Human-Centric AI Solutions

We leverage Harvard AI strategic approach and artificial intelligence certificate that prioritizes human needs, ethics, and values in the design of assessments, platform solutions, workflows, and experiences, education, and governance and implementation strategies.

Our Services

Leverage Harvard AI strategic approach prioritizing human needs, ethics, and values in platform design, experiences, governance, and implementation.

Listen Phrist AI

Platform to elevate patient voices and for innovators to design solutions and strategies co-designing with communities they served.

Overview

Community Co-Design and Partnerships

Apply co-design approach partnering with community partners to conduct participatory research to validate needs, conduct research and design products jointly with researchers addressing real needs and establish learning collaboratives

Health Equity Data Strategy & Dashboard

A holistic AI enabled platform with 250+ indicators to measure outcomes, investments, workforce and community program impact, co-design, and clinical trial participation measuring performance, track investments, assess health outcomes, workforce, and patient experiences.

Tables of Patient Centric vs. Human-Centric

Dimension	Patient-Centric	Human-Centric
Primary Focus	The individual as a patient receiving clinical care	The individual as a whole person with lived experiences
Scope	Health-related needs, conditions, and clinical interactions	Emotional, social, cultural, economic, and spiritual dimensions
Setting	Healthcare environments (hospitals, clinics, telehealth)	Healthcare ecosystem collaboration and broader life contexts (home, community, workplace)
Goals	Improve health outcomes, satisfaction, and treatment adherence	Beyond curing disease to enhance overall well-being and quality of life
Design Lens	Tailored medical care and shared decision-making	Co-designed systems, innovation that reflect human, cultural values and norms, and patient burden
Equity Implications	Addresses disparities in clinical access and treatment	Tackles structural inequities across sectors, addressing social, economic, political, cultural/linguistic barriers
Measurement	Clinical KPIs, patient satisfaction, and treatment adherence	Measure success not just in ROI, but in human outcomes—health, dignity, community partnerships, affordability, changes in policies and behaviors

Human-Centric-AI Enabled Health Ecosystem Framework

Pillar	Key Actions	Sample KPIs
Purpose-Driven Culture	Embed human values; align R&D with societal needs; promote ethical AI	% of R&D aligned with health disparities, community needs; workforce alignment with mission; Ethical AI compliance score
Inclusive Design & Environments	Apply biophilic design; create flexible workspaces; foster belonging and psychological safety	Neurodiversity accommodation index; Psychological safety score; Workspace adaptability rating
Empowered Workforce	Launch cultural empathy and responsiveness, empathy and digital literacy training; promote human-centric leadership; support well-being	% of staff completing equity training; Leadership inclusivity score; Mental health utilization rate
Community Co-Design and Innovation	Co-design with communities; implement participatory research; build health ecosystem cross-sector partnerships	Community co-design participation rate; # of participatory research projects; Compassion Index; Ecosystem Cross-sector Partnership Index
Ethical & Equitable Data and AI	Ensure consistent data collection transparency; disaggregate data; use digital tools to amplify — not replace — human judgment and empathy	% of datasets disaggregated by equity dimensions; Community partners-in-loop key decision ratio; AI lived experience experts teaching and testing machine learning
Sustainable Impact & Measurements	Minimize environmental harm; apply circular economy; measure human and health outcomes	Circular economy adoption affordability score; Health & dignity human experience index; Clinical Trial Burden Score; Policy and behavior changes

Human Centric-AI Principles

Core principles and design approaches that define Human-Centric AI

Human Empowerment It should extend human capabilities, promoting decision-making, creativity, and productivity instead of pursuing full automation
Transparency It entitles end users to comprehend how AI techniques function
Explainability It furnishes perspicuity into detailed AI findings or suggestions
Fairness It seeks to underrate biases and control prejudice by providing righteousness in it’s algorithms
Equity AI systems should function satisfactorily across various demographic classes confirming inclusivity and fair treatment for all users
Data Security It highlights reliable data techniques, such as managing minimal information assuring a protected repository, and delivering users authority over their data
Responsibility It encompasses explicit responsibility for AI-generated results, enabling customers, to argue against decisions or pursue redress
Autonomy It underscores the significance of user autonomy in AI-driven processes. It serves as a mechanism that acknowledges humans’ autonomy, permitting users to overrule, modify, or dismiss AI recommendations at their discretion
Ethical It promotes the evaluation of long-term community consequences, fostering solutions that enhance societal welfare while confining any detriment.

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