Needs assessments are steps taken to find out what a group or organization really needs. In healthcare, this means learning about what patients, caregivers, doctors, and office staff need. When AI technology is added, these assessments make sure the tools help the people who use them. This leads to better acceptance of the technology and better results.
The Massachusetts AI and Technology Center for Connected Care in Aging and Alzheimer’s Disease (MassAITC) works on involving different people to help bring in AI. Their Stakeholder Engagement Core, led by experts like Holly Jimison, PhD, FACMI, involves older adults, informal caregivers, and clinicians. This helps make sure AI is easy to use for all patients.
Getting many kinds of people involved is important when making AI tools for healthcare. Older adults, family caregivers, healthcare workers, and office staff each have different views on what works best. Including these views helps find problems others might miss.
For example, doctors care about accuracy and fitting AI into their routine. Caregivers may want easy-to-use tools and clear communication. Older adults with conditions like Alzheimer’s need technologies that consider their abilities. The Stakeholder Advisory Board at MassAITC, including Dr. Carmen Castaneda Sceppa and Deborah Costello, shows that this mix of opinions helps both AI makers and health providers make better tools.
Human-centered design thinking puts the users first. It means testing tools, getting feedback, and making changes based on what people say. This method helps AI fit better into real healthcare work and daily patient life.
Jenna Marquard, PhD, from the Stakeholder Engagement Core, says this is especially useful with groups that need more care. Instead of forcing AI to change workflows, human-centered design lets AI fit in smoothly. This lowers resistance and makes staff and patients happier.
Using AI in healthcare comes with ethical duties. A review by Siala and Wang (2022) looked at over 250 studies on AI ethics in healthcare. They created the SHIFT framework to guide careful AI use. SHIFT means:
Healthcare managers and IT staff need to use SHIFT principles. This helps avoid problems like bias or lack of responsibility. These points build trust among staff and patients. Trust helps people use the technology more.
Good needs assessments connect new technology with patient-focused care. When clinics and hospitals involve people early on, AI tools match real work and patient needs better. This matters in the United States where health care must follow rules and serve diverse patients.
Talking with stakeholders can show how AI can help front desk staff with tasks like scheduling, answering questions, and follow-ups. By working with these groups, developers of AI phone services, like Simbo AI, make systems that handle many calls well and give personalized answers. This reduces missed calls and wait times, letting staff focus on more important jobs.
Healthcare offices often have many repeated tasks like answering calls for appointments, refills, and billing. AI phone systems, such as Simbo AI, use natural language processing to talk like a human and handle these tasks accurately.
This technology works with existing electronic health records and scheduling systems, making work run smoother by automating simple tasks. Automating the front desk calls helps both patients get answers faster and staff work better.
AI can be set up to help many patients, including older adults who may need clear and slow speech or repeated information. This lowers confusion for patients and front desk workers, and makes sure important information is given right.
Besides phones, AI can also help with tasks like sending reminders, verifying insurance, and sorting out non-urgent medical questions. When these tools fit with human-centered design and good needs assessments, they help healthcare work better and make patients feel cared for.
Keeping stakeholders involved is important even after the first needs assessment. The Massachusetts AI and Technology Center says ongoing contact helps check how well AI works and when to make changes. Ivan Lee, PhD, points out that knowing new needs from doctors and caregivers helps update AI tools.
Healthcare managers need to set up ways for staff and patients to give feedback after AI is in use. This ongoing process helps AI meet real communication needs, fit clinical work, and change as needs evolve. Without this, AI might not match needs and could waste money.
IT managers in healthcare who use AI phone services like Simbo AI have to think about security and law. Laws such as HIPAA in the U.S. protect patient information. Automated systems must keep data safe and private.
Needs assessments should make clear what security and tech standards are needed to follow rules. Transparent AI, as encouraged by SHIFT, helps IT managers watch data flow and keep accountability.
Training staff is very important. Teams need to know how to use AI, fix small problems, and report errors fast. Well-trained staff reduce risks and help AI work well.
Studies from MassAITC’s Stakeholder Engagement Core show benefits of AI made with input from users. Including family caregivers and older adults helps create tools that deal with communication problems common in Alzheimer’s and dementia care. Health providers serving different groups also gain when AI is made to be inclusive, reducing issues from language or access differences.
Medical managers can look at companies like Simbo AI, which focus on AI phone automation for front offices. These tools help reduce missed appointments, improve talks between patients and providers, and increase office efficiency at a lower cost.
As AI keeps changing, using solid needs assessments, ethical ideas like SHIFT, and human-centered design will stay important for success in healthcare across the country. The teamwork between AI makers, healthcare workers, patients, and caregivers keeps helping create tools that match real needs and respect all kinds of people.
By using well-planned needs assessments that involve many healthcare stakeholders, medical offices in the U.S. can bring in AI tools that improve work without losing focus on patients. Automated phone answering systems, shaped by ongoing feedback and ethical principles, help workflows run better and lead to improved health results.
The Stakeholder Engagement Core at the Massachusetts AI and Technology Center is tasked with convening stakeholder groups and conducting needs assessments to ensure AI-enhanced technologies are adoptable and accessible for end-users, particularly in the context of aging and Alzheimer’s disease.
The Core is led by Holly Jimison, PhD, along with Jenna Marquard, PhD, and Ivan Lee, PhD, all of whom hold significant academic positions at various universities.
Core activities include ongoing stakeholder needs assessments, utilizing human-centered design thinking methods to ensure solutions meet user needs, and disseminating findings to the broader research community.
The Stakeholder Advisory Board aims to guide the needs assessment process and ensure that various perspectives from older adults, caregivers, and clinicians are incorporated into technology development.
Stakeholders include older adults, family or informal caregivers, and clinicians, whose needs and preferences are crucial for the development of effective AI technologies.
Human-centered design thinking is employed by pilot project teams to effectively conduct thorough needs assessments and to create solutions that directly address the identified needs of stakeholders.
Expected outcomes include improved understanding of user needs, leading to the design and implementation of more effective AI technologies in healthcare settings.
By conducting rigorous needs assessments and involving stakeholders in the design process, the Core helps ensure that technologies are user-friendly and relevant, thereby enhancing adoption rates.
Notable members include Carmen Castaneda Sceppa, Deborah Costello, and Mary Sano, representing various healthcare and academic institutions.
Stakeholder engagement is critical as it ensures solutions are tailored to the needs of users, increases the likelihood of successful implementation, and promotes equity in healthcare technology access.
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