Using patient lists to add value to integrated data repositories

A narrative review of data collection and analysis guidelines for comparative effectiveness research in chronic pain using patient-reported outcomes and electronic health records

Chronic pain is a widespread and complex set of conditions that are often difficult and expensive to treat. Comparative effectiveness research (CER) is an evolving research method that is useful in determining which treatments are most effective for medical conditions such as chronic pain. An underutilized mechanism for conducting CER in pain medicine involves combining patient-reported outcomes (PROs) with electronic health records (EHRs). Patient-reported pain and mental and physical health outcomes are increasingly collected during clinic visits, and these data can be linked to EHR data that are relevant to the treatment of a patient's pain, such as diagnoses, medications ordered, and medical comorbidities. When aggregated, this information forms a data repository that can be used for high-quality CER. This review provides a blueprint for conducting CER using PROs combined with EHRs. As an example, the University of Pittsburgh's patient outcomes repository for treatment is described. This system includes PROs collected via the Collaborative Health Outcomes Information Registry software and cross-linked data from the University of Pittsburgh Medical Center EHR. The requirements, best practice guidelines, statistical considerations, and caveats for performing CER with this type of data repository are also discussed.

Evaluating the Efficacy of a Registry linked to a Consent to Re-Contact Program and Communication Strategies for Recruiting and Enrolling Participants into Clinical Trials

INTRODUCTION

Although registries can rapidly identify clinical study participants, it is unknown which follow up methods for recruiting are most effective. Our goal is to examine the efficacy of three communication strategies for recruiting and enrolling patients who were identified via a contact registry (i.e., registry linked to a consent to re-contact program).

METHODS

Patients who met the study criteria were identified via the contact registry and targeted for recruitment. In condition 1, patients established in the university hepatology specialty clinics were contacted one time via phone call by the study coordinator and asked to participate (C1). In condition 2, non-established specialty clinic patients were mailed an IRB-approved letter with study information and instructions for calling the study coordinator to participate (C2). Condition 2A included patients who called within two weeks of receiving the letter (C2A); condition 2B included patients who did not call after receiving the letter but were subsequently contacted via phone call.

RESULTS

A registry identified 1,060 patients, of which 661were eligible and targeted for recruiting. All 37 patients were reached in C1 and 17 (45.9%) were recruited. Nineteen of the 624 patients in C2A were reached and 10 were recruited whereas 120 of the 605 patients in C2B were reached and 53 (8.7%) were recruited. Seventy patients enrolled with C2B being the most effective (total, cost) recruitment strategy (n = 50) (p < .001).

CONCLUSION

The efficacy of enrolling patients identified via a contact registry into clinical trials varies based on the communication strategies used for recruiting.

A Consensus-Based Approach for Harmonizing the OHDSI Common Data Model with HL7 FHIR

A variety of data models have been developed to provide a standardized data interface that supports organizing clinical research data into a standard structure for building the integrated data repositories. HL7 Fast Healthcare Interoperability Resources (FHIR) is emerging as a next generation standards framework for facilitating health care and electronic health records-based data exchange. The objective of the study was to design and assess a consensus-based approach for harmonizing the OHDSI CDM with HL7 FHIR. We leverage a FHIR W5 (Who, What, When, Where, and Why) Classification System for designing the harmonization approaches and assess their utility in achieving the consensus among curators using a standard inter-rater agreement measure. Moderate agreement was achieved for the model-level harmonization (kappa = 0.50) whereas only fair agreement was achieved for the property-level harmonization (kappa = 0.21). FHIR W5 is a useful tool in designing the harmonization approaches between data models and FHIR, and facilitating the consensus achievement.

Establishing the role of honest broker: bridging the gap between protecting personal health data and clinical research efficiency

Background. The objective of this study is to propose the four conditions for the roles of honest brokers through a review of literature published by ten institutions that are successfully utilizing honest brokers. Furthermore, the study aims to examine whether the Asan Medical Center's (AMC) honest brokers satisfy the four conditions, and examine the need to enhance their roles. Methods. We analyzed the roles, tasks, and types of honest brokers at 10 organizations by reviewing the literature. We also established a Task Force (TF) in our institution for setting the roles and processes of the honest broker system and the honest brokers. The findings of the literature search were compared with the existing systems at AMC-which introduced the honest broker system for the first time in Korea. Results. Only one organization employed an honest broker for validating anonymized clinical data and monitoring the anonymity verifications of the honest broker system. Six organizations complied with HIPAA privacy regulations, while four organizations did not disclose compliance. By comparing functions with those of the AMC, the following four main characteristics of honest brokers were determined: (1) de-identification of clinical data; (2) independence; (3) checking that the data are used only for purposes approved by the IRB; and (4) provision of de-identified data to researchers. These roles were then compared with those of honest brokers at the AMC. Discussion. First, guidelines that regulate the definitions, purposes, roles, and requirements for honest brokers are needed, since there are no currently existing regulations. Second, Korean clinical research institutions and national regulatory departments need to reach a consensus on a Korean version of Limited Data Sets (LDS), since there are no lists that describe the use of personal identification information. Lastly, satisfaction surveys on honest brokers by researchers are necessary to improve the quality of honest brokers.