What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Nursing
- The Internet
The scientist’s investigation covers issues in Computerized physician order entry, Pediatrics, Patient safety, Medical emergency and Family medicine.
His work carried out in the field of Computerized physician order entry brings together such families of science as Psychological intervention, Clinical decision support system, Decision support system, Emergency medicine and Clinical pharmacy.
His Emergency medicine study incorporates themes from Intensive care unit and Intensive care.
His Pediatrics research incorporates themes from Medical prescription, Electronic prescribing, Prospective cohort study and Cohort study.
Rainu Kaushal has included themes like Health care quality, Response rate, Ambulatory care and Health records in his Family medicine study.
His study in Ambulatory care is interdisciplinary in nature, drawing from both Incentive and Quality.
His most cited work include:
- Medication errors and adverse drug events in pediatric inpatients. (1476 citations)
- Effect of reducing interns' work hours on serious medical errors in intensive care units. (1403 citations)
- Effects of Computerized Physician Order Entry and Clinical Decision Support Systems on Medication Safety: A Systematic Review (1114 citations)
What are the main themes of his work throughout his whole career to date?
Rainu Kaushal mainly focuses on Family medicine, Health information exchange, Nursing, Health information technology and Medical emergency.
His Family medicine study combines topics from a wide range of disciplines, such as Ambulatory care, Health care quality, Response rate, Meaningful use and Health records.
His Ambulatory care research also works with subjects such as
- Quality, which have a strong connection to Incentive,
- Ambulatory which intersects with area such as Pediatrics.
In his study, Knowledge management and Health policy is strongly linked to Health informatics, which falls under the umbrella field of Health information exchange.
The concepts of his Medical emergency study are interwoven with issues in Clinical decision support system, Emergency department, Patient safety, Emergency medicine and Medical prescription.
His work deals with themes such as Intensive care and Clinical pharmacy, which intersect with Emergency medicine.
He most often published in these fields:
- Family medicine (32.49%)
- Health information exchange (30.32%)
- Nursing (30.69%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (4.69%)
- Social determinants of health (3.97%)
- Disease (3.97%)
In recent papers he was focusing on the following fields of study:
Rainu Kaushal mostly deals with Artificial intelligence, Social determinants of health, Disease, Retrospective cohort study and Severe acute respiratory syndrome coronavirus 2.
The study incorporates disciplines such as Pharmacy and Psychological intervention in addition to Social determinants of health.
He interconnects Ambulatory care and Brier score in the investigation of issues within Pharmacy.
The Disease study combines topics in areas such as Respiratory failure, SOFA score, Cohort and Intensive care medicine.
His studies deal with areas such as Environmental health and Laboratory results as well as Retrospective cohort study.
His work in Primary outcome tackles topics such as Emergency medicine which are related to areas like Claims data.
Between 2017 and 2021, his most popular works were:
- Should Health Care Demand Interpretable Artificial Intelligence or Accept "Black Box" Medicine? (39 citations)
- Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning. (16 citations)
- Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning. (16 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Nursing
- The Internet
Rainu Kaushal mainly focuses on Artificial intelligence, Medical education, Receiver operating characteristic, Stakeholder and Machine learning.
His work on Deep learning, Applications of artificial intelligence and Decision tree is typically connected to Black box as part of general Artificial intelligence study, connecting several disciplines of science.
His Medical education research is multidisciplinary, incorporating elements of Focus group and Patient-centered outcomes.
His Receiver operating characteristic research integrates issues from Young adult, Patient demographics, Retrospective cohort study and Routine laboratory.
His Stakeholder research encompasses a variety of disciplines, including Health information technology, End user, Decision support system, Sociotechnical system and User-centered design.
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