Evidence map›Paper›PMID 34255672›Full record

ArticleJournal of medical Internet research2021

Hospital Productivity After Data Breaches: Difference-in-Differences Analysis.

Jinhyung Lee, Sung J Choi

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jinhyung LeeDepartment of Economics, Sungkyunkwan University, Seoul, Republic of Korea.ORCID 0000-0002-7020-109X
Sung J ChoiSchool of Global Health Management and Informatics, College of Community Innovation and Education, University of Central Florida, Orlando, FL, United States.ORCID 0000-0003-0299-5382

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundData breaches are an inevitable risk to hospitals operating with information technology. The financial costs associated with data breaches are also growing. The costs associated with a data breach may divert resources away from patient care, thus negatively affecting hospital productivity.

objectiveAfter a data breach, the resulting regulatory enforcement and remediation are a shock to a hospital's patient care delivery. Exploiting this shock, this study aimed to investigate the association between hospital data breaches and productivity by using a generalized difference-in-differences model with multiple prebreach and postbreach periods.

methodsThe study analyzed the hospital financial data of the California Office of Statewide Health Planning and Development from 2012 to 2016. The study sample was an unbalanced panel of hospitals with 2610 unique hospital-year observations, including general acute care hospitals. California hospital data were merged with breach data published by the US Department of Health and Human Services. The dependent variable was hospital productivity measured as value added. The difference-in-differences model was estimated using fixed effects regression.

resultsHospital productivity did not significantly differ from the baseline for 3 years after a breach. Data breaches were not significantly associated with a reduction in hospital productivity. Before a breach, the productivity of hospitals that experienced a data breach maintained a parallel trend with control hospitals.

conclusionsHospital productivity was resilient against the shocks from a data breach. Nonetheless, data breaches continue to threaten hospitals; therefore, health care workers should be trained in cybersecurity to mitigate disruptions.

Indexed as

Computer SecurityConfidentialityHospitalsHumansUnited Statescybersecuritydata breachhealth informationhealth information technologyhospital data breachhospital productivityinformation technologyprivacy

Identifiers

PMID34255672
PMCPMC8293158

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.