DEVELOPING AN IPAD® APPLICATION FOR DATA COLLECTION IN A RHEUMATOLOGY RESEARCH CLINIC

Int J Technol Assess Health Care. 2015 Jan;31(1-2):99-102. doi: 10.1017/S0266462315000069. Epub 2015 May 20.

Abstract

Objectives: Clinical research data are often collected on paper and later inputted onto an electronic database. This method is time consuming and potentially introduces errors. Therefore, to make primary data collection more efficient and less error prone we aimed to develop a touch-screen application for data collection in a psoriatic arthritis research clinic and compared it with the pre-existing paper-based system.

Methods: We developed a Web application using Java and optimized it for the iPad®. It highlights missing fields for physicians in real time, and only permits submission of data collection form after corrections are made. For its evaluation, seven physicians participated, and before each patient visit they were randomly assigned paper or iPad® data entry. Number of errors, length of visit, and time between clinic visit and completion of data entry were measured.

Results: A total of 106 patients seen in the clinic who agreed to participate were randomly assigned to be evaluated by clinic physicians using the iPad® (fifty-three patients) or a paper protocol (fifty-three patients). On average, 3.34 omissions were found per paper form, of which 2.24 would have been detected on the iPad®. The iPad® increased the mean patient encounter time from 37.2 minutes to 46.5 minutes, but eliminated delay between a clinic visit and its data entry.

Conclusions: Entering data using the iPad® application makes the patient encounter slightly longer, but reduces "missing fields." It also eliminates the delay between clinic visit and data entry thus improving the efficiency of clinical data capture in a research setting.

Keywords: Data collection; Data quality; Research database; Tablet computers.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomedical Research / methods*
  • Computers, Handheld*
  • Data Collection / methods*
  • Humans
  • Internet*
  • Rheumatology*
  • Time Factors
  • User-Computer Interface