Carve Outs: Comments on the Workshop

Investigating Real-world Consequences of Biases in Commonly Used Clinical Calculators

Using Telemedicine Interventions During COVID-19 to Expand Care Post COVID-19

Classifying Clinical Work Settings Using EHR Audit Logs: A Machine Learning Approach

Primary Care Video and Telephone Telemedicine During the COVID-19 Pandemic: Treatment and Follow-up Health Care Utilization

Integrating Third-Party Apps With Electronic Health Records to Support COVID-19 Response

The Health Information Technology Special Issue: Tackling New Challenges to Digital Transformation

Investigating Real-world Consequences of Biases in Commonly Used Clinical Calculators

Using Telemedicine Interventions During COVID-19 to Expand Care Post COVID-19

Classifying Clinical Work Settings Using EHR Audit Logs: A Machine Learning Approach

Primary Care Video and Telephone Telemedicine During the COVID-19 Pandemic: Treatment and Follow-up Health Care Utilization

Integrating Third-Party Apps With Electronic Health Records to Support COVID-19 Response

The Health Information Technology Special Issue: Tackling New Challenges to Digital Transformation

Investigating Real-world Consequences of Biases in Commonly Used Clinical Calculators

Using Telemedicine Interventions During COVID-19 to Expand Care Post COVID-19

Classifying Clinical Work Settings Using EHR Audit Logs: A Machine Learning Approach

Primary Care Video and Telephone Telemedicine During the COVID-19 Pandemic: Treatment and Follow-up Health Care Utilization

Integrating Third-Party Apps With Electronic Health Records to Support COVID-19 Response

The Health Information Technology Special Issue: Tackling New Challenges to Digital Transformation

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Investigating Real-world Consequences of Biases in Commonly Used Clinical Calculators

A letter from the guest editor highlights the potential for the findings in this special issue help us take steps toward realizing the promise of information technology in health care.

Using Telemedicine Interventions During COVID-19 to Expand Care Post COVID-19

Clinical calculators that do not include demographic variables may be biased, and their equity should be understood in the context of clinical guidelines.

Classifying Clinical Work Settings Using EHR Audit Logs: A Machine Learning Approach

The Patient-Centered Rheumatology Collaborative identified several critical areas for further intervention to improve the delivery of high-quality, patient-centered care during the COVID-19 pandemic and beyond.

Primary Care Video and Telephone Telemedicine During the COVID-19 Pandemic: Treatment and Follow-up Health Care Utilization

An automated pipeline of frequency representation and machine learning models on raw electronic health record (EHR) audit logs can classify work settings based on clinical work activities.

Integrating Third-Party Apps With Electronic Health Records to Support COVID-19 Response

Telephone visits may offer a simple and convenient option to address patient primary care needs without raising safety concerns.