Operationalizing AI-generated data to help providers and patients
With 60% of nurses having no trust in their employers to prioritize patient safety when implementing new artificial intelligence (AI) technology, it’s clear the industry is facing significant challenges when it comes to operationalizing AI-generated data.
These nurses are concerned AI technologies prioritize profit over patient care. With reports of false data, these concerns are valid and must be addressed so our industry can take a responsible approach to using these tools.
In this blog, we explore the challenges of AI-generated data in home health and how it can be operationalized to improve patient care.
AI and healthcare: What are the biggest challenges?
While AI technology is often used to generate predictive data around rehospitalization and fall risk, there is a lack of information as it relates to operationalizing that data and using it to impact patient care. These are two of the industry’s biggest challenges around AI:
Disparate systems
Data points are coming from multiple outside sources, often failing to get integrated into clinicians’ digital tools so they can make use of it effectively.
Distrust
Clinicians are not thoroughly educated on how to verify and validate whether AI-generated data is proven to be accurate — leading to distrust of the information and refusal to use it.
AI to human: How can data empower clinicians?
The type of AI that is best for your organization depends on the level of productivity you need to achieve. But no matter the type, data generated by AI must be operationalized — used within your workflows — to improve decision-making for clinicians and create better patient outcomes. These tips can help your organization operationalize AI:
- Understand where the data is coming from and whether it’s accurate.
Trusting the source of the data and validating it against the patient will ultimately help in using it effectively to care for the patient. - View data as a resource so that the prediction recommendations are relevant to the care setting and patient.
As AI delivers data around predictions and risk factors, clinicians must operationalize that data by being mindful about applying it in productive and impactful ways in the care setting. - Gather real-time recommendations as part of the system workflow.
Getting AI as close to real time as possible will help clinicians make more informed decisions and create more positive impacts for patients.
This approach to operationalizing AI is not meant to replace the clinician, but instead empower them to do their jobs more efficiently to make more time for patient care and achieve better work-life balance.
The future of healthcare technology
Going forward, gaining efficiencies with data will help our industry evolve — and MatrixCare is at the forefront of this evolution. Connect with us today to learn how we can help you operationalize your data.
Request a demo today for a closer look at MatrixCare.
Rob Stoltz
Rob Stoltz, Sr Dir, Business Development Home and Hospice. A long time veteran in the home-based healthcare IT industry with deep experience in EMRs, care transitions, patient engagement, predictive analytics and interoperability. Most recently, Rob has been focused on technology partnerships leveraging interoperability to benefit all stakeholders involved with patient care while enhancing provider efficiency through effective workflows.
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