Currently Viewing:
The American Journal of Managed Care February 2015
A Multidisciplinary Intervention for Reducing Readmissions Among Older Adults in a Patient-Centered Medical Home
Paul M. Stranges, PharmD; Vincent D. Marshall, MS; Paul C. Walker, PharmD; Karen E. Hall, MD, PhD; Diane K. Griffith, LMSW, ACSW; and Tami Remington, PharmD
Quality’s Quarter-Century
Margaret E. O'Kane, MHA, President, National Committee for Quality Assurance
How Pooling Fragmented Healthcare Encounter Data Affects Hospital Profiling
Amresh D. Hanchate, PhD; Arlene S. Ash, PhD; Ann Borzecki, MD, MPH; Hassen Abdulkerim, MS; Kelly L. Stolzmann, MS; Amy K. Rosen, PhD; Aaron S. Fink, MD; Mary Jo V. Pugh, PhD; Priti Shokeen, MS; and Michael Shwartz, PhD
Did Medicare Part D Reduce Disparities?
Julie Zissimopoulos, PhD; Geoffrey F. Joyce, PhD; Lauren M. Scarpati, MA; and Dana P. Goldman, PhD
Health Literacy and Cardiovascular Disease Risk Factors Among the Elderly: A Study From a Patient-Centered Medical Home
Anil Aranha, PhD; Pragnesh Patel, MD; Sidakpal Panaich, MD; and Lavoisier Cardozo, MD
Employers Should Disband Employee Weight Control Programs
Alfred Lewis, JD; Vikram Khanna, MHS; and Shana Montrose, MPH
Race/Ethnicity, Personal Health Record Access, and Quality of Care
Terhilda Garrido, MPH; Michael Kanter, MD; Di Meng, PhD; Marianne Turley, PhD; Jian Wang, MS; Valerie Sue, PhD; Luther Scott, MS
Leveraging Remote Behavioral Health Interventions to Improve Medical Outcomes and Reduce Costs
Reena L. Pande, MD, MSc; Michael Morris; Aimee Peters, LCSW; Claire M. Spettell, PhD; Richard Feifer, MD, MPH; William Gillis, PsyD
Decision Aids for Benign Prostatic Hyperplasia and Prostate Cancer
David Arterburn, MD, MPH; Robert Wellman, MS; Emily O. Westbrook, MHA; Tyler R. Ross, MA; David McCulloch, MD; Matt Handley, MD; Marc Lowe, MD; Chris Cable, MD; Steven B. Zeliadt, PhD; and Richard M. Hoffman, MD, MPH
Faster by a Power of 10: A PLAN for Accelerating National Adoption of Evidence-Based Practices
Natalie D. Erb, MPH; Maulik S. Joshi, DrPH; and Jonathan B. Perlin, MD, PhD, MSHA, FACP, FACMI
Differences in Emergency Colorectal Surgery in Medicaid and Uninsured Patients by Hospital Safety Net Status
Cathy J. Bradley, PhD; Bassam Dahman, PhD; and Lindsay M. Sabik, PhD
The Role of Behavioral Health Services in Accountable Care Organizations
Roger G. Kathol, MD; Kavita Patel, MD, MS; Lee Sacks, MD; Susan Sargent, MBA; and Stephen P. Melek, FSA, MAAA
Patients Who Self-Monitor Blood Glucose and Their Unused Testing Results
Richard W. Grant, MD, MPH; Elbert S. Huang, MD, MPH; Deborah J. Wexler, MD, MSc; Neda Laiteerapong, MD, MS; E. Margaret Warton, MPH; Howard H. Moffet, MPH; and Andrew J. Karter, PhD
Currently Reading
The Use of Claims Data Algorithms to Recruit Eligible Participants Into Clinical Trials
Leonardo Tamariz, MD, MPH; Ana Palacio, MD, MPH; Jennifer Denizard, RN; Yvonne Schulman, MD; and Gabriel Contreras, MD, MPH
Emergency Department Use: A Reflection of Poor Primary Care Access?
Daniel Weisz, MD, MPA; Michael K. Gusmano, PhD; Grace Wong, MBA, MPH; and John Trombley II, MPP

The Use of Claims Data Algorithms to Recruit Eligible Participants Into Clinical Trials

Leonardo Tamariz, MD, MPH; Ana Palacio, MD, MPH; Jennifer Denizard, RN; Yvonne Schulman, MD; and Gabriel Contreras, MD, MPH
Using an ICD-9-CM code algorithm, the authors effectively identified potentially difficult-to-reach populations for a hypertension clinical trial.
The validity of single specific codes has been well described5,6,12; however, most reports do not describe the combination of ICD-9-CM codes with other codes. This validation of single codes is a critical component of outcomes, pharmacovigilance signaling, and comparative effectiveness research. However, there is a lack of data reporting on the validity of combinations of ICD- 9-CM codes or the combination of ICD-9-CM codes with other claims-based information to identify specific clinical presentations or populations of interest. These strategies have been used to report events in cohorts of specific populations, such as intestinal perforation among rheumatoid arthritis patients,13 or progression of liver disease.14 However, there has been no translation of this knowledge to identify the ideal potential clinical trial participant. As we showed in this report, it is possible to identify the subgroup of subjects who are likely to be eligible by creating algorithms of well-validated individual codes that represent the inclusion/exclusion criteria of the clinical trial.


Identifying a set of validated codes and creating algorithms that include the inclusion/exclusion criteria of a randomized study can potentially aid in the recruitment of the study using mailings. It will be necessary to evaluate the impact of this strategy after ICD-10-CM is implemented, as well as in the recruitment of minorities.

Author Affiliations: Department of Medicine, Miller School of Medicine, University of Miami (LT, AP, YS, GC), Miami, FL; Veterans Affairs Medical Center (LT, AP, JD, YS, GC), Miami, FL.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (LT,AP); acquisition of data (LT, JD, GC); analysis and interpretation of data (LT, GC, YS); drafting of the manuscript (LT, GC, AP, YS); critical revision of the manuscript for important intellectual content (LT, AP, GC, YS); statistical analysis (LT, GC); provision of study materials or patients (LT, GC, JD); obtaining funding (LT, GC); administrative, technical, or logistic support (JD); supervision (GC, AP).

Address correspondence to: Leonardo Tamariz, MD, MPH, Miller School of Medicine, University of Miami, 1120 NW 14th St, Ste 971 (H- 201), Miami, FL 33136. E-mail:
1. Lloyd-Jones D, Adams R, Carnethon M, et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2009;119(3):e21-e181.

2. Kennedy BM, Kumanyika S, Ard JD, et al. Overall and minorityfocused recruitment strategies in the PREMIER multicenter trial of lifestyle interventions for blood pressure control. Contemp Clin Trials. 2010;31(1):49-54.

3. West SL, Strom BL, Poole C. Validity of pharmacoepidemiologic drug and diagnosis data. In: Strom BL, ed. Pharmacoepidemiology, 4th ed. Chichester, England: John Wiley; 2005:709-766.

4. McCarthy EP, Iezzoni LI, Davis RB, et al. Does clinical evidence support ICD-9-CM diagnosis coding of complications? Med Care. 2000;38(8):868-876.

5. Birman-Deych E, Waterman AD, Yan Y, Nilasena DS, Radford MJ, Gage BF. Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care. 2005;43(5):480-485.

6. Kokotailo RA, Hill MD. Coding of stroke and stroke risk factors using International Classification of Diseases, revisions 9 and 10. Stroke. 2005;36(8):1776-1781.

7. Sacks DB, Arnold M, Bakris GL, et al; National Academy of Clinical Biochemistry; Evidence-Based Laboratory Medicine Committee of the American Association for Clinical Chemistry. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Diabetes Care. 2011;34(6):e61-e99.

8. Maynard C, Chapko MK. Data resources in the Department of Veterans Affairs. Diabetes Care. 2004;27 (suppl 2):B22-B26.

9. Palacio AM, Tamariz LJ, Uribe C, et al. Can claims-based data be used to recruit black and Hispanic subjects into clinical trials? Health Serv Res. 2012;47(2):770-782.

10. Nasser N, Grady D, Balke CW. Commentary: improving participant recruitment in clinical and translational research. Acad Med. 2011;86(11):1334-1335.

11. Kitterman DR, Cheng SK, Dilts DM, Orwoll ES. The prevalence and economic impact of low-enrolling clinical studies at an academic medical center. Acad Med. 2011;86(11):1360-1366.

12. de Burgos-Lunar C, Salinero-Fort MA, Cárdenas-Valladolid J, et al. Validation of diabetes mellitus and hypertension diagnosis in computerized medical records in primary health care. BMC Med Res Methodol. 2011;11:146.

13. Curtis JR, Chen SY, Werther W, John A, Johnson DA. Validation of ICD-9-CM codes to identify gastrointestinal perforation events in administrative claims data among hospitalized rheumatoid arthritis patients. Pharmacoepidemiol Drug Saf. 2011;20(11):1150-1158.

14. Lo Re V 3rd, Lim JK, Goetz MB, et al. Validity of diagnostic codes and liver-related laboratory abnormalities to identify hepatic decompensation events in the Veterans Aging Cohort Study. Pharmacoepidemiol Drug Saf. 2011;20(7):689-699.
Copyright AJMC 2006-2019 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up