Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide

By Julia Adler-Milstein, A. J. Holmgren, P. Kralovec, C. Worzala, Talisha Searcy, Vaishali Patel. J. Am. Medical Informatics Assoc., 2017

351 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Health Information Management

View Paper: https://www.semanticscholar.org/paper/8f88f4bc420f4cbc86aa925e05b074f0aa76b413

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Ahmed M. Alaa
2. Parisa Rashidi
3. Mihaela van der Schaar
4. Ioana Bica

In Health Information Management

1. Parisa Rashidi
2. Mihaela van der Schaar
3. Patrick James Tighe
4. Azra Bihorac

Citations in the corpus

• From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020, methodology): “EHRs have already been widely adopted in US hospitals.(8) Although data from EHRs have less regulatory acceptance compared with data from RCTs in addition to weaknesses, such as informative missingness and observational biases, it facilitates the quantity, accessibility, and heterogeneity of offline”
• A large language model for electronic health records (2022, background): “In recent decades, hospitals and medical practices in the United States (US) have rapidly adopted EHR systems 1,2 , resulting in massive stores of electronic patient data, including structured (e.g., disease codes, medication codes) and unstructured (i.e., clinical narratives such as progress…”
• Artificial Intelligence and Surgical Decision-Making (2019, background): “66 The volume of data generated by EHRs is staggering and will likely increase over time.”