Integration of new technology in a legacy system for collecting medical data - challenges and lessons learned

A hybrid solution for extracting structured medical information from unstructured data in medical records via a double-reading/entry system

BACKGROUND

Healthcare providers generate a huge amount of biomedical data stored in either legacy system (paper-based) format or electronic medical records (EMR) around the world, which are collectively referred to as big biomedical data (BBD). To realize the promise of BBD for clinical use and research, it is an essential step to extract key data elements from unstructured medical records into patient-centered electronic health records with computable data elements. Our objective is to introduce a novel solution, known as a double-reading/entry system (DRESS), for extracting clinical data from unstructured medical records (MR) and creating a semi-structured electronic health record database, as well as to demonstrate its reproducibility empirically.

METHODS

Utilizing the modern cloud-based technologies, we have developed a comprehensive system that includes multiple subsystems, from capturing MRs in clinics, to securely transferring MRs, storing and managing cloud-based MRs, to facilitating both machine learning and manual reading, and to performing iterative quality control before committing the semi-structured data into the desired database. To evaluate the reproducibility of extracted medical data elements by DRESS, we conduct a blinded reproducibility study, with 100 MRs from patients who have undergone surgical treatment of lung cancer in China. The study uses Kappa statistic to measure concordance of discrete variables, and uses correlation coefficient to measure reproducibility of continuous variables.

RESULTS

Using the DRESS, we have demonstrated the feasibility of extracting clinical data from unstructured MRs to create semi-structured and patient-centered electronic health record database. The reproducibility study with 100 patient's MRs has shown an overall high reproducibility of 98 %, and varies across six modules (pathology, Radio/chemo therapy, clinical examination, surgery information, medical image and general patient information).

CONCLUSIONS

DRESS uses a double-reading, double-entry, and an independent adjudication, to manually curate structured data elements from unstructured clinical data. Further, through distributed computing strategies, DRESS protects data privacy by dividing MR data into de-identified modules. Finally, through internet-based computing cloud, DRESS enables many data specialists to work in a virtual environment to achieve the necessary scale of processing thousands MRs within days. This hybrid system represents probably a workable solution to solve the big medical data challenge.

Development and usability testing of a parent decision support tool for the neonatal intensive care unit

In this paper we present the development and evaluation of a parent decision support tool for a neonatal intensive care unit (NICU), known as PPADS or Physician and Parent Decision Support. The NICU interprofessional (IP) team uses advanced technology to care for the sickest infants in the hospital, some at the edge of viability. Many difficult care decisions are made daily for this vulnerable population. The PPADS tool, a computerized decision support system, aims to augment current NICU decision-making by helping parents make more informed decisions, improving physician-parent communication, increasing parent decision-making satisfaction, decreasing conflict, and increasing decision efficiency when faced with ethically challenging situations. The development and evaluation of the PPADS tool followed a five step methodology: assessing the clinical environment, establishing the design criteria, developing the system design, implementing the system, and performing usability testing. Usability testing of the PPADS tool with parents of neonates who have graduated (survived) from a tertiary level NICU demonstrates the usefulness and ease of use of the tool.