Automating data collection methods in electronic health record systems: a Social Determinant of Health (SDOH) viewpoint

Abstract

Social Determinant of Health (SDOH) data are important targets for research and innovation in Health Information Systems (HIS). The ways we envision SDOH in “smart” information systems will play a considerable role in shaping future population health landscapes. Current methods for data collection can capture wide ranges of SDOH factors, in standardised and non-standardised formats, from both primary and secondary sources. Advances in automating data linkage and text classification show particular promise for enhancing SDOH in HIS. One challenge is that social communication processes embedded in data collection are directly related to the inequalities that HIS attempt to measure and redress. To advance equity, it is imperative thatcare-providers, researchers, technicians, and administrators attend to power dynamics in HIS standards and practices. We recommend: 1. Investing in interdisciplinary and intersectoral knowledge generation and translation. 2. Developing novel methods for data discovery, linkage and analysis through participatory research. 3. Channelling information into upstream evidence-informed policy.

From digital health to learning health systems: four approaches to using data for digital health design

“…Nonetheless, we must note that a common system-level dynamic of digital health innovation processes is a tendency to generate new problems on the way of solving others. Digital health innovation, in fact, is not only prone to the emergence of unintended consequences typical of the field of health information technology (Ash et al, 2004 ; Wachter, 2015 ), but also to the paradoxical effects typical of automation efforts at large (Bainbridge, 1983 ; Strauch, 2017 ), including the risk of inadvertently exacerbating existing health disparities (Berg et al, 2022 ). While we cannot at this stage suggest any of the four approaches to digital health design to be preferable from this point of view, we can note that convergent approaches can in principle enable innovators -as well as indipendent evaluators- to conduct iterative cycles of exploration and detection of possible unintended consequences through longitudinal, holistic system monitoring.…”

Going paperless – Qualitative monitoring of staff morale during the transition from paper to electronic health records

“…Transitioning from a paper-based system to EHR can initially seem daunting, but when split into smaller tasks, it appears to be more achievable [ 3 ]. Despite the challenges associated with the implementation of a new electronic system, it will allow for improved creation, storage, organisation, medical data audits, management and analysis; and edits to the medical records, therefore enhancing the delivery of healthcare [ 3 , 4 , 6 , [8] , [9] , [10] , [11] , [12] , [13] ].…”

Digital Transformation in Measuring Social Determinants of Health

“…To the best of the authors' knowledge, there are eight literature reviews in related fields. Four of the reviews examine SDOH measurement in the context of electronic health records (EHRs; Berg et al, 2022;Chen et al, 2020;Patra et al, 2021;Wark et al, 2021). Kino et al (2021) focused on studies that measure SDOH with machine learning.…”