Jan 24, 2022 – AUDITRON is our newest profile from Brazil

Cerner Enviza's Auditron (Brazil) comprises of oncology patients with information regarding chemotherapy, target therapy, and radiotherapy claims since 2003. Auditron is fed by the private health insurances with anonymized patients’ information regarding diagnosis (histology, staging and mutations), treatment (including chemotherapy / systemic regimens, treatment duration, doses, cycles), and epidemiology data.

AUDITRON (Brazil) §

Database Contact Data

Renato Picoli
Auditron Cerner Enviza
Av. das Nações Unidas, 14.171
15º andar, Marble Tower
São Paulo  
BRAZIL
Email: renato.mantellipicoli@CernerEnviza.com 
Phone: +55 11 3197-5908
Cell phone: +55 11 99216-5038 

Alternate Contact

Luciana Vasconcelos
Auditron Cerner Enviza
Av. das Nações Unidas, 14.171
15º andar, Marble Tower
São Paulo  
BRAZIL
Email: Luciana.Pereiradevasconcelos@cernerenviza.com 
Phone: +55 11 3197 2001
Cell phone: +55 11 97657 6969

References of Studies Using/Describing Database

1. Castro AP, Alves AF, Piedade A, Clark LG, Bueno CC, Minowa E. Burden of Drug Waste in Oncology: Optimization of Resource Use. Value Health. 2014 Nov;17(7):A644.

2. Otávio Clark, Anna Flávia Alves, Ana Paula Castro, Fábio Santos, Eneas Faleiros, Luciana Clark, Luciano Paladini, Tobias Engel, Bruna Pegoretti. Budgetary impact of oral chemotherapy incorporation in Brazil: a real world data
analysis from the private payer perspective. J Bras Econ Saude. 2013;5(1):10-14.

3.Clark O, Faleiros EJ. Cost of the treatment of myelodisplastic syndrome in Brazil. Rev Bras Hematol Hemoter. 2011;33(1):31-4.

4. Clark L, Castro AP, Fortes AF, Santos F, Clark O, Engel T, Pegoretti B, Teich V, Vianna D, Puty F. Ideal vial size for bortezomib: real-world data on waste and cost reduction in treatment of multiple myeloma in Brazil. Value Health. 2011 Jul-Aug;14(5 Suppl 1):S82-4.
 

5. 7. Pegoretti Rosa B, Goes L, Moraes Z, Feijo L, Clark LGO. Newest target therapy and immunotherapy for the treatment of advanced melanoma: pattern of adoption by the private healthcare sector in Brazil. ISPOR USA; Boston: ISPOR;  VOLUME 20, ISSUE 9, PA881, OCTOBER 01, 2017.

6. O.A.C. Clark, L. Paladini, T. Engel, A. Caldas, J. Valentim. Costs of her 2 negative, hormonal receptor positive, metastatic breast cancer (MBC-HR+) treated with Everolimus (EVE) + Exemestane (EXE) in the Brazilian private system (BPS): a real world (RW) and published literature analysis. Value in Health Volume 16, Issue 7, Page A405, November 01, 2013.

7. Clark OAC, Paladini L, Engel T, Nishikawa AM, Borges L, Caldas A, Valentim J. Costs of her 2 negative, hormonal receptor positive, metastatic breast cancer (MBC-HNP) in Brazilian private market (BPM): a real world and published literature analysis. ISPOR 4th Latin America Conference.

Cerner Enviza EHR Database (USA)

Database Contact Data

Nathan Vavroch
Sr. Director RWD Data Products
Oracle
Healthcare & Life Sciences
Kansas City, Missouri
UNITED STATES
Email: nathan.vavroch@cerner.com

Alternate Contact

If you cannot reach the database manager, you may contact the company by completing the contact form at:
https://www.oracle.com/life-sciences/contact-us/

References of Studies Using/Describing Database

1. Oxford MA, McLaughlin CM, McLaughlin CJ, Johnson TS, Roberts JM. Replacing the Scalpel With a Computer Mouse: An Evaluation of Time Spent on Electronic Health Record for Plastic Surgery Residents and Its Impact on Resident Training. Ann Plast Surg. 2024 Apr 1;92(4S Suppl 2):S271-S274.

2. Olopoenia A, Yamaguchi Y, Peeva E, Berman B, Jagun O, George P. Demographics, clinical characteristics, and treatment patterns among keloid patients: United States Electronic Health Records (EHR) Database Study. Int J Dermatol. 2024 Feb 27. doi: 10.1111/ijd.17099. Epub ahead of print. PMID: 38411301.

3. Chieh AY, Willis JG, Carroll CM, Mobley AA, Li Y, Li M, Woodard S. Why Start Now? Retrospective Study Evaluating Baseline Screening Mammography in Patients Age 60 and Older. Curr Probl Diagn Radiol. 2024 Jan-Feb;53(1):62-67.

4. Zhu G, Yuan A, Yu D, Zha A, Wu H. Machine learning to predict mortality for aneurysmal subarachnoid hemorrhage (aSAH) using a large nationwide EHR database. PLOS Digit Health. 2023 Dec 6;2(12):e0000400.

5. Ahlness EA, Orlander J, Brunner J, Cutrona SL, Kim B, Molloy-Paolillo BK, Rinne ST, Rucci J, Sayre G, Anderson E. "Everything's so Role-Specific": VA Employee Perspectives' on Electronic Health Record (EHR) Transition Implications for Roles and Responsibilities. J Gen Intern Med. 2023 Oct;38(Suppl 4):991-998.

6. Molloy-Paolillo B, Mohr D, Levy DR, Cutrona SL, Anderson E, Rucci J, Helfrich C, Sayre G, Rinne ST. Assessing Electronic Health Record (EHR) Use during a Major EHR Transition: An Innovative Mixed Methods Approach. J Gen Intern Med. 2023 Oct;38(Suppl 4):999-1006.

7. Scierka LE, Bradley BA, Glynn E, Davis S, Hoffman M, Tam-Williams JB, Mena-Hurtado C, Smolderen KG. Chronic Cough: Characterizing and Quantifying Burden in Adults Using a Nationwide Electronic Health Records Database. J Healthc Inform Res. 2023 Sep 27;8(1):50-64.

8. Shah NP, Peterson ED, Page C, Blanco R, Navar AM. Generalizability of an EHR-network dataset to the United States for cardiovascular disease conditions: Comparison of Cerner real world data with the national inpatient sample. Am Heart J. 2023 Sep;263:64-72.

9. Espinoza J, Tut M, Shah P, Kingsbury P, Nagaraj G, Meeker D, Bahroos N. Integrating REDCap patient-reported outcomes with the HealtheIntent population health platform: proof of concept. JAMIA Open. 2023 Aug 29;6(3):ooad074.

10. Sivasankar S, Goldman JL, Hoffman MA. Variation in antibiotic resistance patterns for children and adults treated at 166 non-affiliated US facilities using EHR data. JAC Antimicrob Resist. 2023 Jan 2;5(1):dlac128.

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