Adaptability of Existing Feasibility Tools for Clinical Study Research Data Platforms

INTRODUCTION

The increasing need for secondary use of clinical study data requires FAIR infrastructures, i.e. provide findable, accessible, interoperable and reusable data. It is crucial for data scientists to assess the number and distribution of cohorts that meet complex combinations of criteria defined by the research question. This so-called feasibility test is increasingly offered as a self-service, where scientists can filter the available data according to specific parameters. Early feasibility tools have been developed for biosamples or image collections. They are of high interest for clinical study platforms that federate multiple studies and data types, but they pose specific requirements on the integration of data sources and data protection.

METHODS

Mandatory and desired requirements for such tools were acquired from two user groups - primary users and staff managing a platform's transfer office. Open Source feasibility tools were sought by different literature search strategies and evaluated on their adaptability to the requirements.

RESULTS

We identified seven feasibility tools that we evaluated based on six mandatory properties.

DISCUSSION

We determined five feasibility tools to be most promising candidates for adaption to a clinical study research data platform, the Clinical Communication Platform, the German Portal for Medical Research Data, the Feasibility Explorer, Medical Controlling, and the Sample Locator.

Lessons Learned in Providing Claims-Based Data to Participants in Health Care Innovation Models

The Centers for Medicare & Medicaid Services (CMS) has been providing data to organizations participating in a range of innovation models to help them implement interventions and to provide feedback on performance. The authors studied 18 CMS models to gain a better understanding of factors contributing to model participants' use or nonuse of CMS-provided data. Factors that contribute to greater use include providing data that participants view as actionable, some type of accountability for performance, robust learning support, participants having resources to work with the data, and soliciting ongoing feedback about the data and related learning needs. Factors that discourage data uptake include time lag, lack of aggregated multi-payer data, exclusion of data for sensitive diagnoses, and small sample sizes. Claims-based data from payers can be an important source of information to innovation model participants. Lessons from this study can increase the usefulness of such data.