Online Program

Developing data systems for innovative programming: Challenges and lessons learned from three real world case studies

Tuesday, November 3, 2015

Virginia Roncaglione, MSc, Medical Affairs, Children's Health Fund, New York, NY
Anna K. Savage, MPH, CHES, Community Learning Schools, Brooklyn, NY
Jennifer Pruitt, Center for Community Health Technology, Children's Health Fund, New York, NY
Delaney Gracy, MD, MPH (Epidemiology), Medical Affairs, Children's Health Fund, New York, NY
Jeb Weisman, PhD, Center for Community Health Technology, Children's Health Fund, New York, NY
Anupa Fabian, MPA, Medical Affairs, Children's Health Fund, New York, NY

BACKGROUND Technical advances over the past decade have dramatically increased data opportunities and the technical workforce required to build sophisticated data systems.  However, ease of construction and ready data availability do not necessarily imply quality or functionality of data systems. This is especially true in the context of innovative programs where it is difficult to re-use existing data systems.  In this instance, challenges arise with regard to developing a novel data system that satisfy the distinct needs of different types of users (case managers/providers, program managers, donors, evaluators and researchers) and various pressures for rapid implementation.

OBJECTIVE Use three case studies to explore challenges and share best practices related to development of high-value, novel data systems in the context of innovative health programming.

METHODS Qualitative data from three database case studies were collected and analyzed to create a framework of best practices for future data system development. Three case studies spanning ten years of Children’s Health Fund experience were analyzed: The Transportation and Referral Management System (TRMS), Online School Clinic data Repository (OSCR) for school-based reproductive health delivery research, and Nadiba for the integrated school-based Healthy and Ready to Learn initiative.

RESULTS Four areas of best practice were identified for successfully developing novel data systems: (1) Adopting a formal iterative process of system development, acknowledging evolving user needs; (2) Adopting a prototyping approach to system development; (3) Rapidly maximizing data collection in order to understand how the data system meets its users’ expectations; (4) Clearly documenting agreed upon definitions of data and intended users.

CONCLUSIONS By understanding challenges, following core best practices, respecting user needs, and establishing agreed upon definitions of data and intended uses, a truly usable system can be constructed. 

Learning Areas:

Communication and informatics
Public health or related research

Learning Objectives:
Identify challenges in developing novel data systems for innovative healthcare programs Understand the complexity of developing data systems that need to satisfy the requirements of both operational and analytical users/consumers Identify frameworks and best practices for novel data system development Describe 3 different real world cases demonstrating approaches to prototyping novel data systems.

Keyword(s): Information Technology, Data Collection and Surveillance

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: Eight years of experience in program monitoring and evaluation in the health and human services field. I have therefore extended experience in the different needs of programs and evaluators in a variety of settings.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.