Decision Support
Predictive analytics in EHRs aren't yet effective enough for clinical decision support at the point of care.
While understanding and reconciling drugs after discharge from the hospital can be challenging, it's a necessity for greater efficacy of care delivery. HL7's Da Vinci project can help.
AHRQ is helping the transition towards evidence-based recommendations to guide clinical decision-making that incorporates patients' needs and preferences.
While RPA has proved its success for some administrative functions, other technologies are emerging as options to help address the worker shortage and reduce workload in clinical and operational areas.
In the COVID-19 era, health systems recognize that existing data infrastructure is inadequate. Here are three things large datasets need to be useful.
Health systems that refuse to see themselves as engineering houses risk falling behind in their ability to properly leverage artificial intelligence and machine learning.
As COVID-19 continues to surge in Los Angeles, LANES is enabling free-flowing data insights – medical, behavioral and socioeconomic ─ to close information gaps and improve clinical decision support for better outcomes.
CIOs need to look beyond just EHRs and explore stand-alone platforms to enhance care delivery – keeping focused on reliable patient data and streamlined clinical workflows.
An integrated coaction model, or iCaM, is ideal for addressing complex multispecialty parameters associated with health inequities and COVID-19.
COVID-19 has revealed a lot about the world we live in. It has reinforced the possibilities that open up when we collaborate and come together, united towards a common cause; in this case, of defeating the spread of SARS-CoV-2 says Atif Al Braiki, CEO, Abu Dhabi Health Data Services LLC (Malaffi).
