shambhu, Amanda Marshall, Elaine Rose-Kennedy, Holly Robertson, Mark Paullin, William Schuyler Jones, Mark Cziraky and Kevin Haynes in Clinical Trials

Additional file 1.

Supplemental material, Appendix_C._Provider_Follow-up_Survey for Prescribing cascades in persons with Alzheimer’s disease: engaging patients, caregivers, and providers in a qualitative evaluation of print educational materials by Sarah Bloomstone, Kathryn Anzuoni, Noelle Cocoros, Jerry H. Gurwitz, Kevin Haynes, Vinit P. Nair, Richard Platt, Paula A. Rochon, Sonal Singh and Kathleen M. Mazor in Therapeutic Advances in Drug Safety

Supplemental material, Appendix_D._Caregiver_Follow-up_Survey for Prescribing cascades in persons with Alzheimer’s disease: engaging patients, caregivers, and providers in a qualitative evaluation of print educational materials by Sarah Bloomstone, Kathryn Anzuoni, Noelle Cocoros, Jerry H. Gurwitz, Kevin Haynes, Vinit P. Nair, Richard Platt, Paula A. Rochon, Sonal Singh and Kathleen M. Mazor in Therapeutic Advances in Drug Safety

Characteristics of Patients Whose Medical Charts Were Obtained Versus Those Whose Medical Charts Were Unobtainable; comparison of patient characteristics between patients whose charts were obtained vs patients whose charts were unable to be obtained. (DOCX 14 kb)

Code Lists Used to Define Baseline Comorbidity Burden; listing of ICD9/10 codes used to identify comorbidities at baseline. (DOCX 13 kb)

Supplemental material, 846853_Appendix_Table_1 for Administrative claims data to support pragmatic clinical trial outcome ascertainment on cardiovascular health by Qinli Ma, Haechung Chung, Sonali Shambhu, Matthew Roe, Mark Cziraky, W Schuyler Jones and Kevin Haynes in Clinical Trials

Supplemental material, 812776_supp_mat for FDA-Catalyst—Using FDA’s Sentinel Initiative for large-scale pragmatic randomized trials: Approach and lessons learned during the planning phase of the first trial by Noelle M Cocoros, Sean D Pokorney, Kevin Haynes, Crystal Garcia, Hussein R Al-Khalidi, Sana M Al-Khatib, Patrick Archdeacon, Jennifer C Goldsack, Thomas Harkins, Nancy D Lin, David Martin, Debbe McCall, Vinit Nair, Lauren Parlett, Robert Temple, Cheryl McMahill-Walraven, Christopher B Granger and Richard Platt in Clinical Trials

The validity of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes to identify diagnoses of severe acute liver injury (SALI) is not well known. We examined the positive predictive values (PPVs) of hospital ICD-9-CM diagnoses in identifying SALI among health plan members in the Mini-Sentinel Distributed Database (MSDD) for patients without liver/biliary disease and for those with chronic liver disease (CLD).We selected random samples of members (149 without liver/biliary disease; 75 with CLD) with a principal hospital diagnosis suggestive of SALI (ICD-9-CM 570, 572.2, 572.4, 572.8, 573.3, 573.8, or V42.7) in the MSDD (2009-2010). Medical records were reviewed by hepatologists to confirm SALI events. PPVs of codes and code combinations for confirmed SALI were determined by CLD status.Among 105 members with available records and no liver/biliary disease, SALI was confirmed in 26 (PPV, 24.7%; 95%CI, 16.9-34.1%). Combined hospital diagnoses of acute hepatic necrosis (570) and liver disease sequelae (572.8) had high PPV (100%; 95%CI, 59.0-100%) and identified 7/26 (26.9%) events. Among 46 CLD members with available records, SALI was confirmed in 19 (PPV, 41.3%; 95%CI, 27.0-56.8%). Acute hepatic necrosis (570) or hepatorenal syndrome (572.4) plus any other SALI code had a PPV of 83.3% (95%CI, 51.6-97.9%) and identified 10/19 (52.6%) events.Most individual hospital ICD-9-CM diagnoses had low PPV for confirmed SALI events. Select code combinations had high PPV but did not capture all events.