Elephants in the Room: When Digital Data Risks Uncovering the Profitability of Dysfunction

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Over the past decade, we have made marked progress in formalizing the field of digital health. From a 2018 World Health Assembly Resolution to the 2019 launch of the Digital Health Guidelines, digital health has gained unprecedented centrality in the health systems strengthening discourse. A number of countries now boast digital health projects operational at subnational or national scale, providing information services to clients or supporting the delivery of care in a community or clinical setting. Technical investments in digital health "global goods: and in 'backbone' systems to facilitate interoperability have been made by major donors, while the Broadband Commission and ITU have advocated for improvements in the digital infrastructure which supports digital systems. There is, however, a few important "elephants" in the room that are seldom discussed in large fora -- related to whether the health systems in which digital innovations are being introduced are prepared to accept unprecedented levels of data clarity and transparency. In many LMIC settings, decades of unintentional and intentional dysfunction have been masked in the unreliability, obscurity and sluggishness of paper-based systems. This talk will focus on bringing to the forefront a number of critical threats to the widespread adoption and use of digital systems -- important active and passive obstacles to changing status quo. Digital data systems risk uncovering a range of inconvenient, uncomfortable or challenging data which, given its temporal and factual accuracy, challenge well-established practices and sometimes even successes. At the macro-level, systems may begin to report lower coverage than previously reported with paper records and multiple levels of 'editing'. Client denominators previously cobbled together manually may shrink or grow with the use of unique digital identifiers. Performance metrics like attendance and hourly performance can be estimated by mining metadata, revealing patterns of inefficiency easily hidden in paper ledgers.
Abstract ID :
GDHF34421
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Johns Hopkins University, Global mHealth Initiative (JHU-GmI)

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