Clinical data sharing improves quality measurement and patient safety

D'Amore, John D.; McCrary, Laura; Denson, Jody; Li, Chun; Vitale, Christopher J.; Tokachichu, Priyaranjan; Sittig, Dean F.; McCoy, Allison B.; Wright, Adam

doi:10.1093/jamia/ocab039

Cited by 15 publications

(11 citation statements)

References 32 publications

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“…Besides the above facts, data quality is considered very valuable for the health systems, in particular for patient data that contains information about diagnosis, medications, hospital treatments, and so on. Thus, referring to D’Amore et al (2021), “Overall, the integrated data showed that 79% of patients in the study had records located within more than one facility.”…”

Section: Research Backgroundmentioning

confidence: 99%

A Framework for Evaluation and Improvement of Open Government Data Quality: Application to the Western Balkans National Open Data Portals

et al. 2022

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Open Government Data (OGD) has become a widely employed tool by governments around the world to promote transparent and accountable policy-making. In the context of societal actors’ ability to reuse such data, one of the most important features of ODG is its quality. Thus, through this research, we aim to address the level of data quality by introducing a new framework that constantly monitors and evaluates the OGD quality of Western Balkan (WB) National Open Data Portals. The advantage of the proposed framework is the integration on the portal level enabling information exposure about the quality dimension at any time. An additional value of the framework is the ability to provide recommendations with the intention of improving quality issues. The applicability and findings of the proposed framework are not limited to the WB and once contextualized that could be applied to other countries in the Eastern European that face similar data quality challenges.

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Section: Research Backgroundmentioning

confidence: 99%

A Framework for Evaluation and Improvement of Open Government Data Quality: Application to the Western Balkans National Open Data Portals

et al. 2022

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“…Ultimately, the effectiveness of HIE will depend on community understanding of the role of HIE and overcoming barriers to support sharing of health data for enabling effective healthcare delivery [3,4,39,44]. HIE has been shown to improve care, through the availability of health information at critical times of need [45,46]. HIE enables critical information to be disseminated, supporting smooth transitions of care from acute events, such as stroke [47].…”

Section: Information Disseminationmentioning

confidence: 99%

Transforming Health Data to Actionable Information: Recent Progress and Future Opportunities in Health Information Exchange

Sarkar

2022

Yearb Med Inform

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Objectives: Provide a systematic review of literature pertaining to health information exchange (HIE) since 2018. Summarize HIE-associated literature for most frequently occurring topics, as well as within the context of the COVID-19 pandemic and health equity. Finally, provide recommendations for how HIE can advance the vision of a digital healthcare ecosystem. Methods: A computer program was developed to mediate a literature search of primary literature indexed in MEDLINE that was: (1) indexed with “Health Information Exchange” MeSH descriptor as a major topic; and (2) published between January 2018 and December 2021. Frequency of MeSH descriptors was then used to identify and to rank topics associated with the retrieved literature. COVID-19 literature was identified using the general COVID-19 PubMed Clinical Query filter. Health equity literature was identified using additional MeSH descriptor-based searches. The retrieved literature was then reviewed and summarized. Results: A total of 256 articles were retrieved and reviewed for this survey. The major thematic areas summarized were: (1) Information Dissemination; (2) Delivery of Health Care; (3) Hospitals; (4) Hospital Emergency Service; (5) COVID-19; (6) Health Disparities; and (7) Computer Security and Confidentiality. A common theme across all areas examined for this survey was the maturity of HIE to support data-driven healthcare delivery. Recommendations were developed based on opportunities identified across the reviewed literature. Conclusions: HIE is an essential advance in next generation healthcare delivery. The review of the recent literature (2018-2021) indicates that successful HIE improves healthcare delivery, often resulting in improved health outcomes. There remain major opportunities for expanded use of HIE, including the active engagement of clinical and patient stakeholders. The maturity of HIE reflects the maturity of the biomedical informatics and health data science fields.

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“…To develop and implement this framework in practice, an important challenge to overcome is data sharing. Although data sharing has been shown to improve quality measurement and patient safety (D'Amore et al 2021), it is often infeasible to share patientlevel data across hospitals due to confidentiality concerns. Traditional methods for hospital quality measurement have relied on either fixed or random effects models (Austin et al 2003, Jones & Spiegelhalter 2011, Normand et al 1997).…”

Section: Causal Inference For Quality Measurementmentioning

confidence: 99%

Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurement

Han¹,

Li²,

Niknam³

et al. 2022

Preprint

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Data sharing can improve hospital quality measurement, but sharing patientlevel data between hospitals is often infeasible due to privacy concerns. Motivated by the problem of evaluating the quality of care provided by candidate Cardiac Centers of Excellence (CCE), we propose a federated causal inference framework to safely leverage information from peer hospitals to improve the precision of quality estimates for a target hospital. We develop a federated doubly robust estimator that is privacy-preserving (requiring only summary statistics be shared between hospitals) and communication-efficient (requiring only one round of communication between hospitals). We contribute to the quality measurement and causal inference literatures by developing a framework for assessing treatment-specific performance in hospitals without sharing patient-level data. We also propose a penalized regression approach on summary statistics of the influence functions for efficient estimation and valid inference. In so doing, the proposed estimator is data-adaptive, downweighting hospitals with different case-mixes from the target hospital for bias reduction and upweighting hospitals with similar case-mixes for efficiency gain. We show the improved performance of the federated global estimator in extensive simulation studies. Studying candidate CCE, we find that the federated global estimator improves precision of treatment effect estimates by 34% to 86% for target hospitals, qualitatively altering the evaluation of the percutaneous coronary intervention (PCI) treatment effect in 22 of 51 hospitals. Focusing on treatment-specific rankings, we find that hospitals rarely excel in both PCI and medical management (MM), stressing the importance of treatment-specific performance assessments.

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Clinical data sharing improves quality measurement and patient safety

Cited by 15 publications

References 32 publications

A Framework for Evaluation and Improvement of Open Government Data Quality: Application to the Western Balkans National Open Data Portals

A Framework for Evaluation and Improvement of Open Government Data Quality: Application to the Western Balkans National Open Data Portals

Transforming Health Data to Actionable Information: Recent Progress and Future Opportunities in Health Information Exchange

Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurement

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